paperID stringlengths 36 36 | pwc_id stringlengths 8 47 | arxiv_id stringlengths 6 16 ⌀ | nips_id float64 | url_abs stringlengths 18 329 | url_pdf stringlengths 18 742 | title stringlengths 8 325 | abstract stringlengths 1 7.27k ⌀ | authors stringlengths 2 7.06k | published stringlengths 10 10 ⌀ | conference stringlengths 12 47 ⌀ | conference_url_abs stringlengths 16 198 ⌀ | conference_url_pdf stringlengths 27 199 ⌀ | proceeding stringlengths 6 47 ⌀ | taskID stringlengths 7 1.44k | areaID stringclasses 688
values | embedding stringlengths 9.26k 12.5k | umap_embedding stringlengths 29 44 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
d42e3176-008e-45f7-ac40-d9306e4989c4 | trafficbots-towards-world-models-for | 2303.04116 | null | https://arxiv.org/abs/2303.04116v1 | https://arxiv.org/pdf/2303.04116v1.pdf | TrafficBots: Towards World Models for Autonomous Driving Simulation and Motion Prediction | Data-driven simulation has become a favorable way to train and test autonomous driving algorithms. The idea of replacing the actual environment with a learned simulator has also been explored in model-based reinforcement learning in the context of world models. In this work, we show data-driven traffic simulation can b... | ['Luc van Gool', 'Fisher Yu', 'Dengxin Dai', 'Alexander Liniger', 'Zhejun Zhang'] | 2023-03-07 | null | null | null | null | ['motion-prediction'] | ['computer-vision'] | [-4.66619372e-01 8.33950862e-02 -1.09730829e-02 -4.61525112e-01
-2.90300250e-01 -3.34423542e-01 9.79896605e-01 -3.45089048e-01
-6.57230020e-01 7.27674127e-01 1.25077069e-01 -2.10724980e-01
-1.14041433e-01 -1.15790021e+00 -7.33652592e-01 -7.51540005e-01
-1.52885169e-01 1.13478410e+00 4.93304968e-01 -8.93058181... | [5.096607685089111, 1.0661993026733398] |
cb3c7c41-3ae2-4b5a-bda4-aa6124494889 | raddet-range-azimuth-doppler-based-radar | 2105.00363 | null | https://arxiv.org/abs/2105.00363v1 | https://arxiv.org/pdf/2105.00363v1.pdf | RADDet: Range-Azimuth-Doppler based Radar Object Detection for Dynamic Road Users | Object detection using automotive radars has not been explored with deep learning models in comparison to the camera based approaches. This can be attributed to the lack of public radar datasets. In this paper, we collect a novel radar dataset that contains radar data in the form of Range-Azimuth-Doppler tensors along ... | ['Robert Laganiere', 'Farzan Erlik Nowruzi', 'Ao Zhang'] | 2021-05-02 | null | null | null | null | ['radar-object-detection'] | ['robots'] | [-2.82718718e-01 -3.72158736e-01 1.13900959e-01 -7.48543441e-01
-9.38642979e-01 -6.66626394e-01 8.04897904e-01 -4.81465161e-01
-2.57209390e-01 3.49312812e-01 3.22460383e-02 -3.63615960e-01
-3.14582139e-01 -9.15533781e-01 -5.73526740e-01 -5.53018808e-01
-1.65577576e-01 6.77909315e-01 3.24676186e-01 -2.08752453... | [7.864893913269043, -1.4574320316314697] |
7953fa25-e4a1-4202-bcdb-eb0d00067c02 | gercct-an-annotated-corpus-for-mining | null | null | https://aclanthology.org/2022.lrec-1.658 | https://aclanthology.org/2022.lrec-1.658.pdf | GerCCT: An Annotated Corpus for Mining Arguments in German Tweets on Climate Change | While the field of argument mining has grown notably in the last decade, research on the Twitter medium remains relatively understudied. Given the difficulty of mining arguments in tweets, recent work on creating annotated resources mainly utilized simplified annotation schemes that focus on single argument components,... | ['Manfred Stede', 'Robin Schaefer'] | null | null | null | null | lrec-2022-6 | ['argument-mining'] | ['natural-language-processing'] | [ 2.19109282e-01 8.55421782e-01 -3.22830290e-01 -2.44418934e-01
-8.18508565e-01 -9.87362742e-01 1.05451119e+00 1.01541221e+00
-5.62828898e-01 1.00026274e+00 6.74898326e-01 -7.47577786e-01
1.11362757e-02 -5.82225740e-01 -3.49086165e-01 -3.20728421e-01
1.84195101e-01 5.04909039e-01 1.65360391e-01 -4.27378893... | [9.372610092163086, 9.689542770385742] |
984c8247-911b-4eec-9aab-58723d9e911b | prompting-and-evaluating-large-language | 2305.13626 | null | https://arxiv.org/abs/2305.13626v1 | https://arxiv.org/pdf/2305.13626v1.pdf | Prompting and Evaluating Large Language Models for Proactive Dialogues: Clarification, Target-guided, and Non-collaboration | Conversational systems based on Large Language Models (LLMs), such as ChatGPT, show exceptional proficiency in context understanding and response generation. However, despite their impressive capabilities, they still possess limitations, such as providing randomly-guessed answers to ambiguous queries or failing to refu... | ['Tat-Seng Chua', 'Lizi Liao', 'Wenqiang Lei', 'Yang Deng'] | 2023-05-23 | null | null | null | null | ['response-generation'] | ['natural-language-processing'] | [ 7.37831220e-02 9.35199022e-01 -3.11546717e-02 -4.79567856e-01
-5.33158660e-01 -7.09219813e-01 1.20261145e+00 -6.38001487e-02
-6.91199377e-02 8.98447335e-01 7.44642615e-01 -7.14199781e-01
-5.44641390e-02 -8.16296935e-01 3.70836049e-01 -2.52318591e-01
1.65591121e-01 7.46270180e-01 2.32848659e-01 -9.43564296... | [12.794414520263672, 8.032617568969727] |
b9e289ed-fcd2-4e4d-9c2d-3766d2494745 | learning-for-open-world-calibration-with | 2305.12039 | null | https://arxiv.org/abs/2305.12039v1 | https://arxiv.org/pdf/2305.12039v1.pdf | Learning for Open-World Calibration with Graph Neural Networks | We tackle the problem of threshold calibration for open-world recognition by incorporating representation compactness measures into clustering. Unlike the open-set recognition which focuses on discovering and rejecting the unknown, open-world recognition learns robust representations that are generalizable to disjoint ... | ['Yifan Xing', 'Joseph Tighe', 'Ying Nian Wu', 'Qingming Tang', 'Tong He', 'Tianjun Xiao', 'Dongsheng An', 'Qin Zhang'] | 2023-05-19 | null | null | null | null | ['open-set-learning'] | ['miscellaneous'] | [ 9.40925628e-02 1.40407115e-01 -4.22189087e-01 -4.48664635e-01
-7.79242277e-01 -6.73429906e-01 6.47412896e-01 1.17002062e-01
8.78468379e-02 4.60814536e-01 2.23355159e-01 -1.55162647e-01
-5.66209197e-01 -7.65344262e-01 -6.04595184e-01 -9.80116606e-01
-4.01854783e-01 4.94606465e-01 -2.83923149e-02 3.27522397... | [9.595698356628418, 2.8291590213775635] |
b362df8c-520d-450e-b8a8-45565084c88b | generalizable-no-reference-image-quality | null | null | https://ieeexplore.ieee.org/abstract/document/9405680 | https://zhuhancheng.github.io/Hancheng_files/files/2021-TCSVT.pdf | Generalizable No-Reference Image Quality Assessment via Deep Meta-learning | Recently, researchers have shown great interest in using convolutional neural networks (CNNs) for no-reference image quality assessment (NR-IQA). Due to the lack of big training data, the efforts of existing metrics in optimizing CNN-based NR-IQA models remain limited. Furthermore, the diversity of distortions in image... | ['and Guangming Shi', 'Weisheng Dong', 'Jinjian Wu', 'Leida Li', 'Hancheng Zhu'] | 2021-04-15 | null | null | null | ieee-transactions-on-circuits-and-systems-for-4 | ['no-reference-image-quality-assessment'] | ['computer-vision'] | [ 8.29033926e-02 -5.73279500e-01 1.25988409e-01 -3.75805974e-01
-7.29929745e-01 -1.52727976e-01 4.75906283e-01 -4.42448229e-01
-2.53021866e-01 3.30711067e-01 1.24172769e-01 -5.32944314e-02
-3.08130682e-01 -8.67506742e-01 -5.88482976e-01 -5.69129586e-01
2.53465027e-01 -9.77640525e-02 -1.15249880e-01 -3.86382878... | [11.850998878479004, -1.8400912284851074] |
be35f97d-d2c4-4852-b280-3260a6d9d77b | anomaly-detection-in-time-series-with-triadic | 2012.04936 | null | https://arxiv.org/abs/2012.04936v1 | https://arxiv.org/pdf/2012.04936v1.pdf | Anomaly Detection in Time Series with Triadic Motif Fields and Application in Atrial Fibrillation ECG Classification | In the time-series analysis, the time series motifs and the order patterns in time series can reveal general temporal patterns and dynamic features. Triadic Motif Field (TMF) is a simple and effective time-series image encoding method based on triadic time series motifs. Electrocardiography (ECG) signals are time-serie... | ['Xin Chen', 'Yadong Zhang'] | 2020-12-09 | null | null | null | null | ['ecg-classification', 'atrial-fibrillation-detection', 'electrocardiography-ecg'] | ['medical', 'medical', 'methodology'] | [ 2.49341041e-01 -4.55490708e-01 -4.39523198e-02 -2.62410551e-01
-4.13401663e-01 -3.60378027e-01 9.09678731e-03 1.84762388e-01
-1.24229632e-01 6.48207188e-01 1.55566148e-02 -4.96127605e-01
-6.59909248e-01 -6.13428116e-01 -2.47765258e-01 -9.21024501e-01
-9.60187793e-01 1.71021909e-01 -2.96251833e-01 5.71647920... | [14.26470947265625, 3.2451422214508057] |
fce1405c-01aa-4392-9331-91f83256cb7d | modeling-user-behavior-with-interaction | 2207.10767 | null | https://arxiv.org/abs/2207.10767v1 | https://arxiv.org/pdf/2207.10767v1.pdf | Modeling User Behavior With Interaction Networks for Spam Detection | Spam is a serious problem plaguing web-scale digital platforms which facilitate user content creation and distribution. It compromises platform's integrity, performance of services like recommendation and search, and overall business. Spammers engage in a variety of abusive and evasive behavior which are distinct from ... | ['Charles Rosenberg', 'Vishwakarma Singh', 'Manisha Srivastava', 'Prabhat Agarwal'] | 2022-07-21 | null | null | null | null | ['spam-detection'] | ['natural-language-processing'] | [-3.45438868e-01 -1.29793912e-01 -2.34018475e-01 1.43937841e-01
-2.31497899e-01 -1.02957094e+00 7.81629503e-01 2.78911501e-01
9.78011191e-02 2.52628922e-01 -3.02010328e-02 -5.71356416e-01
4.62888293e-02 -1.14509869e+00 -3.65269244e-01 -1.71726003e-01
-4.41084802e-01 4.83410418e-01 9.63285327e-01 -6.49416864... | [7.870263576507568, 10.04216480255127] |
9a4793c9-2ead-47aa-97cc-10ca2b7923e7 | accelerating-inexact-hypergradient-descent | 2307.00126 | null | https://arxiv.org/abs/2307.00126v1 | https://arxiv.org/pdf/2307.00126v1.pdf | Accelerating Inexact HyperGradient Descent for Bilevel Optimization | We present a method for solving general nonconvex-strongly-convex bilevel optimization problems. Our method -- the \emph{Restarted Accelerated HyperGradient Descent} (\texttt{RAHGD}) method -- finds an $\epsilon$-first-order stationary point of the objective with $\tilde{\mathcal{O}}(\kappa^{3.25}\epsilon^{-1.75})$ ora... | ['Michael I. Jordan', 'Chris Junchi Li', 'Luo Luo', 'Haikuo Yang'] | 2023-06-30 | null | null | null | null | ['bilevel-optimization'] | ['methodology'] | [-1.70613244e-01 2.12335065e-01 -1.86669603e-01 -2.26711124e-01
-1.31444597e+00 -6.45841599e-01 -5.47863960e-01 6.69819638e-02
-5.83498955e-01 1.09801495e+00 -2.54829675e-01 -8.85229230e-01
-8.26013863e-01 -5.72279274e-01 -9.82125938e-01 -1.00072384e+00
-5.72352350e-01 3.93816710e-01 -2.70435542e-01 -4.84697461... | [6.5103559494018555, 4.535711288452148] |
12e95028-4250-45f5-a24e-0da5e11074e6 | an-accurate-car-counting-in-aerial-images | null | null | https://link.springer.com/article/10.1007%2Fs12652-021-03377-5 | https://link.springer.com/article/10.1007%2Fs12652-021-03377-5 | An Accurate Car Counting in Aerial Images Based on Convolutional Neural Networks | This paper proposes a simple and effective single-shot detector model to detect and
count cars in aerial images. The proposed model, called heatmap learner convolutional
neural network (HLCNN), is used to predict the heatmap of target car instances. In
order to learn the heatmap of the target cars, we have improved ... | ['Serkan Öztürk', 'Ersin Kılıç'] | 2021-07-13 | null | null | null | journal-of-ambient-intelligence-and-humanized-1 | ['object-counting'] | ['computer-vision'] | [-1.92940027e-01 -2.89844602e-01 1.02508068e-01 -3.19110900e-01
-3.27904135e-01 -3.46827894e-01 6.47723079e-01 -1.26879737e-01
-5.32690287e-01 3.09149772e-01 -3.27214479e-01 -1.18256196e-01
4.30077463e-01 -1.04872572e+00 -6.70517564e-01 -5.43839097e-01
1.69557557e-02 2.57372111e-01 7.95889080e-01 2.60360856... | [8.721612930297852, -0.22463208436965942] |
68d74fc3-31b8-4fac-95ee-4127f1cd82d1 | a-comprehensive-review-of-yolo-from-yolov1-to | 2304.00501 | null | https://arxiv.org/abs/2304.00501v3 | https://arxiv.org/pdf/2304.00501v3.pdf | A Comprehensive Review of YOLO: From YOLOv1 and Beyond | YOLO has become a central real-time object detection system for robotics, driverless cars, and video monitoring applications. We present a comprehensive analysis of YOLO's evolution, examining the innovations and contributions in each iteration from the original YOLO to YOLOv8 and YOLO-NAS. We start by describing the s... | ['Diana Cordova-Esparza', 'Juan Terven'] | 2023-04-02 | null | null | null | null | ['real-time-object-detection'] | ['computer-vision'] | [-3.15505594e-01 -3.10918599e-01 -4.87436056e-01 -1.49905950e-01
-1.32692814e-01 -1.75015941e-01 8.54742602e-02 -4.72692847e-01
-3.15673262e-01 3.38568509e-01 -5.60513258e-01 -3.58524740e-01
6.64657652e-02 -4.50428605e-01 -4.68626648e-01 -5.87060392e-01
-1.98953778e-01 -7.95225352e-02 6.40408576e-01 -1.92269355... | [8.263554573059082, -0.9097298979759216] |
be9e5555-dc98-4899-b44c-ee88186e4900 | sgram-improving-scene-graph-parsing-via | 2210.08675 | null | https://arxiv.org/abs/2210.08675v1 | https://arxiv.org/pdf/2210.08675v1.pdf | SGRAM: Improving Scene Graph Parsing via Abstract Meaning Representation | Scene graph is structured semantic representation that can be modeled as a form of graph from images and texts. Image-based scene graph generation research has been actively conducted until recently, whereas text-based scene graph generation research has not. In this paper, we focus on the problem of scene graph parsin... | ['Byoung-Tak Zhang', 'Yu-Jung Heo', 'Woo Suk Choi'] | 2022-10-17 | null | null | null | null | ['scene-graph-generation', 'dependency-parsing'] | ['computer-vision', 'natural-language-processing'] | [ 8.52510393e-01 3.94787341e-01 2.24628061e-01 -6.43361092e-01
-5.89970648e-01 -3.83997083e-01 8.13138664e-01 1.15342043e-01
-7.92391449e-02 2.95270443e-01 4.26924139e-01 -5.02123952e-01
3.81354928e-01 -1.35695481e+00 -8.31149995e-01 -4.18033242e-01
4.83533382e-01 3.09366345e-01 4.66588974e-01 -4.00037050... | [10.49283218383789, 1.577022671699524] |
