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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
a66f31b0-79e1-4a4d-bc78-6891a7727b67 | distilling-the-knowledge-of-romanian-berts | 2112.12650 | null | https://arxiv.org/abs/2112.12650v3 | https://arxiv.org/pdf/2112.12650v3.pdf | Distilling the Knowledge of Romanian BERTs Using Multiple Teachers | Running large-scale pre-trained language models in computationally constrained environments remains a challenging problem yet to be addressed, while transfer learning from these models has become prevalent in Natural Language Processing tasks. Several solutions, including knowledge distillation, network quantization, o... | ['Dan Tufiş', 'Vasile Păiş', 'Traian Rebedea', 'Mihai Dascălu', 'Dumitru-Clementin Cercel', 'Darius Catrina', 'Andrei-Marius Avram'] | 2021-12-23 | null | https://aclanthology.org/2022.lrec-1.39 | https://aclanthology.org/2022.lrec-1.39.pdf | lrec-2022-6 | ['dialect-identification'] | ['natural-language-processing'] | [-1.51037171e-01 1.17232211e-01 -2.42825300e-01 -6.13986790e-01
-6.38244867e-01 -5.29608607e-01 4.21231359e-01 5.35686135e-01
-9.67905939e-01 7.82554924e-01 -7.64392614e-02 -3.74812514e-01
-1.70069560e-01 -6.92285895e-01 -4.23764378e-01 -4.49520022e-01
1.75667524e-01 9.79585767e-01 2.81419933e-01 -5.00592411... | [10.233713150024414, 9.578152656555176] |
8e24ebfd-512e-4f92-87a6-7f0670961234 | progressive-attention-networks-for-visual | 1606.02393 | null | http://arxiv.org/abs/1606.02393v5 | http://arxiv.org/pdf/1606.02393v5.pdf | Progressive Attention Networks for Visual Attribute Prediction | We propose a novel attention model that can accurately attends to target
objects of various scales and shapes in images. The model is trained to
gradually suppress irrelevant regions in an input image via a progressive
attentive process over multiple layers of a convolutional neural network. The
attentive process in ea... | ['Xiaohui Shen', 'Zhe Lin', 'Scott Cohen', 'Bohyung Han', 'Paul Hongsuck Seo'] | 2016-06-08 | null | null | null | null | ['hard-attention'] | ['methodology'] | [ 4.10510778e-01 -8.61264914e-02 -1.85724825e-01 -3.70050728e-01
-2.74842143e-01 4.60364763e-03 3.85152817e-01 3.54935914e-01
-5.18892705e-01 5.75629115e-01 2.78271735e-01 1.07825510e-01
-7.68196583e-02 -7.97742248e-01 -8.11170757e-01 -5.71188748e-01
-1.99428247e-03 3.43396574e-01 6.81870282e-01 1.20934583... | [9.789026260375977, 1.8759835958480835] |
dee8173a-0922-419b-9a4a-d522bb979b7c | umcc_dlsi-a-probabilistic-automata-for-aspect | null | null | https://aclanthology.org/S14-2129 | https://aclanthology.org/S14-2129.pdf | UMCC\_DLSI: A Probabilistic Automata for Aspect Based Sentiment Analysis | null | ['Rafael Mu{\\~n}oz', "Andr{\\'e}s Montoyo", "David Tom{\\'a}s", "Yoan Guti{\\'e}rrez", 'o', 'Yenier Casta{\\~n}eda', 'Elvis Crego', 'Arm Collazo', 'Jorge L. Garcia'] | 2014-08-01 | null | null | null | semeval-2014-8 | ['subjectivity-analysis'] | ['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... | [-7.356940746307373, 3.6964635848999023] |
f4c57060-ab7e-4ce8-9817-af2619329253 | event-based-star-tracking-via-multiresolution | 1906.07866 | null | https://arxiv.org/abs/1906.07866v2 | https://arxiv.org/pdf/1906.07866v2.pdf | Event-based Star Tracking via Multiresolution Progressive Hough Transforms | Star trackers are state-of-the-art attitude estimation devices which function by recognising and tracking star patterns. Most commercial star trackers use conventional optical sensors. A recent alternative is to use event sensors, which could enable more energy efficient and faster star trackers. However, this demands ... | ['Tat-Jun Chin', 'Samya Bagchi'] | 2019-06-19 | null | null | null | null | ['event-based-motion-estimation'] | ['computer-vision'] | [-1.06222205e-01 -4.39810187e-01 -9.68073234e-02 8.81406069e-02
-1.76666066e-01 -7.19516754e-01 7.71147549e-01 2.41089180e-01
-5.33896983e-01 2.74785191e-01 -2.68935710e-01 -1.09663308e-01
5.58986962e-02 -7.52408803e-01 -4.12934780e-01 -7.02317536e-01
-1.96561575e-01 4.16173846e-01 9.01763618e-01 -6.11035265... | [7.661462306976318, -1.7986226081848145] |
ad098d8b-d329-42bc-aa67-6e01efbf59e1 | learning-the-string-partial-order | 2305.15057 | null | https://arxiv.org/abs/2305.15057v1 | https://arxiv.org/pdf/2305.15057v1.pdf | Learning the String Partial Order | We show that most structured prediction problems can be solved in linear time and space by considering them as partial orderings of the tokens in the input string. Our method computes real numbers for each token in an input string and sorts the tokens accordingly, resulting in as few as 2 total orders of the tokens in ... | ['Ryan Cotterell', 'Mrinmaya Sachan', 'Afra Amini', 'Tianyu Liu'] | 2023-05-24 | null | null | null | null | ['dependency-parsing', 'coreference-resolution'] | ['natural-language-processing', 'natural-language-processing'] | [ 4.08523947e-01 5.41143715e-01 -4.93101865e-01 -3.87083620e-01
-1.04980135e+00 -1.16086924e+00 3.56307044e-03 4.59430784e-01
-5.74667752e-01 7.69618809e-01 3.39783043e-01 -5.05825996e-01
1.33033274e-02 -9.17307377e-01 -8.97292078e-01 -4.58119631e-01
-1.81944668e-01 1.04415452e+00 6.44556761e-01 -1.57279581... | [10.346930503845215, 9.538091659545898] |
99535788-1e7d-419a-aa57-807b790127ef | freevc-towards-high-quality-text-free-one | 2210.15418 | null | https://arxiv.org/abs/2210.15418v1 | https://arxiv.org/pdf/2210.15418v1.pdf | FreeVC: Towards High-Quality Text-Free One-Shot Voice Conversion | Voice conversion (VC) can be achieved by first extracting source content information and target speaker information, and then reconstructing waveform with these information. However, current approaches normally either extract dirty content information with speaker information leaked in, or demand a large amount of anno... | ['Li Xiao', 'Weiping tu', 'Jingyi Li'] | 2022-10-27 | null | null | null | null | ['text-annotation'] | ['natural-language-processing'] | [ 2.64689356e-01 -3.96428704e-02 -4.32146899e-03 -6.28654212e-02
-1.46944356e+00 -8.21652591e-01 1.99059799e-01 -1.91795945e-01
-2.80472320e-02 6.00765169e-01 8.06534886e-01 -1.88117623e-01
3.04153174e-01 -2.70700485e-01 -3.88815045e-01 -6.59212172e-01
3.20786923e-01 -1.40644848e-01 -5.64485714e-02 -1.49376225... | [15.001486778259277, 6.201258659362793] |
1096e5d5-52de-41da-9681-d3987b39b683 | casteism-in-india-but-not-racism-a-study-of | null | null | https://aclanthology.org/2022.lateraisse-1.1 | https://aclanthology.org/2022.lateraisse-1.1.pdf | Casteism in India, but Not Racism - a Study of Bias in Word Embeddings of Indian Languages | In this paper, we studied the gender bias in monolingual word embeddings of two Indian languages Hindi and Tamil. Tamil is one of the classical languages of India from the Dravidian language family. In Indian society and culture, instead of racism, a similar type of discrimination called casteism is against the subgrou... | ['Aravindan Chandrabose', 'Aman Chandra Kumar', 'Pranav Tiwari', 'Senthil Kumar B'] | null | null | null | null | lateraisse-lrec-2022-6 | ['culture'] | ['speech'] | [-6.72634363e-01 5.00879213e-02 -3.01756233e-01 -4.76556331e-01
4.96365279e-01 -6.71894252e-01 1.16071594e+00 3.71138871e-01
-1.04993916e+00 7.27135360e-01 8.29647601e-01 -5.81967473e-01
9.65592861e-02 -9.65949237e-01 -4.33037952e-02 -7.34578848e-01
2.28995711e-01 7.63961434e-01 -3.86610746e-01 -8.96435916... | [9.352226257324219, 10.191417694091797] |
55bb423c-6afc-4fd3-9d28-8406a88ccda6 | amplitude-independent-machine-learning-for | 2305.14062 | null | https://arxiv.org/abs/2305.14062v1 | https://arxiv.org/pdf/2305.14062v1.pdf | Amplitude-Independent Machine Learning for PPG through Visibility Graphs and Transfer Learning | Photoplethysmography (PPG) signals are omnipresent in wearable devices, as they measure blood volume variations using LED technology. These signals provide insight into the body's circulatory system and can be employed to extract various bio-features, such as heart rate and vascular ageing. Although several algorithms ... | ['Danilo P. Mandic', 'Harry J. Davies', 'Yuyang Miao'] | 2023-05-23 | null | null | null | null | ['photoplethysmography-ppg'] | ['medical'] | [ 1.31635964e-01 -2.42810994e-01 -3.74533534e-02 -3.59167278e-01
