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91de2389-e4fa-47a8-b1d8-e711c5f1c68f | neural-concept-formation-in-knowledge-graphs | null | null | https://openreview.net/forum?id=V61-62OS4mZ | https://openreview.net/pdf?id=V61-62OS4mZ | Neural Concept Formation in Knowledge Graphs | In this work, we investigate how to learn novel concepts in Knowledge Graphs (KGs) in a principled way, and how to effectively exploit them to produce more accurate neural link prediction models. Specifically, we show how concept membership relationships learned via unsupervised clustering of entities can be reified an... | ['Pasquale Minervini', 'Antonio Vergari', 'Agnieszka Dobrowolska'] | 2021-06-22 | null | null | null | akbc-2021-10 | ['novel-concepts'] | ['reasoning'] | [ 1.54296324e-01 8.67769837e-01 -6.80537403e-01 -6.45391524e-01
-2.36760721e-01 -3.32420886e-01 3.21055084e-01 4.21708882e-01
-3.44073683e-01 1.20781672e+00 1.37622237e-01 -2.46026158e-01
-3.62160653e-01 -1.14378023e+00 -1.16703248e+00 -1.55001879e-01
-5.35619795e-01 7.03670561e-01 2.03727037e-01 -1.64004818... | [8.88937759399414, 7.979780673980713] |
5657ebcc-0485-4be6-9c31-8ca3fb8c111a | a-large-scale-study-of-language-models-for | 1804.01849 | null | http://arxiv.org/abs/1804.01849v1 | http://arxiv.org/pdf/1804.01849v1.pdf | A Large-Scale Study of Language Models for Chord Prediction | We conduct a large-scale study of language models for chord prediction.
Specifically, we compare N-gram models to various flavours of recurrent neural
networks on a comprehensive dataset comprising all publicly available datasets
of annotated chords known to us. This large amount of data allows us to
systematically exp... | ['Filip Korzeniowski', 'David R. W. Sears', 'Gerhard Widmer'] | 2018-04-05 | null | null | null | null | ['chord-recognition'] | ['audio'] | [ 1.31269753e-01 -1.49378553e-01 -1.12563297e-02 -5.72638437e-02
-6.83169484e-01 -1.00801635e+00 3.60530078e-01 -4.51352932e-02
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-3.11165273e-01 6.61789775e-01 5.04404008e-01 -7.02310681... | [15.893318176269531, 5.330941200256348] |
6bb64aa4-f278-433d-8d44-b75d3ffadc49 | consistent-and-symmetry-preserving-data | 2104.11578 | null | https://arxiv.org/abs/2104.11578v1 | https://arxiv.org/pdf/2104.11578v1.pdf | Consistent and symmetry preserving data-driven interface reconstruction for the level-set method | Recently, machine learning has been used to substitute parts of conventional computational fluid dynamics, e.g. the cell-face reconstruction in finite-volume solvers or the curvature computation in the Volume-of-Fluid (VOF) method. The latter showed improvements in terms of accuracy for coarsely resolved interfaces, ho... | ['Nikolaus Adams', 'Deniz A. Bezgin', 'Aaron B. Buhendwa'] | 2021-04-23 | null | null | null | null | ['face-reconstruction'] | ['computer-vision'] | [ 1.12194330e-01 -1.17852084e-01 4.91536885e-01 2.18671620e-01
-6.04186475e-01 -1.84181243e-01 6.49611294e-01 3.24491858e-01
-3.71626735e-01 9.88221288e-01 -2.43738443e-01 -3.24923217e-01
-3.40482146e-01 -9.80118155e-01 -4.43558455e-01 -1.04268193e+00
1.76164676e-02 7.28078723e-01 1.59679070e-01 -2.62490511... | [6.387056827545166, 3.3242926597595215] |
3963eb53-5252-41fe-a220-3e7e72c7c72f | resources-and-evaluations-for-multi | 2306.12601 | null | https://arxiv.org/abs/2306.12601v1 | https://arxiv.org/pdf/2306.12601v1.pdf | Resources and Evaluations for Multi-Distribution Dense Information Retrieval | We introduce and define the novel problem of multi-distribution information retrieval (IR) where given a query, systems need to retrieve passages from within multiple collections, each drawn from a different distribution. Some of these collections and distributions might not be available at training time. To evaluate m... | ['Simran Arora', 'Omar Khattab', 'Soumya Chatterjee'] | 2023-06-21 | null | null | null | null | ['retrieval', 'question-answering', 'information-retrieval'] | ['methodology', 'natural-language-processing', 'natural-language-processing'] | [-2.10464269e-01 -5.10327697e-01 -5.26404142e-01 -2.33647972e-01
-1.98825192e+00 -1.10904813e+00 6.78387940e-01 5.10692894e-01
-5.34735560e-01 9.06216025e-01 1.99299380e-01 -8.69033709e-02
-6.29670799e-01 -7.66481400e-01 -6.29947305e-01 -3.76926154e-01
1.20171323e-01 1.33626032e+00 6.35550082e-01 -3.41625720... | [11.454911231994629, 7.704309463500977] |
b328b38b-0cbc-44e0-b008-7896e324eaa0 | chili-pepper-disease-diagnosis-via-image | 2306.12057 | null | https://arxiv.org/abs/2306.12057v1 | https://arxiv.org/pdf/2306.12057v1.pdf | Chili Pepper Disease Diagnosis via Image Reconstruction Using GrabCut and Generative Adversarial Serial Autoencoder | With the recent development of smart farms, researchers are very interested in such fields. In particular, the field of disease diagnosis is the most important factor. Disease diagnosis belongs to the field of anomaly detection and aims to distinguish whether plants or fruits are normal or abnormal. The problem can be ... | ['Sungyoung Kim', 'Jongwook Si'] | 2023-06-21 | null | null | null | null | ['image-reconstruction', 'anomaly-detection'] | ['computer-vision', 'methodology'] | [ 3.03958982e-01 -3.16793501e-01 1.62073508e-01 -1.66590855e-01
-6.73410371e-02 -2.61246473e-01 1.68067962e-01 -1.79791704e-01
2.76662922e-03 4.86118883e-01 -4.02705550e-01 6.00404851e-02
-1.01132356e-01 -1.41629064e+00 -4.16858345e-01 -1.01171851e+00
3.96085948e-01 1.16422191e-01 6.36012927e-02 -2.81823158... | [7.676959991455078, 1.907288908958435] |
c13f7897-3384-4256-85c6-222f39ed7c89 | channel-recurrent-attention-networks-for | 2010.03108 | null | https://arxiv.org/abs/2010.03108v1 | https://arxiv.org/pdf/2010.03108v1.pdf | Channel Recurrent Attention Networks for Video Pedestrian Retrieval | Full attention, which generates an attention value per element of the input feature maps, has been successfully demonstrated to be beneficial in visual tasks. In this work, we propose a fully attentional network, termed {\it channel recurrent attention network}, for the task of video pedestrian retrieval. The main atte... | ['Mehrtash Harandi', 'Lars Petersson', 'Jieming Zhou', 'Pan Ji', 'Pengfei Fang'] | 2020-10-07 | null | null | null | null | ['person-retrieval'] | ['computer-vision'] | [ 3.31758559e-01 -4.68979299e-01 -1.05405629e-01 -6.46921322e-02
-6.49204075e-01 -2.54998449e-02 6.67910695e-01 -3.15437496e-01
-3.57983410e-01 5.90136170e-01 6.06874585e-01 1.86678290e-01
1.51827484e-01 -5.35718501e-01 -8.61878991e-01 -7.90900230e-01
-8.10200050e-02 -2.36137047e-01 9.76259857e-02 5.31656183... | [9.441946983337402, 0.6835160255432129] |
07343c04-d472-4117-93fd-aaedd6793ec2 | can-neural-networks-do-arithmetic-a-survey-on | 2303.07735 | null | https://arxiv.org/abs/2303.07735v1 | https://arxiv.org/pdf/2303.07735v1.pdf | Can neural networks do arithmetic? A survey on the elementary numerical skills of state-of-the-art deep learning models | Creating learning models that can exhibit sophisticated reasoning skills is one of the greatest challenges in deep learning research, and mathematics is rapidly becoming one of the target domains for assessing scientific progress in this direction. In the past few years there has been an explosion of neural network arc... | ['Alberto Testolin'] | 2023-03-14 | null | null | null | null | ['numerical-integration', 'automated-theorem-proving', 'automated-theorem-proving'] | ['miscellaneous', 'miscellaneous', 'reasoning'] | [-1.94684893e-01 -1.03545956e-01 -2.23597452e-01 -2.23596275e-01
-2.01922163e-01 -6.41880989e-01 7.34340131e-01 5.51803887e-01
-4.35138553e-01 8.27689946e-01 -2.87558585e-01 -9.73234534e-01
-4.46500242e-01 -1.21110022e+00 -7.15499222e-01 -2.55844891e-01
-3.75246882e-01 5.26835799e-01 -2.19003975e-01 -5.23171842... | [9.254510879516602, 7.161590099334717] |
372e1a7f-7a3a-4cdf-af48-6fd0413ca8d8 | pac-assisted-value-factorisation-with | 2206.11420 | null | https://arxiv.org/abs/2206.11420v3 | https://arxiv.org/pdf/2206.11420v3.pdf | PAC: Assisted Value Factorisation with Counterfactual Predictions in Multi-Agent Reinforcement Learning | Multi-agent reinforcement learning (MARL) has witnessed significant progress with the development of value function factorization methods. It allows optimizing a joint action-value function through the maximization of factorized per-agent utilities due to monotonicity. In this paper, we show that in partially observabl... | ['Vaneet Aggarwal', 'Tian Lan', 'Hanhan Zhou'] | 2022-06-22 | null | null | null | null | ['starcraft-ii'] | ['playing-games'] | [ 9.33378178e-04 3.37637067e-01 -7.03318775e-01 -7.28595704e-02
-1.04379749e+00 -4.86216396e-01 7.58728504e-01 1.25225976e-01
-8.62132728e-01 1.56500614e+00 4.47000980e-01 -1.93858057e-01
-4.48424280e-01 -7.45422244e-01 -8.85571718e-01 -9.10247803e-01
-6.83379650e-01 7.03703523e-01 -2.49959201e-01 -3.14699680... | [3.7670390605926514, 2.0681231021881104] |
86976613-7bf1-456b-82c5-d500533d2921 | monocular-3d-object-detection-using-multi | 2212.11804 | null | https://arxiv.org/abs/2212.11804v1 | https://arxiv.org/pdf/2212.11804v1.pdf | Monocular 3D Object Detection using Multi-Stage Approaches with Attention and Slicing aided hyper inference | 3D object detection is vital as it would enable us to capture objects' sizes, orientation, and position in the world. As a result, we would be able to use this 3D detection in real-world applications such as Augmented Reality (AR), self-driving cars, and robotics which perceive the world the same way we do as humans. M... | ['Ashish Patel', 'Abonia Sojasingarayar'] | 2022-12-22 | null | null | null | null | ['monocular-3d-object-detection'] | ['computer-vision'] | [ 9.89828184e-02 -1.63990825e-01 2.02761710e-01 -2.35028028e-01
9.24237967e-02 -7.84826398e-01 4.96305585e-01 -2.50436049e-02
-4.58533257e-01 2.91924417e-01 -5.47745168e-01 -4.77302819e-01
4.83384699e-01 -6.86647594e-01 -6.26309335e-01 -3.28630507e-01
1.65992066e-01 5.87148786e-01 9.19364214e-01 -2.90166825... | [7.7036943435668945, -2.5074880123138428] |
252dfae8-3c79-4859-8731-65362d70fa17 | towards-a-better-understanding-of | 2305.18491 | null | https://arxiv.org/abs/2305.18491v1 | https://arxiv.org/pdf/2305.18491v1.pdf | Towards a Better Understanding of Representation Dynamics under TD-learning | TD-learning is a foundation reinforcement learning (RL) algorithm for value prediction. Critical to the accuracy of value predictions is the quality of state representations. In this work, we consider the question: how does end-to-end TD-learning impact the representation over time? Complementary to prior work, we prov... | ['Rémi Munos', 'Yunhao Tang'] | 2023-05-29 | null | null | null | null | ['value-prediction'] | ['computer-code'] | [ 3.80673148e-02 2.57456988e-01 -7.59773910e-01 -1.18984714e-01
-8.37758243e-01 -7.46941507e-01 5.37485898e-01 3.18429887e-01
-4.77282286e-01 1.03467464e+00 3.78732532e-01 -5.25980055e-01
-5.67838490e-01 -5.89721203e-01 -7.18707085e-01 -5.01070082e-01
-6.04621530e-01 4.44654077e-01 -6.87963970e-04 -5.53710878... | [4.067844867706299, 1.9328854084014893] |
a4d27304-421c-46e7-8f4e-b6cd0beaa69a | multi-modal-page-stream-segmentation-with | null | null | https://link.springer.com/article/10.1007/s10579-019-09476-2 | https://www.inf.uni-hamburg.de/en/inst/ab/lt/publications/2019-wiedemann-lre-pss.pdf | Multi-modal Page Stream Segmentation with Convolutional Neural Networks | In recent years, (retro-)digitizing paper-based files became a major undertaking for private and public archives as well as an important task in electronic mailroom applications. As first steps, the workflow usually involves batch scanning and optical character recognition (OCR) of documents. In the case of multi-page ... | ['Gerhard Heyer', 'Gregor Wiedemann'] | 2019-09-27 | null | null | null | lang-resources-evaluation-2019-9 | ['page-stream-segmentation'] | ['natural-language-processing'] | [ 6.70916975e-01 -1.95391372e-01 1.54130861e-01 -2.95114279e-01
-1.16281581e+00 -8.68752360e-01 5.69551170e-01 4.09916759e-01
-4.00897682e-01 3.82297307e-01 -6.19797818e-02 -3.81673992e-01
-2.39493340e-01 -6.03458226e-01 -6.65960968e-01 -2.46021464e-01
3.54760557e-01 8.67896736e-01 3.68163407e-01 1.07595190... | [11.750812530517578, 2.6896584033966064] |
31bf57ad-19aa-4903-96d6-71fe643559c7 | video-face-clustering-with-unknown-number-of | 1908.03381 | null | https://arxiv.org/abs/1908.03381v2 | https://arxiv.org/pdf/1908.03381v2.pdf | Video Face Clustering with Unknown Number of Clusters | Understanding videos such as TV series and movies requires analyzing who the characters are and what they are doing. We address the challenging problem of clustering face tracks based on their identity. Different from previous work in this area, we choose to operate in a realistic and difficult setting where: (i) the n... | ['Sanja Fidler', 'Marc T. Law', 'Makarand Tapaswi'] | 2019-08-09 | video-face-clustering-with-unknown-number-of-1 | http://openaccess.thecvf.com/content_ICCV_2019/html/Tapaswi_Video_Face_Clustering_With_Unknown_Number_of_Clusters_ICCV_2019_paper.html | http://openaccess.thecvf.com/content_ICCV_2019/papers/Tapaswi_Video_Face_Clustering_With_Unknown_Number_of_Clusters_ICCV_2019_paper.pdf | iccv-2019-10 | ['face-clustering'] | ['computer-vision'] | [ 6.90754205e-02 -6.53285980e-02 -2.68651247e-01 -3.28140646e-01
-5.03036916e-01 -8.36068809e-01 5.81524193e-01 2.08750412e-01
-4.23652768e-01 3.41593415e-01 -1.22059703e-01 -7.69765377e-02
-1.24244347e-01 -5.36969364e-01 -5.45166552e-01 -7.11296618e-01
-1.84486344e-01 8.21935058e-01 1.97814584e-01 1.63019478... | [13.462641716003418, 1.0532432794570923] |
1fd2aa26-8c5c-4b6d-bea3-fb247190d80c | multi-view-subspace-clustering-via-partition | 1912.01201 | null | https://arxiv.org/abs/1912.01201v1 | https://arxiv.org/pdf/1912.01201v1.pdf | Multi-view Subspace Clustering via Partition Fusion | Multi-view clustering is an important approach to analyze multi-view data in an unsupervised way. Among various methods, the multi-view subspace clustering approach has gained increasing attention due to its encouraging performance. Basically, it integrates multi-view information into graphs, which are then fed into sp... | ['Zenglin Xu', 'Boyu Wang', 'Zhao Kang', 'Juncheng Lv', 'Luping Ji'] | 2019-12-03 | null | null | null | null | ['multi-view-subspace-clustering'] | ['computer-vision'] | [-1.57365635e-01 -5.27896643e-01 -1.45767763e-01 -5.70858158e-02