dd4e1e8b-78c8-486e-9ebf-b89641e16316 | alexa-teacher-model-pretraining-and | 2206.07808 | null | https://arxiv.org/abs/2206.07808v1 | https://arxiv.org/pdf/2206.07808v1.pdf | Alexa Teacher Model: Pretraining and Distilling Multi-Billion-Parameter Encoders for Natural Language Understanding Systems | We present results from a large-scale experiment on pretraining encoders with non-embedding parameter counts ranging from 700M to 9.3B, their subsequent distillation into smaller models ranging from 17M-170M parameters, and their application to the Natural Language Understanding (NLU) component of a virtual assistant s... | ['Prem Natarajan', 'Gokhan Tur', 'Shuai Zheng', 'Haiyang Yu', 'Pan Wei', 'Fabian Triefenbach', 'Liz Tan', 'Mukund Harakere Sridhar', 'Saleh Soltan', 'Anjali Shenoy', 'Andy Rosenbaum', 'Stephen Rawls', 'Chandana Satya Prakash', 'Charith Peris', 'Enrico Palumbo', 'Gokmen Oz', 'Karolina Owczarzak', 'Pradeep Natarajan', 'A... | 2022-06-15 | null | null | null | null | ['cross-lingual-natural-language-inference', 'xlm-r', 'slot-filling'] | ['natural-language-processing', 'natural-language-processing', 'natural-language-processing'] | [ 5.85867427e-02 9.28379118e-01 -4.84033048e-01 -7.27073491e-01
-1.10825193e+00 -5.92612803e-01 5.79929948e-01 -3.73432562e-02
-9.78932202e-01 7.65156806e-01 3.23178411e-01 -1.00168943e+00
2.84394845e-02 -4.10301626e-01 -6.70578241e-01 9.18373913e-02
1.62482336e-01 1.21084225e+00 -2.15308711e-01 -6.11690521... | [11.0014066696167, 8.360823631286621] |
8abad33f-69ad-4098-82e0-72e0626ae18c | mesh-interest-point-detection-based-on | 1604.08806 | null | http://arxiv.org/abs/1604.08806v3 | http://arxiv.org/pdf/1604.08806v3.pdf | Mesh Interest Point Detection Based on Geometric Measures and Sparse Refinement | Three dimensional (3D) interest point detection plays a fundamental role in
3D computer vision and graphics. In this paper, we introduce a new method for
detecting mesh interest points based on geometric measures and sparse
refinement (GMSR). The key point of our approach is to calculate the 3D
interest point response ... | ['Yipeng Liu', 'Ce Zhu', 'Xinyu Lin'] | 2016-04-29 | null | null | null | null | ['interest-point-detection'] | ['computer-vision'] | [-1.39481887e-01 -2.64694184e-01 -1.14158466e-02 1.24395952e-01
-6.43281817e-01 -4.03975584e-02 4.64448512e-01 4.02774572e-01
-1.09475657e-01 7.59943053e-02 -2.04804912e-01 6.29575849e-02
-5.98102845e-02 -9.48359549e-01 -4.22514617e-01 -3.60980898e-01
-3.19890350e-01 6.64927721e-01 6.71273470e-01 -2.05410331... | [7.868456840515137, -2.9994735717773438] |
b5481244-0741-48be-b251-1645c63b631c | empirical-evaluation-of-leveraging-named | 1904.10195 | null | http://arxiv.org/abs/1904.10195v1 | http://arxiv.org/pdf/1904.10195v1.pdf | Empirical Evaluation of Leveraging Named Entities for Arabic Sentiment Analysis | Social media reflects the public attitudes towards specific events. Events
are often related to persons, locations or organizations, the so-called Named
Entities. This can define Named Entities as sentiment-bearing components. In
this paper, we dive beyond Named Entities recognition to the exploitation of
sentiment-ann... | ['Ismail Babaoglu', 'Mourad Gridach', 'Hatem Haddad', 'Hala Mulki'] | 2019-04-23 | null | null | null | null | ['arabic-sentiment-analysis'] | ['natural-language-processing'] | [-4.82842118e-01 1.31894946e-01 1.81733407e-02 -5.27316868e-01
-3.04828674e-01 -8.88690710e-01 6.67533219e-01 5.61747015e-01
-8.25069010e-01 7.03212678e-01 6.18764043e-01 -9.34460089e-02
3.50763708e-01 -1.07451010e+00 -2.72856086e-01 -5.63676655e-01
1.80120394e-01 1.88588873e-01 2.62009025e-01 -5.94396234... | [11.05174732208252, 6.9406867027282715] |
0ac4ac1f-b11d-43ea-8694-7ac5fc37906b | smac-symbiotic-multi-agent-construction | 2010.08473 | null | https://arxiv.org/abs/2010.08473v1 | https://arxiv.org/pdf/2010.08473v1.pdf | SMAC: Symbiotic Multi-Agent Construction | We present a novel concept of a heterogeneous, distributed platform for autonomous 3D construction. The platform is composed of two types of robots acting in a coordinated and complementary fashion: (i) A collection of communicating smart construction blocks behaving as a form of growable smart matter, and capable of p... | ['Carlo Pinciroli', 'Gregory Lewin', 'Hannan Liang', 'Josue Contreras', 'Trevor Rizzo', 'Neel Dhanaraj', 'Caleb Wagner'] | 2020-10-16 | null | null | null | null | ['smac-1', 'smac'] | ['playing-games', 'playing-games'] | [-2.80717641e-01 6.26170516e-01 4.23132598e-01 -4.50779125e-03
1.57057658e-01 -8.18447948e-01 4.98746336e-01 -1.72944427e-01
4.77714807e-01 3.22463930e-01 8.28290880e-02 -3.37725058e-02
-3.14432919e-01 -1.28869104e+00 -6.96875036e-01 -7.92325437e-01
-6.44329965e-01 1.23791838e+00 6.73728347e-01 -8.63581240... | [4.8242292404174805, 0.87785804271698] |
6faf8e1b-93aa-4436-b340-dfd66631209e | one-shot-learning-based-drivers-head-movement | 2306.05291 | null | https://arxiv.org/abs/2306.05291v1 | https://arxiv.org/pdf/2306.05291v1.pdf | One shot learning based drivers head movement identification using a millimetre wave radar sensor | Concentration of drivers on traffic is a vital safety issue; thus, monitoring a driver being on road becomes an essential requirement. The key purpose of supervision is to detect abnormal behaviours of the driver and promptly send warnings to him her for avoiding incidents related to traffic accidents. In this paper, t... | ['Yong Hwa Kim', 'Tien Tung Nguyen', 'Seongwook Lee', 'Hong Nhung Nguyen'] | 2023-05-31 | null | null | null | null | ['one-shot-learning'] | ['methodology'] | [ 2.20722705e-01 -2.64977902e-01 1.20497495e-01 -4.89531219e-01
-1.25788167e-01 1.51970237e-01 2.86042690e-01 -3.95941168e-01
-6.08716488e-01 4.16534215e-01 -1.21634662e-01 -3.62458915e-01
-3.47452819e-01 -7.62266040e-01 -1.05569750e-01 -1.03726006e+00
5.04075170e-01 -1.07472427e-01 4.81900901e-01 -3.24667990... | [7.999454975128174, -0.7330774068832397] |
06d2c7e5-97a5-4efb-b9eb-f1ca6e382295 | invalidator-automated-patch-correctness | 2301.01113 | null | https://arxiv.org/abs/2301.01113v2 | https://arxiv.org/pdf/2301.01113v2.pdf | Invalidator: Automated Patch Correctness Assessment via Semantic and Syntactic Reasoning | Automated program repair (APR) faces the challenge of test overfitting, where generated patches pass validation tests but fail to generalize. Existing methods for patch assessment involve generating new tests or manual inspection, which can be time-consuming or biased. In this paper, we propose a novel technique, INVAL... | ['Quyet-Thang Huynh', 'Bui Quang-Huy', 'Nhat-Hoa Tran', 'David Lo', 'Xuan Bach D. Le', 'Duc-Minh Luong', 'Thanh Le-Cong'] | 2023-01-03 | null | null | null | null | ['program-repair', 'program-repair'] | ['computer-code', 'reasoning'] | [ 2.42302611e-01 1.29720539e-01 -5.90147257e-01 -2.14162394e-01
-1.19907749e+00 -8.95584464e-01 -1.01527400e-01 3.98429841e-01
6.08556390e-01 4.21873927e-01 -2.04730377e-01 -7.81538785e-01
2.85615921e-01 -9.27081943e-01 -1.15522194e+00 1.02350004e-01
-7.13139102e-02 4.53098044e-02 5.93976319e-01 7.77349994... | [7.590681552886963, 7.712461948394775] |
d63c59b2-ce11-4d39-9cd9-a747f345dff0 | fpcc-net-fast-point-cloud-clustering-for | 2012.14618 | null | https://arxiv.org/abs/2012.14618v5 | https://arxiv.org/pdf/2012.14618v5.pdf | FPCC: Fast Point Cloud Clustering based Instance Segmentation for Industrial Bin-picking | Instance segmentation is an important pre-processing task in numerous real-world applications, such as robotics, autonomous vehicles, and human-computer interaction. Compared with the rapid development of deep learning for two-dimensional (2D) image tasks, deep learning-based instance segmentation of 3D point cloud sti... | ['Kazuhiro Kosuge', 'Fangzhou Lin', 'Diyi Liu', 'Shogo Arai', 'Yajun Xu'] | 2020-12-29 | null | null | null | null | ['3d-instance-segmentation-1'] | ['computer-vision'] | [-2.17398852e-01 -1.43781304e-01 -2.96946727e-02 -4.08331394e-01
-4.07986313e-01 -4.34149861e-01 4.37948406e-01 3.23509395e-01
-3.06310356e-01 4.81531136e-02 -4.13112760e-01 -1.91735089e-01
-2.90362328e-01 -9.06557679e-01 -7.84727871e-01 -8.67833734e-01
-7.41330385e-02 9.88699555e-01 4.88542974e-01 5.00938632... | [7.983469009399414, -3.1718127727508545] |
c9ec1740-b1de-414b-9560-3b12847e9844 | codekgc-code-language-model-for-generative | 2304.09048 | null | https://arxiv.org/abs/2304.09048v1 | https://arxiv.org/pdf/2304.09048v1.pdf | CodeKGC: Code Language Model for Generative Knowledge Graph Construction | Current generative knowledge graph construction approaches usually fail to capture structural knowledge by simply flattening natural language into serialized texts or a specification language. However, large generative language model trained on structured data such as code has demonstrated impressive capability in unde... | ['Ningyu Zhang', 'Huajun Chen', 'Wei Guo', 'Feiyu Xiong', 'Yinuo Jiang', 'Jing Chen', 'Zhen Bi'] | 2023-04-18 | null | null | null | null | ['graph-construction'] | ['graphs'] | [ 9.64119807e-02 5.80611587e-01 -4.69838083e-01 -4.42798346e-01
-7.06175804e-01 -7.48394191e-01 5.65307915e-01 8.73880833e-03
4.15869832e-01 5.99384725e-01 4.76558417e-01 -6.66935861e-01
1.21188767e-01 -1.08178186e+00 -9.99815643e-01 -1.05908372e-01
1.86771989e-01 2.79118389e-01 8.05815607e-02 -1.54883996... | [7.886507987976074, 7.874252796173096] |
f76a75ab-defd-44d7-b261-6dd6ae8c64bf | region-adaptive-texture-enhancement-for | 2005.12486 | null | https://arxiv.org/abs/2005.12486v1 | https://arxiv.org/pdf/2005.12486v1.pdf | Region-adaptive Texture Enhancement for Detailed Person Image Synthesis | The ability to produce convincing textural details is essential for the fidelity of synthesized person images. However, existing methods typically follow a ``warping-based'' strategy that propagates appearance features through the same pathway used for pose transfer. However, most fine-grained features would be lost du... | ['Zhanning Gao', 'Xuansong Xie', 'Xinfeng Zhang', 'Wen Gao', 'Shanshe Wang', 'Lingbo Yang', 'Siwei Ma', 'Peiran Ren', 'Pan Wang'] | 2020-05-26 | null | null | null | null | ['pose-transfer'] | ['computer-vision'] | [ 4.15982127e-01 1.62964761e-01 2.57263094e-01 -4.48176265e-01
-3.37814420e-01 -3.71872514e-01 6.05591834e-01 -4.52698886e-01
4.74397019e-02 7.42079556e-01 3.31357628e-01 4.09402072e-01
4.50727418e-02 -8.99242043e-01 -8.31756890e-01 -8.01193416e-01
4.17196721e-01 9.78169031e-03 1.64613664e-01 -3.30773979... | [11.956600189208984, -0.8515157103538513] |
b70530fb-b260-42c9-94d6-e7d014d82940 | tab2kg-semantic-table-interpretation-with | 2302.01150 | null | https://arxiv.org/abs/2302.01150v1 | https://arxiv.org/pdf/2302.01150v1.pdf | Tab2KG: Semantic Table Interpretation with Lightweight Semantic Profiles | Tabular data plays an essential role in many data analytics and machine learning tasks. Typically, tabular data does not possess any machine-readable semantics. In this context, semantic table interpretation is crucial for making data analytics workflows more robust and explainable. This article proposes Tab2KG - a nov... | ['Elena Demidova', 'Simon Gottschalk'] | 2023-02-02 | null | null | null | null | ['one-shot-learning'] | ['methodology'] | [ 3.00638109e-01 7.17331529e-01 -4.61593568e-01 -7.18235195e-01
-5.82971156e-01 -7.34868169e-01 4.56932068e-01 1.11548007e+00
1.83089226e-01 8.45411420e-01 8.50626752e-02 -3.04248661e-01
-4.52216297e-01 -1.38674629e+00 -1.05956829e+00 -1.07324801e-01
7.28405192e-02 1.17885959e+00 5.86550593e-01 -3.66784632... | [9.342069625854492, 8.00100040435791] |
f266d564-6895-4ef7-8052-096632485d82 | cellular-segmentation-and-composition-in | 2203.02510 | null | https://arxiv.org/abs/2203.02510v1 | https://arxiv.org/pdf/2203.02510v1.pdf | Cellular Segmentation and Composition in Routine Histology Images using Deep Learning | Identification and quantification of nuclei in colorectal cancer haematoxylin \& eosin (H\&E) stained histology images is crucial to prognosis and patient management. In computational pathology these tasks are referred to as nuclear segmentation, classification and composition and are used to extract meaningful interpr... | ['Adam Shephard', 'Manahil Raza', 'Srijay Deshpande', 'Raja Muhammad Saad Bashir', 'Muhammad Dawood'] | 2022-03-04 | null | null | null | null | ['nuclear-segmentation'] | ['medical'] | [-2.14269999e-02 2.78307050e-01 8.95233974e-02 -3.52758467e-02
-8.94590616e-01 -7.22749054e-01 3.46354663e-01 9.61738050e-01
-7.79383957e-01 8.22689414e-01 -7.70203546e-02 -3.40038151e-01
-1.23122232e-02 -8.35382879e-01 -2.35309768e-02 -1.20450008e+00
-3.87871545e-03 9.30263460e-01 1.38199121e-01 1.71997979... | [15.057774543762207, -3.1275789737701416] |
69125399-4a06-428a-8ae0-73286eeba4cb | synthesizing-diverse-human-motions-in-3d | 2305.12411 | null | https://arxiv.org/abs/2305.12411v2 | https://arxiv.org/pdf/2305.12411v2.pdf | Synthesizing Diverse Human Motions in 3D Indoor Scenes | We present a novel method for populating 3D indoor scenes with virtual humans that can navigate the environment and interact with objects in a realistic manner. Existing approaches rely on high-quality training sequences that capture a diverse range of human motions in 3D scenes. However, such motion data is costly, di... | ['Siyu Tang', 'Thabo Beeler', 'Shaofei Wang', 'Yan Zhang', 'Kaifeng Zhao'] | 2023-05-21 | null | null | null | null | ['human-object-interaction-detection'] | ['computer-vision'] | [-4.74822409e-02 -2.25558370e-01 8.22947174e-02 -1.93549737e-01
-4.39173818e-01 -4.26365018e-01 4.72052604e-01 -5.37395358e-01
-2.95720100e-01 6.83723509e-01 2.84808964e-01 -1.91134661e-01
-4.67107669e-02 -7.67559111e-01 -9.34664309e-01 -4.94903058e-01
-1.25358239e-01 6.63960576e-01 3.19831014e-01 -4.08676773... | [7.0058417320251465, -0.6705074906349182] |
69f6a9e0-96c6-424c-a6e9-16a1f7dc655f | fast-and-accurate-intrinsic-symmetry | 1807.10162 | null | http://arxiv.org/abs/1807.10162v4 | http://arxiv.org/pdf/1807.10162v4.pdf | Fast and Accurate Intrinsic Symmetry Detection | In computer vision and graphics, various types of symmetries are extensively
studied since symmetry present in objects is a fundamental cue for
understanding the shape and the structure of objects. In this work, we detect
the intrinsic reflective symmetry in triangle meshes where we have to find the
intrinsically symme... | ['Rajendra Nagar', 'Shanmuganathan Raman'] | 2018-07-26 | fast-and-accurate-intrinsic-symmetry-1 | http://openaccess.thecvf.com/content_ECCV_2018/html/Rajendra_Nagar_Fast_and_Accurate_ECCV_2018_paper.html | http://openaccess.thecvf.com/content_ECCV_2018/papers/Rajendra_Nagar_Fast_and_Accurate_ECCV_2018_paper.pdf | eccv-2018-9 | ['symmetry-detection'] | ['computer-vision'] | [ 1.51997998e-01 -8.39298666e-02 4.73640710e-02 -1.11286789e-01