-2.44425200e-02 -5.88325024e-01 1.24399088e-01 4.63606454e-02
-2.69561559e-01 7.90120482e-01 2.21820876e-01 1.04512339e-02
6.69142976e-02 -3.39039356e-01 1.86998427e-01 -7.58139551e-01
-1.91553786e-01 -2.53459781e-01 -4.00148071e-02 1.41306028... | [13.935588836669922, 2.906017541885376] |
b197dfc3-9f9e-47d0-8311-4cfd3fef29b6 | introducing-randomized-high-order-fuzzy | 2201.02158 | null | https://arxiv.org/abs/2201.02158v2 | https://arxiv.org/pdf/2201.02158v2.pdf | Introducing Randomized High Order Fuzzy Cognitive Maps as Reservoir Computing Models: A Case Study in Solar Energy and Load Forecasting | Fuzzy Cognitive Maps (FCMs) have emerged as an interpretable signed weighted digraph method consisting of nodes (concepts) and weights which represent the dependencies among the concepts. Although FCMs have attained considerable achievements in various time series prediction applications, designing an FCM model with ti... | ['Frederico Gadelha Guimarães', 'Petrônio Cândido de Lima Silva', 'Omid Orang'] | 2022-01-06 | null | null | null | null | ['time-series-prediction', 'univariate-time-series-forecasting'] | ['time-series', 'time-series'] | [-2.39185914e-02 -2.78209150e-01 2.55251676e-01 -1.02420554e-01
4.40614372e-01 -5.10064900e-01 8.09077263e-01 4.94461991e-02
-1.07160650e-01 7.54443645e-01 -3.15539911e-02 -4.64131385e-01
-7.81676054e-01 -1.18738449e+00 -4.97636110e-01 -9.29684699e-01
-4.79436725e-01 4.15759206e-01 2.34318331e-01 -6.19414985... | [5.949615001678467, 3.022780656814575] |
cc38add8-889f-47fe-9e79-befcc4dd3d4c | designing-fairness-in-autonomous-peer-to-peer | 2302.04771 | null | https://arxiv.org/abs/2302.04771v1 | https://arxiv.org/pdf/2302.04771v1.pdf | Designing Fairness in Autonomous Peer-to-peer Energy Trading | Several autonomous energy management and peer-to-peer trading mechanisms for future energy markets have been recently proposed based on optimization and game theory. In this paper, we study the impact of trading prices on the outcome of these market designs for energy-hub networks. We prove that, for a generic choice o... | ['Florian Dörfler', 'John Lygeros', 'Philipp Heer', 'Giuseppe Belgioioso', 'Andrew Irvine', 'Varsha Behrunani'] | 2023-02-09 | null | null | null | null | ['energy-management'] | ['time-series'] | [-6.94983780e-01 4.53906029e-01 -3.45394254e-01 -4.57782345e-03
-3.97438347e-01 -9.51935530e-01 3.30358237e-01 2.94538438e-02
-2.21309841e-01 1.02636445e+00 -1.98144212e-01 -1.97033018e-01
-5.58260679e-01 -1.39353991e+00 -4.63007152e-01 -8.03522110e-01
-4.98019964e-01 5.44479430e-01 1.33406386e-01 8.86054430... | [5.5230255126953125, 2.5851080417633057] |
8e2decd0-58c4-4b14-b5ec-5135bfe0b048 | breast-density-classification-with-deep | 1711.03674 | null | http://arxiv.org/abs/1711.03674v1 | http://arxiv.org/pdf/1711.03674v1.pdf | Breast density classification with deep convolutional neural networks | Breast density classification is an essential part of breast cancer
screening. Although a lot of prior work considered this problem as a task for
learning algorithms, to our knowledge, all of them used small and not
clinically realistic data both for training and evaluation of their models. In
this work, we explore the... | ['Nan Wu', 'Kyunghyun Cho', 'Krzysztof J. Geras', 'Stacey Wolfson', 'Linda Moy', 'Eric Kim', 'S. Gene Kim', 'Jingyi Su', 'Yiqiu Shen'] | 2017-11-10 | null | null | null | null | ['breast-density-classification'] | ['medical'] | [ 1.76468223e-01 5.69886088e-01 -4.90285605e-01 -6.71794176e-01
-8.10020983e-01 -3.10989112e-01 5.02741456e-01 4.94244248e-01
-7.05200255e-01 7.34453797e-01 1.02294097e-02 -9.20695007e-01
1.74099430e-01 -8.90503466e-01 -8.27018321e-01 -3.42220187e-01
-2.20129013e-01 7.52081096e-01 6.62338018e-01 1.04443058... | [15.202239036560059, -2.4722073078155518] |
7e1fe4e1-c142-45f4-862f-5ac9f9621f9b | bengali-text-document-categorization-based-on | null | null | https://www.sciencedirect.com/science/article/pii/S0957417421008174 | https://www.sciencedirect.com/science/article/pii/S0957417421008174 | Bengali text document categorization based on very deep convolution neural network | In recent years, the amount of digital text contents or documents in the Bengali language has increased enormously on online platforms due to the effortless access of the Internet via electronic gadgets. As a result, an enormous amount of unstructured data is created that demands much time and effort to organize, searc... | ['Iqbal H.Sarker', 'NazmulSiddiqueb', 'Mohammed MoshiulHoquea', 'Md. RajibHossaina'] | 2021-07-02 | null | null | null | expert-systems-with-applications-2021-7 | ['document-classification'] | ['natural-language-processing'] | [-3.84148091e-01 -2.07274809e-01 2.23166943e-01 -5.45755215e-02
-2.03785107e-01 -7.63289809e-01 8.01696301e-01 4.51729059e-01
-8.58032048e-01 4.70493019e-01 2.86546171e-01 -4.96328235e-01
-3.53883594e-01 -1.13778234e+00 1.55010998e-01 -6.41938388e-01
1.20935217e-01 6.85469985e-01 -8.12883154e-02 -5.97627401... | [10.238934516906738, 8.743565559387207] |
57062724-7a8e-444e-9a46-358d878f07f1 | ecsp-a-new-task-for-emotion-cause-span-pair | 2003.03507 | null | https://arxiv.org/abs/2003.03507v1 | https://arxiv.org/pdf/2003.03507v1.pdf | ECSP: A New Task for Emotion-Cause Span-Pair Extraction and Classification | Emotion cause analysis such as emotion cause extraction (ECE) and emotion-cause pair extraction (ECPE) have gradually attracted the attention of many researchers. However, there are still two shortcomings in the existing research: 1) In most cases, emotion expression and cause are not the whole clause, but the span in ... | ['Hongliang Bi', 'Pengyuan Liu'] | 2020-03-07 | null | null | null | null | ['emotion-cause-pair-extraction', 'emotion-cause-extraction'] | ['natural-language-processing', 'natural-language-processing'] | [ 3.41993392e-01 -9.35725793e-02 -1.95022285e-01 -5.43438673e-01
-5.82080007e-01 -5.72976708e-01 4.49927241e-01 2.44810939e-01
-4.64819185e-02 7.45758414e-01 3.49780619e-01 1.69085041e-02
-1.89097911e-01 -5.60958982e-01 -1.73621058e-01 -7.02702463e-01
4.94664274e-02 8.96001533e-02 -8.90850425e-02 -3.18782866... | [12.629487991333008, 6.211156845092773] |
6955afdc-25a9-4865-9021-32a3947bc1d9 | battery-and-hydrogen-energy-storage-control | 2208.12779 | null | https://arxiv.org/abs/2208.12779v1 | https://arxiv.org/pdf/2208.12779v1.pdf | Battery and Hydrogen Energy Storage Control in a Smart Energy Network with Flexible Energy Demand using Deep Reinforcement Learning | Smart energy networks provide for an effective means to accommodate high penetrations of variable renewable energy sources like solar and wind, which are key for deep decarbonisation of energy production. However, given the variability of the renewables as well as the energy demand, it is imperative to develop effectiv... | ['Jun Cao', 'Zhong Fan', 'Cephas Samende'] | 2022-08-26 | null | null | null | null | ['self-learning'] | ['natural-language-processing'] | [-4.30022180e-01 4.00065444e-02 -2.18355179e-01 3.08094501e-01
-7.18588382e-02 -5.50747216e-01 6.53207242e-01 7.75884986e-02
-1.19601026e-01 1.58684337e+00 -3.39393280e-02 -2.92131424e-01
-5.31767249e-01 -1.17803121e+00 -5.14441729e-01 -1.08030748e+00
-1.84949115e-01 5.46241283e-01 -3.14143270e-01 -3.66123587... | [5.601675033569336, 2.5356552600860596] |
d4a5dd9a-01cc-429a-b808-c9aef91df17b | mining-fine-grained-semantics-via-graph | 2201.06885 | null | https://arxiv.org/abs/2201.06885v2 | https://arxiv.org/pdf/2201.06885v2.pdf | Evidence-aware Fake News Detection with Graph Neural Networks | The prevalence and perniciousness of fake news has been a critical issue on the Internet, which stimulates the development of automatic fake news detection in turn. In this paper, we focus on the evidence-based fake news detection, where several evidences are utilized to probe the veracity of news (i.e., a claim). Most... | ['Liang Wang', 'Shu Wu', 'Qiang Liu', 'Junfei Wu', 'Weizhi Xu'] | 2022-01-18 | null | null | null | null | ['graph-structure-learning'] | ['graphs'] | [-2.12408118e-02 1.44783244e-01 -9.49166775e-01 9.52813402e-02
-4.14516807e-01 -1.84009701e-01 5.62862694e-01 6.55740142e-01