-5.12539804e-01 -7.55040526e-01 3.75528932e-01 7.99397826e-02
1.44503817e-01 2.03171283e-01 4.52410221e-01 1.74532682e-01
-4.09128547e-01 -7.21879840e-01 -1.36038601e-01 -9.43844259e-01
2.30492398e-01 2.18531981e-01 3.64398628e-01 1.33844435... | [8.217903137207031, 4.655970096588135] |
e010b1af-75f1-40d7-8faa-1560020a6b0d | integration-of-workflow-and-pipeline-for | null | null | https://aclanthology.org/L14-1708 | https://aclanthology.org/L14-1708.pdf | Integration of Workflow and Pipeline for Language Service Composition | Integrating language resources and language services is a critical part of building natural language processing applications. Service workflow and processing pipeline are two approaches for sharing and combining language resources. Workflow languages focus on expressive power of the languages to describe variety of wor... | ['Trang Mai Xuan', 'Donghui Lin', 'Yohei Murakami', 'Toru Ishida'] | 2014-05-01 | null | null | null | lrec-2014-5 | ['service-composition'] | ['miscellaneous'] | [-2.33412459e-01 -9.92582738e-02 2.26164266e-01 -7.09883034e-01
-4.54200119e-01 -1.04721403e+00 7.60705411e-01 1.92833424e-01
-3.83341938e-01 1.70149565e-01 2.06915691e-01 -3.69870454e-01
-1.10844292e-01 -8.32143188e-01 9.92725790e-02 -4.15284008e-01
1.32810131e-01 5.96603572e-01 6.23472929e-01 -2.45969221... | [9.079822540283203, 7.8000640869140625] |
ee58f7bd-59ed-46da-abd6-b06771d80c57 | a-min-max-cult-algorithm-for-graph | null | null | https://ieeexplore.ieee.org/document/989507 | https://ieeexplore.ieee.org/document/989507 | A Min-max Cult Algorithm for Graph Partitioning and Data Clustering | An important application of graph partitioning is data clustering using a graph model - the pairwise similarities between all data objects form a weighted graph adjacency matrix that contains all necessary information for clustering. In this paper, we propose a new algorithm for graph partitioning with an objective fun... | ['Horst D. Simon', 'Ming Gu', 'Hongyuan Zhab', 'Xiaofeng He', 'Chris H.Q. Ding'] | 2002-08-07 | null | null | null | proceedings-2001-ieee-international | ['graph-partitioning'] | ['graphs'] | [ 1.37864128e-01 2.79274970e-01 -5.26125193e-01 -4.26543415e-01
-3.39104950e-01 -7.52093494e-01 7.83955380e-02 4.92328078e-01
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-9.36588466e-01 -8.84025097e-01 -8.57959241e-02 -7.78700948e-01
-5.75620592e-01 9.67333674e-01 3.41747195e-01 -2.29957420... | [7.0391387939453125, 5.220965385437012] |
d01b58b4-e14d-4a62-9907-274152cd5cb0 | zerotop-zero-shot-task-oriented-semantic | 2212.10815 | null | https://arxiv.org/abs/2212.10815v1 | https://arxiv.org/pdf/2212.10815v1.pdf | ZEROTOP: Zero-Shot Task-Oriented Semantic Parsing using Large Language Models | We explore the use of large language models (LLMs) for zero-shot semantic parsing. Semantic parsing involves mapping natural language utterances to task-specific meaning representations. Language models are generally trained on the publicly available text and code and cannot be expected to directly generalize to domain... | ['Subhro Roy', 'Jason Wolfe', 'Dheeraj Mekala'] | 2022-12-21 | null | null | null | null | ['semantic-parsing'] | ['natural-language-processing'] | [ 6.59707963e-01 8.37527752e-01 1.37444377e-01 -7.29459643e-01
-1.51722038e+00 -5.82926393e-01 2.86325902e-01 1.56615317e-01
-1.69421718e-01 3.16875279e-01 3.73574644e-01 -5.45538485e-01
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3.34022641e-01 7.55775094e-01 4.13604617e-01 -5.11030376... | [10.796916007995605, 8.973309516906738] |
2d9d0368-6125-43aa-9038-3021a4dbe9bc | video-swin-transformer | 2106.13230 | null | https://arxiv.org/abs/2106.13230v1 | https://arxiv.org/pdf/2106.13230v1.pdf | Video Swin Transformer | The vision community is witnessing a modeling shift from CNNs to Transformers, where pure Transformer architectures have attained top accuracy on the major video recognition benchmarks. These video models are all built on Transformer layers that globally connect patches across the spatial and temporal dimensions. In th... | ['Han Hu', 'Stephen Lin', 'Zheng Zhang', 'Yixuan Wei', 'Yue Cao', 'Jia Ning', 'Ze Liu'] | 2021-06-24 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Liu_Video_Swin_Transformer_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Liu_Video_Swin_Transformer_CVPR_2022_paper.pdf | cvpr-2022-1 | ['classification'] | ['methodology'] | [-1.00641429e-01 -3.30111861e-01 -4.28591311e-01 -3.20141524e-01
-6.41346276e-01 -5.20365536e-01 8.55282545e-01 -5.00707030e-01
-5.84083378e-01 2.82659978e-01 4.83731031e-01 -3.50195289e-01
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-1.73182264e-02 1.42318949e-01 2.77123004e-01 -4.55942675... | [8.981473922729492, 0.6798035502433777] |
4526222b-d2f7-407f-b89e-6d00fe088338 | skg-a-versatile-information-retrieval-and | 2306.04758 | null | https://arxiv.org/abs/2306.04758v1 | https://arxiv.org/pdf/2306.04758v1.pdf | SKG: A Versatile Information Retrieval and Analysis Framework for Academic Papers with Semantic Knowledge Graphs | The number of published research papers has experienced exponential growth in recent years, which makes it crucial to develop new methods for efficient and versatile information extraction and knowledge discovery. To address this need, we propose a Semantic Knowledge Graph (SKG) that integrates semantic concepts from a... | ['Han-Wei Shen', 'Rui Qiu', 'Yamei Tu'] | 2023-06-07 | null | null | null | null | ['knowledge-graphs', 'information-retrieval'] | ['knowledge-base', 'natural-language-processing'] | [-4.57014233e-01 4.34032222e-03 -1.41657129e-01 -1.97841838e-01
-2.00019076e-01 -7.61417270e-01 5.21518826e-01 4.69442368e-01
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-3.77521694e-01 -1.10113931e+00 -2.02128902e-01 -2.05172673e-01
2.47202843e-01 4.95801419e-02 5.16299307e-01 -7.45249689... | [9.297051429748535, 8.170109748840332] |
266ccd50-8b56-4c06-b5a9-c52b89107d15 | stacked-convolutional-and-recurrent-neural | 1706.02292 | null | http://arxiv.org/abs/1706.02292v1 | http://arxiv.org/pdf/1706.02292v1.pdf | Stacked Convolutional and Recurrent Neural Networks for Music Emotion Recognition | This paper studies the emotion recognition from musical tracks in the
2-dimensional valence-arousal (V-A) emotional space. We propose a method based
on convolutional (CNN) and recurrent neural networks (RNN), having
significantly fewer parameters compared with the state-of-the-art method for
the same task. We utilize o... | ['Konstantinos Drossos', 'Roman Jarina', 'Sharath Adavanne', 'Miroslav Malik', 'Tuomas Virtanen', 'Dasa Ticha'] | 2017-06-07 | null | null | null | null | ['music-emotion-recognition'] | ['music'] | [-1.35607094e-01 -9.60591733e-02 1.40792251e-01 -4.09924716e-01
-5.46347260e-01 -4.14343834e-01 1.00686394e-01 -6.55326396e-02
-6.46027029e-01 6.77783549e-01 3.35802376e-01 3.04565966e-01
-4.29260172e-03 -5.47177970e-01 -3.55128258e-01 -5.70135474e-01
-3.35905999e-01 -1.43490225e-01 -5.77729940e-01 -4.33920473... | [13.516203880310059, 5.050252437591553] |
49545c8c-2279-4eef-8447-103cc6968b7e | elepose-unsupervised-3d-human-pose-estimation | 2112.07088 | null | https://arxiv.org/abs/2112.07088v1 | https://arxiv.org/pdf/2112.07088v1.pdf | ElePose: Unsupervised 3D Human Pose Estimation by Predicting Camera Elevation and Learning Normalizing Flows on 2D Poses | Human pose estimation from single images is a challenging problem that is typically solved by supervised learning. Unfortunately, labeled training data does not yet exist for many human activities since 3D annotation requires dedicated motion capture systems. Therefore, we propose an unsupervised approach that learns t... | ['Helge Rhodin', 'James J. Little', 'Bastian Wandt'] | 2021-12-14 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Wandt_ElePose_Unsupervised_3D_Human_Pose_Estimation_by_Predicting_Camera_Elevation_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Wandt_ElePose_Unsupervised_3D_Human_Pose_Estimation_by_Predicting_Camera_Elevation_CVPR_2022_paper.pdf | cvpr-2022-1 | ['unsupervised-3d-human-pose-estimation'] | ['computer-vision'] | [-1.33709326e-01 6.53824583e-02 -1.56642079e-01 -3.79246622e-01
-6.39857233e-01 -4.61197287e-01 4.02413577e-01 -3.97506356e-01
-7.89988399e-01 6.12680852e-01 5.14988840e-01 3.93399715e-01
4.10478443e-01 -2.96838880e-01 -8.58912051e-01 -3.57336581e-01
1.69439139e-04 1.08200753e+00 1.07925966e-01 -1.73367605... | [7.018719673156738, -0.9516367316246033] |
582e6961-aebc-487d-985b-d0fce2f2150e | neuroninspect-detecting-backdoors-in-neural | 1911.07399 | null | https://arxiv.org/abs/1911.07399v1 | https://arxiv.org/pdf/1911.07399v1.pdf | NeuronInspect: Detecting Backdoors in Neural Networks via Output Explanations | Deep neural networks have achieved state-of-the-art performance on various tasks. However, lack of interpretability and transparency makes it easier for malicious attackers to inject trojan backdoor into the neural networks, which will make the model behave abnormally when a backdoor sample with a specific trigger is i... | ['Xijie Huang', 'Moustafa Alzantot', 'Mani Srivastava'] | 2019-11-18 | null | null | null | null | ['traffic-sign-recognition'] | ['computer-vision'] | [ 9.13750008e-02 -1.77872241e-01 -1.72271222e-01 -2.26123512e-01
-3.15983772e-01 -8.07111382e-01 6.38121784e-01 -2.20628679e-02
-6.37624487e-02 3.33617955e-01 -1.38380930e-01 -8.55643153e-01
-7.72784203e-02 -5.61658800e-01 -1.12853277e+00 -8.15913200e-01
-1.19670860e-01 -2.25305989e-01 1.19397938e-01 -1.77170262... | [5.712527275085449, 7.744675159454346] |
0b86aa19-fa9a-4093-8b54-8939814d6cad | safe-distributional-reinforcement-learning | 2102.13446 | null | https://arxiv.org/abs/2102.13446v1 | https://arxiv.org/pdf/2102.13446v1.pdf | Safe Distributional Reinforcement Learning | Safety in reinforcement learning (RL) is a key property in both training and execution in many domains such as autonomous driving or finance. In this paper, we formalize it with a constrained RL formulation in the distributional RL setting. Our general model accepts various definitions of safety(e.g., bounds on expecte... | ['Paul Weng', 'Jianyi Zhang'] | 2021-02-26 | null | null | null | null | ['distributional-reinforcement-learning'] | ['methodology'] | [-6.21690117e-02 4.24771786e-01 -4.81295854e-01 -2.26854473e-01
-8.11249554e-01 -7.64069617e-01 7.11710632e-01 3.40077765e-02
-7.80415118e-01 1.23524857e+00 -2.78590024e-02 -5.82927704e-01
-3.47406358e-01 -7.37486124e-01 -8.97243679e-01 -7.63304949e-01
-4.14842993e-01 1.87525004e-02 3.05574715e-01 -2.52940178... | [4.459441184997559, 2.147900104522705] |
0d522f4d-5258-4151-8e54-b38d76b3a7bf | localtrans-a-multiscale-local-transformer | 2106.04067 | null | https://arxiv.org/abs/2106.04067v2 | https://arxiv.org/pdf/2106.04067v2.pdf | LocalTrans: A Multiscale Local Transformer Network for Cross-Resolution Homography Estimation | Cross-resolution image alignment is a key problem in multiscale gigapixel photography, which requires to estimate homography matrix using images with large resolution gap. Existing deep homography methods concatenate the input images or features, neglecting the explicit formulation of correspondences between them, whic... | ['Yebin Liu', 'Ying Fu', 'Yuemei Zhou', 'Gaochang Wu', 'Ruizhi Shao'] | 2021-06-08 | null | http://openaccess.thecvf.com//content/ICCV2021/html/Shao_LocalTrans_A_Multiscale_Local_Transformer_Network_for_Cross-Resolution_Homography_Estimation_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Shao_LocalTrans_A_Multiscale_Local_Transformer_Network_for_Cross-Resolution_Homography_Estimation_ICCV_2021_paper.pdf | iccv-2021-1 | ['homography-estimation'] | ['computer-vision'] | [ 2.09018156e-01 -3.19905996e-01 2.32420370e-01 -2.95349449e-01
-1.17212856e+00 -4.31388050e-01 4.72535312e-01 -4.55060571e-01
-2.63230413e-01 3.83831590e-01 3.00048470e-01 3.82633567e-01
-4.42331403e-01 -7.54316747e-01 -1.04216504e+00 -5.95811486e-01
4.28676069e-01 4.33564425e-01 7.42652714e-02 -2.36595318... | [8.799187660217285, -2.286741256713867] |
8d4e7cc7-0e5b-43a9-8890-81c1391b89e4 | exemplar-bsaed-pattern-synthesis-with | 2204.01671 | null | https://arxiv.org/abs/2204.01671v2 | https://arxiv.org/pdf/2204.01671v2.pdf | Exemplar-based Pattern Synthesis with Implicit Periodic Field Network | Synthesis of ergodic, stationary visual patterns is widely applicable in texturing, shape modeling, and digital content creation. The wide applicability of this technique thus requires the pattern synthesis approaches to be scalable, diverse, and authentic. In this paper, we propose an exemplar-based visual pattern syn... | ['Yajie Zhao', 'Shichen Liu', 'Weikai Chen', 'Jiayi Liu', 'Haiwei Chen'] | 2022-04-04 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Chen_Exemplar-Based_Pattern_Synthesis_With_Implicit_Periodic_Field_Network_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Chen_Exemplar-Based_Pattern_Synthesis_With_Implicit_Periodic_Field_Network_CVPR_2022_paper.pdf | cvpr-2022-1 | ['texture-synthesis'] | ['computer-vision'] | [ 4.44726527e-01 2.78379083e-01 3.86488847e-02 3.56371611e-01
-1.98484182e-01 -5.71284652e-01 8.99032414e-01 -6.26550794e-01
4.87767011e-01 7.61758983e-01 1.31907701e-01 9.13763344e-02
-9.68853533e-02 -1.06377625e+00 -9.28482473e-01 -1.03968179e+00
8.08845237e-02 2.60280281e-01 -4.14854549e-02 -3.88529599... | [9.125773429870605, -3.46344256401062] |
773755ef-044a-4de3-a853-29e3f17a8a0b | triplet-loss-based-embeddings-for-forensic | 2102.12564 | null | https://arxiv.org/abs/2102.12564v2 | https://arxiv.org/pdf/2102.12564v2.pdf | Triplet loss based embeddings for forensic speaker identification in Spanish | With the advent of digital technology, it is more common that committed crimes or legal disputes involve some form of speech recording where the identity of a speaker is questioned [1]. In face of this situation, the field of forensic speaker identification has been looking to shed light on the problem by quantifying h... | ['Ivan Meza', 'Carlos Mena', 'Javier Alvarez-Jimenez', 'Emmanuel Maqueda'] | 2021-02-24 | null | null | null | null | ['speaker-identification'] | ['speech'] | [ 4.82294671e-02 3.05380225e-01 3.19742024e-01 -4.43130463e-01