-1.50738969e-01 -8.12904894e-01 3.40021253e-01 -6.17297813e-02
7.51874992e-04 8.40623453e-02 -6.12331070e-02 -1.08923770e-01
-2.97432929e-01 -8.96881342e-01 -8.27002704e-01 -6.82201385e-01
-6.23112954e-02 6.67199016e-01 4.31240499e-01 -3.06806952... | [8.15269947052002, -2.3795480728149414] |
3071d261-748f-4646-857d-958029fc0154 | decomposed-meta-learning-for-few-shot-named | 2204.05751 | null | https://arxiv.org/abs/2204.05751v2 | https://arxiv.org/pdf/2204.05751v2.pdf | Decomposed Meta-Learning for Few-Shot Named Entity Recognition | Few-shot named entity recognition (NER) systems aim at recognizing novel-class named entities based on only a few labeled examples. In this paper, we present a decomposed meta-learning approach which addresses the problem of few-shot NER by sequentially tackling few-shot span detection and few-shot entity typing using ... | ['Chin-Yew Lin', 'Tiejun Zhao', 'Qianhui Wu', 'Huiqiang Jiang', 'Tingting Ma'] | 2022-04-12 | null | https://aclanthology.org/2022.findings-acl.124 | https://aclanthology.org/2022.findings-acl.124.pdf | findings-acl-2022-5 | ['few-shot-ner', 'entity-typing'] | ['natural-language-processing', 'natural-language-processing'] | [-1.42951738e-02 -8.44538510e-02 -3.25589895e-01 -3.67355675e-01
-9.81061995e-01 -4.93228853e-01 4.00027156e-01 2.63312548e-01
-6.77524269e-01 5.44247985e-01 1.03432439e-01 -9.20389965e-02
8.35031085e-03 -8.59643757e-01 -4.95576531e-01 -2.65897572e-01
-5.65277562e-02 4.74360645e-01 2.75082409e-01 -2.91689605... | [9.667068481445312, 9.401098251342773] |
f41080a0-aff0-416b-a573-0f4b03d53c9a | un-likelihood-training-for-interpretable | 2207.00282 | null | https://arxiv.org/abs/2207.00282v2 | https://arxiv.org/pdf/2207.00282v2.pdf | (Un)likelihood Training for Interpretable Embedding | Cross-modal representation learning has become a new normal for bridging the semantic gap between text and visual data. Learning modality agnostic representations in a continuous latent space, however, is often treated as a black-box data-driven training process. It is well-known that the effectiveness of representatio... | ['Zhijian Hou', 'Wing-Kwong Chan', 'Chong-Wah Ngo', 'Jiaxin Wu'] | 2022-07-01 | null | null | null | null | ['ad-hoc-video-search'] | ['computer-vision'] | [ 3.50716561e-01 -1.97189406e-01 -8.17545712e-01 -5.08558095e-01
-9.41528261e-01 -6.11919641e-01 6.80909872e-01 2.75193844e-02
-3.32689047e-01 4.55375075e-01 4.91405785e-01 -2.45897487e-01
-1.55279428e-01 -2.23099977e-01 -7.63043463e-01 -5.92539132e-01
3.56391184e-02 3.48969162e-01 -3.11585933e-01 9.01594833... | [10.507333755493164, 1.3317723274230957] |
d2e449af-552d-4ea6-aa72-cd23593bd243 | csclog-a-component-subsequence-correlation | 2307.03359 | null | https://arxiv.org/abs/2307.03359v1 | https://arxiv.org/pdf/2307.03359v1.pdf | CSCLog: A Component Subsequence Correlation-Aware Log Anomaly Detection Method | Anomaly detection based on system logs plays an important role in intelligent operations, which is a challenging task due to the extremely complex log patterns. Existing methods detect anomalies by capturing the sequential dependencies in log sequences, which ignore the interactions of subsequences. To this end, we pro... | ['Feifei Li', 'Dachao Fu', 'Xu Wang', 'Chaodu Song', 'Ling Chen'] | 2023-07-07 | null | null | null | null | ['anomaly-detection'] | ['methodology'] | [-2.29213014e-02 -4.11239058e-01 -1.78757645e-02 -2.55381376e-01
5.52457012e-02 -2.55381733e-01 3.56356949e-01 7.08720386e-01
-2.39125401e-01 6.43649548e-02 4.20913219e-01 -4.78704274e-01
3.22780833e-02 -4.58639473e-01 -9.39361870e-01 -4.95403647e-01
-6.37420058e-01 -3.89288515e-02 2.97006458e-01 -2.40107253... | [7.346531867980957, 2.6302378177642822] |
e24167cb-f8f3-49ee-9548-2c38b06a8448 | rf-based-fall-monitoring-using-convolutional | null | null | https://doi.org/10.1145/3264947 | http://people.csail.mit.edu/yonglong/yonglong/rffall.pdf | RF-Based Fall Monitoring Using Convolutional Neural Networks | Falls are the top reason for fatal and non-fatal injuries among seniors. Existing solutions are based on wearable fall-alert sensors, but medical research has shown that they are ineffective, mostly because seniors do not wear them. These revelations have led to new passive sensors that infer falls by analyzing Radio F... | ['Dina Katabi', 'Chen-Yu Hsu', 'Guang-He Lee', 'Yonglong Tian', 'Hao He'] | 2018-09-01 | null | null | null | proceedings-of-the-acm-on-interactive-mobile | ['rf-based-pose-estimation'] | ['computer-vision'] | [ 3.80331054e-02 -3.09270173e-01 -6.32843226e-02 -3.58702302e-01
-5.45344472e-01 -1.67539805e-01 -2.45566994e-01 -1.17684109e-02
-8.31705093e-01 1.01128292e+00 5.47908604e-01 -1.47447854e-01
-3.11169773e-02 -9.97981071e-01 -4.23561573e-01 -2.66015917e-01
-2.18495235e-01 2.00092316e-01 8.61674726e-01 -4.87348527... | [7.197625160217285, 0.5851206183433533] |
fcc7c05f-1b16-4836-99da-5ae63cc00f4a | cascaded-deep-monocular-3d-human-pose-1 | 2006.07778 | null | https://arxiv.org/abs/2006.07778v3 | https://arxiv.org/pdf/2006.07778v3.pdf | Cascaded deep monocular 3D human pose estimation with evolutionary training data | End-to-end deep representation learning has achieved remarkable accuracy for monocular 3D human pose estimation, yet these models may fail for unseen poses with limited and fixed training data. This paper proposes a novel data augmentation method that: (1) is scalable for synthesizing massive amount of training data (o... | ['Kwang-Ting Cheng', 'Chi-Keung Tang', 'Yu-Wing Tai', 'Lei Ke', 'Shichao Li', 'Kevin Pratama'] | 2020-06-14 | cascaded-deep-monocular-3d-human-pose | http://openaccess.thecvf.com/content_CVPR_2020/html/Li_Cascaded_Deep_Monocular_3D_Human_Pose_Estimation_With_Evolutionary_Training_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Li_Cascaded_Deep_Monocular_3D_Human_Pose_Estimation_With_Evolutionary_Training_CVPR_2020_paper.pdf | cvpr-2020-6 | ['monocular-3d-human-pose-estimation', 'weakly-supervised-3d-human-pose-estimation'] | ['computer-vision', 'computer-vision'] | [ 4.99725789e-02 1.48735285e-01 -2.88600236e-01 -1.97695211e-01
-8.02347720e-01 -3.08539987e-01 3.58874232e-01 -5.93068659e-01
-3.52051765e-01 7.61594951e-01 6.44427657e-01 6.32199571e-02
3.69238853e-01 -3.98153514e-01 -1.00154030e+00 -1.15067407e-01
-1.30196661e-01 1.08095348e+00 1.04733348e-01 -1.56712070... | [6.9748406410217285, -0.886874794960022] |
8259321d-c68f-4cd1-ae81-2c50f1dd91eb | object-driven-active-mapping-for-more | 2012.01788 | null | https://arxiv.org/abs/2012.01788v3 | https://arxiv.org/pdf/2012.01788v3.pdf | Object SLAM-Based Active Mapping and Robotic Grasping | This paper presents the first active object mapping framework for complex robotic manipulation and autonomous perception tasks. The framework is built on an object SLAM system integrated with a simultaneous multi-object pose estimation process that is optimized for robotic grasping. Aiming to reduce the observation unc... | ['Zhiqiang Deng', 'Xinggang Hu', 'Wenkai Sun', 'Sonya Coleman', 'Xin Chen', 'Delong Zhu', 'Yunzhou Zhang', 'Yanmin Wu'] | 2020-12-03 | null | null | null | null | ['object-slam'] | ['computer-vision'] | [-1.02777466e-01 7.25988373e-02 -1.65414855e-01 -3.97821993e-01
-2.80569315e-01 -4.94228512e-01 3.68868172e-01 -1.65890902e-02
-1.45257249e-01 4.09583360e-01 -2.46728197e-01 3.07208419e-01
-8.01086247e-01 -8.41425121e-01 -6.99520767e-01 -4.27432209e-01
-2.11284563e-01 9.46752131e-01 3.76041740e-01 -1.17735989... | [5.897825717926025, -0.9387659430503845] |
1903a1a4-5863-4c2f-9738-5eaf997bcb04 | meta-voice-fast-few-shot-style-transfer-for | 2111.07218 | null | https://arxiv.org/abs/2111.07218v1 | https://arxiv.org/pdf/2111.07218v1.pdf | Meta-Voice: Fast few-shot style transfer for expressive voice cloning using meta learning | The task of few-shot style transfer for voice cloning in text-to-speech (TTS) synthesis aims at transferring speaking styles of an arbitrary source speaker to a target speaker's voice using very limited amount of neutral data. This is a very challenging task since the learning algorithm needs to deal with few-shot voic... | ['Dong Yu', 'Dan Su', 'Songxiang Liu'] | 2021-11-14 | null | null | null | null | ['voice-cloning'] | ['speech'] | [ 5.52625656e-01 7.59155676e-02 -1.19581111e-01 -2.72632360e-01
-1.33381212e+00 -4.51906592e-01 5.62180340e-01 -4.53681886e-01
-3.79378766e-01 7.98224330e-01 2.75858968e-01 -2.31637537e-01
4.62338239e-01 -2.30750412e-01 -5.86847842e-01 -7.79572964e-01
3.61123651e-01 5.41877151e-01 9.38642547e-02 -4.00455832... | [14.929698944091797, 6.614534854888916] |
c1b7cb3d-d19d-49ab-8c5d-1ab4daa102a7 | safe-exploration-in-linear-equality | null | null | https://openreview.net/forum?id=5vjyt5JHmaU | https://openreview.net/pdf?id=5vjyt5JHmaU | Safe Exploration in Linear Equality Constraint | With the extensive research and application, some shortcomings of reinforcement learning methods are gradually revealed. One of the considerable problems is that it is difficult for reinforcement learning methods to strictly satisfy the constraints. In this paper, a Singular Value Decomposition-based non-training metho... | ['Jinwei Liu', 'Wang Yao', 'Zijia Niu', 'Xiaohu Jia'] | 2021-09-29 | null | null | null | null | ['safe-exploration'] | ['robots'] | [-6.58722371e-02 1.34189636e-01 -5.65103590e-01 2.96522379e-01
-1.26738427e-02 -3.92778248e-01 2.57577360e-01 -3.76951396e-01
-5.36739349e-01 1.35181999e+00 3.76760960e-02 -3.30190569e-01
-4.15642083e-01 -7.32397199e-01 -4.01386440e-01 -1.10029233e+00
-1.48127764e-01 -9.66542438e-02 2.24208593e-01 -6.86394393... | [4.291292190551758, 2.15521502494812] |
b39dc31b-1996-4d5d-bec8-8d892e9bb2cc | perceiving-the-world-question-guided | 2204.09597 | null | https://arxiv.org/abs/2204.09597v2 | https://arxiv.org/pdf/2204.09597v2.pdf | Perceiving the World: Question-guided Reinforcement Learning for Text-based Games | Text-based games provide an interactive way to study natural language processing. While deep reinforcement learning has shown effectiveness in developing the game playing agent, the low sample efficiency and the large action space remain to be the two major challenges that hinder the DRL from being applied in the real ... | ['Chengqi Zhang', 'Joey Tianyi Zhou', 'Yali Du', 'Ling Chen', 'Meng Fang', 'Yunqiu Xu'] | 2022-03-20 | null | https://aclanthology.org/2022.acl-long.41 | https://aclanthology.org/2022.acl-long.41.pdf | acl-2022-5 | ['text-based-games'] | ['playing-games'] | [-6.40298799e-02 -1.27686873e-01 5.19077405e-02 -1.38596511e-02
-6.66970372e-01 -5.60161233e-01 5.91352761e-01 -9.48412642e-02
-9.10914302e-01 6.60173595e-01 -9.00482107e-03 -4.93348420e-01
-2.86644381e-02 -1.11874676e+00 -3.62147689e-01 -4.94950533e-01
1.21363133e-01 3.65941554e-01 5.08940399e-01 -4.12523091... | [3.8535170555114746, 1.5352736711502075] |
891d50e8-64cd-431f-b2e1-55f84ba9c25b | the-first-proven-performance-guarantees-for | 2305.13459 | null | https://arxiv.org/abs/2305.13459v2 | https://arxiv.org/pdf/2305.13459v2.pdf | The First Proven Performance Guarantees for the Non-Dominated Sorting Genetic Algorithm II (NSGA-II) on a Combinatorial Optimization Problem | The Non-dominated Sorting Genetic Algorithm-II (NSGA-II) is one of the most prominent algorithms to solve multi-objective optimization problems. Recently, the first mathematical runtime guarantees have been obtained for this algorithm, however only for synthetic benchmark problems. In this work, we give the first prove... | ['Simon Wietheger', 'Yakob Kahane', 'Benjamin Hebras', 'Benjamin Doerr', 'Sacha Cerf'] | 2023-05-22 | null | null | null | null | ['combinatorial-optimization'] | ['methodology'] | [ 4.83320326e-01 7.76615217e-02 5.96661605e-02 1.16958961e-01
-5.21977961e-01 -6.60224915e-01 -3.99968386e-01 4.07356203e-01
-5.98030567e-01 1.02173674e+00 -6.50884926e-01 -3.62349033e-01
-1.05125868e+00 -1.13699162e+00 -7.95533717e-01 -1.07503939e+00
-6.54469967e-01 7.39888728e-01 1.54883951e-01 -3.87813479... | [6.301247596740723, 4.466187953948975] |
1101a9b7-5830-4ffe-af75-ee78a3c570e3 | glt-t-global-local-transformer-for-3d-siamese | 2304.00242 | null | https://arxiv.org/abs/2304.00242v1 | https://arxiv.org/pdf/2304.00242v1.pdf | GLT-T++: Global-Local Transformer for 3D Siamese Tracking with Ranking Loss | Siamese trackers based on 3D region proposal network (RPN) have shown remarkable success with deep Hough voting. However, using a single seed point feature as the cue for voting fails to produce high-quality 3D proposals. Additionally, the equal treatment of seed points in the voting process, regardless of their signif... | ['Jing Zhang', 'Mingyu Gao', 'Xudong Lv', 'Yuxiang Yang', 'Zhiwei He', 'Jiahao Nie'] | 2023-04-01 | null | null | null | null | ['3d-single-object-tracking'] | ['computer-vision'] | [-2.60981232e-01 -1.67448923e-01 -3.28884840e-01 -3.37548077e-01
-9.45643127e-01 -6.12284005e-01 6.46510303e-01 2.18179282e-02
-4.19879615e-01 3.75976533e-01 -7.04126209e-02 -2.37485711e-02
-1.17719807e-01 -6.37556136e-01 -8.86138499e-01 -5.81319332e-01
2.78495601e-04 5.71771383e-01 7.19866753e-01 -3.70251350... | [6.514237880706787, -2.2397820949554443] |
890bbbc2-7beb-434f-811f-b65bd9566a61 | heart-sound-classification-considering | 2106.01865 | null | https://arxiv.org/abs/2106.01865v1 | https://arxiv.org/pdf/2106.01865v1.pdf | Heart Sound Classification Considering Additive Noise and Convolutional Distortion | Cardiac auscultation is an essential point-of-care method used for the early diagnosis of heart diseases. Automatic analysis of heart sounds for abnormality detection is faced with the challenges of additive noise and sensor-dependent degradation. This paper aims to develop methods to address the cardiac abnormality de... | ['Taufiq Hasan', 'Ian Mclane', 'Md. Istiaq Ansari', 'Farhat Binte Azam'] | 2021-06-03 | null | null | null | null | ['sound-classification'] | ['audio'] | [ 4.05843467e-01 -3.54680657e-01 5.47082365e-01 -6.39029872e-03
-7.24424958e-01 -4.50265855e-01 -1.67599529e-01 2.65003979e-01
-3.74814332e-01 3.45939100e-01 7.93425832e-03 -5.19746900e-01
-2.40365118e-01 -3.89519483e-01 -1.86793238e-01 -6.51554465e-01
-4.47959363e-01 -2.79514611e-01 5.03837503e-02 1.37875333... | [14.271682739257812, 3.2607057094573975] |