8.39345083e-02 7.09148765e-01 5.28357506e-01 -3.69480312e-01
-3.18997175e-01 -1.05115855e+00 -7.57916451e-01 -1.98672861e-01
2.38128558e-01 2.58991361e-01 7.68713951e-01 -5.78622520... | [8.133291244506836, 10.212803840637207] |
1c6d40fc-f1ed-4806-ab2f-8522021fcfc7 | multi-output-learning-for-camera | null | null | http://openaccess.thecvf.com/content_cvpr_2014/html/Guzman-Rivera_Multi-Output_Learning_for_2014_CVPR_paper.html | http://openaccess.thecvf.com/content_cvpr_2014/papers/Guzman-Rivera_Multi-Output_Learning_for_2014_CVPR_paper.pdf | Multi-Output Learning for Camera Relocalization | We address the problem of estimating the pose of a cam- era relative to a known 3D scene from a single RGB-D frame. We formulate this problem as inversion of the generative rendering procedure, i.e., we want to find the camera pose corresponding to a rendering of the 3D scene model that is most similar with the observe... | ['Andrew Fitzgibbon', 'Ben Glocker', 'Abner Guzman-Rivera', 'Toby Sharp', 'Shahram Izadi', 'Jamie Shotton', 'Pushmeet Kohli'] | 2014-06-01 | null | null | null | cvpr-2014-6 | ['camera-relocalization'] | ['computer-vision'] | [ 4.16101843e-01 -5.87185025e-02 3.62712033e-02 -5.70459604e-01
-1.32919061e+00 -5.55998087e-01 5.73811114e-01 -2.74389893e-01
-2.11589172e-01 2.65396923e-01 2.14378461e-01 4.94503081e-02
-7.33421941e-04 -5.21163583e-01 -1.11335516e+00 -7.99880505e-01
2.89343625e-01 1.08602500e+00 3.04592878e-01 9.01987031... | [8.143527030944824, -2.643231153488159] |
d1669b2f-475a-4496-be98-512e76fd55a0 | s-2-me-spatial-spectral-mutual-teaching-and | 2306.00451 | null | https://arxiv.org/abs/2306.00451v1 | https://arxiv.org/pdf/2306.00451v1.pdf | S$^2$ME: Spatial-Spectral Mutual Teaching and Ensemble Learning for Scribble-supervised Polyp Segmentation | Fully-supervised polyp segmentation has accomplished significant triumphs over the years in advancing the early diagnosis of colorectal cancer. However, label-efficient solutions from weak supervision like scribbles are rarely explored yet primarily meaningful and demanding in medical practice due to the expensiveness ... | ['Hongliang Ren', 'Mobarakol Islam', 'Yang Zhang', 'Mengya Xu', 'An Wang'] | 2023-06-01 | null | null | null | null | ['pseudo-label'] | ['miscellaneous'] | [ 4.08239216e-01 1.43802091e-01 -5.06780803e-01 -3.27786565e-01
-1.27542710e+00 -4.67478663e-01 2.35545158e-01 4.35661435e-01
-2.85371512e-01 7.05175102e-01 2.58568943e-01 -3.19005311e-01
-5.81717908e-01 -4.25153762e-01 -5.30786097e-01 -1.14834523e+00
6.44517783e-03 -3.19200940e-02 -1.01268910e-01 8.90257116... | [14.726409912109375, -2.0860626697540283] |
e471a7d8-c28e-4dc6-a8d2-47d627b509cc | worst-case-performance-of-greedy-policies-in | 2204.04773 | null | https://arxiv.org/abs/2204.04773v2 | https://arxiv.org/pdf/2204.04773v2.pdf | Worst-case Performance of Greedy Policies in Bandits with Imperfect Context Observations | Contextual bandits are canonical models for sequential decision-making under uncertainty in environments with time-varying components. In this setting, the expected reward of each bandit arm consists of the inner product of an unknown parameter with the context vector of that arm. The classical bandit settings heavily ... | ['Mohamad Kazem Shirani Faradonbeh', 'Hongju Park'] | 2022-04-10 | null | null | null | null | ['decision-making-under-uncertainty', 'decision-making-under-uncertainty'] | ['medical', 'reasoning'] | [ 1.63880199e-01 2.71851987e-01 -9.22106326e-01 -1.84724092e-01
-9.13252473e-01 -7.93442130e-01 3.47882897e-01 1.98843047e-01
-5.67485869e-01 1.21243227e+00 2.35314533e-01 -6.25832260e-01
-5.04417539e-01 -6.92126393e-01 -1.16138291e+00 -1.06027496e+00
-2.61677027e-01 7.70056307e-01 -1.40678898e-01 2.98465550... | [4.465785503387451, 3.194587469100952] |
e8d3e276-f7a2-48c9-b319-ef7759e37de7 | using-a-frustratingly-easy-domain-and-tagset | null | null | https://aclanthology.org/2021.bsnlp-1.12 | https://aclanthology.org/2021.bsnlp-1.12.pdf | Using a Frustratingly Easy Domain and Tagset Adaptation for Creating Slavic Named Entity Recognition Systems | We present a collection of Named Entity Recognition (NER) systems for six Slavic languages: Bulgarian, Czech, Polish, Slovenian, Russian and Ukrainian. These NER systems have been trained using different BERT models and a Frustratingly Easy Domain Adaptation (FEDA). FEDA allow us creating NER systems using multiple dat... | ['Antoine Doucet', 'Jose G. Moreno', 'Luis Adrián Cabrera-Diego'] | null | null | null | null | eacl-bsnlp-2021-4 | ['miscellaneous'] | ['miscellaneous'] | [-5.77038944e-01 5.81886480e-03 1.29445434e-01 -3.60395044e-01
-5.31372607e-01 -1.18489909e+00 9.23816264e-01 4.33793932e-01
-1.02513134e+00 1.15524054e+00 3.80586565e-01 -4.12869155e-01
-7.01691210e-03 -9.00630355e-01 -3.77295971e-01 -1.96931884e-01
5.36223780e-03 7.22489595e-01 3.13607812e-01 -2.63885766... | [9.751922607421875, 9.646135330200195] |
52ea4c69-1a9c-4c33-95ed-8d760979fef8 | explaining-chest-x-ray-pathologies-in-natural | 2207.04343 | null | https://arxiv.org/abs/2207.04343v1 | https://arxiv.org/pdf/2207.04343v1.pdf | Explaining Chest X-ray Pathologies in Natural Language | Most deep learning algorithms lack explanations for their predictions, which limits their deployment in clinical practice. Approaches to improve explainability, especially in medical imaging, have often been shown to convey limited information, be overly reassuring, or lack robustness. In this work, we introduce the ta... | ['Thomas Lukasiewicz', 'Bartlomiej Papiez', 'Guy Parsons', 'Oana-Maria Camburu', 'Cornelius Emde', 'Maxime Kayser'] | 2022-07-09 | null | null | null | null | ['explainable-models'] | ['computer-vision'] | [ 3.54221761e-01 9.75469410e-01 -3.40006977e-01 -8.21286380e-01
-7.36494958e-01 -2.39710584e-01 2.40688041e-01 2.80956209e-01
3.30987751e-01 9.84778762e-01 5.15133202e-01 -8.32187176e-01
-2.82235533e-01 -3.03624392e-01 -7.22997189e-01 -1.83852389e-01
1.03553228e-01 7.93929338e-01 -3.12237203e-01 2.22463757... | [8.626194953918457, 5.856684684753418] |
73d07c97-5ccd-4099-b5f7-b66f10c0c366 | boosting-on-the-shoulders-of-giants-in | 2005.06194 | null | https://arxiv.org/abs/2005.06194v1 | https://arxiv.org/pdf/2005.06194v1.pdf | Boosting on the shoulders of giants in quantum device calibration | Traditional machine learning applications, such as optical character recognition, arose from the inability to explicitly program a computer to perform a routine task. In this context, learning algorithms usually derive a model exclusively from the evidence present in a massive dataset. Yet in some scientific discipline... | ['Mile Gu', 'Jayne Thompson', 'Felix Binder', 'Alex Wozniakowski'] | 2020-05-13 | null | null | null | null | ['multi-target-regression'] | ['miscellaneous'] | [ 3.92880440e-01 -1.45462677e-02 -3.09345216e-01 -3.69720787e-01
-7.84772575e-01 -4.21898216e-01 5.57542086e-01 1.18727274e-01
-3.91306579e-01 1.04792345e+00 -5.28377593e-01 -6.56776905e-01
-1.51375413e-01 -7.65450418e-01 -9.79746878e-01 -8.50663483e-01
3.62055421e-01 5.57777584e-01 7.94373602e-02 -2.19345465... | [5.722500324249268, 4.917947292327881] |
c87f6d44-06a7-4920-8513-24d0f3e2ac73 | sharp-bounds-for-generalized-causal | 2305.16988 | null | https://arxiv.org/abs/2305.16988v1 | https://arxiv.org/pdf/2305.16988v1.pdf | Sharp Bounds for Generalized Causal Sensitivity Analysis | Causal inference from observational data is crucial for many disciplines such as medicine and economics. However, sharp bounds for causal effects under relaxations of the unconfoundedness assumption (causal sensitivity analysis) are subject to ongoing research. So far, works with sharp bounds are restricted to fairly s... | ['Stefan Feuerriegel', 'Valentyn Melnychuk', 'Dennis Frauen'] | 2023-05-26 | null | null | null | null | ['causal-inference', 'causal-inference'] | ['knowledge-base', 'miscellaneous'] | [ 3.97298008e-01 1.28511444e-01 -1.02540064e+00 -5.01080632e-01
-6.39079154e-01 -6.07977629e-01 3.02538693e-01 1.95913255e-01
-1.93877116e-01 1.14190912e+00 7.27039516e-01 -6.56819880e-01
-8.02471519e-01 -8.17929924e-01 -8.89309824e-01 -6.66088700e-01
-3.31634104e-01 2.58720130e-01 -1.76975295e-01 1.48224473... | [7.949225425720215, 5.251655101776123] |