-9.32079554e-01 -5.05957484e-01 8.46647859e-01 1.65427104e-01
-5.89861095e-01 5.35814464e-01 3.86510462e-01 -3.45686108e-01
-1.08735710e-02 -5.72766006e-01 -5.88576913e-01 -6.90141261e-01
6.69837520e-02 4.14901257e-01 -2.14888513e-01 1.52513608... | [14.321846961975098, 6.046711444854736] |
6b1f1145-0c19-461b-958d-be9ca721a8e7 | sparse-winograd-convolutional-neural-networks | 1810.01973 | null | http://arxiv.org/abs/1810.01973v1 | http://arxiv.org/pdf/1810.01973v1.pdf | Sparse Winograd Convolutional neural networks on small-scale systolic arrays | The reconfigurability, energy-efficiency, and massive parallelism on FPGAs
make them one of the best choices for implementing efficient deep learning
accelerators. However, state-of-art implementations seldom consider the balance
between high throughput of computation power and the ability of the memory
subsystem to su... | ['Song-Chun Zhu', 'Haochen Li', 'Feng Shi', 'Yuhe Gao', 'Benjamin Kuschner'] | 2018-10-03 | null | null | null | null | ['layout-design'] | ['computer-vision'] | [-6.26766860e-01 -4.06499594e-01 -2.53311574e-01 -2.62697339e-01
3.32357556e-01 -8.28645751e-02 4.08602089e-01 1.54904678e-01
-5.01733720e-01 4.56718296e-01 1.94817394e-01 -7.06111252e-01
-1.25626743e-01 -1.04136789e+00 -3.69546533e-01 -8.54696929e-01
-3.20480131e-02 -1.63181603e-01 3.23041886e-01 -6.06137700... | [8.32797908782959, 2.852698564529419] |
d6fcaaea-307e-4c09-b057-5a95cdd9ffd0 | handseg-an-automatically-labeled-dataset-for | 1711.05944 | null | http://arxiv.org/abs/1711.05944v4 | http://arxiv.org/pdf/1711.05944v4.pdf | HandSeg: An Automatically Labeled Dataset for Hand Segmentation from Depth Images | We propose an automatic method for generating high-quality annotations for
depth-based hand segmentation, and introduce a large-scale hand segmentation
dataset. Existing datasets are typically limited to a single hand. By
exploiting the visual cues given by an RGBD sensor and a pair of colored
gloves, we automatically ... | ['Sri Raghu Malireddi', 'Andrea Tagliasacchi', 'Vincent Lepetit', 'Kwang Moo Yi', 'Franziska Mueller', 'Christian Theobalt', 'Abhishake Kumar Bojja', 'Markus Oberweger'] | 2017-11-16 | null | null | null | null | ['hand-segmentation'] | ['computer-vision'] | [ 2.09061667e-01 7.17273429e-02 -1.94352403e-01 -3.05076122e-01
-6.62361920e-01 -1.06067550e+00 1.13159351e-01 -2.93637246e-01
-2.75647908e-01 5.97416818e-01 4.56217974e-02 -2.01267332e-01
2.75577515e-01 -6.52870595e-01 -4.28546041e-01 -3.88728648e-01
4.15358931e-01 7.69384027e-01 6.37123644e-01 6.25100732... | [6.614871025085449, -0.6746015548706055] |
61a92636-85c9-4466-93cc-47fb449ffe9d | learning-by-aligning-2d-skeleton-sequences-in | 2305.19480 | null | https://arxiv.org/abs/2305.19480v2 | https://arxiv.org/pdf/2305.19480v2.pdf | Learning by Aligning 2D Skeleton Sequences in Time | This paper presents a novel self-supervised temporal video alignment framework which is useful for several fine-grained human activity understanding applications. In contrast with the state-of-the-art method of CASA, where sequences of 3D skeleton coordinates are taken directly as input, our key idea is to use sequence... | ['M. Zeeshan Zia', 'Andrey Konin', 'Murad Popattia', 'M. Hassan Ahmed', 'Ahmed Mehmood', 'Muhammad Ahmed', 'Quoc-Huy Tran'] | 2023-05-31 | null | null | null | null | ['video-alignment'] | ['computer-vision'] | [ 3.80930752e-01 -2.19832599e-01 -3.70850891e-01 -2.03847975e-01
-6.82049155e-01 -3.43851268e-01 7.24738359e-01 -3.02118585e-02
-5.41669965e-01 3.80115569e-01 4.26188111e-01 8.93470645e-02
-1.22252978e-01 -6.82981551e-01 -8.66694570e-01 -4.75872874e-01
-1.16728634e-01 2.66088784e-01 4.29211497e-01 -3.13694268... | [7.950120449066162, 0.4408995509147644] |
1a53c36e-1577-45df-aea8-8462c5335c98 | thompson-sampling-for-combinatorial-semi-2 | 2005.06725 | null | https://arxiv.org/abs/2005.06725v1 | https://arxiv.org/pdf/2005.06725v1.pdf | Thompson Sampling for Combinatorial Semi-bandits with Sleeping Arms and Long-Term Fairness Constraints | We study the combinatorial sleeping multi-armed semi-bandit problem with long-term fairness constraints~(CSMAB-F). To address the problem, we adopt Thompson Sampling~(TS) to maximize the total rewards and use virtual queue techniques to handle the fairness constraints, and design an algorithm called \emph{TS with beta ... | ['QiPeng Wang', 'Yifan Xu', 'Bingshan Hu', 'Zhiming Huang', 'Jianping Pan'] | 2020-05-14 | null | null | null | null | ['movie-recommendation'] | ['miscellaneous'] | [ 1.73415914e-02 -5.59541993e-02 -5.42206943e-01 -3.71507585e-01
-9.19930696e-01 -6.62254870e-01 -3.49745750e-01 -3.34699482e-01
-8.46328378e-01 1.23205948e+00 -4.04488295e-01 -7.98564672e-01
-8.54282737e-01 -7.53988862e-01 -6.33561075e-01 -8.95643711e-01
-1.91471264e-01 7.46189475e-01 -7.94368535e-02 -1.74855188... | [4.556407451629639, 3.3446969985961914] |
a957fe64-e92f-4e6f-9cee-ee234fbb72ab | avatarclip-zero-shot-text-driven-generation | 2205.08535 | null | https://arxiv.org/abs/2205.08535v1 | https://arxiv.org/pdf/2205.08535v1.pdf | AvatarCLIP: Zero-Shot Text-Driven Generation and Animation of 3D Avatars | 3D avatar creation plays a crucial role in the digital age. However, the whole production process is prohibitively time-consuming and labor-intensive. To democratize this technology to a larger audience, we propose AvatarCLIP, a zero-shot text-driven framework for 3D avatar generation and animation. Unlike professional... | ['Ziwei Liu', 'Lei Yang', 'Zhongang Cai', 'Liang Pan', 'Mingyuan Zhang', 'Fangzhou Hong'] | 2022-05-17 | null | null | null | null | ['texture-synthesis'] | ['computer-vision'] | [-1.06867269e-01 1.95246786e-01 2.59190142e-01 1.05807610e-01
-3.41446370e-01 -8.50192130e-01 7.97489524e-01 -6.29765272e-01
1.37845069e-01 1.93032280e-01 2.17162326e-01 -1.59054205e-01
5.36878288e-01 -8.93389344e-01 -6.50401294e-01 -5.42295635e-01
3.43984008e-01 6.08155251e-01 -6.34400696e-02 -6.13998592... | [11.995943069458008, -0.6641638278961182] |
b15f86a0-e3f6-43f8-897e-f48494b658bf | agents-that-listen-high-throughput | 2107.02195 | null | https://arxiv.org/abs/2107.02195v1 | https://arxiv.org/pdf/2107.02195v1.pdf | Agents that Listen: High-Throughput Reinforcement Learning with Multiple Sensory Systems | Humans and other intelligent animals evolved highly sophisticated perception systems that combine multiple sensory modalities. On the other hand, state-of-the-art artificial agents rely mostly on visual inputs or structured low-dimensional observations provided by instrumented environments. Learning to act based on com... | ['Aleksei Petrenko', 'Anssi Kanervisto', 'Shashank Hegde'] | 2021-07-05 | null | null | null | null | ['game-of-doom'] | ['playing-games'] | [-3.89978811e-02 -9.83988792e-02 4.13071781e-01 -1.21497102e-01
-4.64649945e-01 -7.84749925e-01 7.02365279e-01 -1.75476484e-02
-8.74900043e-01 5.46715021e-01 -1.23116754e-01 -2.88886815e-01
2.54819810e-01 -4.97850806e-01 -4.97830451e-01 -5.58749080e-01
-1.60829529e-01 6.23501122e-01 5.25414824e-01 -3.82857770... | [4.265717029571533, 1.0354903936386108] |
b549c1cd-ffc0-4d09-b5b5-b6fd702e7fce | stn-homography-estimate-homography-parameters | 1906.02539 | null | https://arxiv.org/abs/1906.02539v1 | https://arxiv.org/pdf/1906.02539v1.pdf | STN-Homography: estimate homography parameters directly | In this paper, we introduce the STN-Homography model to directly estimate the homography matrix between image pair. Different most CNN-based homography estimation methods which use an alternative 4-point homography parameterization, we use prove that, after coordinate normalization, the variance of elements of coordina... | ['Qiang Zhou', 'Xin Li'] | 2019-06-06 | null | null | null | null | ['homography-estimation'] | ['computer-vision'] | [-2.00287059e-01 -4.19155173e-02 1.54997855e-01 -2.99253035e-02
-3.11068773e-01 -2.65347987e-01 3.82019341e-01 -3.84999156e-01
-3.42910409e-01 3.30017716e-01 -4.08039689e-02 -2.04325780e-01
2.72941202e-01 -9.31103110e-01 -1.06881177e+00 -5.22253990e-01
1.92498595e-01 3.74913007e-01 2.65289128e-01 -2.24997163... | [8.586602210998535, -2.2151410579681396] |
366bd791-618d-494f-8cfb-070a4c5f75cf | multimodal-multipart-learning-for-action | 1507.08761 | null | http://arxiv.org/abs/1507.08761v1 | http://arxiv.org/pdf/1507.08761v1.pdf | Multimodal Multipart Learning for Action Recognition in Depth Videos | The articulated and complex nature of human actions makes the task of action
recognition difficult. One approach to handle this complexity is dividing it to
the kinetics of body parts and analyzing the actions based on these partial
descriptors. We propose a joint sparse regression based learning method which
utilizes ... | ['Tian-Tsong Ng', 'Gang Wang', 'Amir Shahroudy', 'Qingxiong Yang'] | 2015-07-31 | null | null | null | null | ['multimodal-activity-recognition'] | ['computer-vision'] | [ 3.30860436e-01 -1.16352409e-01 -4.37159270e-01 -1.55359030e-01
-5.04915893e-01 -2.07372010e-01 7.26554096e-01 -1.29799575e-01
-1.85607970e-01 6.74071014e-01 7.02373505e-01 7.16835439e-01
-4.31160778e-01 -3.35173160e-01 -3.76660645e-01 -9.70444441e-01
-6.23337738e-02 5.09700954e-01 2.48006791e-01 -2.24964842... | [7.89186954498291, 0.40472736954689026] |
5ce598d7-fdf3-4b2c-be2e-67e6a1e468ff | counterfactual-explanations-for-predictive | 2202.12018 | null | https://arxiv.org/abs/2202.12018v1 | https://arxiv.org/pdf/2202.12018v1.pdf | Counterfactual Explanations for Predictive Business Process Monitoring | Predictive business process monitoring increasingly leverages sophisticated prediction models. Although sophisticated models achieve consistently higher prediction accuracy than simple models, one major drawback is their lack of interpretability, which limits their adoption in practice. We thus see growing interest in ... | ['Klaus Pohl', 'Andreas Metzger', 'Tsung-Hao Huang'] | 2022-02-24 | null | null | null | null | ['counterfactual-explanation', 'predictive-process-monitoring'] | ['miscellaneous', 'time-series'] | [ 5.68670094e-01 1.01922774e+00 -5.82780480e-01 -5.15720844e-01
-2.45808829e-02 -2.95872718e-01 1.03231537e+00 1.57060504e-01
3.61840755e-01 9.44677889e-01 4.42749947e-01 -9.16353047e-01
-4.43656474e-01 -8.90699089e-01 -7.00025260e-01 -9.32364985e-02
1.23950183e-01 7.65777349e-01 -3.10230702e-01 2.99043775... | [8.716687202453613, 5.800018310546875] |
fe7ed137-9a8d-4d20-98e8-8fcd95f8deaf | fast-hand-detection-in-collaborative-learning | 2110.07070 | null | https://arxiv.org/abs/2110.07070v1 | https://arxiv.org/pdf/2110.07070v1.pdf | Fast Hand Detection in Collaborative Learning Environments | Long-term object detection requires the integration of frame-based results over several seconds. For non-deformable objects, long-term detection is often addressed using object detection followed by video tracking. Unfortunately, tracking is inapplicable to objects that undergo dramatic changes in appearance from frame... | ['Carlos LopezLeiva', 'Sylvia Celedon Pattichis', 'Marios S. Pattichis', 'Venkatesh Jatla', 'Sravani Teeparthi'] | 2021-10-13 | null | null | null | null | ['hand-detection'] | ['computer-vision'] | [-3.58706564e-02 -3.95641804e-01 1.66903540e-01 2.25956410e-01
-8.47813547e-01 -7.71307290e-01 1.74690947e-01 2.49733310e-02
-6.73936963e-01 6.49438083e-01 -2.06492618e-01 -1.75320059e-02
1.14948750e-01 -2.79320598e-01 -8.12249780e-01 -6.41059756e-01
-1.83407709e-01 3.22757810e-01 9.37530935e-01 2.70652294... | [6.711970329284668, -0.8019370436668396] |
b80ff0a5-3fd2-4fcf-a463-f02bf77a4c9a | incorporating-the-rhetoric-of-scientific | null | null | https://aclanthology.org/2022.sdp-1.7 | https://aclanthology.org/2022.sdp-1.7.pdf | Incorporating the Rhetoric of Scientific Language into Sentence Embeddings using Phrase-guided Distant Supervision and Metric Learning | Communicative functions are an important rhetorical feature of scientific writing. Sentence embeddings that contain such features are highly valuable for the argumentative analysis of scientific documents, with applications in document alignment, recommendation, and academic writing assistance. Moreover, embeddings can... | ['Akiko Aizawa', 'Kaito Sugimoto'] | null | null | null | null | sdp-coling-2022-10 | ['sentence-embeddings', 'sentence-embeddings'] | ['methodology', 'natural-language-processing'] | [ 7.83272013e-02 7.27319270e-02 -3.84925008e-01 -4.33920622e-01
-5.74041843e-01 -5.52378595e-01 6.65060163e-01 7.40918517e-01
-5.06105363e-01 6.47949755e-01 5.69413364e-01 -3.96716505e-01
-3.28231424e-01 -6.92175508e-01 -4.46818560e-01 -4.89128798e-01
3.93246889e-01 3.78285021e-01 -2.59046882e-01 -5.01320422... | [11.01823616027832, 8.688685417175293] |
bd9655b8-2221-413d-9563-f32aac645869 | eica-at-semeval-2017-task-4-a-simple | null | null | https://aclanthology.org/S17-2124 | https://aclanthology.org/S17-2124.pdf | EICA at SemEval-2017 Task 4: A Simple Convolutional Neural Network for Topic-based Sentiment Classification | This paper describes our approach for SemEval-2017 Task 4 - Sentiment Analysis in Twitter (SAT). Its five subtasks are divided into two categories: (1) sentiment classification, i.e., predicting topic-based tweet sentiment polarity, and (2) sentiment quantification, that is, estimating the sentiment distributions of a ... | ['Yufei Xie', 'Shiyun Chen', 'Maoquan Wang', 'Lu Zhao'] | 2017-08-01 | null | null | null | semeval-2017-8 | ['twitter-sentiment-analysis'] | ['natural-language-processing'] | [-1.28870383e-01 -1.32760525e-01 -3.40650916e-01 -1.10083926e+00
-7.18573570e-01 -4.60919559e-01 6.46720529e-01 2.50220865e-01
-4.07731771e-01 5.05738497e-01 6.35467291e-01 -3.31034303e-01
7.64062583e-01 -6.93219602e-01 -4.66342807e-01 -4.18535829e-01
1.58647537e-01 3.67804676e-01 -3.81874926e-02 -6.51666641... | [11.183066368103027, 6.8962578773498535] |