61e41678-b564-4bdc-a981-cedd67abc1f8 | new-frontiers-in-graph-autoencoders-joint | 2211.08972 | null | https://arxiv.org/abs/2211.08972v1 | https://arxiv.org/pdf/2211.08972v1.pdf | New Frontiers in Graph Autoencoders: Joint Community Detection and Link Prediction | Graph autoencoders (GAE) and variational graph autoencoders (VGAE) emerged as powerful methods for link prediction (LP). Their performances are less impressive on community detection (CD), where they are often outperformed by simpler alternatives such as the Louvain method. It is still unclear to what extent one can im... | ['Michalis Vazirgiannis', 'Romain Hennequin', 'George Dasoulas', 'Johannes F. Lutzeyer', 'Guillaume Salha-Galvan'] | 2022-11-16 | null | null | null | null | ['community-detection'] | ['graphs'] | [-1.86144397e-01 3.65507096e-01 8.27059075e-02 3.66126060e-01
-2.47177690e-01 -4.78107929e-01 6.99967980e-01 5.59650421e-01
-2.61165679e-01 6.97341263e-01 2.84201473e-01 -2.52412647e-01
-3.88606191e-01 -1.06701279e+00 -8.60517263e-01 -6.41488969e-01
-4.87673759e-01 5.48699439e-01 8.37058350e-02 -1.64487839... | [7.1987624168396, 6.099703788757324] |
cc0db26e-a445-4f2d-a2ad-9956d7d7dde4 | graph-based-aspect-representation-learning | null | null | https://aclanthology.org/2020.textgraphs-1.2 | https://aclanthology.org/2020.textgraphs-1.2.pdf | Graph-based Aspect Representation Learning for Entity Resolution | Entity Resolution (ER) identifies records that refer to the same real-world entity. Deep learning approaches improved the generalization ability of entity matching models, but hardly overcame the impact of noisy or incomplete data sources. In real scenes, an entity usually consists of multiple semantic facets, called a... | ['Bin Gu', 'Xiangnan He', 'Yufan Huang', 'Dingxian Wang', 'Yuchen Guo', 'Zhenqi Zhao'] | null | null | null | null | coling-textgraphs-2020-12 | ['entity-resolution'] | ['natural-language-processing'] | [-2.55250931e-01 4.14825201e-01 -3.93146425e-01 -2.82044470e-01
-5.15212476e-01 -2.34057009e-01 5.61519504e-01 5.17066777e-01
-5.72213113e-01 6.56283617e-01 4.31505054e-01 -5.78308702e-02
-2.85326153e-01 -1.34994209e+00 -1.05915558e+00 -3.37015837e-01
-4.90682013e-02 7.17275620e-01 2.53790021e-01 -2.69442201... | [8.947144508361816, 8.177873611450195] |
b7c75cd1-4e0d-4567-b59d-8ddba8de40a9 | using-machine-learning-methods-for-automation | 2306.09775 | null | https://arxiv.org/abs/2306.09775v1 | https://arxiv.org/pdf/2306.09775v1.pdf | Using Machine Learning Methods for Automation of Size Grid Building and Management | Fashion apparel companies require planning for the next season, a year in advance for supply chain management. This study focuses on size selection decision making for Levi Strauss. Currently, the region and planning group level size grids are built and managed manually. The company suffers from the workload it creates... | ['Filipa Peleja', 'Dries Benoit', 'Salim Yunus'] | 2023-06-16 | null | null | null | null | ['management'] | ['miscellaneous'] | [-1.61082953e-01 2.90456321e-02 -3.78352404e-01 -3.82979155e-01
1.38331190e-01 -4.52796698e-01 4.23442051e-02 5.22351444e-01
-2.90093511e-01 7.19697833e-01 -1.49158090e-02 -3.00979912e-01
-4.66223210e-01 -1.12825704e+00 -4.42123920e-01 -5.71050882e-01
7.88328201e-02 1.22384477e+00 -1.95854008e-01 -3.65016550... | [9.111751556396484, 5.92186164855957] |
cbafa84d-a8fa-4f82-aba6-f34f81c0488a | adaptive-action-supervision-in-reinforcement | 2305.13030 | null | https://arxiv.org/abs/2305.13030v2 | https://arxiv.org/pdf/2305.13030v2.pdf | Adaptive action supervision in reinforcement learning from real-world multi-agent demonstrations | Modeling of real-world biological multi-agents is a fundamental problem in various scientific and engineering fields. Reinforcement learning (RL) is a powerful framework to generate flexible and diverse behaviors in cyberspace; however, when modeling real-world biological multi-agents, there is a domain gap between beh... | ['Yoshinobu Kawahara', 'Naoya Takeishi', 'Hiroshi Nakahara', 'Atom Scott', 'Kazushi Tsutsui', 'Keisuke Fujii'] | 2023-05-22 | null | null | null | null | ['dynamic-time-warping'] | ['time-series'] | [-1.38344780e-01 -2.61844575e-01 7.60667911e-03 2.60608107e-01
-2.13473141e-01 -7.13100314e-01 6.11101747e-01 -3.36169332e-01
-8.42478871e-01 1.14864147e+00 -4.78918672e-01 5.82147799e-02
-3.40281785e-01 -4.68513429e-01 -9.03286636e-01 -1.15338218e+00
-3.88565898e-01 4.59091246e-01 4.84631598e-01 -6.53644145... | [4.2978835105896, 1.598680853843689] |
2ba23d17-20d0-4011-802a-7ad1120daf38 | portmanteauing-features-for-scene-text | 2211.05036 | null | https://arxiv.org/abs/2211.05036v1 | https://arxiv.org/pdf/2211.05036v1.pdf | Portmanteauing Features for Scene Text Recognition | Scene text images have different shapes and are subjected to various distortions, e.g. perspective distortions. To handle these challenges, the state-of-the-art methods rely on a rectification network, which is connected to the text recognition network. They form a linear pipeline which uses text rectification on all i... | ['Joo Hwee Lim', 'Jung-jae Kim', 'Adams Wai-Kin Kong', 'Ernest Yu Kai Chew', 'Yew Lee Tan'] | 2022-11-09 | null | null | null | null | ['scene-text-recognition'] | ['computer-vision'] | [ 6.31160438e-01 -4.02528763e-01 1.54470280e-01 -2.92116404e-01
-1.17913492e-01 -1.81761086e-01 7.80225217e-01 -2.47029915e-01
-3.70867908e-01 2.51900285e-01 1.36603564e-01 -2.69584246e-02
1.36028096e-01 -7.15768516e-01 -6.73042238e-01 -7.78416693e-01
8.82630169e-01 3.05367678e-01 2.83741623e-01 -2.99495161... | [11.87619400024414, 2.162376642227173] |
c1c04ff7-46b5-4d66-a334-60843c1c8a6c | pre-trained-models-or-feature-engineering-the | null | null | https://aclanthology.org/2022.osact-1.5 | https://aclanthology.org/2022.osact-1.5.pdf | Pre-trained Models or Feature Engineering: The Case of Dialectal Arabic | The usage of social media platforms has resulted in the proliferation of work on Arabic Natural Language Processing (ANLP), including the development of resources. There is also an increased interest in processing Arabic dialects and a number of models and algorithms have been utilised for the purpose of Dialectal Arab... | ['Simon Dobnik', 'Stergios Chatzikyriakidis', 'Kathrein Abu Kwaik'] | null | null | null | null | osact-lrec-2022-6 | ['dialect-identification'] | ['natural-language-processing'] | [-4.11867917e-01 -1.19419225e-01 2.10652515e-01 -4.66377467e-01
-3.06725532e-01 -6.89335525e-01 1.07968676e+00 7.28403449e-01
-8.28166842e-01 3.95896584e-01 4.36047375e-01 -4.36278135e-01
7.00727701e-02 -1.11899686e+00 -3.48383993e-01 -6.07600212e-01
-2.42645890e-01 7.94313312e-01 -2.13662572e-02 -1.17387569... | [11.148672103881836, 7.1966071128845215] |
1658c0ed-7d43-4c8c-8652-732114104f66 | learning-multi-scale-deep-features-for-high | 1611.03591 | null | http://arxiv.org/abs/1611.03591v1 | http://arxiv.org/pdf/1611.03591v1.pdf | Learning Multi-Scale Deep Features for High-Resolution Satellite Image Classification | In this paper, we propose a multi-scale deep feature learning method for
high-resolution satellite image classification. Specifically, we firstly warp
the original satellite image into multiple different scales. The images in each
scale are employed to train a deep convolutional neural network (DCNN).
However, simultan... | ['Zhi Li', 'Renlong Hang', 'Qingshan Liu', 'Huihui Song'] | 2016-11-11 | null | null | null | null | ['satellite-image-classification'] | ['computer-vision'] | [ 2.72955024e-03 -6.01735234e-01 5.69465756e-02 -4.10297155e-01
-6.67883992e-01 -3.34283978e-01 2.34810919e-01 -1.80756509e-01
-7.84400165e-01 4.94867802e-01 -1.29028887e-01 -2.77431915e-03
-2.06837267e-01 -1.20603228e+00 -6.47838056e-01 -9.39669907e-01
-3.35390359e-01 -2.23922685e-01 5.74833393e-01 -1.00276047... | [9.886358261108398, -1.4813718795776367] |
a230dc40-c232-4540-a5d7-570fd2402d3b | seastar-vertex-centric-programming-for-graph | null | null | https://dl.acm.org/doi/10.1145/3447786.3456247 | https://dl.acm.org/doi/pdf/10.1145/3447786.3456247 | Seastar: vertex-centric programming for graph neural networks | Graph neural networks (GNNs) have achieved breakthrough performance in graph analytics such as node classification, link prediction and graph clustering. Many GNN training frameworks have been developed, but they are usually designed as a set of manually written, GNN-specific operators plugged into existing deep learni... | ['Fan Yu', 'James Cheng', 'Chenguang Zheng', 'Boyang Li', 'Tatiana Jin', 'Zhenkun Cai', 'Kaihao Ma', 'Yidi Wu'] | 2021-04-21 | null | null | null | proceedings-of-the-sixteenth-european | ['graph-clustering'] | ['graphs'] | [-5.45588791e-01 -1.54297084e-01 -3.02658647e-01 -2.00812921e-01
3.73940431e-02 -3.83403689e-01 4.15190488e-01 3.93546134e-01
-1.89488590e-01 2.06604078e-01 -4.20556515e-01 -9.26401675e-01
3.83554921e-02 -1.49897075e+00 -5.24306953e-01 -4.36414778e-01
-2.16705844e-01 6.42540812e-01 5.44302642e-01 -2.47807845... | [7.027835845947266, 5.768289566040039] |
d5e56cc7-fc0a-4ee7-99a8-dc9110e99c55 | coevolution-of-camouflage | 2304.11793 | null | https://arxiv.org/abs/2304.11793v2 | https://arxiv.org/pdf/2304.11793v2.pdf | Coevolution of Camouflage | Camouflage in nature seems to arise from competition between predator and prey. To survive, predators must find prey, and prey must avoid being found. This work simulates an abstract model of that adversarial relationship. It looks at crypsis through evolving prey camouflage patterns (as color textures) in competition ... | ['Craig Reynolds'] | 2023-04-24 | null | null | null | null | ['artificial-life'] | ['miscellaneous'] | [ 1.73777919e-02 -3.82464468e-01 6.21480465e-01 6.24078512e-01
6.85719669e-01 -1.02320421e+00 5.44834614e-01 -4.84588534e-01
-6.61697328e-01 8.88921976e-01 -1.01465508e-01 9.84024554e-02
5.19307077e-01 -6.85870886e-01 -6.19938076e-01 -1.10153437e+00
-4.21991944e-01 1.61362402e-02 5.10086298e-01 -4.43531662... | [8.333562850952148, -0.7586168646812439] |
a8f9c2f9-723d-45d7-9743-252e7384b243 | implicit-bias-of-sgd-in-l-2-regularized | 2305.16038 | null | https://arxiv.org/abs/2305.16038v1 | https://arxiv.org/pdf/2305.16038v1.pdf | Implicit bias of SGD in $L_{2}$-regularized linear DNNs: One-way jumps from high to low rank | The $L_{2}$-regularized loss of Deep Linear Networks (DLNs) with more than one hidden layers has multiple local minima, corresponding to matrices with different ranks. In tasks such as matrix completion, the goal is to converge to the local minimum with the smallest rank that still fits the training data. While rank-un... | ['Arthur Jacot', 'Zihan Wang'] | 2023-05-25 | null | null | null | null | ['matrix-completion'] | ['methodology'] | [-1.49089769e-01 5.06244957e-01 -5.15111051e-02 -3.87356400e-01
-1.07482648e+00 -3.78552079e-01 -1.19100526e-01 1.40080407e-01
-6.29835606e-01 8.91114175e-01 -3.67960222e-02 -3.99744123e-01
-6.49823964e-01 -8.87524545e-01 -9.36568677e-01 -9.80253220e-01
-7.40405500e-01 5.07174850e-01 -1.21932983e-01 -1.97344989... | [7.768314361572266, 3.79764461517334] |
21a3c6a2-51dd-463a-895e-52cd5165ff52 | multi-modal-relational-graph-for-cross-modal | null | null | http://openaccess.thecvf.com//content/CVPR2021/html/Zeng_Multi-Modal_Relational_Graph_for_Cross-Modal_Video_Moment_Retrieval_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Zeng_Multi-Modal_Relational_Graph_for_Cross-Modal_Video_Moment_Retrieval_CVPR_2021_paper.pdf | Multi-Modal Relational Graph for Cross-Modal Video Moment Retrieval | Given an untrimmed video and a query sentence, cross-modal video moment retrieval aims to rank a video moment from pre-segmented video moment candidates that best matches the query sentence. Pioneering work typically learns the representations of the textual and visual content separately and then obtains the intera... | ['Zheng Qin', 'Zhou Zhao', 'Meng Liu', 'Xiaochi Wei', 'Da Cao', 'Yawen Zeng'] | 2021-06-19 | null | null | null | cvpr-2021-1 | ['moment-retrieval'] | ['computer-vision'] | [ 1.65455416e-01 -4.57341939e-01 -5.76530874e-01 -3.64739269e-01
-9.88478780e-01 -6.03122771e-01 7.22388208e-01 5.95634818e-01
-7.54187778e-02 9.39509496e-02 4.53276426e-01 2.70437002e-01
-4.03142571e-01 -4.89546746e-01 -7.39842772e-01 -3.60891074e-01
-3.20454240e-01 1.88175887e-01 3.49344879e-01 -1.27454132... | [10.182877540588379, 0.8766908645629883] |
8dbdf16a-ff1f-4d51-815d-4a2fd990096c | neuse-neural-se-3-equivariant-embedding-for | 2303.07308 | null | https://arxiv.org/abs/2303.07308v2 | https://arxiv.org/pdf/2303.07308v2.pdf | NeuSE: Neural SE(3)-Equivariant Embedding for Consistent Spatial Understanding with Objects | We present NeuSE, a novel Neural SE(3)-Equivariant Embedding for objects, and illustrate how it supports object SLAM for consistent spatial understanding with long-term scene changes. NeuSE is a set of latent object embeddings created from partial object observations. It serves as a compact point cloud surrogate for co... | ['John J. Leonard', 'Joshua B. Tenenbaum', 'Kurran Singh', 'Yilun Du', 'Jiahui Fu'] | 2023-03-13 | null | null | null | null | ['object-slam'] | ['computer-vision'] | [-8.60901643e-03 -4.63612638e-02 2.39990093e-02 -5.88930726e-01
-4.27142799e-01 -7.87584424e-01 9.74852324e-01 1.17709942e-01
-3.40479612e-01 3.48715752e-01 1.39748991e-01 2.02491462e-01
-3.08956534e-01 -4.88077670e-01 -1.03764737e+00 -4.11667019e-01
-1.93223551e-01 1.04577446e+00 3.50193888e-01 -4.09522951... | [7.396956920623779, -2.331049680709839] |
f1145e32-a01e-4230-afe8-acbf10951099 | automatic-design-of-semantic-similarity | 2307.00925 | null | https://arxiv.org/abs/2307.00925v1 | https://arxiv.org/pdf/2307.00925v1.pdf | Automatic Design of Semantic Similarity Ensembles Using Grammatical Evolution | Semantic similarity measures are widely used in natural language processing to catalyze various computer-related tasks. However, no single semantic similarity measure is the most appropriate for all tasks, and researchers often use ensemble strategies to ensure performance. This research work proposes a method for auto... | ['Jorge Martinez-Gil'] | 2023-07-03 | null | null | null | null | ['semantic-textual-similarity', 'semantic-similarity'] | ['natural-language-processing', 'natural-language-processing'] | [ 3.68341833e-01 -3.75507087e-01 2.86228567e-01 -3.86365175e-01
-3.60761464e-01 -3.33216935e-01 6.94126546e-01 6.19301975e-01