91f3b2f9-868e-425d-884e-664ed5110800 | a-simple-and-general-strategy-for-referential | null | null | https://openreview.net/forum?id=IazZhsJK7wJ | https://openreview.net/pdf?id=IazZhsJK7wJ | A Simple and General Strategy for Referential Problem in Low-Resource Neural Machine Translation | This paper aims to solve a series of referential problems in sequence decoding caused by data sparsity and corpus scarce in low-resource Neural Machine Translation (NMT), including pronoun missing, reference error, bias and so on. It is difficult to find the essential reason of these problems because they are only show... | ['Hongxu Hou', 'Nier Wu', 'Yatu Ji'] | 2021-01-01 | null | null | null | null | ['low-resource-neural-machine-translation'] | ['natural-language-processing'] | [ 6.55942798e-01 2.16331601e-01 -3.05813104e-02 -4.02892441e-01
-7.99122751e-01 -3.88642639e-01 5.31031966e-01 -3.68938029e-01
-6.30346000e-01 1.19043076e+00 1.64912537e-01 -4.85909641e-01
1.14800997e-01 -4.85671610e-01 -8.85812283e-01 -7.64971316e-01
1.76647127e-01 6.17470264e-01 -1.82053939e-01 -6.82346225... | [11.598018646240234, 10.10793685913086] |
74ff16bc-cbfd-44dc-906a-8a8de73e4cc7 | performance-modeling-of-data-storage-systems | 2307.02073 | null | https://arxiv.org/abs/2307.02073v1 | https://arxiv.org/pdf/2307.02073v1.pdf | Performance Modeling of Data Storage Systems using Generative Models | High-precision modeling of systems is one of the main areas of industrial data analysis. Models of systems, their digital twins, are used to predict their behavior under various conditions. We have developed several models of a storage system using machine learning-based generative models. The system consists of severa... | ['Mikhail Hushchyn', 'Artem Ryzhikov', 'Aziz Temirkhanov', 'Abdalaziz Rashid Al-Maeeni'] | 2023-07-05 | null | null | null | null | ['benchmarking', 'benchmarking'] | ['miscellaneous', 'robots'] | [-6.51812136e-01 -1.10928118e-01 -9.40091684e-02 -4.30726945e-01
-3.67359728e-01 -8.60688686e-02 6.18941844e-01 2.05530003e-01
2.05298424e-01 9.23878014e-01 -4.01594132e-01 -3.99828762e-01
-3.15810323e-01 -7.64232993e-01 -6.46801949e-01 -9.25573289e-01
-1.76581025e-01 1.10403728e+00 7.05030501e-01 9.30233970... | [6.641984939575195, 2.845038890838623] |
06136303-8f67-4e26-b3b0-3d92568c8e28 | friends-in-need-how-chaperonins-recognize-and | 2211.08623 | null | https://arxiv.org/abs/2211.08623v1 | https://arxiv.org/pdf/2211.08623v1.pdf | Friends in need: how chaperonins recognize and remodel proteins that require folding assistance | Chaperonins are biological nanomachines that help newly translated proteins to fold by rescuing them from kinetically trapped misfolded states. Protein folding assistance by the chaperonin machinery is obligatory in vivo for a subset of proteins in the bacterial proteome. Chaperonins are large oligomeric complexes, wit... | ['D. Thirumalai', 'George H. Lorimer', 'George Stan'] | 2022-11-16 | null | null | null | null | ['protein-folding'] | ['natural-language-processing'] | [ 1.65860891e-01 -2.93488920e-01 -1.12616926e-01 -2.77548563e-02
2.82494634e-01 -9.92530763e-01 -4.89831679e-02 1.40640855e-01
-2.67096698e-01 1.15105855e+00 5.13297468e-02 -6.99748814e-01
4.30592388e-01 -3.78039092e-01 -6.96559131e-01 -1.04185069e+00
-2.54326314e-01 5.82031429e-01 8.42975900e-02 -2.36007437... | [4.724524021148682, 5.276699542999268] |
7b0d6954-827e-4316-b29c-f921cad64496 | learning-target-domain-specific-classifier | 2008.10785 | null | https://arxiv.org/abs/2008.10785v1 | https://arxiv.org/pdf/2008.10785v1.pdf | Learning Target Domain Specific Classifier for Partial Domain Adaptation | Unsupervised domain adaptation~(UDA) aims at reducing the distribution discrepancy when transferring knowledge from a labeled source domain to an unlabeled target domain. Previous UDA methods assume that the source and target domains share an identical label space, which is unrealistic in practice since the label infor... | ['PengFei Ge', 'Chuan-Xian Ren', 'Peiyi Yang', 'Shuicheng Yan'] | 2020-08-25 | null | null | null | null | ['partial-domain-adaptation'] | ['methodology'] | [ 2.43037075e-01 8.19034427e-02 -3.04318786e-01 -6.44885957e-01
-8.84418488e-01 -5.23433387e-01 2.46378466e-01 9.83625948e-02
-1.28118217e-01 9.48942661e-01 -2.63590753e-01 1.95123702e-02
-1.10238671e-01 -7.08580077e-01 -6.23994231e-01 -9.57270682e-01
3.71259272e-01 7.51114190e-01 3.55518311e-01 -1.33610994... | [10.354382514953613, 3.1214656829833984] |
05a471bf-1775-47b4-820f-319d019ab58c | a-optimization-framework-for-herbal | 2304.12828 | null | https://arxiv.org/abs/2304.12828v1 | https://arxiv.org/pdf/2304.12828v1.pdf | A optimization framework for herbal prescription planning based on deep reinforcement learning | Treatment planning for chronic diseases is a critical task in medical artificial intelligence, particularly in traditional Chinese medicine (TCM). However, generating optimized sequential treatment strategies for patients with chronic diseases in different clinical encounters remains a challenging issue that requires f... | ['Xuezhong Zhou', 'Tiancai Wen', 'Zhuang Liu', 'Feidie Yu', 'Qiguang Zheng', 'Ning Wang', 'Xiong He', 'Xin Su', 'Zecong Yu', 'Kuo Yang'] | 2023-04-25 | null | null | null | null | ['sequential-diagnosis'] | ['medical'] | [ 1.78150818e-01 1.26638874e-01 -9.53725219e-01 -3.13053995e-01
-4.61681664e-01 -1.28064722e-01 -7.40127638e-02 3.07670951e-01
-9.82502624e-02 9.43331361e-01 3.32686037e-01 -2.55462945e-01
-5.92045307e-01 -1.02558637e+00 -3.79606932e-01 -7.53581703e-01
-5.39591797e-02 1.11015940e+00 -7.22455084e-01 5.79337478... | [7.972714900970459, 5.624350070953369] |
8cddc061-1b01-47b3-b54c-2f1470a1a422 | nemf-neural-motion-fields-for-kinematic | 2206.03287 | null | https://arxiv.org/abs/2206.03287v3 | https://arxiv.org/pdf/2206.03287v3.pdf | NeMF: Neural Motion Fields for Kinematic Animation | We present an implicit neural representation to learn the spatio-temporal space of kinematic motions. Unlike previous work that represents motion as discrete sequential samples, we propose to express the vast motion space as a continuous function over time, hence the name Neural Motion Fields (NeMF). Specifically, we u... | ['Yi Zhou', 'Holly Rushmeier', 'James Zachary', 'Jun Saito', 'Chengan He'] | 2022-06-04 | null | null | null | null | ['miscellaneous'] | ['miscellaneous'] | [-1.71027452e-01 -1.90976679e-01 -2.58852899e-01 -2.14269698e-01
-4.95928854e-01 -4.95085239e-01 7.02255726e-01 -8.77721965e-01
-2.44306728e-01 6.39736176e-01 5.85013330e-01 -1.21759564e-01
1.36610135e-01 -9.50222015e-01 -1.00094569e+00 -7.00983167e-01
8.38338286e-02 2.63177127e-01 -3.91330793e-02 -1.32170409... | [7.335374355316162, -0.17010927200317383] |
91653456-fb70-4a65-b9bd-f6979819508c | gmd-controllable-human-motion-synthesis-via | 2305.12577 | null | https://arxiv.org/abs/2305.12577v1 | https://arxiv.org/pdf/2305.12577v1.pdf | GMD: Controllable Human Motion Synthesis via Guided Diffusion Models | Denoising diffusion models have shown great promise in human motion synthesis conditioned on natural language descriptions. However, it remains a challenge to integrate spatial constraints, such as pre-defined motion trajectories and obstacles, which is essential for bridging the gap between isolated human motion and i... | ['Siyu Tang', 'Supasorn Suwajanakorn', 'Konpat Preechakul', 'Korrawe Karunratanakul'] | 2023-05-21 | null | null | null | null | ['motion-synthesis', 'imputation', 'imputation', 'imputation'] | ['computer-vision', 'computer-vision', 'miscellaneous', 'time-series'] | [-1.48242256e-02 -2.24709794e-01 -3.59709077e-02 -2.93857474e-02
-4.69711870e-01 -3.01155329e-01 6.91455662e-01 -4.19577926e-01
-1.60699219e-01 6.06348753e-01 8.54442179e-01 2.23882809e-01
-1.30160317e-01 -8.33874941e-01 -4.43401188e-01 -8.54500294e-01
2.49618858e-01 1.18658632e-01 2.75327384e-01 -3.22441161... | [10.776041984558105, -0.7806311249732971] |