2e359dfd-14cf-47ca-a71d-b07f646249b1 | faster-person-re-identification | 2008.06826 | null | https://arxiv.org/abs/2008.06826v1 | https://arxiv.org/pdf/2008.06826v1.pdf | Faster Person Re-Identification | Fast person re-identification (ReID) aims to search person images quickly and accurately. The main idea of recent fast ReID methods is the hashing algorithm, which learns compact binary codes and performs fast Hamming distance and counting sort. However, a very long code is needed for high accuracy (e.g. 2048), which c... | ['Zeng-Guang Hou', "Guan'an Wang", 'Shaogang Gong', 'Jian Cheng'] | 2020-08-16 | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/566_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123530273.pdf | eccv-2020-8 | ['code-search', 'code-search', '2048'] | ['computer-code', 'computer-vision', 'playing-games'] | [-2.76119351e-01 -5.14926434e-01 -1.43638089e-01 -4.15911615e-01
-7.63717413e-01 -5.29914320e-01 3.08182001e-01 4.39979523e-01
-7.83390522e-01 5.03719032e-01 3.18088919e-01 1.30787149e-01
-1.27458379e-01 -9.48639274e-01 -5.87571323e-01 -7.44241059e-01
-2.29028583e-01 5.95267415e-01 2.44828254e-01 -1.47229508... | [14.733536720275879, 0.8730013370513916] |
a99da683-b8f4-423b-8629-38772f111a50 | exploiting-bilateral-symmetry-in-brain-lesion | 1907.08196 | null | https://arxiv.org/abs/1907.08196v1 | https://arxiv.org/pdf/1907.08196v1.pdf | Exploiting bilateral symmetry in brain lesion segmentation | Brain lesions, including stroke and tumours, have a high degree of variability in terms of location, size, intensity and form, making automatic segmentation difficult. We propose an improvement to existing segmentation methods by exploiting the bilateral quasi-symmetry of healthy brains, which breaks down when lesions ... | ['Tanya Schmah', 'Uladzimir Yahorau', 'Kevin Raina'] | 2019-07-18 | null | null | null | null | ['ischemic-stroke-lesion-segmentation'] | ['medical'] | [ 4.46333319e-01 1.25115365e-01 4.14693505e-02 -4.92518932e-01
-8.74715388e-01 -7.42589355e-01 8.37609649e-01 2.12291047e-01
-7.86356628e-01 4.45251614e-01 6.28542364e-01 -1.31133333e-01
-1.07697077e-01 -5.86362839e-01 -4.60270494e-01 -8.04348350e-01
-2.13966921e-01 7.30095565e-01 5.67336380e-01 -1.47386909... | [14.223960876464844, -2.133103370666504] |
7a9ed627-f935-4394-8a22-4424b525bd26 | dukweb-diachronic-word-representations-from | 2107.01076 | null | https://arxiv.org/abs/2107.01076v2 | https://arxiv.org/pdf/2107.01076v2.pdf | DUKweb: Diachronic word representations from the UK Web Archive corpus | Lexical semantic change (detecting shifts in the meaning and usage of words) is an important task for social and cultural studies as well as for Natural Language Processing applications. Diachronic word embeddings (time-sensitive vector representations of words that preserve their meaning) have become the standard reso... | ['Barbara McGillivray', 'Mihai Cucuringu', 'Marya Bazzi', 'Pierpaolo Basile', 'Adam Tsakalidis'] | 2021-07-02 | null | null | null | null | ['diachronic-word-embeddings'] | ['natural-language-processing'] | [-6.40339404e-02 -3.80859137e-01 -2.66424268e-01 -8.91901031e-02
-4.84185338e-01 -8.84350836e-01 9.21138525e-01 8.07963073e-01
-1.22710371e+00 4.89652395e-01 1.05367863e+00 -1.74329996e-01
-2.54711092e-01 -8.77956033e-01 -1.15172908e-01 -4.70917076e-01
-7.84999579e-02 2.67072558e-01 3.71759593e-01 -5.65783799... | [10.154223442077637, 8.931760787963867] |
95ff1f23-75dc-4ab8-8e22-f2cf822a42a2 | meshdepth-disconnected-mesh-based-deep-depth | 1905.01312 | null | https://arxiv.org/abs/1905.01312v2 | https://arxiv.org/pdf/1905.01312v2.pdf | TriDepth: Triangular Patch-based Deep Depth Prediction | We propose a novel and efficient representation for single-view depth estimation using Convolutional Neural Networks (CNNs). Point-cloud is generally used for CNN-based 3D scene reconstruction; however it has some drawbacks: (1) it is redundant as a representation for planar surfaces, and (2) no spatial relationships b... | ['Kiyoharu Aizawa', 'Ken Sakurada', 'Masaya Kaneko'] | 2019-05-03 | null | null | null | null | ['3d-scene-reconstruction'] | ['computer-vision'] | [ 8.61441046e-02 2.87300795e-02 1.44940257e-01 -1.73576429e-01
-3.71446401e-01 -3.03342521e-01 2.52572864e-01 -1.76963180e-01
-1.13250203e-01 1.57859355e-01 -1.88932121e-01 -6.90866038e-02
1.70278281e-01 -1.20519829e+00 -9.29130733e-01 -4.96057421e-01
2.70213604e-01 5.17680228e-01 5.55921495e-01 -2.00044751... | [8.749921798706055, -3.046511650085449] |
fb2a077a-5c43-4405-8631-8f052943f492 | co-saliency-detection-for-rgbd-images-based | 1710.05172 | null | http://arxiv.org/abs/1710.05172v1 | http://arxiv.org/pdf/1710.05172v1.pdf | Co-saliency Detection for RGBD Images Based on Multi-constraint Feature Matching and Cross Label Propagation | Co-saliency detection aims at extracting the common salient regions from an
image group containing two or more relevant images. It is a newly emerging
topic in computer vision community. Different from the most existing
co-saliency methods focusing on RGB images, this paper proposes a novel
co-saliency detection model ... | ['Qingming Huang', 'Huazhu Fu', 'Runmin Cong', 'Xiaochun Cao', 'Jianjun Lei', 'Chunping Hou'] | 2017-10-14 | null | null | null | null | ['co-saliency-detection'] | ['computer-vision'] | [ 5.05433202e-01 -1.53068304e-01 -8.20734873e-02 -1.02361232e-01
-5.00595272e-01 6.74022734e-02 2.29021832e-01 3.38854104e-01
-4.05917943e-01 2.86798924e-01 1.10636078e-01 1.60368726e-01
-1.73566222e-01 -6.72008872e-01 -4.64376479e-01 -6.67290807e-01
3.70338172e-01 -3.72508764e-01 1.13806307e+00 -2.05015242... | [9.783515930175781, -0.5411754250526428] |
f7da1c4c-58d2-4d1f-869a-c5d78e67749e | a-regularized-framework-for-sparse-and | 1705.07704 | null | http://arxiv.org/abs/1705.07704v3 | http://arxiv.org/pdf/1705.07704v3.pdf | A Regularized Framework for Sparse and Structured Neural Attention | Modern neural networks are often augmented with an attention mechanism, which
tells the network where to focus within the input. We propose in this paper a
new framework for sparse and structured attention, building upon a smoothed max
operator. We show that the gradient of this operator defines a mapping from
real val... | ['Mathieu Blondel', 'Vlad Niculae'] | 2017-05-22 | a-regularized-framework-for-sparse-and-1 | http://papers.nips.cc/paper/6926-a-regularized-framework-for-sparse-and-structured-neural-attention | http://papers.nips.cc/paper/6926-a-regularized-framework-for-sparse-and-structured-neural-attention.pdf | neurips-2017-12 | ['abstractive-sentence-summarization'] | ['natural-language-processing'] | [ 7.12793350e-01 6.76521480e-01 -2.73515344e-01 -6.38099134e-01
-6.69109941e-01 -4.45772737e-01 6.54370964e-01 2.45435297e-01
-6.67016983e-01 7.55451202e-01 6.31551981e-01 -4.89487469e-01
1.32207617e-01 -5.55580080e-01 -1.09911203e+00 -3.77341390e-01
9.08711553e-02 3.77445042e-01 -1.09077245e-01 -3.15147847... | [11.520644187927246, 8.797663688659668] |
afffb7d4-c168-41ac-92d8-7f66e9d2d520 | nsitnlp4if-2019-propaganda-detection-from | null | null | https://aclanthology.org/D19-5021 | https://aclanthology.org/D19-5021.pdf | NSIT@NLP4IF-2019: Propaganda Detection from News Articles using Transfer Learning | In this paper, we describe our approach and system description for NLP4IF 2019 Workshop: Shared Task on Fine-Grained Propaganda Detection. Given a sentence from a news article, the task is to detect whether the sentence contains a propagandistic agenda or not. The main contribution of our work is to evaluate the effect... | ['Anubhav Sadana', 'Kartik Aggarwal'] | 2019-11-01 | null | null | null | ws-2019-11 | ['propaganda-detection'] | ['natural-language-processing'] | [-7.70180002e-02 1.12728581e-01 -1.55552462e-01 -1.72218695e-01
-1.08832467e+00 -4.62987632e-01 1.24878120e+00 3.96317363e-01
-6.25802994e-01 6.34686470e-01 1.08946478e+00 -6.27492130e-01
1.31030068e-01 -7.61559188e-01 -6.94815576e-01 -5.85078418e-01
-2.02150047e-02 1.56773314e-01 1.33007895e-02 -4.04641896... | [8.478108406066895, 10.664602279663086] |
5370bf12-587b-4f7a-bf09-7795ae2b8a46 | indexing-ai-risks-with-incidents-issues-and | 2211.10384 | null | https://arxiv.org/abs/2211.10384v1 | https://arxiv.org/pdf/2211.10384v1.pdf | Indexing AI Risks with Incidents, Issues, and Variants | Two years after publicly launching the AI Incident Database (AIID) as a collection of harms or near harms produced by AI in the world, a backlog of "issues" that do not meet its incident ingestion criteria have accumulated in its review queue. Despite not passing the database's current criteria for incidents, these iss... | ['Khoa Lam', 'Kevin Paeth', 'Sean McGregor'] | 2022-11-18 | null | null | null | null | ['computer-security'] | ['miscellaneous'] | [ 9.88370627e-02 2.90241361e-01 3.53116659e-03 -2.45786041e-01
-8.17009866e-01 -5.21093845e-01 8.86284232e-01 8.97896469e-01
-4.92954552e-01 7.32644141e-01 8.58569741e-01 -4.63803560e-01
-4.43181455e-01 -9.11677361e-01 -5.00652850e-01 -3.27250808e-02
-1.30727157e-01 5.24394035e-01 7.28888214e-02 -1.81087554... | [8.803709030151367, 9.244643211364746] |
ea4ad3f3-cf9e-456c-934c-9a75e79fb08e | repurposing-knowledge-graph-embeddings-for | 2208.10328 | null | https://arxiv.org/abs/2208.10328v1 | https://arxiv.org/pdf/2208.10328v1.pdf | Repurposing Knowledge Graph Embeddings for Triple Representation via Weak Supervision | The majority of knowledge graph embedding techniques treat entities and predicates as separate embedding matrices, using aggregation functions to build a representation of the input triple. However, these aggregations are lossy, i.e. they do not capture the semantics of the original triples, such as information contain... | ['Yuan An', 'Alexander Kalinowski'] | 2022-08-22 | null | null | null | null | ['triple-classification', 'knowledge-graph-embedding', 'knowledge-graph-embeddings', 'knowledge-graph-embeddings'] | ['graphs', 'graphs', 'graphs', 'methodology'] | [-1.89940825e-01 2.82487631e-01 -7.13050246e-01 -4.05342728e-01
-6.22394383e-01 -4.66984093e-01 6.54829681e-01 6.44160986e-01
-1.96020558e-01 5.56069672e-01 4.85177964e-01 -2.80034561e-02
-2.12816939e-01 -1.12800038e+00 -8.86239171e-01 -4.84489053e-01
2.04073489e-02 8.52372289e-01 1.77081630e-01 -2.83608913... | [8.747817993164062, 7.881862163543701] |
48a084b2-d0be-4993-92be-b280d3e254c9 | dotat-a-domain-oriented-text-annotation-tool-1 | null | null | https://aclanthology.org/2022.acl-demo.1 | https://aclanthology.org/2022.acl-demo.1.pdf | DoTAT: A Domain-oriented Text Annotation Tool | We propose DoTAT, a domain-oriented text annotation tool. The tool designs and implements functions heavily in need in domain-oriented information extraction. Firstly, the tool supports a multi-person collaborative process with automatically merging and review, which can greatly improve the annotation accuracy. Secondl... | ['Yi Wang', 'Wen Du', 'Tingting Cai', 'Ming Liang', 'Tong Ruan', 'Yupian Lin'] | null | null | null | null | acl-2022-5 | ['text-annotation'] | ['natural-language-processing'] | [-4.86569136e-01 2.30433494e-01 -1.33269921e-01 -3.60535592e-01
-8.54750693e-01 -7.15242445e-01 6.25675738e-01 3.63666952e-01
-4.99743909e-01 8.25556576e-01 4.47192371e-01 -7.43457377e-02
7.66527876e-02 -4.91701305e-01 -1.87227711e-01 -1.81973100e-01
3.38838071e-01 7.32444942e-01 3.51003289e-01 4.53259163... | [9.314022064208984, 9.049840927124023] |
2b4fd209-eedc-4f66-84fa-1664666c91da | restorex-ai-a-contrastive-approach-towards | 2204.01719 | null | https://arxiv.org/abs/2204.01719v1 | https://arxiv.org/pdf/2204.01719v1.pdf | RestoreX-AI: A Contrastive Approach towards Guiding Image Restoration via Explainable AI Systems | Modern applications such as self-driving cars and drones rely heavily upon robust object detection techniques. However, weather corruptions can hinder the object detectability and pose a serious threat to their navigation and reliability. Thus, there is a need for efficient denoising, deraining, and restoration techniq... | ['Ketan Kotecha', 'Rahee Walambe', 'Pushkar Jain', 'Aboli Marathe'] | 2022-04-03 | null | null | null | null | ['robust-object-detection'] | ['computer-vision'] | [ 4.36215132e-01 1.63514495e-01 4.38286245e-01 -1.37871012e-01
-7.61479318e-01 -5.94064653e-01 8.51069927e-01 -2.84881353e-01
-4.51993763e-01 7.54340351e-01 -1.68968141e-01 -3.77637208e-01
1.75414562e-01 -8.57223451e-01 -1.00321877e+00 -8.94385934e-01
2.29828984e-01 -2.87376996e-03 2.83177793e-01 -4.98520225... | [8.287908554077148, -1.4129247665405273] |
746c60b2-5fe1-4509-b47e-8dc527fdcc1a | a-prototypical-semantic-decoupling-method-via | 2302.13610 | null | https://arxiv.org/abs/2302.13610v2 | https://arxiv.org/pdf/2302.13610v2.pdf | A Prototypical Semantic Decoupling Method via Joint Contrastive Learning for Few-Shot Name Entity Recognition | Few-shot named entity recognition (NER) aims at identifying named entities based on only few labeled instances. Most existing prototype-based sequence labeling models tend to memorize entity mentions which would be easily confused by close prototypes. In this paper, we proposed a Prototypical Semantic Decoupling method... | ['Weiran Xu', 'QiXiang Gao', 'Xinyue Cui', 'Keqing He', 'Tingfeng Hui', 'Xuefeng Li', 'Chen Zeng', 'Yuxiang Wu', 'Dayuan Fu', 'Daichi Guo', 'LiWen Wang', 'Zechen Wang', 'Guanting Dong'] | 2023-02-27 | null | null | null | null | ['few-shot-ner'] | ['natural-language-processing'] | [-1.59095317e-01 3.16459909e-02 -2.04747766e-01 -4.91137743e-01
-5.88994145e-01 -4.91910577e-01 6.11484349e-01 3.94616187e-01
-7.19572961e-01 7.51765668e-01 2.37176761e-01 -2.80629955e-02
-1.32756401e-02 -7.68719375e-01 -3.13541114e-01 -4.04697150e-01
1.09331645e-01 4.11740154e-01 4.74353939e-01 -2.58111000... | [9.664918899536133, 9.380733489990234] |
92db60b1-82f9-405d-ad52-b8b738b7e07d | separable-structure-modeling-for-semi | null | null | https://ieeexplore.ieee.org/document/9356697 | https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9356697 | Separable Structure Modeling for Semi-supervised Video Object Segmentation | In this paper, we propose a separable structure modeling approach for semi-supervised video object segmentation.