-4.65733081e-01 5.70602894e-01 1.14164211e-01 1.37654275e-01
-4.16853637e-01 -1.02411366e+00 1.41470850e-01 -4.21372294e-01
3.84958208e-01 5.01814663e-01 1.98581159e-01 -6.28299594... | [10.266336441040039, 8.860600471496582] |
01e51062-4783-460c-9fb8-fa13f2fd5f1b | active-universal-domain-adaptation | null | null | http://openaccess.thecvf.com//content/ICCV2021/html/Ma_Active_Universal_Domain_Adaptation_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Ma_Active_Universal_Domain_Adaptation_ICCV_2021_paper.pdf | Active Universal Domain Adaptation | Most unsupervised domain adaptation methods rely on rich prior knowledge about the source-target label set relationship, and they cannot recognize categories beyond the source classes, which limits their applicability in practical scenarios. This paper proposes a new paradigm for unsupervised domain adaptation, ter... | ['Changsheng Xu', 'Junyu Gao', 'Xinhong Ma'] | 2021-01-01 | null | null | null | iccv-2021-1 | ['universal-domain-adaptation'] | ['computer-vision'] | [ 4.95484471e-01 1.77848563e-01 -6.83245361e-01 -5.07469893e-01
-9.81056035e-01 -8.15212429e-01 4.33111161e-01 1.08243123e-01
-2.49315485e-01 8.69216502e-01 4.83475588e-02 -3.96917900e-03
-5.46863489e-02 -8.44918907e-01 -6.79952085e-01 -9.04791951e-01
2.51986355e-01 5.68928242e-01 3.73166859e-01 -3.65642644... | [10.347002029418945, 3.093291997909546] |
17b532ed-5318-4c90-9fdf-bc31bad56aab | learning-a-general-clause-to-clause | 2208.13549 | null | https://arxiv.org/abs/2208.13549v2 | https://arxiv.org/pdf/2208.13549v2.pdf | Learning a General Clause-to-Clause Relationships for Enhancing Emotion-Cause Pair Extraction | Emotion-cause pair extraction (ECPE) is an emerging task aiming to extract potential pairs of emotions and corresponding causes from documents. Previous approaches have focused on modeling the pair-to-pair relationship and achieved promising results. However, the clause-to-clause relationship, which fundamentally symbo... | ['Xiang Li', 'Xinyu Yang', 'Hang Chen'] | 2022-08-29 | null | null | null | null | ['emotion-cause-pair-extraction'] | ['natural-language-processing'] | [ 2.31925547e-01 2.87869334e-01 -4.71511126e-01 -9.13356066e-01
-1.06470346e+00 -5.13305187e-01 4.88638103e-01 4.33631361e-01
9.72292423e-02 6.13225996e-01 3.54098350e-01 3.60177383e-02
-7.25759938e-02 -8.00328553e-01 -3.65381777e-01 -4.93272930e-01
-3.86419833e-01 2.74383724e-01 -3.58228564e-01 -3.16432148... | [12.63239860534668, 6.213926792144775] |
633c7235-3815-44bc-b9a5-85f92d3202e7 | complex-relation-extraction-challenges-and | 2012.04821 | null | https://arxiv.org/abs/2012.04821v1 | https://arxiv.org/pdf/2012.04821v1.pdf | Complex Relation Extraction: Challenges and Opportunities | Relation extraction aims to identify the target relations of entities in texts. Relation extraction is very important for knowledge base construction and text understanding. Traditional binary relation extraction, including supervised, semi-supervised and distant supervised ones, has been extensively studied and signif... | ['Yanghua Xiao', 'Li Wang', 'Deqing Yang', 'Qiao Cheng', 'Qiaoben Bao', 'Haiyun Jiang'] | 2020-12-09 | null | null | null | null | ['binary-relation-extraction'] | ['natural-language-processing'] | [ 2.34979123e-01 6.16600096e-01 -8.23764622e-01 -2.66334236e-01
-1.38748825e-01 -4.15906608e-01 8.07258427e-01 7.07452655e-01
-2.06521481e-01 1.41522872e+00 -8.03763717e-02 -5.89746773e-01
-3.48977447e-01 -1.13507342e+00 2.71662660e-02 -3.37235838e-01
-2.62677252e-01 8.30693126e-01 3.09932798e-01 -2.21767351... | [9.153907775878906, 8.709939956665039] |
2283d8a5-4180-4890-b72a-f21ad2497089 | modeling-4d-fmri-data-via-spatio-temporal | 1805.12564 | null | http://arxiv.org/abs/1805.12564v3 | http://arxiv.org/pdf/1805.12564v3.pdf | Modeling 4D fMRI Data via Spatio-Temporal Convolutional Neural Networks (ST-CNN) | Simultaneous modeling of the spatio-temporal variation patterns of brain
functional network from 4D fMRI data has been an important yet challenging
problem for the field of cognitive neuroscience and medical image analysis.
Inspired by the recent success in applying deep learning for functional brain
decoding and encod... | ['Wei zhang', 'Yu Zhao', 'Mo Zhang', 'Tianming Liu', 'Shijie Zhao', 'Quanzheng Li', 'Milad Makkie', 'Xiang Li'] | 2018-05-31 | null | null | null | null | ['brain-decoding', 'brain-decoding'] | ['medical', 'miscellaneous'] | [ 2.27836475e-01 -3.39688361e-01 1.24526015e-02 -4.83315349e-01
-1.12285160e-01 -3.44296128e-01 5.42018294e-01 -2.36170873e-01
-5.10631144e-01 2.84280658e-01 1.80591360e-01 -5.37427031e-02
-6.58135235e-01 -2.87390292e-01 -4.57586259e-01 -7.97165155e-01
-4.72135007e-01 2.99657941e-01 2.25207508e-01 2.26623878... | [12.526934623718262, 3.342341423034668] |
7084d139-de17-48de-86e1-b60e47b18c45 | demograsp-few-shot-learning-for-robotic | 2112.02849 | null | https://arxiv.org/abs/2112.02849v1 | https://arxiv.org/pdf/2112.02849v1.pdf | DemoGrasp: Few-Shot Learning for Robotic Grasping with Human Demonstration | The ability to successfully grasp objects is crucial in robotics, as it enables several interactive downstream applications. To this end, most approaches either compute the full 6D pose for the object of interest or learn to predict a set of grasping points. While the former approaches do not scale well to multiple obj... | ['Benjamin Busam', 'Nassir Navab', 'Sven Meie', 'Lorenzo Garattoni', 'Luca Minciullo', 'Fabian Manhardt', 'Pengyuan Wang'] | 2021-12-06 | null | null | null | null | ['robotic-grasping'] | ['robots'] | [ 1.91504180e-01 2.30627641e-01 1.85710728e-01 -2.47338921e-01
-3.98566306e-01 -7.02721179e-01 4.49068427e-01 8.55817944e-02
-1.49227053e-01 4.70847219e-01 -5.90690255e-01 2.93332078e-02
-4.83782738e-01 -6.26646996e-01 -8.38747978e-01 -6.17782593e-01
-1.36851713e-01 1.09255219e+00 4.17861938e-01 -2.71082252... | [5.892202854156494, -0.8650883436203003] |
8a9f0107-aba4-4895-949d-27bc20ebeec1 | can-transfer-entropy-infer-causality-in | 1901.07589 | null | https://arxiv.org/abs/1901.07589v2 | https://arxiv.org/pdf/1901.07589v2.pdf | Can Transfer Entropy Infer Information Flow in Neuronal Circuits for Cognitive Processing? | To infer information flow in any network of agents, it is important first and foremost to establish causal temporal relations between the nodes. Practical and automated methods that can infer causality are difficult to find, and the subject of ongoing research. While Shannon information only detects correlation, there ... | ['Ali Tehrani-Saleh', 'Christoph Adami'] | 2019-01-22 | null | null | null | null | ['motion-detection'] | ['computer-vision'] | [ 6.40227020e-01 1.08128726e-01 1.36255309e-01 8.73584747e-02
3.04952651e-01 -8.98423731e-01 9.51879680e-01 3.83005828e-01
-2.23910108e-01 9.40318346e-01 2.58353680e-01 -5.51633775e-01
-7.69146323e-01 -8.98930967e-01 -5.45842528e-01 -6.55962825e-01
-4.50458884e-01 1.70971170e-01 2.82517046e-01 -1.79176763... | [7.949141979217529, 3.517305850982666] |
a4d1968c-bd9d-433b-b49e-cf24e86f1676 | advanced-customer-activity-prediction-based | 1904.07687 | null | https://arxiv.org/abs/1904.07687v4 | https://arxiv.org/pdf/1904.07687v4.pdf | Advanced Customer Activity Prediction based on Deep Hierarchic Encoder-Decoders | Product recommender systems and customer profiling techniques have always been a priority in online retail. Recent machine learning research advances and also wide availability of massive parallel numerical computing has enabled various approaches and directions of recommender systems advancement. Worth to mention is t... | ['Laurentiu Piciu', 'Andrei Damian', 'Sergiu Turlea', 'Nicolae Tapus'] | 2019-04-11 | null | null | null | null | ['activity-prediction', 'product-recommendation', 'activity-prediction'] | ['computer-vision', 'miscellaneous', 'time-series'] | [ 3.70702855e-02 -1.16286930e-02 -1.86983332e-01 -7.17437565e-01
-6.38876557e-01 -4.15835619e-01 3.55457217e-01 1.86738089e-01
1.87491323e-03 1.80362865e-01 1.31393313e-01 -4.93749797e-01
-5.14481425e-01 -8.16616833e-01 -4.64085639e-01 -3.97236735e-01
-1.88744500e-01 9.93156135e-01 -3.86231124e-01 -1.03389633... | [9.924210548400879, 5.8663716316223145] |
383fcdfc-0177-4b83-aa3f-b6c94074c409 | an-end-to-end-review-of-gaze-estimation-and | 2307.00122 | null | https://arxiv.org/abs/2307.00122v1 | https://arxiv.org/pdf/2307.00122v1.pdf | An End-to-End Review of Gaze Estimation and its Interactive Applications on Handheld Mobile Devices | In recent years we have witnessed an increasing number of interactive systems on handheld mobile devices which utilise gaze as a single or complementary interaction modality. This trend is driven by the enhanced computational power of these devices, higher resolution and capacity of their cameras, and improved gaze est... | ['Juan Ye', 'Mohamed Khamis', 'Shijing He', 'Yaxiong Lei'] | 2023-06-30 | null | null | null | null | ['gaze-estimation'] | ['computer-vision'] | [ 3.26641589e-01 5.92880696e-03 -3.79460216e-01 -2.83484161e-01
-1.72941655e-01 -3.31238538e-01 3.11773121e-01 -3.89271796e-01
-3.89515996e-01 5.51252961e-01 -7.54644349e-02 -2.04770073e-01
-1.41045690e-01 -2.55429116e-03 -1.27317369e-01 -5.48902810e-01
1.70767412e-01 -6.08260408e-02 -2.15098247e-01 6.50534332... | [14.10536003112793, 0.11554199457168579] |
004b9e85-9c7d-4830-af9c-8f554eb1bc43 | detecting-histologic-glioblastoma-regions-of | 2302.00669 | null | https://arxiv.org/abs/2302.00669v2 | https://arxiv.org/pdf/2302.00669v2.pdf | Detecting Histologic & Clinical Glioblastoma Patterns of Prognostic Relevance | Glioblastoma is the most common and aggressive malignant adult tumor of the central nervous system, with a grim prognosis and heterogeneous morphologic and molecular profiles. Since adopting the current standard-of-care treatment 18 years ago, no substantial prognostic improvement has been noticed. Accurate prediction ... | ['Sharath Chandra Guntuku', 'Garv Mehdiratta', 'Sunny Rai', 'Spyridon Bakas', 'MacLean P. Nasrallah', 'Shubham Innani', 'Bhakti Baheti'] | 2023-02-01 | null | null | null | null | ['whole-slide-images', 'multiple-instance-learning'] | ['computer-vision', 'methodology'] | [ 5.19603074e-01 1.29112720e-01 -3.36725086e-01 -2.31298149e-01
-1.12993383e+00 -1.58871099e-01 2.96651810e-01 8.99797320e-01
-6.29364729e-01 1.02004898e+00 5.18299162e-01 -5.86693466e-01
-8.14753830e-01 -3.64298254e-01 7.78454728e-03 -1.33554840e+00
-1.50960684e-01 8.47080827e-01 -3.11631739e-01 -2.87573412... | [14.796714782714844, -2.5916895866394043] |
82fda4e4-ff1c-420b-aa83-a2d7ee5a9b87 | few-shot-3d-point-cloud-semantic-segmentation-1 | 2303.15654 | null | https://arxiv.org/abs/2303.15654v1 | https://arxiv.org/pdf/2303.15654v1.pdf | Few-Shot 3D Point Cloud Semantic Segmentation via Stratified Class-Specific Attention Based Transformer Network | 3D point cloud semantic segmentation aims to group all points into different semantic categories, which benefits important applications such as point cloud scene reconstruction and understanding. Existing supervised point cloud semantic segmentation methods usually require large-scale annotated point clouds for trainin... | ['Song Wang', 'Ziyu Zhao', 'Xinyi Wu', 'Zhenyao Wu', 'Canyu Zhang'] | 2023-03-28 | null | null | null | null | ['point-cloud-segmentation', 'graph-construction'] | ['computer-vision', 'graphs'] | [ 1.22103058e-01 -4.56792749e-02 -3.90979171e-01 -6.41311467e-01
-7.92597651e-01 -1.16567016e-01 3.77211571e-01 4.14984465e-01
-3.04580688e-01 6.86073527e-02 -3.88925761e-01 -8.21141340e-03
-2.78354347e-01 -1.15050340e+00 -8.41525793e-01 -5.28071821e-01
-9.24989432e-02 7.25923181e-01 9.32634175e-01 4.29589823... | [8.008505821228027, -3.1560308933258057] |
71d8b7dd-8c9b-488d-9e0f-0f54919c2442 | deep-insights-of-learning-based-micro | 2210.04935 | null | https://arxiv.org/abs/2210.04935v1 | https://arxiv.org/pdf/2210.04935v1.pdf | Deep Insights of Learning based Micro Expression Recognition: A Perspective on Promises, Challenges and Research Needs | Micro expression recognition (MER) is a very challenging area of research due to its intrinsic nature and fine-grained changes. In the literature, the problem of MER has been solved through handcrafted/descriptor-based techniques. However, in recent times, deep learning (DL) based techniques have been adopted to gain h... | ['Girdhari Singh', 'Santosh Kumar Vipparthi', 'Monu Verma'] | 2022-10-10 | null | null | null | null | ['micro-expression-recognition'] | ['computer-vision'] | [-1.25815555e-01 -9.75702628e-02 -4.98672813e-01 -5.65164626e-01
7.32416511e-02 -3.15520585e-01 2.06059843e-01 -1.40765170e-02
-2.66556680e-01 5.44868946e-01 -1.23633511e-01 2.67981917e-01
-2.80614078e-01 -7.13706672e-01 2.68605072e-02 -8.50619435e-01
-2.25499392e-01 1.05772614e-01 -5.31996310e-01 -5.38860977... | [13.593334197998047, 1.8767642974853516] |
b39d5b8d-4888-43f9-9156-918403bb4be3 | time-and-cost-efficient-bathymetric-mapping | 2210.10263 | null | https://arxiv.org/abs/2210.10263v1 | https://arxiv.org/pdf/2210.10263v1.pdf | Time and Cost-Efficient Bathymetric Mapping System using Sparse Point Cloud Generation and Automatic Object Detection | Generating 3D point cloud (PC) data from noisy sonar measurements is a problem that has potential applications for bathymetry mapping, artificial object inspection, mapping of aquatic plants and fauna as well as underwater navigation and localization of vehicles such as submarines. Side-scan sonar sensors are available... | ['Jaejeong Shin', 'Peter Ifju', 'Andrew Ortega', 'Antonio Diaz', 'Ruoyao Qin', 'Andres Pulido'] | 2022-10-19 | null | null | null | null | ['point-cloud-generation'] | ['computer-vision'] | [ 2.09506318e-01 -1.32926971e-01 9.48726296e-01 -3.98872018e-01
-3.12127322e-01 -6.10678136e-01 2.90374070e-01 2.94528324e-02
-7.74388313e-01 3.93694282e-01 -3.03473592e-01 -2.56305993e-01
-1.12647846e-01 -9.66327965e-01 -7.39991724e-01 -7.43876576e-01
-6.72046006e-01 5.59819579e-01 4.12284434e-01 -7.51263499... | [7.4679741859436035, -1.7527503967285156] |