aca9bb80-d1f3-419c-a5bc-9b1a415e46f6 | convolutional-neural-networks-can-be-deceived | null | null | http://openaccess.thecvf.com/content_CVPR_2019/html/Gomez-Villa_Convolutional_Neural_Networks_Can_Be_Deceived_by_Visual_Illusions_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Gomez-Villa_Convolutional_Neural_Networks_Can_Be_Deceived_by_Visual_Illusions_CVPR_2019_paper.pdf | Convolutional Neural Networks Can Be Deceived by Visual Illusions | Visual illusions teach us that what we see is not always what is represented in the physical world. Their special nature make them a fascinating tool to test and validate any new vision model proposed. In general, current vision models are based on the concatenation of linear and non-linear operations. The similarity o... | [' Marcelo Bertalmio', ' Javier Vazquez-Corral', ' Adrian Martin', 'Alexander Gomez-Villa'] | 2019-06-01 | null | null | null | cvpr-2019-6 | ['color-constancy'] | ['computer-vision'] | [ 9.18366387e-02 -5.29212430e-02 5.39921761e-01 -2.63313740e-01
6.64914131e-01 -6.63261652e-01 8.65078330e-01 -1.36913419e-01
-3.79322588e-01 3.74414742e-01 1.60897553e-01 -2.94482529e-01
2.45877877e-01 -5.57523608e-01 -7.77271748e-01 -7.12658048e-01
2.26720661e-01 -1.93607926e-01 2.71710545e-01 -5.86078048... | [10.105268478393555, 2.3059496879577637] |
59b212a8-be00-4f47-bd33-aaaa70a9ff86 | d-score-a-white-box-diagnosis-score-for-cnns | 2304.00697 | null | https://arxiv.org/abs/2304.00697v1 | https://arxiv.org/pdf/2304.00697v1.pdf | D-Score: A White-Box Diagnosis Score for CNNs Based on Mutation Operators | Convolutional neural networks (CNNs) have been widely applied in many safety-critical domains, such as autonomous driving and medical diagnosis. However, concerns have been raised with respect to the trustworthiness of these models: The standard testing method evaluates the performance of a model on a test set, while l... | ['Fei Zuo', 'XiaoFeng Wang', 'Yuqi Song', 'Xin Zhang'] | 2023-04-03 | null | null | null | null | ['medical-diagnosis'] | ['medical'] | [ 5.31283498e-01 9.56071168e-02 2.05653712e-01 -5.98523140e-01
-2.81885564e-01 -3.12386751e-01 3.41105014e-01 -7.40735680e-02
-5.70285022e-01 7.03045905e-01 -3.83101195e-01 -3.96939009e-01
-2.75858074e-01 -7.63172448e-01 -5.86183727e-01 -6.39570177e-01
2.16729820e-01 2.53132153e-02 3.66581947e-01 -1.44847050... | [6.555032730102539, 7.592701435089111] |
a1576a31-a666-40b3-9b11-e55c216576cb | sequential-embedding-induced-text-clustering | 1811.12500 | null | http://arxiv.org/abs/1811.12500v1 | http://arxiv.org/pdf/1811.12500v1.pdf | Sequential Embedding Induced Text Clustering, a Non-parametric Bayesian Approach | Current state-of-the-art nonparametric Bayesian text clustering methods model
documents through multinomial distribution on bags of words. Although these
methods can effectively utilize the word burstiness representation of documents
and achieve decent performance, they do not explore the sequential information
of text... | ['Sargur N. Srihari', 'Tiehang Duan', 'Xiaohui Xie', 'Qi Lou'] | 2018-11-29 | null | null | null | null | ['text-clustering'] | ['natural-language-processing'] | [-4.44404602e-01 -3.36472601e-01 -2.83070207e-01 -5.01564622e-01
-6.47031367e-01 -2.42085919e-01 9.86249268e-01 3.74560863e-01
-5.44085205e-01 3.67815346e-01 5.73300421e-01 5.21345288e-02
-2.02839002e-01 -5.97058475e-01 -2.35534802e-01 -9.79387164e-01
-1.54319257e-01 8.41939688e-01 1.02767251e-01 4.00751591... | [10.397241592407227, 6.928426742553711] |
ced6984f-7d25-4d1f-9a00-99f5b8887381 | automatic-video-object-segmentation-via | 1912.01373 | null | https://arxiv.org/abs/1912.01373v1 | https://arxiv.org/pdf/1912.01373v1.pdf | Automatic Video Object Segmentation via Motion-Appearance-Stream Fusion and Instance-aware Segmentation | This paper presents a method for automatic video object segmentation based on the fusion of motion stream, appearance stream, and instance-aware segmentation. The proposed scheme consists of a two-stream fusion network and an instance segmentation network. The two-stream fusion network again consists of motion and appe... | ['Wonkyo Seo', 'Sungkwon Choo', 'Nam Ik Cho'] | 2019-12-03 | null | null | null | null | ['foreground-segmentation'] | ['computer-vision'] | [ 5.19755781e-01 -2.91789085e-01 -1.86081558e-01 -2.25241706e-01
-8.74263883e-01 -3.17942977e-01 3.21783751e-01 -7.97387734e-02
-4.87331122e-01 4.33555514e-01 -2.25048587e-01 1.13024630e-01
4.30444404e-02 -8.44438970e-01 -7.03822017e-01 -9.67168570e-01
1.42510682e-01 1.34349540e-01 9.92649853e-01 2.99285620... | [9.165498733520508, -0.3150150775909424] |
e023b9e4-7c34-4404-b73b-dfca73b556d5 | semi-supervised-video-paragraph-grounding | null | null | http://openaccess.thecvf.com//content/CVPR2022/html/Jiang_Semi-Supervised_Video_Paragraph_Grounding_With_Contrastive_Encoder_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Jiang_Semi-Supervised_Video_Paragraph_Grounding_With_Contrastive_Encoder_CVPR_2022_paper.pdf | Semi-Supervised Video Paragraph Grounding With Contrastive Encoder | Video events grounding aims at retrieving the most relevant moments from an untrimmed video in terms of a given natural language query. Most previous works focus on Video Sentence Grounding (VSG), which localizes the moment with a sentence query. Recently, researchers extended this task to Video Paragraph Grounding... | ['Heng Tao Shen', 'Zuo Cao', 'Fumin Shen', 'Jingran Zhang', 'Xing Xu', 'Xun Jiang'] | 2022-01-01 | null | null | null | cvpr-2022-1 | ['video-grounding'] | ['computer-vision'] | [ 2.51551539e-01 -9.19912606e-02 -4.80964780e-01 -2.87553757e-01
-1.15672696e+00 -3.67088526e-01 7.29252934e-01 2.15618461e-01
-3.38482618e-01 5.30631363e-01 5.86438656e-01 8.14069211e-02
2.44065851e-01 -4.77212578e-01 -1.05450249e+00 -4.77118134e-01
1.02032468e-01 1.20759912e-01 5.87069273e-01 -1.75247923... | [10.188530921936035, 0.7441869378089905] |
f4477abf-4ee8-4aa9-babc-c5abe058b395 | uir-pku-twitter-opinminer-system-for | null | null | https://aclanthology.org/S15-2111 | https://aclanthology.org/S15-2111.pdf | UIR-PKU: Twitter-OpinMiner System for Sentiment Analysis in Twitter at SemEval 2015 | null | ['Kam-Fai Wong', 'Gaoyan Ou', 'Jing Ma', 'Yuxiao Zhang', 'Xu Han', 'Tengjiao Wang', 'Binyang Li'] | 2015-06-01 | null | null | null | semeval-2015-6 | ['twitter-sentiment-analysis'] | ['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... | [-7.202101230621338, 3.728757858276367] |
58f39764-adaa-4e9e-bfe7-dde1e56646a5 | reusing-keywords-for-fine-grained | 2210.11806 | null | https://arxiv.org/abs/2210.11806v2 | https://arxiv.org/pdf/2210.11806v2.pdf | Multi-view Semantic Matching of Question retrieval using Fine-grained Semantic Representations | As a key task of question answering, question retrieval has attracted much attention from the communities of academia and industry. Previous solutions mainly focus on the translation model, topic model, and deep learning techniques. Distinct from the previous solutions, we propose to construct fine-grained semantic rep... | ['Yueguo Chen', 'Denghao Ma', 'Li Chong'] | 2022-10-21 | null | null | null | null | ['keyword-extraction'] | ['natural-language-processing'] | [ 1.31309882e-01 2.52543781e-02 -2.30781198e-01 -4.86929208e-01
-1.38209236e+00 -4.48509932e-01 6.91570342e-01 3.51823270e-01
-4.68223214e-01 2.94405192e-01 6.68052614e-01 7.86627680e-02
-3.26071292e-01 -9.18054342e-01 -6.50302589e-01 -2.44841829e-01
6.40141070e-01 5.23395181e-01 4.23465014e-01 -2.73677200... | [11.042142868041992, 8.11634635925293] |
9496406d-b32e-403e-a685-284bcc88d585 | ecg-segmentation-by-neural-networks-errors | 1812.10386 | null | http://arxiv.org/abs/1812.10386v1 | http://arxiv.org/pdf/1812.10386v1.pdf | ECG Segmentation by Neural Networks: Errors and Correction | In this study we examined the question of how error correction occurs in an
ensemble of deep convolutional networks, trained for an important applied
problem: segmentation of Electrocardiograms(ECG). We also explore the
possibility of using the information about ensemble errors to evaluate a
quality of data representat... | ['Aleksandra Koneva', 'Roman Kataev', 'Grigory Osipov', 'Sergey Alekseev', 'Iana Sereda'] | 2018-12-26 | null | null | null | null | ['electrocardiography-ecg'] | ['methodology'] | [ 2.93503910e-01 4.01745111e-01 6.51749492e-01 -5.83732486e-01
-2.88375527e-01 -2.29525372e-01 1.36813402e-01 5.87764323e-01
-7.41301894e-01 1.04318392e+00 -8.54101554e-02 -4.11213189e-01