Unlike most existing methods which preclude the semantically structural information of target objects, our method not only captures pixel-level similarity relationships between the reference and target fram... | ['Jie zhou', 'Jiwen Lu', 'Jiahao Li', 'Wencheng Zhu'] | 2021-02-18 | null | null | null | null | ['one-shot-visual-object-segmentation'] | ['computer-vision'] | [ 3.56362402e-01 -2.96784133e-01 -4.95837986e-01 -3.98822606e-01
-6.81364000e-01 -3.61773610e-01 1.39092281e-01 -7.43386149e-02
-3.27363759e-01 3.27263236e-01 -2.26413161e-02 1.82670295e-01
-8.15811679e-02 -5.49778402e-01 -5.27045131e-01 -7.84804046e-01
1.18312322e-01 3.20422351e-01 8.67255569e-01 3.25096726... | [9.34609603881836, -0.10150997340679169] |
d1e0cd9f-0d1e-4ce5-b540-acb2b1f053d0 | beyond-supervised-vs-unsupervised-1 | 2206.08347 | null | https://arxiv.org/abs/2206.08347v1 | https://arxiv.org/pdf/2206.08347v1.pdf | Beyond Supervised vs. Unsupervised: Representative Benchmarking and Analysis of Image Representation Learning | By leveraging contrastive learning, clustering, and other pretext tasks, unsupervised methods for learning image representations have reached impressive results on standard benchmarks. The result has been a crowded field - many methods with substantially different implementations yield results that seem nearly identica... | ['Abhinav Shrivastava', 'Matthew Gwilliam'] | 2022-06-16 | beyond-supervised-vs-unsupervised | http://openaccess.thecvf.com//content/CVPR2022/html/Gwilliam_Beyond_Supervised_vs._Unsupervised_Representative_Benchmarking_and_Analysis_of_Image_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Gwilliam_Beyond_Supervised_vs._Unsupervised_Representative_Benchmarking_and_Analysis_of_Image_CVPR_2022_paper.pdf | cvpr-2022-1 | ['graph-similarity'] | ['graphs'] | [ 1.08607978e-01 -2.42028572e-02 -3.80331784e-01 -5.78784227e-01
-5.82305312e-01 -7.91174769e-01 9.50013995e-01 4.95038629e-01
-4.32688028e-01 2.08544105e-01 7.90980399e-01 -2.70982295e-01
-4.32792842e-01 -5.15031815e-01 -4.90219921e-01 -6.49095833e-01
-3.48452479e-01 9.57619324e-02 1.89401075e-01 -2.95921594... | [9.152427673339844, 3.0998342037200928] |
65e1126b-dd73-4b1d-baa8-4764da2b483c | unified-modeling-of-multi-talker-overlapped | 2305.16263 | null | https://arxiv.org/abs/2305.16263v1 | https://arxiv.org/pdf/2305.16263v1.pdf | Unified Modeling of Multi-Talker Overlapped Speech Recognition and Diarization with a Sidecar Separator | Multi-talker overlapped speech poses a significant challenge for speech recognition and diarization. Recent research indicated that these two tasks are inter-dependent and complementary, motivating us to explore a unified modeling method to address them in the context of overlapped speech. A recent study proposed a cos... | ['Helen Meng', 'Xixin Wu', 'Haibin Wu', 'Mingyu Cui', 'Jiawen Kang', 'Lingwei Meng'] | 2023-05-25 | null | null | null | null | ['automatic-speech-recognition'] | ['speech'] | [ 2.48483464e-01 1.20878667e-01 7.43886456e-02 -7.55512834e-01
-1.64946043e+00 -6.52490377e-01 3.95423591e-01 -3.20415884e-01
-4.05276954e-01 8.34199935e-02 3.49025071e-01 -5.54517627e-01
3.27252746e-01 1.56512097e-01 -3.64944756e-01 -8.78589034e-01
3.47847015e-01 3.23867887e-01 6.80591315e-02 -1.86852917... | [14.628008842468262, 6.302112102508545] |
56cb79b7-b83b-4dac-b216-714f9065706c | multiview-compressive-coding-for-3d | 2301.08247 | null | https://arxiv.org/abs/2301.08247v1 | https://arxiv.org/pdf/2301.08247v1.pdf | Multiview Compressive Coding for 3D Reconstruction | A central goal of visual recognition is to understand objects and scenes from a single image. 2D recognition has witnessed tremendous progress thanks to large-scale learning and general-purpose representations. Comparatively, 3D poses new challenges stemming from occlusions not depicted in the image. Prior works try to... | ['Georgia Gkioxari', 'Christoph Feichtenhofer', 'Jitendra Malik', 'Justin Johnson', 'Chao-yuan Wu'] | 2023-01-19 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Wu_Multiview_Compressive_Coding_for_3D_Reconstruction_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Wu_Multiview_Compressive_Coding_for_3D_Reconstruction_CVPR_2023_paper.pdf | cvpr-2023-1 | ['single-view-3d-reconstruction'] | ['computer-vision'] | [ 4.66992944e-01 1.11703672e-01 -1.76866487e-01 -4.84029114e-01
-8.82879496e-01 -7.60946631e-01 5.96516371e-01 -4.49979156e-01
2.35207930e-01 2.86739707e-01 1.46998942e-01 -8.38699937e-03
6.13748934e-03 -5.27900755e-01 -1.02420282e+00 -3.60142410e-01
1.94531903e-01 5.18158793e-01 1.33458987e-01 -1.10341264... | [8.403233528137207, -3.029836893081665] |
5f195e09-8777-4be4-a49f-39ef59fae576 | the-mapillary-vistas-dataset-for-semantic | null | null | http://openaccess.thecvf.com/content_iccv_2017/html/Neuhold_The_Mapillary_Vistas_ICCV_2017_paper.html | http://openaccess.thecvf.com/content_ICCV_2017/papers/Neuhold_The_Mapillary_Vistas_ICCV_2017_paper.pdf | The Mapillary Vistas Dataset for Semantic Understanding of Street Scenes | The Mapillary Vistas Dataset is a novel, large-scale street-level image dataset containing 25,000 high-resolution images annotated into 66 object categories with additional, instance-specific labels for 37 classes. Annotation is performed in a dense and fine-grained style by using polygons for delineating individual ob... | ['Gerhard Neuhold', 'Peter Kontschieder', 'Tobias Ollmann', 'Samuel Rota Bulo'] | 2017-10-01 | null | null | null | iccv-2017-10 | ['road-scene-understanding'] | ['computer-vision'] | [ 3.88021678e-01 1.77897885e-01 -1.47004709e-01 -4.74245727e-01
-5.71276546e-01 -9.28737819e-01 9.67001975e-01 2.13886902e-01
-3.53289157e-01 6.33585930e-01 2.57357270e-01 -2.98156381e-01
1.78569719e-01 -1.24180734e+00 -6.83601439e-01 -3.23735595e-01
-4.93565612e-02 6.52862012e-01 6.33449495e-01 -2.63480276... | [8.574701309204102, -1.5598591566085815] |
5b3e4597-cab3-4ebb-80e2-28c8c899d4cf | an-inductive-formalization-of-self | 1806.08925 | null | http://arxiv.org/abs/1806.08925v1 | http://arxiv.org/pdf/1806.08925v1.pdf | An Inductive Formalization of Self Reproduction in Dynamical Hierarchies | Formalizing self reproduction in dynamical hierarchies is one of the
important problems in Artificial Life (AL) studies. We study, in this paper, an
inductively defined algebraic framework for self reproduction on macroscopic
organizational levels under dynamical system setting for simulated AL models
and explore some ... | ['Janardan Misra'] | 2018-06-23 | null | null | null | null | ['artificial-life'] | ['miscellaneous'] | [ 1.16443209e-01 6.93672121e-01 4.23499882e-01 1.81894898e-01
7.25168586e-01 -8.07647586e-01 9.81629252e-01 2.96455652e-01
-1.67072207e-01 1.04547763e+00 3.44126582e-01 1.23167671e-01
-4.55827564e-01 -1.10321081e+00 -4.88281935e-01 -9.66054380e-01
-6.21075094e-01 6.15506887e-01 3.76307160e-01 -8.62883151... | [5.578807830810547, 4.15610933303833] |
d46b62e5-77fe-43f4-b074-d18b7e5c987f | investigating-generative-adversarial-networks | 1803.10132 | null | http://arxiv.org/abs/1803.10132v3 | http://arxiv.org/pdf/1803.10132v3.pdf | Investigating Generative Adversarial Networks based Speech Dereverberation for Robust Speech Recognition | We investigate the use of generative adversarial networks (GANs) in speech
dereverberation for robust speech recognition. GANs have been recently studied
for speech enhancement to remove additive noises, but there still lacks of a
work to examine their ability in speech dereverberation and the advantages of
using GANs ... | ['Ke Wang', 'Fei Xiang', 'Sining Sun', 'Yujun Wang', 'Junbo Zhang', 'Lei Xie'] | 2018-03-27 | null | null | null | null | ['robust-speech-recognition', 'speech-dereverberation'] | ['speech', 'speech'] | [ 1.02270819e-01 6.51721656e-02 6.40699506e-01 -4.62081134e-02
-9.87108111e-01 -5.04592717e-01 4.29085106e-01 -6.24614537e-01
-2.82319397e-01 7.36171961e-01 5.34532428e-01 -5.92552841e-01
3.87679577e-01 -4.70520765e-01 -7.54624844e-01 -1.01316655e+00
4.02963549e-01 -3.66156809e-02 -1.47750124e-01 -4.92048591... | [15.067460060119629, 6.044388771057129] |
a218b965-8230-4cc5-97df-6bb2a8ff869c | probabilistic-polargmm-unsupervised-cluster | 2206.12959 | null | https://arxiv.org/abs/2206.12959v1 | https://arxiv.org/pdf/2206.12959v1.pdf | Probabilistic PolarGMM: Unsupervised Cluster Learning of Very Noisy Projection Images of Unknown Pose | A crucial step in single particle analysis (SPA) of cryogenic electron microscopy (Cryo-EM), 2D classification and alignment takes a collection of noisy particle images to infer orientations and group similar images together. Averaging these aligned and clustered noisy images produces a set of clean images, ready for f... | ['Chandrajit L. Bajaj', 'Supawit Chockchowwat'] | 2022-06-26 | null | null | null | null | ['cryogenic-electron-microscopy-cryo-em'] | ['computer-vision'] | [ 2.23451421e-01 -4.66346771e-01 5.12266278e-01 -3.26260567e-01
-9.37110245e-01 -7.02918112e-01 8.13003242e-01 1.14341609e-01
-7.92845607e-01 8.32455158e-01 -1.53819963e-01 -1.90857694e-01
-3.82964641e-01 -1.50472865e-01 -6.31588995e-01 -1.41763651e+00
-1.04577079e-01 1.28141642e+00 1.29639283e-01 2.77895957... | [13.295042991638184, -3.065812587738037] |
433cdfd9-370e-4923-b812-05edd9d42cea | dds-decoupled-dynamic-scene-graph-generation | 2301.07666 | null | https://arxiv.org/abs/2301.07666v1 | https://arxiv.org/pdf/2301.07666v1.pdf | DDS: Decoupled Dynamic Scene-Graph Generation Network | Scene-graph generation involves creating a structural representation of the relationships between objects in a scene by predicting subject-object-relation triplets from input data. However, existing methods show poor performance in detecting triplets outside of a predefined set, primarily due to their reliance on depen... | ['B. S. Manjunath', 'Suya You', 'Satish Kumar', 'Raphael Ruschel', 'A S M Iftekhar'] | 2023-01-18 | null | null | null | null | ['scene-graph-generation'] | ['computer-vision'] | [ 3.74599278e-01 -1.15641423e-01 8.92726704e-02 -5.06651461e-01
-3.69418234e-01 -5.72786808e-01 5.99658966e-01 3.13204497e-01
3.09124868e-02 5.37191927e-01 1.32538944e-01 1.13142073e-01
-3.57630879e-01 -6.89214230e-01 -5.16044915e-01 -6.34777367e-01
-1.77961633e-01 4.98931289e-01 5.84317148e-01 -1.56131193... | [10.251051902770996, 1.6911972761154175] |
f48c0b1e-b07d-47ab-a5aa-395c344329df | generative-ode-modeling-with-known-unknowns | 2003.10775 | null | https://arxiv.org/abs/2003.10775v2 | https://arxiv.org/pdf/2003.10775v2.pdf | Generative ODE Modeling with Known Unknowns | In several crucial applications, domain knowledge is encoded by a system of ordinary differential equations (ODE), often stemming from underlying physical and biological processes. A motivating example is intensive care unit patients: the dynamics of vital physiological functions, such as the cardiovascular system with... | ['Neta Ravid', 'Danny Eytan', 'Ori Linial', 'Uri Shalit'] | 2020-03-24 | null | https://openreview.net/forum?id=pmvEzAbl7M | https://openreview.net/pdf?id=pmvEzAbl7M | iclr-workshop-deepdiffeq-2019-12 | ['known-unknowns'] | ['miscellaneous'] | [ 5.91522008e-02 2.12698743e-01 -3.17217886e-01 1.13496691e-01
-7.94504303e-03 -7.57000029e-01 3.40231806e-01 -6.33178428e-02
-5.44194840e-02 1.36927593e+00 3.77086699e-02 -2.45700508e-01
-2.34718114e-01 -5.06176472e-01 -8.34387302e-01 -1.10403514e+00
-3.37691516e-01 6.89843774e-01 -3.72646004e-01 -1.52944565... | [6.534353256225586, 3.57250714302063] |
632616b7-ac3b-4094-ba5d-517940f99090 | dynamic-graph-attention-for-anomaly-detection | 2307.03761 | null | https://arxiv.org/abs/2307.03761v1 | https://arxiv.org/pdf/2307.03761v1.pdf | Dynamic Graph Attention for Anomaly Detection in Heterogeneous Sensor Networks | In the era of digital transformation, systems monitored by the Industrial Internet of Things (IIoTs) generate large amounts of Multivariate Time Series (MTS) data through heterogeneous sensor networks. While this data facilitates condition monitoring and anomaly detection, the increasing complexity and interdependencie... | ['Olga Fink', 'Mengjie Zhao'] | 2023-07-07 | null | null | null | null | ['graph-attention', 'anomaly-detection', 'fault-detection'] | ['graphs', 'methodology', 'miscellaneous'] | [ 3.91518831e-01 4.45751697e-02 1.77685112e-01 1.17321447e-01
-5.57114147e-02 -5.61815083e-01 3.51915807e-01 9.26114321e-01
4.61647034e-01 4.79513764e-01 -9.96495187e-02 -3.54702383e-01
-7.49522030e-01 -9.31004107e-01 -5.02201200e-01 -5.51747799e-01
-7.62950361e-01 3.23050797e-01 3.15269738e-01 -2.89125800... | [7.250041961669922, 2.871300220489502] |
617abf86-c6c5-40bf-aab8-b90d713a6d61 | unsupervised-cross-lingual-representation-1 | 1911.02116 | null | https://arxiv.org/abs/1911.02116v2 | https://arxiv.org/pdf/1911.02116v2.pdf | Unsupervised Cross-lingual Representation Learning at Scale | This paper shows that pretraining multilingual language models at scale leads to significant performance gains for a wide range of cross-lingual transfer tasks. We train a Transformer-based masked language model on one hundred languages, using more than two terabytes of filtered CommonCrawl data. Our model, dubbed XLM-... | ['Francisco Guzmán', 'Vishrav Chaudhary', 'Kartikay Khandelwal', 'Alexis Conneau', 'Myle Ott', 'Luke Zettlemoyer', 'Guillaume Wenzek', 'Edouard Grave', 'Veselin Stoyanov', 'Naman Goyal'] | 2019-11-05 | unsupervised-cross-lingual-representation-2 | https://aclanthology.org/2020.acl-main.747 | https://aclanthology.org/2020.acl-main.747.pdf | acl-2020-6 | ['multilingual-nlp'] | ['natural-language-processing'] | [-6.58531010e-01 -2.12673113e-01 -4.63324040e-01 -3.26200753e-01
-1.80716300e+00 -8.27063143e-01 6.85899913e-01 -4.28233622e-03
-7.90341973e-01 9.56023276e-01 2.42874965e-01 -8.24525714e-01
3.67789775e-01 -6.42308116e-01 -1.07303822e+00 -1.85143173e-01
-1.89560309e-01 5.99725008e-01 9.72770900e-02 -5.55073917... | [10.965686798095703, 9.960070610046387] |
4d6bbe70-45d7-4de6-aa42-30e94844053b | spiking-two-stream-methods-with-unsupervised | 2306.13783 | null | https://arxiv.org/abs/2306.13783v1 | https://arxiv.org/pdf/2306.13783v1.pdf | Spiking Two-Stream Methods with Unsupervised STDP-based Learning for Action Recognition | Video analysis is a computer vision task that is useful for many applications like surveillance, human-machine interaction, and autonomous vehicles. Deep Convolutional Neural Networks (CNNs) are currently the state-of-the-art methods for video analysis. However they have high computational costs, and need a large amoun... | ['Ioan Marius Bilasco', 'Pierre Tirilly', 'Mireille El-Assal'] | 2023-06-23 | null | null | null | null | ['action-classification', 'autonomous-vehicles', 'action-recognition-in-videos'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 5.92971027e-01 -3.76197428e-01 1.15552358e-01 5.85631542e-02
1.64307043e-01 -3.84760529e-01 6.27538979e-01 3.74389328e-02