a5c3abe4-9c13-4d94-8f51-d171012f39ad | physics-informed-machine-learning-with | 2206.10718 | null | https://arxiv.org/abs/2206.10718v1 | https://arxiv.org/pdf/2206.10718v1.pdf | Physics-informed machine learning with differentiable programming for heterogeneous underground reservoir pressure management | Avoiding over-pressurization in subsurface reservoirs is critical for applications like CO2 sequestration and wastewater injection. Managing the pressures by controlling injection/extraction are challenging because of complex heterogeneity in the subsurface. The heterogeneity typically requires high-fidelity physics-ba... | ['Hari Viswanathan', 'Dylan Robert Harp', "Daniel O'Malley", 'Aleksandra Pachalieva'] | 2022-06-21 | null | null | null | null | ['physics-informed-machine-learning'] | ['graphs'] | [-3.33608925e-01 -6.38901070e-02 9.71905217e-02 3.22630256e-02
-7.43888438e-01 -4.51350808e-01 5.84974885e-01 4.98139203e-01
-1.15445167e-01 1.07807553e+00 -1.15783051e-01 -7.78327942e-01
-2.31211215e-01 -1.21286714e+00 -1.09170318e+00 -6.49950981e-01
-7.58050621e-01 8.07509422e-01 1.14871249e-01 -2.45323941... | [6.438438415527344, 3.246082067489624] |
2153ea79-4f44-4581-b49b-9b101586756d | deep-mds-framework-for-recovering-the-3d | 2210.15200 | null | https://arxiv.org/abs/2210.15200v1 | https://arxiv.org/pdf/2210.15200v1.pdf | Deep-MDS Framework for Recovering the 3D Shape of 2D Landmarks from a Single Image | In this paper, a low parameter deep learning framework utilizing the Non-metric Multi-Dimensional scaling (NMDS) method, is proposed to recover the 3D shape of 2D landmarks on a human face, in a single input image. Hence, NMDS approach is used for the first time to establish a mapping from a 2D landmark space to the co... | ['Zohreh Azimifar', 'Shima Kamyab'] | 2022-10-27 | null | null | null | null | ['face-model'] | ['computer-vision'] | [-1.90087646e-01 3.48086916e-02 4.19597656e-01 -4.01706815e-01
-1.54099271e-01 -1.51384294e-01 7.69252717e-01 -4.04924065e-01
-3.40417236e-01 3.16841573e-01 1.69963434e-01 1.75786287e-01
-3.75911891e-01 -7.75947988e-01 -6.14078224e-01 -8.63421500e-01
-7.92325195e-03 6.95851088e-01 -4.75071728e-01 -8.72000530... | [13.211397171020508, 0.3007567226886749] |
5d4af61e-f502-48be-9c1c-44aa282397aa | exbrainable-an-open-source-gui-for-cnn-based | 2201.04065 | null | https://arxiv.org/abs/2201.04065v1 | https://arxiv.org/pdf/2201.04065v1.pdf | ExBrainable: An Open-Source GUI for CNN-based EEG Decoding and Model Interpretation | We have developed a graphic user interface (GUI), ExBrainable, dedicated to convolutional neural networks (CNN) model training and visualization in electroencephalography (EEG) decoding. Available functions include model training, evaluation, and parameter visualization in terms of temporal and spatial representations.... | ['Chun-Shu Wei', 'Jian-Xue Huang', 'Chia-Ying Hsieh', 'Ya-Lin Huang'] | 2022-01-10 | null | null | null | null | ['eeg-decoding', 'eeg-decoding'] | ['medical', 'time-series'] | [-2.11562306e-01 -5.49043775e-01 4.08607692e-01 -5.15649438e-01
-1.46269664e-01 -3.76638025e-01 3.15487385e-01 -2.67089218e-01
-5.89179337e-01 8.00136626e-01 7.52555206e-02 -7.47267425e-01
-2.34478787e-01 -1.45750031e-01 -7.01154709e-01 -3.92752528e-01
-6.53739512e-01 2.05769405e-01 -3.48321766e-01 -3.24172266... | [13.133481979370117, 3.449796438217163] |
5ea28d69-95ae-4023-ba8e-90d74981c68f | nanoflow-scalable-normalizing-flows-with | 2006.06280 | null | https://arxiv.org/abs/2006.06280v4 | https://arxiv.org/pdf/2006.06280v4.pdf | NanoFlow: Scalable Normalizing Flows with Sublinear Parameter Complexity | Normalizing flows (NFs) have become a prominent method for deep generative models that allow for an analytic probability density estimation and efficient synthesis. However, a flow-based network is considered to be inefficient in parameter complexity because of reduced expressiveness of bijective mapping, which renders... | ['Sang-gil Lee', 'Sungwon Kim', 'Sungroh Yoon'] | 2020-06-11 | null | http://proceedings.neurips.cc/paper/2020/hash/a1c3ae6c49a89d92aef2d423dadb477f-Abstract.html | http://proceedings.neurips.cc/paper/2020/file/a1c3ae6c49a89d92aef2d423dadb477f-Paper.pdf | neurips-2020-12 | ['normalising-flows'] | ['methodology'] | [-1.88564345e-01 4.17531170e-02 -3.60001802e-01 -1.34652749e-01
-5.15458882e-01 -4.51704741e-01 5.99490404e-01 -3.51274282e-01
-3.11880022e-01 9.46278751e-01 1.20682627e-01 -2.81572819e-01
-1.26156777e-01 -9.79262888e-01 -7.59641588e-01 -8.36161017e-01
1.53995484e-01 3.81231695e-01 -2.47516483e-02 1.53937489... | [7.2060112953186035, 3.82244610786438] |
5e5b1284-5a19-48eb-af7c-c4922a2cf115 | out-of-distribution-detection-with-distance | 2002.03328 | null | https://arxiv.org/abs/2002.03328v5 | https://arxiv.org/pdf/2002.03328v5.pdf | Kullback-Leibler Divergence-Based Out-of-Distribution Detection with Flow-Based Generative Models | Recent research has revealed that deep generative models including flow-based models and Variational Autoencoders may assign higher likelihoods to out-of-distribution (OOD) data than in-distribution (ID) data. However, we cannot sample OOD data from the model. This counterintuitive phenomenon has not been satisfactoril... | ['Hongmei Wei', 'Kenli Li', 'Zhiming Liu', 'Ji Wang', 'Zhenbang Chen', 'Wanwei Liu', 'Jialu Pan', 'Yufeng Zhang'] | 2020-02-09 | null | null | null | null | ['group-anomaly-detection'] | ['methodology'] | [-3.60033691e-01 -2.13570625e-01 -8.38241801e-02 2.33691055e-02
-6.62953436e-01 -3.95588070e-01 6.15020216e-01 3.48158143e-02
-1.45393968e-01 5.50745845e-01 -5.62467128e-02 -5.07552922e-01
-1.37412354e-01 -7.88452446e-01 -6.05627477e-01 -8.08933377e-01
-3.42616707e-01 2.43411422e-01 2.59869903e-01 1.21014453... | [7.656364917755127, 2.2893428802490234] |
132eaafc-3c33-4ea5-9337-bfd74e51fc69 | generating-multiple-choice-questions-for | 2303.07069 | null | https://arxiv.org/abs/2303.07069v1 | https://arxiv.org/pdf/2303.07069v1.pdf | Generating multiple-choice questions for medical question answering with distractors and cue-masking | Medical multiple-choice question answering (MCQA) is particularly difficult. Questions may describe patient symptoms and ask for the correct diagnosis, which requires domain knowledge and complex reasoning. Standard language modeling pretraining alone is not sufficient to achieve the best results. \citet{jin2020disease... | ['Marie-Francine Moens', 'Kanimozhi Uma', 'Damien Sileo'] | 2023-03-13 | null | null | null | null | ['multiple-choice-qa'] | ['natural-language-processing'] | [ 1.16069885e-02 4.50210214e-01 -3.20618868e-01 -4.20133680e-01
-1.90533769e+00 -4.03068423e-01 1.22358315e-01 4.50769067e-01
-4.67672974e-01 1.02633595e+00 6.69242799e-01 -5.71369767e-01
-3.74197245e-01 -6.90168440e-01 -6.19148731e-01 -1.27165541e-01
1.76351443e-01 1.06359565e+00 2.56905645e-01 -4.43403304... | [8.767420768737793, 8.564485549926758] |
0b5c7936-4b13-4acd-af8c-2adf2d353467 | an-emg-gesture-recognition-system-with | 1802.10237 | null | http://arxiv.org/abs/1802.10237v2 | http://arxiv.org/pdf/1802.10237v2.pdf | An EMG Gesture Recognition System with Flexible High-Density Sensors and Brain-Inspired High-Dimensional Classifier | EMG-based gesture recognition shows promise for human-machine interaction.
Systems are often afflicted by signal and electrode variability which degrades
performance over time. We present an end-to-end system combating this
variability using a large-area, high-density sensor array and a robust
classification algorithm.... | ['Luca Benini', 'Fred Burghardt', 'Natasha Yamamoto', 'Simone Benatti', 'Jonathan Ting', 'Jan M. Rabaey', 'Alisha Menon', 'Ali Moin', 'Abbas Rahimi', 'Yasser Khan', 'Senam Tamakloe', 'Andy Zhou', 'Ana C. Arias'] | 2018-02-28 | null | null | null | null | ['emg-gesture-recognition'] | ['medical'] | [ 7.50415027e-01 -2.93900400e-01 -1.59479193e-02 -2.39085674e-01
-1.28570390e+00 -2.97490478e-01 -1.81346878e-01 -3.77254516e-01
-9.33723748e-01 5.04562855e-01 -5.04449755e-02 4.86809671e-01
-5.37950173e-02 1.04017928e-01 -6.90323174e-01 -6.30326033e-01
-5.27874768e-01 1.79167420e-01 2.50142395e-01 2.99058318... | [6.808749198913574, 0.1495652049779892] |
307beb6e-b789-40c1-a866-1e3392fa10da | self-constrained-inference-optimization-on | 2207.02425 | null | https://arxiv.org/abs/2207.02425v1 | https://arxiv.org/pdf/2207.02425v1.pdf | Self-Constrained Inference Optimization on Structural Groups for Human Pose Estimation | We observe that human poses exhibit strong group-wise structural correlation and spatial coupling between keypoints due to the biological constraints of different body parts. This group-wise structural correlation can be explored to improve the accuracy and robustness of human pose estimation. In this work, we develop ... | ['Zhihai He', 'Zeng Li', 'Shuoshuo Chen', 'Zhehan Kan'] | 2022-07-06 | null | null | null | null | ['inference-optimization', 'multi-person-pose-estimation'] | ['audio', 'computer-vision'] | [-2.42127895e-01 1.35565653e-01 -3.30793470e-01 -3.38129610e-01
-5.77846110e-01 -3.89306456e-01 1.75293639e-01 2.13544935e-01
-3.57977837e-01 6.71723962e-01 1.14349894e-01 3.65902752e-01
-2.27149665e-01 -6.17353857e-01 -9.45346534e-01 -6.00253880e-01
-3.52519631e-01 7.12356865e-01 4.21405733e-01 -2.54541814... | [7.057668685913086, -0.862555205821991] |
ff6ad74a-eae9-4007-861a-56bc2ed37bda | guided-slot-attention-for-unsupervised-video | 2303.08314 | null | https://arxiv.org/abs/2303.08314v1 | https://arxiv.org/pdf/2303.08314v1.pdf | Guided Slot Attention for Unsupervised Video Object Segmentation | Unsupervised video object segmentation aims to segment the most prominent object in a video sequence. However, the existence of complex backgrounds and multiple foreground objects make this task challenging. To address this issue, we propose a guided slot attention network to reinforce spatial structural information an... | ['Sangyoun Lee', 'Jungho Lee', 'Chaewon Park', 'Dogyoon Lee', 'Suhwan Cho', 'Minhyeok Lee'] | 2023-03-15 | null | null | null | null | ['video-object-segmentation', 'video-semantic-segmentation', 'unsupervised-video-object-segmentation'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 0.3250427 -0.3860547 -0.22344491 -0.44157916 -0.66072166 -0.31183377
0.22643471 -0.11953242 -0.45530334 0.53283226 0.1297529 0.02806459
-0.08414364 -0.5188712 -0.51505 -0.8900051 0.18748954 0.01905362
1.0383058 0.1544415 0.30088368 0.46071634 -1.4619257 0.4471816
0.9595632 1.0703572 0.6... | [9.252098083496094, -0.29938170313835144] |
7b9562ab-a8c0-4dc6-8165-28d8ac29b8f5 | weakly-supervised-action-localization-with-2 | 2004.00163 | null | https://arxiv.org/abs/2004.00163v2 | https://arxiv.org/pdf/2004.00163v2.pdf | Weakly-Supervised Action Localization with Expectation-Maximization Multi-Instance Learning | Weakly-supervised action localization requires training a model to localize the action segments in the video given only video level action label. It can be solved under the Multiple Instance Learning (MIL) framework, where a bag (video) contains multiple instances (action segments). Since only the bag's label is known,... | ['Huijuan Xu', 'Fang Wan', 'Zhekun Luo', 'Baifeng Shi', 'Devin Guillory', 'Wei Ke', 'Trevor Darrell'] | 2020-03-31 | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/6965_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123740715.pdf | eccv-2020-8 | ['weakly-supervised-action-localization'] | ['computer-vision'] | [ 5.83578467e-01 3.03741872e-01 -8.17897499e-01 -5.57008684e-01
-1.23141336e+00 -3.60974193e-01 4.99867171e-01 -1.14321388e-01
-4.33280766e-01 8.10601890e-01 2.24349812e-01 4.43622768e-02
2.78292060e-01 -5.00834882e-01 -1.11428082e+00 -9.28041160e-01
-4.82742637e-02 4.78269637e-01 1.71747997e-01 5.06790221... | [8.642057418823242, 0.7422336339950562] |
351973fc-39d6-4aab-960b-c28a61ecd821 | interpretable-summaries-of-black-box-incident | 2108.03013 | null | https://arxiv.org/abs/2108.03013v1 | https://arxiv.org/pdf/2108.03013v1.pdf | Interpretable Summaries of Black Box Incident Triaging with Subgroup Discovery | The need of predictive maintenance comes with an increasing number of incidents reported by monitoring systems and equipment/software users. In the front line, on-call engineers (OCEs) have to quickly assess the degree of severity of an incident and decide which service to contact for corrective actions. To automate th... | ['Mehdi Kaytoue', 'Céline Robardet', 'Marc Plantevit', 'Anes Bendimerad', 'Youcef Remil'] | 2021-08-06 | null | null | null | null | ['subgroup-discovery'] | ['methodology'] | [ 2.13953018e-01 4.56138939e-01 -6.63439482e-02 -6.37025595e-01
-5.91417924e-02 -1.62552238e-01 1.01608515e-01 5.90353966e-01
1.73269287e-01 7.41141438e-01 9.63319372e-03 -6.02687299e-01
-1.03200936e+00 -7.92133927e-01 -2.05996230e-01 -3.73440236e-01
-2.09617272e-01 1.02423847e+00 3.41140516e-02 -3.45783770... | [8.463934898376465, 5.869870185852051] |
11da22ec-a946-41f0-8b63-927adba81448 | wdr-face-the-first-database-for-studying-face | 2101.03826 | null | https://arxiv.org/abs/2101.03826v1 | https://arxiv.org/pdf/2101.03826v1.pdf | WDR FACE: The First Database for Studying Face Detection in Wide Dynamic Range | Currently, face detection approaches focus on facial information by varying specific parameters including pose, occlusion, lighting, background, race, and gender. These studies only utilized the information obtained from low dynamic range images, however, face detection in wide dynamic range (WDR) scenes has received l... | ['Orly Yadid-Pecht', 'Svetlana Yanushkevich', 'Kenneth Kam Fai Lai', 'Mengchen Lin', 'Jie Yang', 'Ziyi Liu'] | 2021-01-11 | null | null | null | null | ['tone-mapping'] | ['computer-vision'] | [ 1.62443310e-01 -7.60589063e-01 -3.33018675e-02 -4.90577400e-01
-3.82166713e-01 -3.55062127e-01 2.22633064e-01 -8.37279856e-01
-4.48711336e-01 5.09480178e-01 -1.02842197e-01 -9.63164866e-02
1.47666216e-01 -6.72424495e-01 -1.59680814e-01 -6.16512656e-01
-1.60005361e-01 -8.07102025e-02 1.99140698e-01 -4.23459142... | [13.23963737487793, 0.7978472709655762] |
e063e00d-04d4-4d9d-808c-fb875caba203 | face-recognition-using-synthetic-face-data | 2305.10079 | null | https://arxiv.org/abs/2305.10079v1 | https://arxiv.org/pdf/2305.10079v1.pdf | Face Recognition Using Synthetic Face Data | In the field of deep learning applied to face recognition, securing large-scale, high-quality datasets is vital for attaining precise and reliable results. However, amassing significant volumes of high-quality real data faces hurdles such as time limitations, financial burdens, and privacy issues. Furthermore, prevalen... | ['Orly Zvitia', 'Max Kogan', 'Vladimir Loginov', 'Alexey Gruzdev', 'Omer Granoviter'] | 2023-05-17 | null | null | null | null | ['face-recognition'] | ['computer-vision'] | [ 2.67443001e-01 -8.77811201e-03 2.35869467e-01 -8.46733689e-01