-4.14379895e-01 -7.43685722e-01 -7.72156119e-01 -7.24406540e-01
-4.77201253e-01 2.62023091e-01 -3.16861838e-01 -4.12694328... | [14.328688621520996, 3.3062567710876465] |
52749405-0de8-4900-9922-4cc62541718e | content-aware-unsupervised-deep-homography | 1909.05983 | null | https://arxiv.org/abs/1909.05983v2 | https://arxiv.org/pdf/1909.05983v2.pdf | Content-Aware Unsupervised Deep Homography Estimation | Homography estimation is a basic image alignment method in many applications. It is usually conducted by extracting and matching sparse feature points, which are error-prone in low-light and low-texture images. On the other hand, previous deep homography approaches use either synthetic images for supervised learning or... | ['Jue Wang', 'Nianjin Ye', 'Lanpeng Jia', 'Shuaicheng Liu', 'Chuan Wang', 'Jirong Zhang', 'Jian Sun', 'Ji Zhou'] | 2019-09-12 | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/2503_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123460630.pdf | eccv-2020-8 | ['homography-estimation'] | ['computer-vision'] | [ 3.01708400e-01 -2.73674816e-01 -3.70348891e-04 -4.38250422e-01
-5.85978031e-01 -2.97977597e-01 3.82078439e-01 -3.17393392e-01
-2.79040039e-01 5.83016455e-01 9.55323577e-02 2.57547021e-01
-2.45612606e-01 -8.04942667e-01 -9.00566280e-01 -7.82187104e-01
3.41208726e-01 3.82122755e-01 1.48980394e-01 -2.22208023... | [8.726795196533203, -2.3044636249542236] |
6235f4ad-ff86-4913-a730-3b9a3082229e | self-calibrating-neural-probabilistic-model | 2106.11196 | null | https://arxiv.org/abs/2106.11196v1 | https://arxiv.org/pdf/2106.11196v1.pdf | Self-Calibrating Neural-Probabilistic Model for Authorship Verification Under Covariate Shift | We are addressing two fundamental problems in authorship verification (AV): Topic variability and miscalibration. Variations in the topic of two disputed texts are a major cause of error for most AV systems. In addition, it is observed that the underlying probability estimates produced by deep learning AV mechanisms of... | ['Robert M. Nickel', 'Dorothea Kolossa', 'Benedikt Boenninghoff'] | 2021-06-21 | null | null | null | null | ['authorship-verification'] | ['natural-language-processing'] | [-2.22543761e-01 3.57076749e-02 -3.88504326e-01 -4.97104645e-01
-1.16736603e+00 -7.36158669e-01 1.09066498e+00 3.20618033e-01
-3.44807714e-01 1.01944494e+00 1.44461304e-01 -5.12477338e-01
-1.77248418e-02 -5.03336549e-01 -6.90565050e-01 -2.69568145e-01
5.41417897e-01 8.87292624e-01 3.00104674e-02 1.28882095... | [9.62933349609375, 10.555913925170898] |
d7c2c725-ec6d-44bb-bb85-631f1cbc04bb | similarity-kernel-and-clustering-via-random | 1908.10506 | null | https://arxiv.org/abs/1908.10506v1 | https://arxiv.org/pdf/1908.10506v1.pdf | Similarity Kernel and Clustering via Random Projection Forests | Similarity plays a fundamental role in many areas, including data mining, machine learning, statistics and various applied domains. Inspired by the success of ensemble methods and the flexibility of trees, we propose to learn a similarity kernel called rpf-kernel through random projection forests (rpForests). Our theor... | ['Donghui Yan', 'Songxiang Gu', 'Ying Xu', 'Zhiwei Qin'] | 2019-08-28 | null | null | null | null | ['clustering-ensemble'] | ['graphs'] | [ 2.17271775e-01 -1.02618419e-01 -2.72988319e-01 -4.31703061e-01
-1.45502836e-01 -5.19958913e-01 7.57299423e-01 1.80560336e-01
-2.21554488e-01 5.99537730e-01 2.69159853e-01 -3.17973286e-01
-6.45495594e-01 -1.00854468e+00 -4.34371799e-01 -1.19393897e+00
-3.72972816e-01 5.86087584e-01 4.68237251e-01 1.46487445... | [7.589538097381592, 4.533164978027344] |
9d60fb4d-b3ca-41c8-a349-93cbe11d23a3 | bias-and-fairness-on-multimodal-emotion | 2205.08383 | null | https://arxiv.org/abs/2205.08383v1 | https://arxiv.org/pdf/2205.08383v1.pdf | Bias and Fairness on Multimodal Emotion Detection Algorithms | Numerous studies have shown that machine learning algorithms can latch onto protected attributes such as race and gender and generate predictions that systematically discriminate against one or more groups. To date the majority of bias and fairness research has been on unimodal models. In this work, we explore the bias... | ['Jimi Cao', 'Rehan Ahmed', 'Matheus Schmitz'] | 2022-05-11 | null | null | null | null | ['multimodal-emotion-recognition', 'multimodal-emotion-recognition'] | ['computer-vision', 'speech'] | [ 1.58548132e-01 1.40559733e-01 -5.44185221e-01 -7.43758678e-01
-2.42362767e-01 -5.85042894e-01 7.30937541e-01 3.72057974e-01
-6.31328881e-01 6.88291490e-01 5.05538821e-01 -5.21662176e-01
-6.37812614e-02 -3.49478126e-01 -6.79351389e-02 -3.76764417e-01
3.15087050e-01 2.30360925e-02 -5.35437882e-01 -3.30917239... | [13.003338813781738, 1.4409419298171997] |
df07ea95-145d-47f5-9b3e-bf6468d14b86 | eeg-decoding-for-datasets-with-heterogenous | 2306.13109 | null | https://arxiv.org/abs/2306.13109v1 | https://arxiv.org/pdf/2306.13109v1.pdf | EEG Decoding for Datasets with Heterogenous Electrode Configurations using Transfer Learning Graph Neural Networks | Brain-Machine Interfacing (BMI) has greatly benefited from adopting machine learning methods for feature learning that require extensive data for training, which are often unavailable from a single dataset. Yet, it is difficult to combine data across labs or even data within the same lab collected over the years due to... | ['A. Aldo Faisal', 'Xiaoxi Wei', 'Jinpei Han'] | 2023-06-20 | null | null | null | null | ['eeg-decoding', 'transfer-learning', 'eeg-decoding'] | ['medical', 'miscellaneous', 'time-series'] | [ 4.58698183e-01 -4.52290565e-01 1.80134535e-01 -3.05525780e-01
-4.78576571e-01 -5.74095786e-01 5.42580858e-02 9.22064483e-02
-5.71622014e-01 1.05717874e+00 -2.04260185e-01 -2.87410647e-01
-7.87255585e-01 -4.88988638e-01 -9.25418437e-01 -6.29032075e-01
-4.15647119e-01 2.04996616e-01 -1.06118537e-01 -7.28173628... | [13.1327543258667, 3.4434077739715576] |
79e8da24-be4a-4cb3-8306-62da2b9f0e80 | multi-modal-deep-learning-system-for | 2212.14490 | null | https://arxiv.org/abs/2212.14490v1 | https://arxiv.org/pdf/2212.14490v1.pdf | Multi-modal deep learning system for depression and anxiety detection | Traditional screening practices for anxiety and depression pose an impediment to monitoring and treating these conditions effectively. However, recent advances in NLP and speech modelling allow textual, acoustic, and hand-crafted language-based features to jointly form the basis of future mental health screening and co... | ['Jekaterina Novikova', 'Marija Stanojevic', 'Brian Diep'] | 2022-12-30 | null | null | null | null | ['anxiety-detection'] | ['medical'] | [ 1.59392595e-01 2.76295841e-01 -2.88422614e-01 -7.32551455e-01
-1.40088308e+00 -3.18209589e-01 3.69795471e-01 6.88322544e-01
-1.61483273e-01 2.21581578e-01 6.96861982e-01 -7.74992332e-02
-2.82220840e-01 -6.70950413e-01 -1.14578359e-01 -1.58358067e-01
-2.10149705e-01 2.89004803e-01 -2.46986732e-01 -2.52341777... | [13.767669677734375, 5.0406928062438965] |
bbd4a7f9-c67d-4229-9118-c29f4198ec9f | evaluating-multilingual-sentence | null | null | https://aclanthology.org/2022.lrec-1.314 | https://aclanthology.org/2022.lrec-1.314.pdf | Evaluating Multilingual Sentence Representation Models in a Real Case Scenario | In this paper, we present an evaluation of sentence representation models on the paraphrase detection task. The evaluation is designed to simulate a real-world problem of plagiarism and is based on one of the most important cases of forgery in modern history: the so-called “Protocols of the Elders of Zion”. The sentenc... | ['Simon Levis Sullam', 'Rexhina Blloshmi', 'Rocco Tripodi'] | null | null | null | null | lrec-2022-6 | ['paraphrase-identification'] | ['natural-language-processing'] | [ 3.53700593e-02 8.85004997e-02 1.29710034e-01 -3.22884880e-02
-7.52633452e-01 -8.34383309e-01 1.03376365e+00 6.44436598e-01
-3.67003262e-01 6.28740132e-01 6.83978617e-01 -4.03632909e-01
-1.73534408e-01 -7.09260166e-01 -5.08751988e-01 -3.88260752e-01
1.97157577e-01 4.39440489e-01 -5.05066849e-02 -8.96870494... | [8.842330932617188, 10.022235870361328] |