-6.78646743e-01 6.97078526e-01 -3.79809767e-01 -3.19059892e-03
8.20849016e-02 -8.31963480e-01 -1.06747842e+00 -1.09259498e+00
-1.92842156e-01 -2.57510036e-01 1.08038509e+00 -1.20963655... | [8.222579956054688, 2.4170591831207275] |
6ecb1ff8-4b8f-4f6d-9537-7f0b1f67bfc8 | latency-control-for-keyword-spotting | 2206.07261 | null | https://arxiv.org/abs/2206.07261v1 | https://arxiv.org/pdf/2206.07261v1.pdf | Latency Control for Keyword Spotting | Conversational agents commonly utilize keyword spotting (KWS) to initiate voice interaction with the user. For user experience and privacy considerations, existing approaches to KWS largely focus on accuracy, which can often come at the expense of introduced latency. To address this tradeoff, we propose a novel approac... | ['Brian Kulis', 'Yuriy Mishchenko', 'Mohammad Omar Khursheed', 'Grant P. Strimel', 'Joseph Wang', 'Christin Jose'] | 2022-06-15 | null | null | null | null | ['keyword-spotting'] | ['speech'] | [ 8.92030448e-02 1.61206126e-01 -2.37833232e-01 -4.10586208e-01
-1.25170231e+00 -7.34746993e-01 7.22363591e-01 3.06723952e-01
-9.03490245e-01 5.45462251e-01 4.79474403e-02 -3.46061289e-01
2.04565346e-01 -3.68699849e-01 -4.09192920e-01 -4.79820102e-01
1.15891192e-02 3.17052037e-01 2.80728728e-01 4.76038828... | [14.009051322937012, 6.891024112701416] |
4619ab15-6a13-468d-82ad-b5a2f3b1c897 | textdefense-adversarial-text-detection-based | 2302.05892 | null | https://arxiv.org/abs/2302.05892v1 | https://arxiv.org/pdf/2302.05892v1.pdf | TextDefense: Adversarial Text Detection based on Word Importance Entropy | Currently, natural language processing (NLP) models are wildly used in various scenarios. However, NLP models, like all deep models, are vulnerable to adversarially generated text. Numerous works have been working on mitigating the vulnerability from adversarial attacks. Nevertheless, there is no comprehensive defense ... | ['Yanghe Feng', 'Xing Yang', 'Chunpeng Ge', 'Yuwen Pu', 'Shouling Ji', 'Xuhong Zhang', 'Lujia Shen'] | 2023-02-12 | null | null | null | null | ['adversarial-text'] | ['adversarial'] | [ 1.32105485e-01 -1.10408925e-01 7.62715340e-02 -1.33392587e-01
-6.54040992e-01 -1.34876204e+00 8.79595697e-01 1.67911932e-01
-5.92300259e-02 3.61345917e-01 4.00394946e-01 -5.32046914e-01
1.91102087e-01 -1.00679302e+00 -6.77973092e-01 -5.44658840e-01
3.63493823e-02 4.44969498e-02 1.65587470e-01 -5.60020506... | [5.956864356994629, 8.023102760314941] |
1a97341f-76f1-49d1-8957-95686c9fdade | toward-multi-target-self-organizing-pursuit | 2206.12330 | null | https://arxiv.org/abs/2206.12330v3 | https://arxiv.org/pdf/2206.12330v3.pdf | Toward multi-target self-organizing pursuit in a partially observable Markov game | The multiple-target self-organizing pursuit (SOP) problem has wide applications and has been considered a challenging self-organization game for distributed systems, in which intelligent agents cooperatively pursue multiple dynamic targets with partial observations. This work proposes a framework for decentralized mult... | ['Chin-Teng Lin', 'Yuhui Shi', 'Ye Shi', 'Chao Lyu', 'Yu-Cheng Chang', 'Lijun Sun'] | 2022-06-24 | null | null | null | null | ['multi-agent-path-finding'] | ['playing-games'] | [-4.65391040e-01 1.35185085e-02 -1.14086419e-01 3.80970359e-01
-5.06710649e-01 -5.16894102e-01 4.44039971e-01 -2.28163928e-01
-2.17267990e-01 7.26469159e-01 -5.77073209e-02 1.64054960e-01
-8.00097346e-01 -5.40894687e-01 -2.89649993e-01 -1.38308370e+00
-5.88132918e-01 1.00664437e+00 2.09204748e-01 -6.18190050... | [3.747713565826416, 1.995650291442871] |
23b5aa20-ffe7-4976-b977-880c34b1f75d | hypercon-image-to-video-model-transfer-for | 1912.04950 | null | https://arxiv.org/abs/1912.04950v2 | https://arxiv.org/pdf/1912.04950v2.pdf | HyperCon: Image-To-Video Model Transfer for Video-To-Video Translation Tasks | Video-to-video translation is more difficult than image-to-image translation due to the temporal consistency problem that, if unaddressed, leads to distracting flickering effects. Although video models designed from scratch produce temporally consistent results, training them to match the vast visual knowledge captured... | ['Mostafa El-Khamy', 'Jungwon Lee', 'Ryan Szeto', 'Jason J. Corso'] | 2019-12-10 | null | null | null | null | ['video-style-transfer', 'video-inpainting'] | ['computer-vision', 'computer-vision'] | [ 3.90134454e-01 -4.96944450e-02 -3.53984505e-01 -1.23884499e-01
-9.77502048e-01 -7.14831948e-01 6.53348863e-01 -7.73938060e-01
-1.12269912e-02 7.47541726e-01 2.34109417e-01 -1.45010576e-01
6.43078208e-01 -2.84759641e-01 -1.38954973e+00 -2.85671383e-01
2.61880964e-01 1.42011181e-01 2.02324867e-01 2.01747447... | [10.871296882629395, -0.6865136027336121] |
de8c6bed-3fcd-4665-b5ac-092ad5303eb7 | incremental-deep-learning-for-robust-object | 1810.10323 | null | http://arxiv.org/abs/1810.10323v1 | http://arxiv.org/pdf/1810.10323v1.pdf | Incremental Deep Learning for Robust Object Detection in Unknown Cluttered Environments | Object detection in streaming images is a major step in different
detection-based applications, such as object tracking, action recognition,
robot navigation, and visual surveillance applications. In mostcases, image
quality is noisy and biased, and as a result, the data distributions are
disturbed and imbalanced. Most... | ['Rhee Phill Kyu', 'Ahmed Minhaz Uddin', 'Shin Dong Kyun'] | 2018-10-13 | null | null | null | null | ['robust-object-detection'] | ['computer-vision'] | [ 1.49596378e-01 -8.73757526e-02 -5.17617464e-01 -3.18794996e-01
-6.77177608e-01 -3.60709280e-01 4.17720824e-01 1.33808091e-01
-5.67473352e-01 6.52561307e-01 -3.55513006e-01 5.13038151e-02
-1.44703507e-01 -6.60019636e-01 -7.80651093e-01 -1.15524578e+00
3.89850475e-02 2.97504067e-01 8.76649916e-01 2.62316912... | [9.240062713623047, 1.4009829759597778] |
b34596f6-12b3-4f77-98a1-ce910ef2dbc8 | fast-matrix-multiplication-without-tears-a | 2306.01097 | null | https://arxiv.org/abs/2306.01097v1 | https://arxiv.org/pdf/2306.01097v1.pdf | Fast Matrix Multiplication Without Tears: A Constraint Programming Approach | It is known that the multiplication of an $N \times M$ matrix with an $M \times P$ matrix can be performed using fewer multiplications than what the naive $NMP$ approach suggests. The most famous instance of this is Strassen's algorithm for multiplying two $2\times 2$ matrices in 7 instead of 8 multiplications. This gi... | ['Elias B. Khalil', 'Pashootan Vaezipoor', 'Chang Liu', 'Arnaud Deza'] | 2023-06-01 | null | null | null | null | ['problem-decomposition'] | ['miscellaneous'] | [ 1.54585168e-01 9.65838209e-02 -8.23723339e-03 -1.37539163e-01
-5.18988252e-01 -6.91892385e-01 3.56207564e-02 1.44369513e-01
-5.30605555e-01 9.16640520e-01 -4.79199618e-01 -7.92192698e-01
-6.34705603e-01 -8.85071695e-01 -1.05066741e+00 -6.97885573e-01
-7.25399554e-01 4.72667187e-01 -3.79552901e-01 -5.72875440... | [6.444575786590576, 4.599170684814453] |
b367c441-67de-40aa-add0-dac4f6b46881 | deep-sequential-segmentation-of-organs-in | 1807.02437 | null | http://arxiv.org/abs/1807.02437v2 | http://arxiv.org/pdf/1807.02437v2.pdf | Deep Sequential Segmentation of Organs in Volumetric Medical Scans | Segmentation in 3D scans is playing an increasingly important role in current
clinical practice supporting diagnosis, tissue quantification, or treatment
planning. The current 3D approaches based on convolutional neural networks
usually suffer from at least three main issues caused predominantly by
implementation const... | ['Katja Bühler', 'Maria Wimmer', 'Dimitrios Lenis', 'Alexey Novikov', 'David Major'] | 2018-07-06 | null | null | null | null | ['liver-segmentation'] | ['medical'] | [ 1.74821422e-01 1.35351807e-01 -2.09716499e-01 -4.45778400e-01
-5.87040782e-01 -2.91435510e-01 2.59722292e-01 3.55618387e-01
-4.92588311e-01 5.32434106e-01 1.87609524e-01 -7.27738082e-01
8.09102952e-02 -9.21272635e-01 -6.03107274e-01 -4.74123895e-01
-4.47331041e-01 5.68008184e-01 5.15559077e-01 3.15409034... | [14.490823745727539, -2.5437774658203125] |
86e6fad4-49af-4b0c-9eef-a22b7f119fe6 | saltinet-scan-path-prediction-on-360-degree | 1707.03123 | null | http://arxiv.org/abs/1707.03123v5 | http://arxiv.org/pdf/1707.03123v5.pdf | SaltiNet: Scan-path Prediction on 360 Degree Images using Saliency Volumes | We introduce SaltiNet, a deep neural network for scanpath prediction trained
on 360-degree images. The model is based on a temporal-aware novel
representation of saliency information named the saliency volume. The first
part of the network consists of a model trained to generate saliency volumes,
whose parameters are f... | ["Noel E. O'Connor", 'Xavier Giro-i-Nieto', 'Kevin McGuinness', 'Marc Assens'] | 2017-07-11 | null | null | null | null | ['scanpath-prediction'] | ['computer-vision'] | [ 2.06148788e-01 4.45254624e-01 -4.42953378e-01 -6.36453450e-01
-6.54022336e-01 -1.16785049e-01 5.55493534e-01 -1.91351473e-01
-5.67934057e-03 3.46119881e-01 5.50334275e-01 -2.68077791e-01
1.27856480e-02 -9.42948580e-01 -1.08682728e+00 -2.00237259e-01
-2.74086952e-01 2.08670363e-01 6.56713486e-01 -4.05192405... | [9.795345306396484, -0.23709633946418762] |
39adc51d-2ae0-44fa-8205-0f508d57772c | diversity-aware-coherence-loss-for-improving | 2305.16199 | null | https://arxiv.org/abs/2305.16199v2 | https://arxiv.org/pdf/2305.16199v2.pdf | Diversity-Aware Coherence Loss for Improving Neural Topic Models | The standard approach for neural topic modeling uses a variational autoencoder (VAE) framework that jointly minimizes the KL divergence between the estimated posterior and prior, in addition to the reconstruction loss. Since neural topic models are trained by recreating individual input documents, they do not explicitl... | ['Giuseppe Carenini', 'Gabriel Murray', 'Linzi Xing', 'Felipe González-Pizarro', 'Raymond Li'] | 2023-05-25 | null | null | null | null | ['topic-models'] | ['natural-language-processing'] | [-2.66994596e-01 4.42870438e-01 -2.38382325e-01 -5.01734853e-01
-8.62835586e-01 -9.13246274e-02 8.44531596e-01 9.08896048e-03
-1.57537133e-01 6.29622877e-01 4.88564372e-01 6.91035911e-02
1.05711661e-01 -7.72935987e-01 -8.47555339e-01 -7.73512900e-01
1.83962315e-01 6.44966543e-01 8.47531110e-02 9.69182253... | [10.424806594848633, 6.980449676513672] |
12f772a1-4f55-4ab1-8250-026850c038d7 | improving-speech-translation-by-understanding | 2107.05782 | null | https://arxiv.org/abs/2107.05782v1 | https://arxiv.org/pdf/2107.05782v1.pdf | Improving Speech Translation by Understanding and Learning from the Auxiliary Text Translation Task | Pretraining and multitask learning are widely used to improve the speech to text translation performance. In this study, we are interested in training a speech to text translation model along with an auxiliary text to text translation task. We conduct a detailed analysis to understand the impact of the auxiliary task o... | ['Dmitriy Genzel', 'Changhan Wang', 'Xian Li', 'Juan Pino', 'Yun Tang'] | 2021-07-12 | null | https://aclanthology.org/2021.acl-long.328 | https://aclanthology.org/2021.acl-long.328.pdf | acl-2021-5 | ['speech-to-text-translation'] | ['natural-language-processing'] | [ 2.35947192e-01 9.03306678e-02 -3.14385772e-01 -3.20509821e-01
-1.35760546e+00 -4.67255741e-01 8.15056920e-01 -3.19574505e-01
-5.14572263e-01 8.35479200e-01 6.84280872e-01 -4.06169057e-01
3.38585794e-01 -3.12286973e-01 -1.00552022e+00 -8.04839969e-01
7.67429888e-01 4.78600472e-01 -7.19198808e-02 -4.43364114... | [14.469467163085938, 7.225993633270264] |
247a2e33-01ab-4a8f-bee5-cc73dbe4f4bd | ransomai-ai-powered-ransomware-for-stealthy | 2306.15559 | null | https://arxiv.org/abs/2306.15559v1 | https://arxiv.org/pdf/2306.15559v1.pdf | RansomAI: AI-powered Ransomware for Stealthy Encryption | Cybersecurity solutions have shown promising performance when detecting ransomware samples that use fixed algorithms and encryption rates. However, due to the current explosion of Artificial Intelligence (AI), sooner than later, ransomware (and malware in general) will incorporate AI techniques to intelligently and dyn... | ['Burkhard Stiller', 'Gregorio Martínez Pérez', 'Gérôme Bovet', 'Pedro Miguel Sánchez Sánchez', 'Janik Luechinger', 'Alberto Huertas Celdrán', 'Jan von der Assen'] | 2023-06-27 | null | null | null | null | ['q-learning'] | ['methodology'] | [-6.43869415e-02 -2.85428911e-01 1.69282164e-02 3.13602000e-01
4.95118767e-01 -1.04431629e+00 5.53611815e-01 -1.00738287e-01
-4.60233837e-01 6.86408162e-01 -4.84950721e-01 -4.64474857e-01
-3.17540079e-01 -7.73684204e-01 -3.53233665e-01 -6.98703706e-01
-1.15193069e-01 7.09754467e-01 1.75714239e-01 -1.76857665... | [5.409328937530518, 7.384851932525635] |
cff32782-e117-43db-b1b7-a87921abb67d | towards-better-characterization-of | null | null | https://openreview.net/forum?id=t2UJIFZVyz4 | https://openreview.net/pdf?id=t2UJIFZVyz4 | Towards Better Characterization of Paraphrases | To effectively characterize the nature of paraphrase pairs without expert human annotation, we proposes two new metrics: word position deviation (WPD) and lexical deviation (LD). WPD measures the degree of structural alteration, while LD measures the difference in vocabulary used. We apply these metrics to better under... | ['Anonymous'] | 2021-09-17 | null | null | null | acl-arr-september-2021-9 | ['paraphrase-generation', 'paraphrase-identification', 'paraphrase-generation'] | ['computer-code', 'natural-language-processing', 'natural-language-processing'] | [ 5.21799862e-01 -3.62944640e-02 -2.86716968e-01 -2.30377346e-01
-8.74551177e-01 -1.16411090e+00 6.72434032e-01 5.63707471e-01
-3.94551039e-01 5.58185637e-01 7.11941242e-01 -5.62044442e-01
-8.82438645e-02 -6.12799227e-01 -7.01704264e-01 -6.01618588e-02
6.20232284e-01 3.70892674e-01 2.08988309e-01 -3.11985999... | [11.33221435546875, 9.224584579467773] |
59e15ac2-84e3-474e-ae81-8c6ce383e113 | retrieving-to-answer-zero-shot-video-question | 2306.11732 | null | https://arxiv.org/abs/2306.11732v1 | https://arxiv.org/pdf/2306.11732v1.pdf | Retrieving-to-Answer: Zero-Shot Video Question Answering with Frozen Large Language Models | Video Question Answering (VideoQA) has been significantly advanced from the scaling of recent Large Language Models (LLMs). The key idea is to convert the visual information into the language feature space so that the capacity of LLMs can be fully exploited. Existing VideoQA methods typically take two paradigms: (1) le... | ['Hongsheng Li', 'Yu Qiao', 'Yi Wang', 'Renrui Zhang', 'Xiatian Zhu', 'Yuying Ge', 'Ziyi Lin', 'Junting Pan'] | 2023-06-15 | null | null | null | null | ['video-question-answering', 'domain-generalization', 'question-answering'] | ['computer-vision', 'methodology', 'natural-language-processing'] | [ 8.55148062e-02 -2.24641055e-01 -8.95929337e-02 -2.61809856e-01
-1.58454156e+00 -8.83075476e-01 6.47936404e-01 -6.42782524e-02
-3.78374994e-01 3.12936366e-01 3.15561622e-01 -3.17625433e-01