-7.87515581e-01 -4.85571474e-01 7.06345975e-01 -3.99768084e-01
-4.09898579e-01 7.22833693e-01 3.83455269e-02 -1.45536378e-01
3.08087338e-02 -7.79898167e-01 -9.61677432e-01 -3.82230192e-01
1.18938342e-01 2.46853799e-01 -5.34683526e-01 1.66523992... | [12.902861595153809, 0.8113130331039429] |
1dbaedc7-6c52-4e69-a19c-511190a6a3e0 | word-embeddings-for-banking-industry | 2306.01807 | null | https://arxiv.org/abs/2306.01807v1 | https://arxiv.org/pdf/2306.01807v1.pdf | Word Embeddings for Banking Industry | Applications of Natural Language Processing (NLP) are plentiful, from sentiment analysis to text classification. Practitioners rely on static word embeddings (e.g. Word2Vec or GloVe) or static word representation from contextual models (e.g. BERT or ELMo) to perform many of these NLP tasks. These widely available word ... | ['Avnish Patel'] | 2023-06-02 | null | null | null | null | ['word-embeddings', 'sentiment-analysis'] | ['methodology', 'natural-language-processing'] | [-5.82421601e-01 -7.22870976e-02 -3.93419832e-01 -5.67952216e-01
-4.30916339e-01 -8.09412420e-01 8.26373816e-01 7.98631072e-01
-8.37555408e-01 4.82295394e-01 9.47592199e-01 -5.80382884e-01
-3.24400561e-03 -9.71416712e-01 -3.04158814e-02 -5.25051236e-01
1.82368606e-01 5.31123519e-01 6.87041581e-02 -8.36975574... | [10.461445808410645, 8.719318389892578] |
72a5962e-9eb4-44e0-8c75-13238b89130e | asdot-any-shot-data-to-text-generation-with | 2210.04325 | null | https://arxiv.org/abs/2210.04325v3 | https://arxiv.org/pdf/2210.04325v3.pdf | ASDOT: Any-Shot Data-to-Text Generation with Pretrained Language Models | Data-to-text generation is challenging due to the great variety of the input data in terms of domains (e.g., finance vs sports) or schemata (e.g., diverse predicates). Recent end-to-end neural methods thus require substantial training examples to learn to disambiguate and describe the data. Yet, real-world data-to-text... | ['Zhiting Hu', 'Eric P. Xing', 'Yucheng Zhou', 'Zhengzhong Liu', 'Jiannan Xiang'] | 2022-10-09 | null | null | null | null | ['data-to-text-generation'] | ['natural-language-processing'] | [ 5.31163216e-01 1.95165589e-01 -1.81257412e-01 -5.63623905e-01
-1.33051550e+00 -7.76945591e-01 6.96105242e-01 4.92009163e-01
-4.92520630e-01 1.04044044e+00 2.60787189e-01 -3.51417512e-01
-1.26459211e-01 -7.24859715e-01 -7.43835568e-01 -2.97405243e-01
4.44079280e-01 1.02879536e+00 8.40392411e-02 -7.05197871... | [11.589054107666016, 8.82402229309082] |
36a4b8d6-763a-42e8-af36-6ced43d0fe43 | efficient-vertical-federated-learning-method | null | null | https://ieeexplore.ieee.org/abstract/document/9930870 | https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9930870 | Efficient Vertical Federated Learning Method for Ridge Regression of Large-Scale Samples via Least-Squares Solution | Integrating data from multiple parties to achieve cross-institutional machine learning is an important trend in Industry 4.0 era. However, the privacy risks from sharing data pose a significant challenge to data integration. To integrate data without sharing data and meet large-scale samples' modeling needs, we propose... | ['Jiayin Li', 'Kun Guo', 'Zhiyong Yu', 'Ximeng Liu', 'Jianping Cai'] | 2022-10-26 | null | null | null | ieee-transactions-on-emerging-topics-in | ['data-integration'] | ['knowledge-base'] | [-3.15218657e-01 -1.99056625e-01 -4.08944279e-01 -2.96772450e-01
-1.12120461e+00 -9.20162678e-01 1.90237120e-01 9.96019468e-02
-5.90087056e-01 5.25849998e-01 -4.54597712e-01 -5.18234313e-01
-2.17082694e-01 -7.48569012e-01 -9.46960926e-01 -7.99512565e-01
-2.96904713e-01 4.73300308e-01 -1.99575260e-01 2.00137449... | [5.834895610809326, 6.667096138000488] |
63645777-50cc-4c10-bd54-d8e3e4e3aad0 | sft-kd-recon-learning-a-student-friendly | 2304.05057 | null | https://arxiv.org/abs/2304.05057v1 | https://arxiv.org/pdf/2304.05057v1.pdf | SFT-KD-Recon: Learning a Student-friendly Teacher for Knowledge Distillation in Magnetic Resonance Image Reconstruction | Deep cascaded architectures for magnetic resonance imaging (MRI) acceleration have shown remarkable success in providing high-quality reconstruction. However, as the number of cascades increases, the improvements in reconstruction tend to become marginal, indicating possible excess model capacity. Knowledge distillatio... | ['Mohanasankar Sivaprakasam', 'Keerthi Ram', 'Rahul G S', 'Mohammad Al Fahim', 'Sriprabha Ramanarayanan', 'Matcha Naga Gayathri'] | 2023-04-11 | null | null | null | null | ['image-reconstruction'] | ['computer-vision'] | [ 1.07162483e-01 3.47505718e-01 -1.01399757e-01 -3.10354978e-01
-7.07187831e-01 -1.81412175e-01 2.92582035e-01 -2.79282425e-02
-5.91584802e-01 4.89639759e-01 2.84530252e-01 -4.72093284e-01
-1.21354144e-02 -4.74410236e-01 -1.04387987e+00 -9.32706535e-01
-1.98744133e-01 4.69218314e-01 3.69081855e-01 5.87935634... | [13.75632381439209, -2.348465919494629] |
485ea4f8-7e77-45e9-a864-a7f73491d3a8 | an-adaptive-simulated-annealing-based-machine | 2212.11892 | null | https://arxiv.org/abs/2212.11892v1 | https://arxiv.org/pdf/2212.11892v1.pdf | An Adaptive Simulated Annealing-Based Machine Learning Approach for Developing an E-Triage Tool for Hospital Emergency Operations | Patient triage at emergency departments (EDs) is necessary to prioritize care for patients with critical and time-sensitive conditions. Different tools are used for patient triage and one of the most common ones is the emergency severity index (ESI), which has a scale of five levels, where level 1 is the most urgent an... | ['Dursun Delen', 'Mohammad Firouz', 'Mohammed Al-Maamari', 'Abdulaziz Ahmed'] | 2022-12-22 | null | null | null | null | ['metaheuristic-optimization'] | ['methodology'] | [ 7.01962709e-02 -4.53464359e-01 3.78102474e-02 -3.49361926e-01
-5.75186610e-01 -2.58995861e-01 -6.65525498e-04 7.92376220e-01
-5.09025693e-01 9.25586879e-01 2.83725232e-01 -5.80871165e-01
-9.40973461e-01 -5.60523689e-01 -8.53972360e-02 -8.41690481e-01
-1.19597219e-01 7.88686514e-01 -1.37449829e-02 -2.93236166... | [8.453534126281738, 4.792398452758789] |
964edbc9-66c4-4a11-9227-ff7c763152a3 | comparing-rule-based-and-deep-learning-models | 1703.08705 | null | http://arxiv.org/abs/1703.08705v1 | http://arxiv.org/pdf/1703.08705v1.pdf | Comparing Rule-Based and Deep Learning Models for Patient Phenotyping | Objective: We investigate whether deep learning techniques for natural
language processing (NLP) can be used efficiently for patient phenotyping.
Patient phenotyping is a classification task for determining whether a patient
has a medical condition, and is a crucial part of secondary analysis of
healthcare data. We ass... | ['Leo Anthony Celi', 'John Foote Jr.', 'Franck Dernoncourt', 'David W. Grant', 'Yeran Li', 'Edward T. Moseley', 'Joy T. Wu', 'Jonathan Welt', 'Eric T. Carlson', 'Sebastian Gehrmann', 'Patrick D. Tyler'] | 2017-03-25 | null | null | null | null | ['patient-phenotyping'] | ['medical'] | [ 3.10097575e-01 4.36746091e-01 -2.94580072e-01 -4.40905720e-01
-9.37629759e-01 -4.48914737e-01 -1.01125814e-01 1.00174999e+00
-5.30228496e-01 8.28060448e-01 5.22227705e-01 -5.85292220e-01
-4.42227453e-01 -6.86058402e-01 -4.39470708e-01 -5.75334311e-01
-1.24564976e-01 1.15998423e+00 -5.75329602e-01 2.18273237... | [8.062433242797852, 7.078860759735107] |
8c998420-1431-4fd4-a8ec-b4ccf6b38a48 | image-shape-manipulation-from-a-single | 2109.06151 | null | https://arxiv.org/abs/2109.06151v3 | https://arxiv.org/pdf/2109.06151v3.pdf | Image Shape Manipulation from a Single Augmented Training Sample | In this paper, we present DeepSIM, a generative model for conditional image manipulation based on a single image. We find that extensive augmentation is key for enabling single image training, and incorporate the use of thin-plate-spline (TPS) as an effective augmentation. Our network learns to map between a primitive ... | ['Yedid Hoshen', 'Nir Zabari', 'Eliahu Horwitz', 'Yael Vinker'] | 2021-09-13 | null | http://openaccess.thecvf.com//content/ICCV2021/html/Vinker_Image_Shape_Manipulation_From_a_Single_Augmented_Training_Sample_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Vinker_Image_Shape_Manipulation_From_a_Single_Augmented_Training_Sample_ICCV_2021_paper.pdf | iccv-2021-1 | ['sketch-to-image-translation'] | ['computer-vision'] | [ 6.95301712e-01 4.39012825e-01 -1.89183224e-02 -2.07753018e-01
-3.82277429e-01 -8.75714362e-01 9.91031945e-01 -3.85965407e-01
-3.88977349e-01 4.54809934e-01 -5.94461784e-02 -4.93888229e-01
2.86178827e-01 -9.38352644e-01 -1.16982365e+00 -5.47447264e-01
-1.87182648e-03 4.54360098e-01 2.51276344e-01 -3.58290911... | [11.48619270324707, -0.41026636958122253] |
85fa9172-c98b-4057-a7b7-d88f3ce1c341 | change-detection-needs-change-information | 2304.12639 | null | https://arxiv.org/abs/2304.12639v1 | https://arxiv.org/pdf/2304.12639v1.pdf | Change detection needs change information: improving deep 3D point cloud change detection | Change detection is an important task to rapidly identify modified areas, in particular when multi-temporal data are concerned. In landscapes with complex geometry such as urban environment, vertical information turn out to be a very useful knowledge not only to highlight changes but also to classify them into differen... | ['Sébastien Lefèvre', 'Thomas Corpetti', 'Iris de Gélis'] | 2023-04-25 | null | null | null | null | ['change-detection'] | ['computer-vision'] | [ 1.90322503e-01 -1.77930892e-01 1.83628544e-01 -3.01919878e-01
-2.99398333e-01 -6.02905631e-01 1.07674611e+00 4.70190406e-01
-8.66595924e-01 6.78650081e-01 -5.98034337e-02 -2.43595153e-01
-3.53761226e-01 -1.17431915e+00 -9.00501907e-01 -6.43929183e-01
-3.70645911e-01 4.08016235e-01 6.22124970e-01 -6.03416264... | [9.710628509521484, -1.600701093673706] |
bafd5637-8333-42b9-b1dc-9b80de3d68da | inverse-path-tracing-for-joint-material-and | 1903.07145 | null | http://arxiv.org/abs/1903.07145v1 | http://arxiv.org/pdf/1903.07145v1.pdf | Inverse Path Tracing for Joint Material and Lighting Estimation | Modern computer vision algorithms have brought significant advancement to 3D
geometry reconstruction. However, illumination and material reconstruction
remain less studied, with current approaches assuming very simplified models
for materials and illumination. We introduce Inverse Path Tracing, a novel
approach to join... | ['Matthias Nießner', 'Tzu-Mao Li', 'Dejan Azinović', 'Anton Kaplanyan'] | 2019-03-17 | null | null | null | null | ['lighting-estimation'] | ['computer-vision'] | [ 6.27840757e-01 -6.54314280e-01 8.01988542e-01 -3.89941752e-01
-4.45283502e-01 -4.66484487e-01 6.56253040e-01 1.05846375e-02
-1.88175544e-01 8.11500072e-01 -2.04455405e-01 1.94080211e-02
-2.14209035e-01 -8.84941876e-01 -6.39916003e-01 -8.21315825e-01
4.27099109e-01 6.78011894e-01 -7.37121701e-02 1.12557001... | [9.736343383789062, -3.0645880699157715] |
f85fcf0e-bcf3-4d46-85ad-28f35e1250da | panoramic-image-reflection-removal | null | null | http://openaccess.thecvf.com//content/CVPR2021/html/Hong_Panoramic_Image_Reflection_Removal_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Hong_Panoramic_Image_Reflection_Removal_CVPR_2021_paper.pdf | Panoramic Image Reflection Removal | This paper studies the problem of panoramic image reflection removal, aiming at reliving the content ambiguity between reflection and transmission scenes. Although a partial view of the reflection scene is included in the panoramic image, it cannot be utilized directly due to its misalignment with the reflection-co... | ['Boxin Shi', 'Alex C. Kot', 'Xudong Jiang', 'Lingran Zhao', 'Qian Zheng', 'Yuchen Hong'] | 2021-06-19 | null | null | null | cvpr-2021-1 | ['reflection-removal'] | ['computer-vision'] | [ 1.11995411e+00 -2.04042464e-01 4.27955300e-01 -1.08646020e-01
-6.38516128e-01 -2.76443332e-01 6.76742256e-01 -9.03585374e-01
-1.90139338e-01 5.08654058e-01 3.22745919e-01 -2.45344311e-01
-3.24073911e-01 -8.69416595e-01 -6.50527954e-01 -9.08952773e-01
6.57085001e-01 -1.05782673e-01 9.58145782e-03 -2.66489685... | [10.156574249267578, -2.813112735748291] |
26282afa-61bd-4aed-90a5-fb4bdb2871b0 | stereoscene-bev-assisted-stereo-matching | 2303.13959 | null | https://arxiv.org/abs/2303.13959v2 | https://arxiv.org/pdf/2303.13959v2.pdf | StereoScene: BEV-Assisted Stereo Matching Empowers 3D Semantic Scene Completion | 3D semantic scene completion (SSC) is an ill-posed task that requires inferring a dense 3D scene from incomplete observations. Previous methods either explicitly incorporate 3D geometric input or rely on learnt 3D prior behind monocular RGB images. However, 3D sensors such as LiDAR are expensive and intrusive while mon... | ['Dalong Du', 'Hang Xiao', 'James Okae', 'Yunpeng Zhang', 'Xiaoefeng Wang', 'Zheng Zhu', 'Wenjun Zeng', 'Xin Jin', 'Yasheng Sun', 'Bohan Li'] | 2023-03-24 | null | null | null | null | ['3d-semantic-scene-completion', 'stereo-matching-1'] | ['computer-vision', 'computer-vision'] | [ 2.55650729e-01 1.44281015e-01 1.57156155e-01 -4.91346538e-01
-6.05938256e-01 -4.23573852e-01 5.38603127e-01 -3.12373608e-01
-1.27255693e-01 3.30500931e-01 3.44553769e-01 -1.80280685e-01
7.82052428e-02 -6.89704895e-01 -7.67463863e-01 -5.92861414e-01
4.20712203e-01 2.19885543e-01 3.18428010e-01 -2.26118460... | [8.607855796813965, -2.801826000213623] |
3c73c41a-591b-4e4d-ba73-73ba0ee70cf4 | astra-a-novel-algorithm-level-approach-to | 2209.01685 | null | https://arxiv.org/abs/2209.01685v1 | https://arxiv.org/pdf/2209.01685v1.pdf | ASTra: A Novel Algorithm-Level Approach to Imbalanced Classification | We propose a novel output layer activation function, which we name ASTra (Asymmetric Sigmoid Transfer function), which makes the classification of minority examples, in scenarios of high imbalance, more tractable. We combine this with a loss function that helps to effectively target minority misclassification. These tw... | ['Denise Gorse', 'David Twomey'] | 2022-09-04 | null | null | null | null | ['imbalanced-classification'] | ['miscellaneous'] | [ 3.76573503e-01 2.80594677e-01 -2.51552820e-01 -4.97540861e-01
-3.94388944e-01 -3.51673067e-01 5.19442856e-01 5.18824220e-01
-7.24764526e-01 1.17219841e+00 -2.57701278e-01 -5.93287766e-01
-3.45723897e-01 -8.55395555e-01 -4.64302808e-01 -7.55677640e-01
-6.70036748e-02 4.13757920e-01 2.07942780e-02 -2.39861324... | [8.69035530090332, 4.244235992431641] |