7b5ca296-36da-4425-850b-ef1c939c7c27 | zero-shot-event-causality-identification-with | null | null | https://aclanthology.org/2022.clib-1.13 | https://aclanthology.org/2022.clib-1.13.pdf | Zero-shot Event Causality Identification with Question Answering | Extraction of event causality and especially implicit causality from text data is a challenging task. Causality is often treated as a specific relation type and can be considered as a part of relation extraction or relation classification task. Many causality identification-related tasks are designed to select the most... | ['Sven Schlarb', 'Daria Liakhovets'] | null | null | null | null | clib-2022-9 | ['relation-classification', 'passage-retrieval', 'event-causality-identification'] | ['natural-language-processing', 'natural-language-processing', 'natural-language-processing'] | [ 3.21733981e-01 4.04905051e-01 -1.84161320e-01 -3.83891612e-01
-1.20647001e+00 -7.50765324e-01 1.02198756e+00 1.06104183e+00
-6.28656387e-01 1.21770990e+00 7.19958425e-01 -3.55500251e-01
-5.64272642e-01 -9.36326325e-01 -7.33214915e-01 -1.67601705e-01
-2.35820308e-01 8.38520586e-01 6.28833652e-01 -2.50229895... | [9.150571823120117, 9.141562461853027] |
0502354d-ee12-4673-949b-48214f6cbb3f | a-deep-learning-approach-to-solar-irradiance | 1901.04881 | null | http://arxiv.org/abs/1901.04881v1 | http://arxiv.org/pdf/1901.04881v1.pdf | A deep learning approach to solar-irradiance forecasting in sky-videos | Ahead-of-time forecasting of incident solar-irradiance on a panel is
indicative of expected energy yield and is essential for efficient grid
distribution and planning. Traditionally, these forecasts are based on
meteorological physics models whose parameters are tuned by coarse-grained
radiometric tiles sensed from geo... | ['Talha A. Siddiqui', 'Shivkumar Kalyanaraman', 'Samarth Bharadwaj'] | 2019-01-15 | null | null | null | null | ['solar-irradiance-forecasting'] | ['time-series'] | [-4.25685272e-02 -6.16462111e-01 2.31382266e-01 -6.22762203e-01
-3.35283190e-01 -9.08265829e-01 6.67126715e-01 -3.31909955e-01
-2.38652363e-01 1.12310290e+00 2.34260529e-01 -1.53692141e-01
2.31900234e-02 -1.04986274e+00 -1.01106358e+00 -1.13592994e+00
-1.45818129e-01 -1.05476283e-01 -4.09996837e-01 -2.18059085... | [6.41500997543335, 2.7090020179748535] |
decf02e9-3277-47ad-9e0e-b31a4ee85827 | learning-transferable-pedestrian | 2304.05554 | null | https://arxiv.org/abs/2304.05554v1 | https://arxiv.org/pdf/2304.05554v1.pdf | Learning Transferable Pedestrian Representation from Multimodal Information Supervision | Recent researches on unsupervised person re-identification~(reID) have demonstrated that pre-training on unlabeled person images achieves superior performance on downstream reID tasks than pre-training on ImageNet. However, those pre-trained methods are specifically designed for reID and suffer flexible adaption to oth... | ['Qi Tian', 'Houqiang Li', 'Wengang Zhou', 'Xiaoyu Qiu', 'Longhui Wei', 'Liping Bao'] | 2023-04-12 | null | null | null | null | ['person-re-identification', 'person-search', 'unsupervised-person-re-identification'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 3.67522538e-02 -3.80501360e-01 -7.47777820e-02 -8.19870055e-01
-2.63152242e-01 -1.88417047e-01 6.14114940e-01 -1.79937715e-03
-8.04554164e-01 7.12839186e-01 2.84746975e-01 -5.50710876e-03
2.42268905e-01 -8.38571370e-01 -7.16350496e-01 -8.19741905e-01
1.78713724e-01 7.23996818e-01 -1.81750372e-01 -1.82696328... | [14.592623710632324, 0.9568402171134949] |
9b184c38-389c-42a8-b754-cedd19204671 | learning-multilingual-sentence | 2306.06919 | null | https://arxiv.org/abs/2306.06919v1 | https://arxiv.org/pdf/2306.06919v1.pdf | Learning Multilingual Sentence Representations with Cross-lingual Consistency Regularization | Multilingual sentence representations are the foundation for similarity-based bitext mining, which is crucial for scaling multilingual neural machine translation (NMT) system to more languages. In this paper, we introduce MuSR: a one-for-all Multilingual Sentence Representation model that supports more than 220 languag... | ['Haifeng Wang', 'Hua Wu', 'Zhongjun He', 'Liwen Zhang', 'Pengzhi Gao'] | 2023-06-12 | null | null | null | null | ['nmt'] | ['computer-code'] | [ 1.35065421e-01 -2.15651006e-01 -6.97281420e-01 -4.97842252e-01
-1.36707234e+00 -6.40946209e-01 7.30661690e-01 -8.53344947e-02
-5.73931217e-01 8.69871378e-01 4.11707103e-01 -9.07434702e-01
2.46902227e-01 -5.29929042e-01 -1.02400875e+00 -5.63473292e-02
3.03894788e-01 7.17515826e-01 -4.16352391e-01 -6.33681893... | [11.357203483581543, 10.091445922851562] |
c17fdae4-f0a8-4fb5-abf8-380aea8c285b | a-review-of-probabilistic-forecasting-and | 2209.08307 | null | https://arxiv.org/abs/2209.08307v1 | https://arxiv.org/pdf/2209.08307v1.pdf | A review of probabilistic forecasting and prediction with machine learning | Predictions and forecasts of machine learning models should take the form of probability distributions, aiming to increase the quantity of information communicated to end users. Although applications of probabilistic prediction and forecasting with machine learning models in academia and industry are becoming more freq... | ['Georgia Papacharalampous', 'Hristos Tyralis'] | 2022-09-17 | null | null | null | null | ['additive-models'] | ['methodology'] | [-2.30757892e-02 5.19358180e-02 -3.67650449e-01 -8.54093015e-01
-8.40072334e-01 -5.67552686e-01 7.19169319e-01 2.96728849e-01
-5.51359951e-02 1.02225041e+00 1.77905470e-01 -3.99970025e-01
-6.29725099e-01 -8.85266125e-01 -5.50235629e-01 -8.93264413e-01
-4.12724346e-01 7.19655573e-01 1.40956029e-01 -4.03370820... | [7.677577018737793, 3.9699289798736572] |
a37d72de-743e-4b70-b12b-dc6d20526a69 | exploring-transformers-for-ranking-portuguese | null | null | https://aclanthology.org/2022.lrec-1.275 | https://aclanthology.org/2022.lrec-1.275.pdf | Exploring Transformers for Ranking Portuguese Semantic Relations | We explored transformer-based language models for ranking instances of Portuguese lexico-semantic relations. Weights were based on the likelihood of natural language sequences that transmitted the relation instances, and expectations were that they would be useful for filtering out noisier instances. However, after ana... | ['Hugo Gonçalo Oliveira'] | null | null | null | null | lrec-2022-6 | ['word-similarity'] | ['natural-language-processing'] | [-1.05292210e-02 6.45066500e-01 -2.35287428e-01 -2.26244569e-01
-1.23647936e-01 -7.33664572e-01 1.08137536e+00 9.49191988e-01
-6.63450599e-01 7.18636811e-01 8.16615939e-01 -6.61105573e-01
-8.38958263e-01 -1.15231335e+00 -4.40238416e-01 -6.16895854e-01
-4.36240703e-01 7.43236840e-01 5.43486595e-01 -5.63476503... | [10.274543762207031, 9.050971031188965] |
564d7d27-8c3e-43a4-8967-a7b5280de954 | hltsuda-at-semeval-2019-task-1-ucca-graph-1 | null | null | https://aclanthology.org/S19-2002 | https://aclanthology.org/S19-2002.pdf | HLT@SUDA at SemEval-2019 Task 1: UCCA Graph Parsing as Constituent Tree Parsing | This paper describes a simple UCCA semantic graph parsing approach. The key idea is to convert a UCCA semantic graph into a constituent tree, in which extra labels are deliberately designed to mark remote edges and discontinuous nodes for future recovery. In this way, we can make use of existing syntactic parsing techn... | ['Wei Jiang', 'Zhenghua Li', 'Min Zhang', 'Yu Zhang'] | 2019-06-01 | null | null | null | semeval-2019-6 | ['ucca-parsing'] | ['natural-language-processing'] | [-7.09655136e-02 6.06640577e-01 -4.56022322e-01 -4.18398112e-01
-1.20067620e+00 -8.41700554e-01 4.99731958e-01 2.02905223e-01
-5.38647652e-01 4.63597417e-01 2.94753551e-01 -4.93465871e-01
1.44522220e-01 -8.97919595e-01 -7.49107897e-01 -5.50414741e-01
1.10742196e-01 5.81562221e-01 2.79619455e-01 -2.19849944... | [10.52525806427002, 9.657309532165527] |
1bba08ac-1839-4039-8cc2-62f79ab3ea94 | multi-agent-path-finding-with-continuous-time | 1903.09820 | null | http://arxiv.org/abs/1903.09820v1 | http://arxiv.org/pdf/1903.09820v1.pdf | Multi-agent Path Finding with Continuous Time Viewed Through Satisfiability Modulo Theories (SMT) | This paper addresses a variant of multi-agent path finding (MAPF) in
continuous space and time. We present a new solving approach based on
satisfiability modulo theories (SMT) to obtain makespan optimal solutions. The
standard MAPF is a task of navigating agents in an undirected graph from given