2.36434475e-01 -5.94970465e-01 -9.23285246e-01 -4.67243582e-01
3.84606630e-01 6.45472467e-01 3.79317194e-01 -3.64145219... | [10.630904197692871, 1.1666724681854248] |
fa8f711c-8c88-4e38-aa6a-9ad846c44b6b | learning-depth-guided-convolutions-for | 1912.04799 | null | https://arxiv.org/abs/1912.04799v2 | https://arxiv.org/pdf/1912.04799v2.pdf | Learning Depth-Guided Convolutions for Monocular 3D Object Detection | 3D object detection from a single image without LiDAR is a challenging task due to the lack of accurate depth information. Conventional 2D convolutions are unsuitable for this task because they fail to capture local object and its scale information, which are vital for 3D object detection. To better represent 3D struct... | ['Yuqi Huo', 'Zhiwu Lu', 'Mingyu Ding', 'Ping Luo', 'Hongwei Yi', 'Zhe Wang', 'Jianping Shi'] | 2019-12-10 | learning-depth-guided-convolutions-for-1 | http://openaccess.thecvf.com/content_CVPR_2020/html/Ding_Learning_Depth-Guided_Convolutions_for_Monocular_3D_Object_Detection_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Ding_Learning_Depth-Guided_Convolutions_for_Monocular_3D_Object_Detection_CVPR_2020_paper.pdf | cvpr-2020-6 | ['vehicle-pose-estimation'] | ['computer-vision'] | [ 6.90925196e-02 -2.06487507e-01 -8.49822536e-02 -2.74065077e-01
-3.59719068e-01 -4.84042585e-01 4.76747930e-01 -1.54725879e-01
-5.25306463e-01 1.68805912e-01 -2.45283231e-01 -4.01697159e-01
2.37735078e-01 -9.41123068e-01 -9.08313036e-01 -4.24398750e-01
8.87181163e-02 2.86533713e-01 7.36108661e-01 2.41292212... | [7.954402923583984, -2.720723867416382] |
afdec513-f5f4-48c2-b1a2-81aef76a8bc5 | updet-universal-multi-agent-rl-via-policy | null | null | https://openreview.net/forum?id=v9c7hr9ADKx | https://openreview.net/pdf?id=v9c7hr9ADKx | UPDeT: Universal Multi-agent RL via Policy Decoupling with Transformers | Recent advances in multi-agent reinforcement learning have been largely limited in training one model from scratch for every new task. The limitation is due to the restricted model architecture related to fixed input and output dimensions. This hinders the experience accumulation and transfer of the learned agent over ... | ['Xiaodan Liang', 'Xiaojun Chang', 'Fengda Zhu', 'Siyi Hu'] | 2021-01-01 | null | null | null | iclr-2021-1 | ['smac-1', 'smac'] | ['playing-games', 'playing-games'] | [-1.34242907e-01 1.62419468e-01 -1.54671654e-01 1.44339323e-01
-3.63652349e-01 -4.54277217e-01 6.58258677e-01 6.70209620e-03
-8.85684133e-01 8.56861353e-01 -3.26317586e-02 -2.58946776e-01
-3.48509192e-01 -6.39822066e-01 -6.90965354e-01 -7.65508413e-01
5.74603900e-02 7.88868368e-01 4.61977243e-01 -6.10648036... | [3.855250358581543, 1.857831358909607] |
d2d1fe6f-6aaf-49cd-afb5-3a344ad660fc | te-yolof-tiny-and-efficient-yolof-for-blood | 2108.12313 | null | https://arxiv.org/abs/2108.12313v1 | https://arxiv.org/pdf/2108.12313v1.pdf | TE-YOLOF: Tiny and efficient YOLOF for blood cell detection | Blood cell detection in microscopic images is an essential branch of medical image processing research. Since disease detection based on manual checking of blood cells is time-consuming and full of errors, testing of blood cells using object detectors with Deep Convolutional Neural Network can be regarded as a feasible... | ['Wei Xiang', 'Yali Wang', 'Hang Yang', 'Xiangkui Li', 'Fanxin Xu'] | 2021-08-27 | null | null | null | null | ['cell-detection', 'blood-cell-detection'] | ['computer-vision', 'medical'] | [-3.60295385e-01 -3.38563353e-01 6.67543262e-02 -7.24265799e-02
2.29182959e-01 7.47607276e-02 8.19996893e-02 2.62456626e-01
-7.81082869e-01 5.02473891e-01 -1.48362190e-01 -3.87757085e-02
2.84167945e-01 -1.01530659e+00 -1.26945123e-01 -9.78206456e-01
1.50794417e-01 9.13021937e-02 5.45365870e-01 1.83648184... | [14.803963661193848, -3.0896670818328857] |
cee113b7-dc02-438f-9197-c38b377b7342 | unleash-the-potential-of-3d-point-cloud | 2306.00552 | null | https://arxiv.org/abs/2306.00552v1 | https://arxiv.org/pdf/2306.00552v1.pdf | Unleash the Potential of 3D Point Cloud Modeling with A Calibrated Local Geometry-driven Distance Metric | Quantifying the dissimilarity between two unstructured 3D point clouds is a challenging task, with existing metrics often relying on measuring the distance between corresponding points that can be either inefficient or ineffective. In this paper, we propose a novel distance metric called Calibrated Local Geometry Dista... | ['Junhui Hou', 'Siyu Ren'] | 2023-06-01 | null | null | null | null | ['scene-flow-estimation'] | ['computer-vision'] | [-1.52824089e-01 -5.52357197e-01 -3.67733352e-02 -1.95777193e-01
-8.31810653e-01 -5.93693018e-01 6.91360474e-01 4.95929569e-01
-1.74249977e-01 2.09231615e-01 2.47220173e-02 -5.24173826e-02
-1.26634285e-01 -8.35723758e-01 -4.76385236e-01 -5.05876422e-01
1.45202219e-01 6.22114837e-01 1.40979737e-01 7.48032853... | [7.712401866912842, -2.8573415279388428] |
0a66b1eb-18c7-4cd6-95e0-21fdfb9f4173 | inspecting-spoken-language-understanding-from | 2306.00482 | null | https://arxiv.org/abs/2306.00482v1 | https://arxiv.org/pdf/2306.00482v1.pdf | Inspecting Spoken Language Understanding from Kids for Basic Math Learning at Home | Enriching the quality of early childhood education with interactive math learning at home systems, empowered by recent advances in conversational AI technologies, is slowly becoming a reality. With this motivation, we implement a multimodal dialogue system to support play-based learning experiences at home, guiding kid... | ['Lama Nachman', 'Saurav Sahay', 'Roddy Fuentes Alba', 'Eda Okur'] | 2023-06-01 | null | null | null | null | ['intent-recognition', 'spoken-language-understanding', 'spoken-language-understanding', 'automatic-speech-recognition'] | ['natural-language-processing', 'natural-language-processing', 'speech', 'speech'] | [-2.04298645e-02 3.56235564e-01 3.03736061e-01 -6.25088751e-01
-8.48617733e-01 -6.67779028e-01 4.69761461e-01 1.78436458e-01
-2.04928711e-01 2.76914299e-01 8.98483217e-01 -5.15801609e-01
1.73915431e-01 -9.28380370e-01 -3.96457464e-01 3.58639732e-02
2.04464287e-01 6.37924135e-01 3.30071926e-01 -9.09637213... | [12.623919486999512, 7.918766975402832] |
a8ee294f-4e5c-4c92-9724-fdffcf9c53ab | tmu-nmt-system-with-automatic-post-editing-by | null | null | https://aclanthology.org/2022.wat-1.4 | https://aclanthology.org/2022.wat-1.4.pdf | TMU NMT System with Automatic Post-Editing by Multi-Source Levenshtein Transformer for the Restricted Translation Task of WAT 2022 | In this paper, we describe our TMU English–Japanese systems submitted to the restricted translation task at WAT 2022 (Nakazawa et al., 2022). In this task, we translate an input sentence with the constraint that certain words or phrases (called restricted target vocabularies (RTVs)) should be contained in the output se... | ['Mamoru Komachi', 'Seiichiro Kondo'] | null | null | null | null | wat-2022-10 | ['automatic-post-editing', 'automatic-post-editing'] | ['computer-vision', 'natural-language-processing'] | [ 5.61282337e-01 4.26702723e-02 9.07851458e-02 -3.71910363e-01
-1.07924771e+00 -6.40794635e-01 5.13596892e-01 -4.34788585e-01
-4.62662905e-01 1.15061569e+00 3.29140455e-01 -6.68833435e-01
3.60151023e-01 -6.91861153e-01 -7.91705251e-01 -4.25244659e-01
6.42012715e-01 4.83752519e-01 1.33032337e-01 -4.67443943... | [11.645397186279297, 10.24954891204834] |
38e427b4-5145-43a0-890e-9023c304f2eb | rigidity-strengthening-is-a-vital-mechanism | 1704.05883 | null | http://arxiv.org/abs/1704.05883v1 | http://arxiv.org/pdf/1704.05883v1.pdf | Rigidity strengthening is a vital mechanism for protein-ligand binding | Protein-ligand binding is essential to almost all life processes. The
understanding of protein-ligand interactions is fundamentally important to
rational drug design and protein design. Based on large scale data sets, we
show that protein rigidity strengthening or flexibility reduction is a pivoting
mechanism in protei... | [] | 2017-03-31 | null | null | null | null | ['protein-design'] | ['medical'] | [ 1.98635742e-01 -1.12959124e-01 -5.46175420e-01 -3.68692040e-01
-2.94956356e-01 -5.47546148e-01 2.73823947e-01 2.53144175e-01
-4.06955481e-01 1.34033239e+00 2.47396931e-01 -5.66155374e-01
-3.66529971e-01 -5.32307804e-01 -8.29543889e-01 -1.34190965e+00
-8.68668482e-02 6.85826302e-01 5.04996419e-01 -6.48911119... | [4.8114190101623535, 5.416579246520996] |
6f1620a6-9ed8-4b36-abf7-abaa4562b1d4 | factify3m-a-benchmark-for-multimodal-fact | 2306.05523 | null | https://arxiv.org/abs/2306.05523v1 | https://arxiv.org/pdf/2306.05523v1.pdf | FACTIFY3M: A Benchmark for Multimodal Fact Verification with Explainability through 5W Question-Answering | Combating disinformation is one of the burning societal crises -- about 67% of the American population believes that disinformation produces a lot of uncertainty, and 10% of them knowingly propagate disinformation. Evidence shows that disinformation can manipulate democratic processes and public opinion, causing disrup... | ['Amitava Das', 'Amit Sheth', 'Aman Chadha', 'Parth Patwa', 'Shreyash Mishra', 'Suryavardan S', 'Dwip Dalal', 'Kinjal Sensharma', 'Shreyas Chatterjee', 'Samahriti Mukherjee', 'Ritvik G', 'Preethi Gurumurthy', 'Janvita Reddy', 'Ishan Paul', 'Harshit Dave', 'Arghya Sarkar', 'Aditya Pakala', 'Adarsh Mahor', 'Anku Rani', '... | 2023-05-22 | null | null | null | null | ['fact-verification'] | ['natural-language-processing'] | [ 3.44797254e-01 4.77557391e-01 -4.29969698e-01 -7.52420202e-02
-1.09581268e+00 -1.04636300e+00 9.65257585e-01 4.14302826e-01
2.87054088e-02 6.49892747e-01 9.05814111e-01 -6.56451941e-01
3.19214433e-01 -6.63577557e-01 -7.09059596e-01 -1.94654614e-01
3.55926603e-01 1.43402755e-01 -5.88309020e-02 -5.67276180... | [8.142041206359863, 10.252302169799805] |
007956d2-8408-414f-bacb-43a1416675f6 | probabilistic-time-series-forecasting-with-1 | null | null | http://proceedings.neurips.cc/paper/2020/hash/2f2b265625d76a6704b08093c652fd79-Abstract.html | http://proceedings.neurips.cc/paper/2020/file/2f2b265625d76a6704b08093c652fd79-Paper.pdf | Probabilistic Time Series Forecasting with Shape and Temporal Diversity | Probabilistic forecasting consists in predicting a distribution of possible future outcomes. In this paper, we address this problem for non-stationary time series, which is very challenging yet crucially important. We introduce the STRIPE model for representing structured diversity based on shape and time features, ens... | ['Nicolas Thome', 'Vincent Le Guen'] | 2020-12-01 | null | null | null | neurips-2020-12 | ['probabilistic-time-series-forecasting'] | ['time-series'] | [-1.17402412e-01 -1.99152544e-01 -1.02071129e-01 -2.78408796e-01
-7.21594274e-01 -7.87662923e-01 1.23916423e+00 2.02439595e-02
1.84475631e-01 7.30355740e-01 3.68551582e-01 -2.85354167e-01
-5.88702381e-01 -7.63026595e-01 -5.41507363e-01 -1.00799012e+00
-4.33691829e-01 5.96590400e-01 2.26862967e-01 -1.55787632... | [7.020274639129639, 3.4163975715637207] |
a9c1d0f0-4595-4848-a602-f1a6b4375cd4 | a-database-for-face-presentation-attack-using | 1906.11900 | null | https://arxiv.org/abs/1906.11900v1 | https://arxiv.org/pdf/1906.11900v1.pdf | A database for face presentation attack using wax figure faces | Compared to 2D face presentation attacks (e.g. printed photos and video replays), 3D type attacks are more challenging to face recognition systems (FRS) by presenting 3D characteristics or materials similar to real faces. Existing 3D face spoofing databases, however, mostly based on 3D masks, are restricted to small da... | ['Zhengquan Xu', 'Guodong Guo', 'Shan Jia', 'Chuanbo Hu'] | 2019-06-06 | null | null | null | null | ['face-presentation-attack-detection', 'small-data'] | ['computer-vision', 'computer-vision'] | [ 2.50297546e-01 -2.26316735e-01 1.31449163e-01 -1.17463671e-01
-1.64686099e-01 -8.27202141e-01 7.99711108e-01 -4.91312742e-01
1.31475747e-01 3.74953568e-01 -9.53007787e-02 -5.08086562e-01
-3.86569910e-02 -6.59333050e-01 -5.62898755e-01 -6.75498784e-01
-6.46034300e-01 -6.27875626e-02 2.35008821e-01 -3.46965313... | [13.016554832458496, 1.0865051746368408] |
4950f89e-6ae9-4f36-8d43-fa562df09307 | explanation-based-handwriting-verification | 1909.02548 | null | https://arxiv.org/abs/1909.02548v1 | https://arxiv.org/pdf/1909.02548v1.pdf | Explanation based Handwriting Verification | Deep learning system have drawback that their output is not accompanied with ex-planation. In a domain such as forensic handwriting verification it is essential to provideexplanation to jurors. The goal of handwriting verification is to find a measure of confi-dence whether the given handwritten samples are written by ... | ['Mohammad Abuzar Shaikh', 'Mihir Chauhan', 'Sargur N. Srihari'] | 2019-08-14 | null | null | null | null | ['handwriting-verification'] | ['computer-vision'] | [ 3.80313396e-02 3.42688352e-01 -5.45810342e-01 -9.40360367e-01
-6.88700259e-01 -6.09144807e-01 7.30326355e-01 5.11853881e-02
-1.02141194e-01 9.35576320e-01 2.03296885e-01 -7.17629194e-01
-2.66981423e-01 -6.60022080e-01 -6.58798754e-01 -6.83472216e-01
2.46368408e-01 5.69670677e-01 -2.47365966e-01 2.91278630... | [11.630926132202148, 2.2039272785186768] |
1207a8af-23e6-4f8e-903a-0713b027c318 | endowing-robots-with-longer-term-autonomy-by | 1809.03979 | null | http://arxiv.org/abs/1809.03979v1 | http://arxiv.org/pdf/1809.03979v1.pdf | Endowing Robots with Longer-term Autonomy by Recovering from External Disturbances in Manipulation through Grounded Anomaly Classification and Recovery Policies | Robot manipulation is increasingly poised to interact with humans in
co-shared workspaces. Despite increasingly robust manipulation and control
algorithms, failure modes continue to exist whenever models do not capture the
dynamics of the unstructured environment. To obtain longer-term horizons in
robot automation, rob... | ['Sakmongkon Chumkamon', 'Longxin Chen', 'Shuangqi Luo', 'Shuangda Duan', 'Yisheng Guan', 'Hongmin Wu', 'Juan Rojas', 'Dong Liu'] | 2018-09-11 | null | null | null | null | ['anomaly-classification'] | ['computer-vision'] | [ 1.92409307e-01 2.80481994e-01 1.13254912e-01 -1.62062924e-02
-2.74299055e-01 -2.31883958e-01 4.38990533e-01 1.04002528e-01
-3.27801228e-01 8.27280939e-01 -1.36077687e-01 -1.06227115e-01
-6.08174801e-01 -1.71571955e-01 -7.08875835e-01 -4.76360828e-01
-5.59794366e-01 1.02146244e+00 4.53695416e-01 -3.50980133... | [4.472651958465576, 1.412313461303711] |
c468d417-0b74-4d29-bd1f-f46af94b85f9 | sts-uhh-at-semeval-2017-task-1-scoring | null | null | https://aclanthology.org/S17-2025 | https://aclanthology.org/S17-2025.pdf | STS-UHH at SemEval-2017 Task 1: Scoring Semantic Textual Similarity Using Supervised and Unsupervised Ensemble | This paper reports the STS-UHH participation in the SemEval 2017 shared Task 1 of Semantic Textual Similarity (STS). Overall, we submitted 3 runs covering monolingual and cross-lingual STS tracks. Our participation involves two approaches: unsupervised approach, which estimates a word alignment-based similarity score, ... | ['Chris Biemann', 'Sarah Kohail', 'Amr Rekaby Salama'] | 2017-08-01 | null | null | null | semeval-2017-8 | ['graph-similarity'] | ['graphs'] | [-1.66281238e-01 2.51619041e-01 -3.46661806e-01 -3.43704224e-01