e038945d-23e2-41a7-8315-103cb613fa60 | a-bayesian-treatment-of-real-to-sim-for | 2112.05068 | null | https://arxiv.org/abs/2112.05068v1 | https://arxiv.org/pdf/2112.05068v1.pdf | A Bayesian Treatment of Real-to-Sim for Deformable Object Manipulation | Deformable object manipulation remains a challenging task in robotics research. Conventional techniques for parameter inference and state estimation typically rely on a precise definition of the state space and its dynamics. While this is appropriate for rigid objects and robot states, it is challenging to define the s... | ['Jeannette Bohg', 'Fabio Ramos', 'Dieter Fox', 'Priya Sundaresan', 'Jingyun Yang', 'Rika Antonova'] | 2021-12-09 | null | null | null | null | ['deformable-object-manipulation'] | ['robots'] | [ 1.24113886e-02 -1.17881931e-01 -9.80062559e-02 -6.22876473e-02
-2.29403615e-01 -7.53927410e-01 6.47255003e-01 -2.11838841e-01
-3.15847486e-01 7.75799572e-01 -1.83827907e-01 6.23492673e-02
-3.53195250e-01 -6.66437685e-01 -9.09297585e-01 -1.01030421e+00
-1.41687049e-02 1.06753981e+00 6.47619605e-01 3.21126916... | [5.601956367492676, -0.4974064528942108] |
fbcd5ea8-4540-4fd6-8de2-b000beb6a9ff | knowledge-driven-answer-generation-for | 2104.06892 | null | https://arxiv.org/abs/2104.06892v1 | https://arxiv.org/pdf/2104.06892v1.pdf | Knowledge-driven Answer Generation for Conversational Search | The conversational search paradigm introduces a step change over the traditional search paradigm by allowing users to interact with search agents in a multi-turn and natural fashion. The conversation flows naturally and is usually centered around a target field of knowledge. In this work, we propose a knowledge-driven ... | ['João Magalhães', 'David Semedo', 'Rafael Ferreira', 'Mariana Leite'] | 2021-04-14 | null | null | null | null | ['conversational-search'] | ['natural-language-processing'] | [ 2.59398311e-01 6.05747759e-01 -4.84520316e-01 -1.50809869e-01
-1.17097712e+00 -7.45409131e-01 1.14935458e+00 2.12417096e-01
-3.81369978e-01 8.95622194e-01 9.23567235e-01 -1.72620475e-01
-2.32374325e-01 -8.45449030e-01 -2.38437667e-01 -1.59072146e-01
2.09104344e-01 1.13896215e+00 5.04060328e-01 -8.30682456... | [12.106024742126465, 7.9031596183776855] |
20fae2f8-bd94-4e72-a1fc-8a172d1e79f0 | cross-attention-is-not-enough-incongruity | 2305.13583 | null | https://arxiv.org/abs/2305.13583v2 | https://arxiv.org/pdf/2305.13583v2.pdf | Cross-Attention is Not Enough: Incongruity-Aware Hierarchical Multimodal Sentiment Analysis and Emotion Recognition | Fusing multiple modalities for affective computing tasks has proven effective for performance improvement. However, how multimodal fusion works is not well understood, and its use in the real world usually results in large model sizes. In this work, on sentiment and emotion analysis, we first analyze how the salient af... | ['Catherine Lai', 'Peter Bell', 'Yuanchao Li', 'Yaoting Wang'] | 2023-05-23 | null | null | null | null | ['multimodal-sentiment-analysis', 'sentiment-analysis', 'multimodal-sentiment-analysis'] | ['computer-vision', 'natural-language-processing', 'natural-language-processing'] | [ 2.25527465e-01 -9.24005955e-02 -6.10041954e-02 -3.72160822e-01
-6.95297062e-01 -3.96200776e-01 5.22680521e-01 2.57044703e-01
-3.24523926e-01 5.37455142e-01 5.05497754e-01 1.48770258e-01
6.39774799e-02 -3.86102796e-01 -3.87904376e-01 -7.70271122e-01
3.67923737e-01 2.48404264e-01 -1.19798847e-01 -4.89130735... | [13.183351516723633, 5.165999412536621] |
bdd726f9-e8db-4bbf-be91-963d3daa6837 | an-order-invariant-and-interpretable | 2302.06243 | null | https://arxiv.org/abs/2302.06243v1 | https://arxiv.org/pdf/2302.06243v1.pdf | An Order-Invariant and Interpretable Hierarchical Dilated Convolution Neural Network for Chemical Fault Detection and Diagnosis | Fault detection and diagnosis is significant for reducing maintenance costs and improving health and safety in chemical processes. Convolution neural network (CNN) is a popular deep learning algorithm with many successful applications in chemical fault detection and diagnosis tasks. However, convolution layers in CNN a... | ['Hongwei Wang', 'Min Wang', 'Peng Peng', 'Mengxuan Li'] | 2023-02-13 | null | null | null | null | ['fault-detection'] | ['miscellaneous'] | [ 2.19089314e-01 -1.30437806e-01 3.17489058e-01 -1.57341763e-01
2.69593596e-01 -3.53466958e-01 1.47068188e-01 2.76501924e-01
2.88713863e-03 5.65034926e-01 -8.48395228e-02 -3.57136935e-01
-7.96636105e-01 -8.65736604e-01 -5.19276917e-01 -9.14242566e-01
-8.22912231e-02 2.65841544e-01 -1.37436688e-01 -3.74936238... | [7.244388103485107, 2.2036118507385254] |
5acc523e-314f-4202-aa92-03ffceb166ba | a-multimodal-dataset-for-deception-detection | null | null | https://aclanthology.org/L14-1673 | https://aclanthology.org/L14-1673.pdf | A Multimodal Dataset for Deception Detection | This paper presents the construction of a multimodal dataset for deception detection, including physiological, thermal, and visual responses of human subjects under three deceptive scenarios. We present the experimental protocol, as well as the data acquisition process. To evaluate the usefulness of the dataset for the... | ["Ver{\\'o}nica P{\\'e}rez-Rosas", 'Mihai Burzo', 'Alexis Narvaez', 'Rada Mihalcea'] | 2014-05-01 | null | null | null | lrec-2014-5 | ['deception-detection'] | ['miscellaneous'] | [-5.19216731e-02 -4.57418144e-01 3.17204982e-01 -7.92507946e-01
-4.21847731e-01 -8.37349892e-01 7.77193189e-01 -3.25635560e-02
-3.69089335e-01 7.80353487e-01 2.20658854e-01 2.93544456e-02
2.03143939e-01 2.66638130e-01 -5.92270680e-03 -7.27503419e-01
-4.02194215e-03 -3.46981019e-01 -6.34679794e-01 1.65193319... | [13.31544017791748, 2.079537868499756] |
b6c1a0ce-2e27-4284-a50a-6e5a6b6c92c3 | depth-infused-binaural-audio-generation-using | 2108.04906 | null | https://arxiv.org/abs/2108.04906v1 | https://arxiv.org/pdf/2108.04906v1.pdf | Depth Infused Binaural Audio Generation using Hierarchical Cross-Modal Attention | Binaural audio gives the listener the feeling of being in the recording place and enhances the immersive experience if coupled with AR/VR. But the problem with binaural audio recording is that it requires a specialized setup which is not possible to fabricate within handheld devices as compared to traditional mono audi... | ['Gaurav Sharma', 'Neeraj Matiyali', 'Siddharth Srivastava', 'Kranti Kumar Parida'] | 2021-08-10 | null | null | null | null | ['audio-generation'] | ['audio'] | [ 4.06435370e-01 -1.75535023e-01 6.31931126e-01 -2.59217620e-01
-7.79231608e-01 -5.65243125e-01 3.71121615e-01 1.81351498e-01
-3.69741142e-01 2.55934834e-01 4.60035443e-01 1.27238765e-01
-3.64821292e-02 -6.95758402e-01 -9.42329347e-01 -6.43422425e-01
1.72359362e-01 7.51783475e-02 4.76425856e-01 -7.24228323... | [14.992269515991211, 5.061923027038574] |
5e180310-8930-41fb-9dc4-482169e685bd | duta-vc-a-duration-aware-typical-to-atypical | 2306.10588 | null | https://arxiv.org/abs/2306.10588v1 | https://arxiv.org/pdf/2306.10588v1.pdf | DuTa-VC: A Duration-aware Typical-to-atypical Voice Conversion Approach with Diffusion Probabilistic Model | We present a novel typical-to-atypical voice conversion approach (DuTa-VC), which (i) can be trained with nonparallel data (ii) first introduces diffusion probabilistic model (iii) preserves the target speaker identity (iv) is aware of the phoneme duration of the target speaker. DuTa-VC consists of three parts: an enco... | ['Laureano Moro-Velazquez', 'Najim Dehak', 'Becky Lammers', 'Myra Sydnor', 'Jesus Villalba', 'Thomas Thebaud', 'Helin Wang'] | 2023-06-18 | null | null | null | null | ['voice-conversion', 'voice-conversion'] | ['audio', 'speech'] | [ 1.95593163e-01 2.09726825e-01 1.70588002e-01 -7.63857961e-02
-1.17806196e+00 -5.07015169e-01 1.35166287e-01 -4.48093206e-01
-1.19647786e-01 5.62688887e-01 8.91159296e-01 -4.23365295e-01
2.30024233e-02 -3.07226866e-01 -3.55366588e-01 -6.91340268e-01
1.14916921e-01 4.49749619e-01 2.51473606e-01 -1.63419873... | [14.749100685119629, 6.486413478851318] |
c695caac-eb3e-4e04-b7ff-c0f021dd53b3 | on-robustness-of-prompt-based-semantic | 2301.12868 | null | https://arxiv.org/abs/2301.12868v3 | https://arxiv.org/pdf/2301.12868v3.pdf | On Robustness of Prompt-based Semantic Parsing with Large Pre-trained Language Model: An Empirical Study on Codex | Semantic parsing is a technique aimed at constructing a structured representation of the meaning of a natural-language question. Recent advancements in few-shot language models trained on code have demonstrated superior performance in generating these representations compared to traditional unimodal language models, wh... | ['Fatemeh Shiri', 'Gholamreza Haffari', 'Weiqing Wang', 'Yuan-Fang Li', 'Yujin Huang', 'Zhuang Li', 'Terry Yue Zhuo'] | 2023-01-30 | null | null | null | null | ['semantic-parsing'] | ['natural-language-processing'] | [ 4.15724248e-01 7.88385868e-01 1.75106451e-01 -4.43185240e-01
-1.35391164e+00 -8.89422596e-01 5.26401103e-01 1.26108736e-01
-1.47013918e-01 4.10971552e-01 3.11943442e-01 -7.47316539e-01
4.88483131e-01 -8.75070572e-01 -1.11831033e+00 -1.36051044e-01
-1.34562543e-02 1.19587503e-01 3.88184428e-01 -5.18794537... | [7.040821075439453, 7.926254749298096] |
ca060c59-7417-4cae-9efa-72625f09901f | negation-scope-detection-for-twitter | null | null | https://aclanthology.org/W15-2914 | https://aclanthology.org/W15-2914.pdf | Negation Scope Detection for Twitter Sentiment Analysis | null | ['Bj{\\"o}rn Gamb{\\"a}ck', 'J{\\o}rgen Faret', 'Lars Bungum', 'Johan Reitan'] | 2015-09-01 | null | null | null | ws-2015-9 | ['twitter-sentiment-analysis', 'negation-detection'] | ['natural-language-processing', 'natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
-2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00
-3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01
-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.256717681884766, 3.84771466255188] |
43fde8f4-5bac-4842-8206-fcd601afe822 | affinity-aware-graph-networks | 2206.11941 | null | https://arxiv.org/abs/2206.11941v1 | https://arxiv.org/pdf/2206.11941v1.pdf | Affinity-Aware Graph Networks | Graph Neural Networks (GNNs) have emerged as a powerful technique for learning on relational data. Owing to the relatively limited number of message passing steps they perform -- and hence a smaller receptive field -- there has been significant interest in improving their expressivity by incorporating structural aspect... | ['Sreenivas Gollapudi', 'Petar Veličković', 'Ira Ktena', 'Ali Kemal Sinop', 'Ameya Velingker'] | 2022-06-23 | null | null | null | null | ['graph-property-prediction', 'graph-regression'] | ['graphs', 'graphs'] | [ 1.15244448e-01 1.34279191e-01 -4.77987498e-01 -2.61984169e-01
7.98630640e-02 -4.68517601e-01 6.52688682e-01 8.17869604e-01
-5.74631155e-01 5.93710721e-01 -1.60411485e-02 -6.82753921e-01
-4.97994810e-01 -1.25613701e+00 -7.88159251e-01 -4.11020964e-01
-7.24559963e-01 5.60216486e-01 3.61732185e-01 -4.66755748... | [6.9211931228637695, 6.1598334312438965] |
e3fd38c2-08a4-4649-bddf-80d64c999155 | learning-graph-embeddings-for-open-world | 2105.01017 | null | https://arxiv.org/abs/2105.01017v3 | https://arxiv.org/pdf/2105.01017v3.pdf | Learning Graph Embeddings for Open World Compositional Zero-Shot Learning | Compositional Zero-Shot learning (CZSL) aims to recognize unseen compositions of state and object visual primitives seen during training. A problem with standard CZSL is the assumption of knowing which unseen compositions will be available at test time. In this work, we overcome this assumption operating on the open wo... | ['Zeynep Akata', 'Yongqin Xian', 'Muhammad Ferjad Naeem', 'Massimiliano Mancini'] | 2021-05-03 | null | null | null | null | ['compositional-zero-shot-learning'] | ['computer-vision'] | [ 3.22376788e-02 6.98385388e-02 -2.15730499e-02 1.92715794e-01
-1.08164161e-01 -7.08867788e-01 5.83404422e-01 3.76107514e-01
-2.30833203e-01 2.54570305e-01 1.43205151e-01 -8.50885808e-02
2.61783917e-02 -1.09952199e+00 -8.72708678e-01 -6.71278298e-01
-1.65497258e-01 7.34061420e-01 5.46187699e-01 -1.71056524... | [10.247544288635254, 2.2520911693573] |
79b1e219-1583-4155-ba10-f5ca9c483432 | bulk-production-augmentation-towards | 2103.02198 | null | https://arxiv.org/abs/2103.02198v1 | https://arxiv.org/pdf/2103.02198v1.pdf | Bulk Production Augmentation Towards Explainable Melanoma Diagnosis | Although highly accurate automated diagnostic techniques for melanoma have been reported, the realization of a system capable of providing diagnostic evidence based on medical indices remains an open issue because of difficulties in obtaining reliable training data. In this paper, we propose bulk production augmentatio... | ['Hitoshi Iyatomi', 'Masaru Tanaka', 'Noriko Umegaki-Arao', 'Quan Huu Cap', 'Kasumi Obi'] | 2021-03-03 | null | null | null | null | ['melanoma-diagnosis'] | ['computer-vision'] | [ 1.00018704e+00 3.07411373e-01 -2.93345541e-01 -2.86071226e-02
-9.71988976e-01 -2.99691379e-01 5.16014695e-01 7.01257139e-02
-4.29056108e-01 8.14633250e-01 -9.60895717e-02 -4.23127145e-01
1.27919286e-01 -6.91991985e-01 -4.01836187e-01 -1.14233851e+00
2.45132491e-01 1.71986476e-01 1.11043490e-01 -5.49723953... | [15.632957458496094, -2.9627397060394287] |
6b0ac06c-2648-4e4c-9ebd-6e909ce961e4 | recovering-compressed-images-for-automatic | 2003.03028 | null | https://arxiv.org/abs/2003.03028v1 | https://arxiv.org/pdf/2003.03028v1.pdf | Recovering compressed images for automatic crack segmentation using generative models | In a structural health monitoring (SHM) system that uses digital cameras to monitor cracks of structural surfaces, techniques for reliable and effective data compression are essential to ensure a stable and energy efficient crack images transmission in wireless devices, e.g., drones and robots with high definition came... | ['Haoyu Zhang', 'Stephen Wu', 'Hui Li', 'Yong Huang'] | 2020-03-06 | null | null | null | null | ['crack-segmentation'] | ['computer-vision'] | [ 9.52173412e-01 -6.35302439e-02 2.60576189e-01 1.86326280e-01
-5.32293677e-01 -9.22495499e-02 5.89591824e-02 -1.55214384e-01
-1.82007939e-01 6.01958692e-01 -1.20539092e-01 1.11874126e-01
-3.77025366e-01 -9.75921750e-01 -6.28583789e-01 -9.68977511e-01
1.39857888e-01 1.12828009e-01 3.33603770e-01 -2.06168875... | [11.841713905334473, -2.343186378479004] |
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