-1.35384297e+00 -6.38465881e-01 7.28186250e-01 7.48846889e-01
-6.81626916e-01 5.37030041e-01 9.04479980e-01 -4.09613885e-02
-2.01354362e-02 -1.55269206e-01 -4.19004023e-01 -1.52459340e-02
9.75685269e-02 7.66607404e-01 2.85683751e-01 -5.64630210... | [10.903227806091309, 9.618900299072266] |
579f4c9d-e896-4169-b8f9-fc237b0dc1fc | compound-multi-branch-feature-fusion-for-real-1 | 2206.02748 | null | https://arxiv.org/abs/2206.02748v1 | https://arxiv.org/pdf/2206.02748v1.pdf | Compound Multi-branch Feature Fusion for Real Image Restoration | Image restoration is a challenging and ill-posed problem which also has been a long-standing issue. However, most of learning based restoration methods are proposed to target one degradation type which means they are lack of generalization. In this paper, we proposed a multi-branch restoration model inspired from the H... | ['Kuan-Hsien Liu', 'Tsung-Jung Liu', 'Chi-Mao Fan'] | 2022-06-02 | compound-multi-branch-feature-fusion-for-real | https://openreview.net/forum?id=WQIdU90Gsu | https://openreview.net/pdf?id=WQIdU90Gsu | null | ['image-dehazing'] | ['computer-vision'] | [ 9.55607221e-02 -3.62559944e-01 4.78391722e-02 -1.16267866e-02
-4.61395085e-01 9.63849872e-02 5.63189089e-01 -4.92665589e-01
-1.55700982e-01 7.02725291e-01 3.34073365e-01 -1.47677287e-01
1.51811376e-01 -6.94823742e-01 -7.48199582e-01 -1.05467236e+00
5.67005038e-01 -2.77257949e-01 3.94054770e-01 -4.16548312... | [11.07313060760498, -2.618635892868042] |
8f56e30b-87e4-44e2-a812-c6ec8ed70437 | vision-through-the-veil-differential-privacy | 2306.17794 | null | https://arxiv.org/abs/2306.17794v1 | https://arxiv.org/pdf/2306.17794v1.pdf | Vision Through the Veil: Differential Privacy in Federated Learning for Medical Image Classification | The proliferation of deep learning applications in healthcare calls for data aggregation across various institutions, a practice often associated with significant privacy concerns. This concern intensifies in medical image analysis, where privacy-preserving mechanisms are paramount due to the data being sensitive in na... | ['Balasubramanian Raman', 'Uppala Vivek Narayan', 'Pradeep Singh', 'Kishore Babu Nampalle'] | 2023-06-30 | null | null | null | null | ['medical-image-classification'] | ['medical'] | [ 4.28582020e-02 2.54702389e-01 -2.24186808e-01 -5.35204709e-01
-8.92233670e-01 -8.38557422e-01 2.21234202e-01 4.95972425e-01
-6.09512925e-01 6.42293990e-01 1.94773719e-01 -7.72738576e-01
-3.40819627e-01 -7.36366451e-01 -5.10745168e-01 -9.61708069e-01
-2.12983176e-01 -3.08455765e-01 -6.06671453e-01 3.95966738... | [5.98087739944458, 6.607274532318115] |
75ab4984-3701-49f3-899a-de0b9feb25c9 | skill-critic-refining-learned-skills-for | 2306.08388 | null | https://arxiv.org/abs/2306.08388v2 | https://arxiv.org/pdf/2306.08388v2.pdf | Skill-Critic: Refining Learned Skills for Reinforcement Learning | Hierarchical reinforcement learning (RL) can accelerate long-horizon decision-making by temporally abstracting a policy into multiple levels. Promising results in sparse reward environments have been seen with skills, i.e. sequences of primitive actions. Typically, a skill latent space and policy are discovered from of... | ['Wei Zhan', 'Masayoshi Tomizuka', 'Kenta Kawamoto', 'Chen Tang', 'Catherine Weaver', 'Ce Hao'] | 2023-06-14 | null | null | null | null | ['hierarchical-reinforcement-learning'] | ['methodology'] | [-3.20611931e-02 -2.48545483e-02 -4.79071647e-01 -2.07968682e-01
-9.02139604e-01 -4.68174517e-01 5.73257029e-01 -1.58495083e-01
-6.73612356e-01 1.07195795e+00 4.51840609e-01 -7.32212067e-02
-6.29694164e-02 -3.07898611e-01 -9.00638163e-01 -8.06569695e-01
-4.29342717e-01 5.22313952e-01 2.84846812e-01 -2.27803007... | [4.168341159820557, 1.5664150714874268] |
77de38be-5ba9-4e0e-99df-f592419ba16c | tyger-task-type-generic-active-learning-for | 2205.11279 | null | https://arxiv.org/abs/2205.11279v1 | https://arxiv.org/pdf/2205.11279v1.pdf | Tyger: Task-Type-Generic Active Learning for Molecular Property Prediction | How to accurately predict the properties of molecules is an essential problem in AI-driven drug discovery, which generally requires a large amount of annotation for training deep learning models. Annotating molecules, however, is quite costly because it requires lab experiments conducted by experts. To reduce annotatio... | ['Xinchao Wang', 'Tingyang Xu', 'Jian Tang', 'Jiashi Feng', 'Kaixin Wang', 'Kuangqi Zhou'] | 2022-05-23 | null | null | null | null | ['molecular-property-prediction'] | ['miscellaneous'] | [ 4.35953587e-01 -5.14848046e-02 -6.03118360e-01 -3.38656276e-01
-1.00831890e+00 -6.84688032e-01 2.98061192e-01 5.86078346e-01
-3.91027749e-01 9.69397545e-01 -1.39963076e-01 -3.05594891e-01
-1.09337382e-01 -8.67433548e-01 -8.99703443e-01 -1.02301729e+00
1.01446941e-01 6.49442196e-01 6.61040097e-02 1.75156236... | [5.202031135559082, 5.933951377868652] |
1d5475db-3242-40ba-a7c8-abb8ba30bf33 | contrastive-code-representation-learning | 2007.04973 | null | https://arxiv.org/abs/2007.04973v4 | https://arxiv.org/pdf/2007.04973v4.pdf | Contrastive Code Representation Learning | Recent work learns contextual representations of source code by reconstructing tokens from their context. For downstream semantic understanding tasks like summarizing code in English, these representations should ideally capture program functionality. However, we show that the popular reconstruction-based BERT model is... | ['Joseph E. Gonzalez', 'Ion Stoica', 'Ajay Jain', 'Pieter Abbeel', 'Paras Jain', 'Tianjun Zhang'] | 2020-07-09 | null | https://aclanthology.org/2021.emnlp-main.482 | https://aclanthology.org/2021.emnlp-main.482.pdf | emnlp-2021-11 | ['code-summarization', 'type-prediction', 'method-name-prediction'] | ['computer-code', 'computer-code', 'natural-language-processing'] | [ 4.87905979e-01 3.68487924e-01 -4.75797802e-01 -4.26974714e-01
-1.21298444e+00 -1.00384653e+00 4.99404311e-01 5.35067856e-01
-8.51429403e-02 3.11230600e-01 5.08241773e-01 -6.53562784e-01
5.47930777e-01 -9.26949799e-01 -1.42624640e+00 -5.06736189e-02
-3.64696421e-02 -3.62186581e-02 1.70458958e-01 -1.59455627... | [7.534585952758789, 7.873610973358154] |
c4b2d7cc-b67c-44e1-bcd4-8944044a8166 | open-vocabulary-panoptic-segmentation-with-2 | 2303.11324 | null | https://arxiv.org/abs/2303.11324v1 | https://arxiv.org/pdf/2303.11324v1.pdf | Open-vocabulary Panoptic Segmentation with Embedding Modulation | Open-vocabulary image segmentation is attracting increasing attention due to its critical applications in the real world. Traditional closed-vocabulary segmentation methods are not able to characterize novel objects, whereas several recent open-vocabulary attempts obtain unsatisfactory results, i.e., notable performanc... | ['Hengshuang Zhao', 'Antonio Torralba', 'Ser-Nam Lim', 'Shuang Li', 'Xi Chen'] | 2023-03-20 | null | null | null | null | ['panoptic-segmentation', 'open-vocabulary-panoptic-segmentation'] | ['computer-vision', 'computer-vision'] | [ 3.32136184e-01 7.67564168e-03 -2.18736783e-01 -1.76439837e-01
-8.83649290e-01 -5.37769258e-01 4.14847702e-01 -1.11725509e-01
-5.54125190e-01 5.46152294e-01 -5.39903715e-02 -6.22325838e-02
-3.21664242e-03 -5.05955637e-01 -5.29642582e-01 -7.00734079e-01
1.46283999e-01 3.19893897e-01 3.62082154e-01 -2.70079434... | [9.636449813842773, 0.7192903161048889] |
1817b2ec-576e-4c29-a638-c0891ccb6552 | simpleclick-interactive-image-segmentation | 2210.11006 | null | https://arxiv.org/abs/2210.11006v3 | https://arxiv.org/pdf/2210.11006v3.pdf | SimpleClick: Interactive Image Segmentation with Simple Vision Transformers | Click-based interactive image segmentation aims at extracting objects with a limited user clicking. A hierarchical backbone is the de-facto architecture for current methods. Recently, the plain, non-hierarchical Vision Transformer (ViT) has emerged as a competitive backbone for dense prediction tasks. This design allow... | ['Marc Niethammer', 'Gedas Bertasius', 'Zhenlin Xu', 'Qin Liu'] | 2022-10-20 | null | null | null | null | ['interactive-segmentation'] | ['computer-vision'] | [ 3.65457535e-01 5.91842592e-01 -1.92250803e-01 -4.10984129e-01
-7.78306365e-01 -4.21974152e-01 1.64275438e-01 -1.69411376e-01
-4.77479607e-01 1.34219885e-01 1.36091754e-01 -3.98828208e-01
5.13016403e-01 -5.78294635e-01 -1.04486823e+00 -7.71538854e-01
1.67396933e-01 3.46394628e-01 8.06721866e-01 1.72362030... | [9.64409065246582, 0.12779301404953003] |
d581db44-60d3-491b-80c1-26e5d422dbe1 | intervention-generalization-a-view-from | 2306.04027 | null | https://arxiv.org/abs/2306.04027v1 | https://arxiv.org/pdf/2306.04027v1.pdf | Intervention Generalization: A View from Factor Graph Models | One of the goals of causal inference is to generalize from past experiments and observational data to novel conditions. While it is in principle possible to eventually learn a mapping from a novel experimental condition to an outcome of interest, provided a sufficient variety of experiments is available in the training... | ['Ricardo Silva', 'Jakob Zeitler', 'Jialin Yu', 'David S. Watson', 'Gecia Bravo-Hermsdorff'] | 2023-06-06 | null | null | null | null | ['causal-inference', 'experimental-design', 'causal-inference'] | ['knowledge-base', 'methodology', 'miscellaneous'] | [ 8.36162150e-01 1.11653440e-01 -4.75253880e-01 -1.76756874e-01
-2.63944864e-01 -6.17029369e-01 4.84966964e-01 3.62887353e-01
-2.44076550e-01 1.12082434e+00 7.71420971e-02 -9.91159797e-01
-9.36836362e-01 -5.23792624e-01 -1.17041218e+00 -6.82350576e-01
-4.79975492e-01 2.63809085e-01 -3.95930558e-01 1.61879152... | [7.844381332397461, 5.262531280517578] |
a4099606-d1a6-428a-bd2e-ab99183a9440 | cross-lingual-citations-in-english-papers-a | 2111.05097 | null | https://arxiv.org/abs/2111.05097v2 | https://arxiv.org/pdf/2111.05097v2.pdf | Cross-Lingual Citations in English Papers: A Large-Scale Analysis of Prevalence, Usage, and Impact | Citation information in scholarly data is an important source of insight into the reception of publications and the scholarly discourse. Outcomes of citation analyses and the applicability of citation based machine learning approaches heavily depend on the completeness of such data. One particular shortcoming of schola... | ['Tornike Tsereteli', 'Michael Färber', 'Tarek Saier'] | 2021-11-07 | null | null | null | null | ['cross-lingual-entity-linking', 'citation-intent-classification'] | ['natural-language-processing', 'natural-language-processing'] | [-6.86332107e-01 -5.09255111e-01 -1.04474556e+00 1.82961062e-01
-1.13885760e+00 -9.82172132e-01 1.30717862e+00 5.94959557e-01
-5.93685329e-01 9.48804855e-01 6.92065656e-01 -9.40717280e-01
-3.88283819e-01 -4.68631715e-01 -6.31244659e-01 -2.43543684e-01
5.30749202e-01 1.59274936e-01 -2.85192281e-01 2.00484186... | [9.559171676635742, 8.23935604095459] |
83cdfdd7-fd5f-4ec0-aa4c-9e8999d369e1 | tfdet-target-aware-fusion-for-rgb-t | 2305.16580 | null | https://arxiv.org/abs/2305.16580v1 | https://arxiv.org/pdf/2305.16580v1.pdf | TFDet: Target-aware Fusion for RGB-T Pedestrian Detection | Pedestrian detection is a critical task in computer vision because of its role in ensuring traffic safety. However, existing methods that rely solely on RGB images suffer from performance degradation under low-light conditions due to the lack of useful information. To address this issue, recent multispectral detection ... | ['Hui-Liang Shen', 'Zehua Sheng', 'Xiaohan Zhang', 'Xue Zhang'] | 2023-05-26 | null | null | null | null | ['pedestrian-detection'] | ['computer-vision'] | [ 2.74875164e-01 -8.69732440e-01 2.61999965e-01 -2.08636492e-01
-8.22993279e-01 -2.64598846e-01 6.21212900e-01 6.27386793e-02
-7.58204281e-01 7.58718848e-01 -2.79170990e-01 -1.42524630e-01
2.39454865e-01 -8.55182827e-01 -4.09711927e-01 -1.26433170e+00
6.57751501e-01 -2.74893492e-01 8.25823247e-01 -3.36008310... | [9.828218460083008, -1.390444040298462] |
37b69289-49fb-4ed9-8598-52d4cd85f980 | improving-toponym-resolution-with-better | 2305.11315 | null | https://arxiv.org/abs/2305.11315v1 | https://arxiv.org/pdf/2305.11315v1.pdf | Improving Toponym Resolution with Better Candidate Generation, Transformer-based Reranking, and Two-Stage Resolution | Geocoding is the task of converting location mentions in text into structured data that encodes the geospatial semantics. We propose a new architecture for geocoding, GeoNorm. GeoNorm first uses information retrieval techniques to generate a list of candidate entries from the geospatial ontology. Then it reranks the ca... | ['Steven Bethard', 'Zeyu Zhang'] | 2023-05-18 | null | null | null | null | ['toponym-resolution'] | ['natural-language-processing'] | [-2.17765898e-01 1.65802911e-01 -3.03191334e-01 -3.99608910e-01
-1.01548696e+00 -5.05436182e-01 8.23486507e-01 8.25916231e-01
-4.50615525e-01 8.16811442e-01 1.04893184e+00 -3.70577067e-01
-1.49914518e-01 -1.25249600e+00 -5.68970203e-01 -1.55356333e-01
9.76954773e-02 5.53839087e-01 1.74872890e-01 -2.42452264... | [9.373022079467773, 9.082247734069824] |
4ca5751e-806a-4b7a-9096-bfc3b1fcb15c | pose-disentangled-contrastive-learning-for | 2211.13490 | null | https://arxiv.org/abs/2211.13490v2 | https://arxiv.org/pdf/2211.13490v2.pdf | Pose-disentangled Contrastive Learning for Self-supervised Facial Representation | Self-supervised facial representation has recently attracted increasing attention due to its ability to perform face understanding without relying on large-scale annotated datasets heavily. However, analytically, current contrastive-based self-supervised learning (SSL) still performs unsatisfactorily for learning facia... | ['Shaoze Feng', 'Kejun Liu', 'Zhe Chen', 'Yibing Zhan', 'Wenbin Wang', 'Yuanyuan Liu'] | 2022-11-24 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Liu_Pose-Disentangled_Contrastive_Learning_for_Self-Supervised_Facial_Representation_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Liu_Pose-Disentangled_Contrastive_Learning_for_Self-Supervised_Facial_Representation_CVPR_2023_paper.pdf | cvpr-2023-1 | ['head-pose-estimation', 'facial-expression-recognition'] | ['computer-vision', 'computer-vision'] | [ 2.42366735e-02 1.28780678e-01 -3.51287305e-01 -8.40252221e-01
-7.81485617e-01 -2.90426642e-01 5.22641599e-01 -4.79141563e-01
-5.35399653e-02 5.84358215e-01 3.82101953e-01 2.61283845e-01
-8.29438865e-02 -3.98224920e-01 -7.71319687e-01 -8.96171451e-01
-6.08716570e-02 2.32522249e-01 -5.66418052e-01 -3.06131274... | [13.246153831481934, 0.6103165149688721] |
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