starting vertices to gi... | ['Pavel Surynek'] | 2019-03-23 | null | null | null | null | ['multi-agent-path-finding'] | ['playing-games'] | [ 3.18158984e-01 2.86677659e-01 2.55143363e-02 6.92655817e-02
1.13926586e-02 -8.09475660e-01 3.19557488e-01 5.81806064e-01
-6.79467618e-01 1.20900822e+00 -6.58328116e-01 -6.43942118e-01
-1.10665023e+00 -1.35089946e+00 -5.57413518e-01 -5.83438933e-01
-8.46127033e-01 1.15106726e+00 5.23392260e-01 -6.29750729... | [4.982133865356445, 1.8188738822937012] |
3e969341-0d79-442b-9667-ac0bec4329c1 | r-monet-region-based-unsupervised-scene | null | null | https://openreview.net/forum?id=pAJ3svHLDV | https://openreview.net/pdf?id=pAJ3svHLDV | R-MONet: Region-Based Unsupervised Scene Decomposition and Representation via Consistency of Object Representations | Decomposing a complex scene into multiple objects is a natural instinct of an intelligent vision system. Recently, the interest in unsupervised scene representation learning emerged and many previous works tackle this by decomposing scene into object representations either in the form of segmentation masks or position ... | ['Shengxin Qian'] | 2021-01-01 | null | null | null | null | ['foreground-segmentation'] | ['computer-vision'] | [ 6.77477300e-01 2.66463608e-01 3.58017795e-02 -5.91844022e-01
-3.52015167e-01 -7.78091788e-01 8.60527694e-01 -2.59355810e-02
1.25951588e-01 4.24850315e-01 -3.98414470e-02 -1.84567610e-03
-1.10856786e-01 -1.17887890e+00 -8.64896297e-01 -9.85234261e-01
2.35260099e-01 6.51670694e-01 5.53057730e-01 1.86396226... | [9.640202522277832, 0.7493057250976562] |
b0486d5a-a4e1-4fa6-a92f-e7406286e631 | combinational-q-learning-for-dou-di-zhu | 1901.08925 | null | http://arxiv.org/abs/1901.08925v2 | http://arxiv.org/pdf/1901.08925v2.pdf | Combinational Q-Learning for Dou Di Zhu | Deep reinforcement learning (DRL) has gained a lot of attention in recent
years, and has been proven to be able to play Atari games and Go at or above
human levels. However, those games are assumed to have a small fixed number of
actions and could be trained with a simple CNN network. In this paper, we study
a special ... | ['Liangwei Li', 'Yang You', 'Baisong Guo', 'Cewu Lu', 'Weiming Wang'] | 2019-01-24 | null | null | null | null | ['card-games'] | ['playing-games'] | [-1.14414811e-01 -7.33170286e-02 2.76200101e-03 1.30208924e-01
-6.15163624e-01 -9.34054732e-01 8.38382781e-01 -4.27020967e-01
-8.32856238e-01 8.85338843e-01 -1.64213404e-01 -3.31167936e-01
-5.88050112e-02 -1.07669628e+00 -8.91271830e-01 -6.05407417e-01
-2.35984668e-01 5.63074768e-01 4.06160831e-01 -8.88885677... | [3.6487374305725098, 1.5051696300506592] |
f24e3060-a55e-49ce-baae-dd658fc2ec16 | one-step-bipartite-graph-cut-a-normalized | 2305.07386 | null | https://arxiv.org/abs/2305.07386v1 | https://arxiv.org/pdf/2305.07386v1.pdf | One-step Bipartite Graph Cut: A Normalized Formulation and Its Application to Scalable Subspace Clustering | The bipartite graph structure has shown its promising ability in facilitating the subspace clustering and spectral clustering algorithms for large-scale datasets. To avoid the post-processing via k-means during the bipartite graph partitioning, the constrained Laplacian rank (CLR) is often utilized for constraining the... | ['Jian-Huang Lai', 'Chang-Dong Wang', 'Dong Huang', 'Si-Guo Fang'] | 2023-05-12 | null | null | null | null | ['graph-partitioning'] | ['graphs'] | [ 8.27371329e-02 1.24210175e-02 -2.30389744e-01 -9.54003334e-02
-6.18111908e-01 -7.18552887e-01 -3.03235371e-03 1.29826397e-01
-6.30358160e-02 5.68174362e-01 -1.82213098e-01 -3.76893610e-01
-6.11933708e-01 -5.75525522e-01 -4.80570048e-01 -9.87097502e-01
-2.33245388e-01 6.20730102e-01 1.61220461e-01 3.00833791... | [7.3773345947265625, 4.892287254333496] |
aa58b3ff-e136-4619-bef0-d1db024562a0 | neural-texture-synthesis-with-guided | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Zhou_Neural_Texture_Synthesis_With_Guided_Correspondence_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Zhou_Neural_Texture_Synthesis_With_Guided_Correspondence_CVPR_2023_paper.pdf | Neural Texture Synthesis With Guided Correspondence | Markov random fields (MRFs) are the cornerstone of classical approaches to example-based texture synthesis. Yet, it is not fully valued in the deep learning era. This paper aims to re-promote the combination of MRFs and neural networks, i.e., the CNNMRF model, for texture synthesis, with two key observations made. ... | ['Hui Huang', 'Rongjun Xiao', 'Kaijian Chen', 'Yang Zhou'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['texture-synthesis'] | ['computer-vision'] | [ 7.36395895e-01 2.04444543e-01 2.89437152e-03 -3.25589240e-01
-5.98057210e-01 -1.33733690e-01 8.49242628e-01 -1.75640225e-01
-1.51069745e-01 7.96587884e-01 -7.47391433e-02 -5.05420088e-04
-3.47918421e-01 -1.14020479e+00 -1.08554482e+00 -8.72645855e-01
4.13692325e-01 4.53579575e-01 7.34894797e-02 -2.37772167... | [11.488895416259766, -0.5958898663520813] |
ed5e5612-fa72-4dec-a3e7-82bb33e07fb4 | action-genome-actions-as-compositions-of | null | null | http://openaccess.thecvf.com/content_CVPR_2020/html/Ji_Action_Genome_Actions_As_Compositions_of_Spatio-Temporal_Scene_Graphs_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Ji_Action_Genome_Actions_As_Compositions_of_Spatio-Temporal_Scene_Graphs_CVPR_2020_paper.pdf | Action Genome: Actions As Compositions of Spatio-Temporal Scene Graphs | Action recognition has typically treated actions and activities as monolithic events that occur in videos. However, there is evidence from Cognitive Science and Neuroscience that people actively encode activities into consistent hierarchical part structures. However, in Computer Vision, few explorations on representati... | [' Juan Carlos Niebles', ' Li Fei-Fei', ' Ranjay Krishna', 'Jingwei Ji'] | 2020-06-01 | null | null | null | cvpr-2020-6 | ['few-shot-action-recognition'] | ['computer-vision'] | [ 4.48740214e-01 -6.15686625e-02 -2.22366378e-01 -3.98051798e-01
-1.02764413e-01 -6.60490990e-01 9.69384611e-01 3.40663493e-01
-2.23541915e-01 3.83133560e-01 8.53706241e-01 1.08299397e-01
-2.32595906e-01 -6.16978049e-01 -9.24408257e-01 -5.86477578e-01
-6.26556098e-01 1.98359415e-01 6.21074021e-01 1.88497841... | [8.54984188079834, 0.7400519847869873] |
19bd2b95-3a40-4480-8dc1-0eb059421ba7 | deep-reinforcement-learning-for-1 | 2209.05559 | null | https://arxiv.org/abs/2209.05559v6 | https://arxiv.org/pdf/2209.05559v6.pdf | Deep Reinforcement Learning for Cryptocurrency Trading: Practical Approach to Address Backtest Overfitting | Designing profitable and reliable trading strategies is challenging in the highly volatile cryptocurrency market. Existing works applied deep reinforcement learning methods and optimistically reported increased profits in backtesting, which may suffer from the false positive issue due to overfitting. In this paper, we ... | ['Berend Jelmer Dirk Gort', 'Christina Dan Wang', 'Shuaiyu Chen', 'Jiechao Gao', 'Xinghang Sun', 'Xiao-Yang Liu'] | 2022-09-12 | null | null | null | null | ['algorithmic-trading'] | ['time-series'] | [-7.87563145e-01 -1.17713720e-01 8.41073543e-02 5.01916185e-02
-6.74977183e-01 -7.77587891e-01 4.85175610e-01 -1.50973886e-01
-5.12301147e-01 1.09496319e+00 -4.76013511e-01 -7.43205190e-01
-5.47642484e-02 -1.04835963e+00 -8.04527044e-01 -9.24725711e-01
-5.71351051e-01 9.72853541e-01 1.11958366e-02 -3.10164988... | [4.457311630249023, 3.9754748344421387] |
ca4f6807-f08c-4d13-957e-8a39ce0ec474 | learning-to-see-the-wood-for-the-trees-deep | 1902.10194 | null | http://arxiv.org/abs/1902.10194v1 | http://arxiv.org/pdf/1902.10194v1.pdf | Learning to See the Wood for the Trees: Deep Laser Localization in Urban and Natural Environments on a CPU | Localization in challenging, natural environments such as forests or
woodlands is an important capability for many applications from guiding a robot
navigating along a forest trail to monitoring vegetation growth with handheld
sensors. In this work we explore laser-based localization in both urban and
natural environme... | ['Adrian Penate-Sanchez', 'Maurice Fallon', 'Georgi Tinchev'] | 2019-02-26 | null | null | null | null | ['loop-closure-detection'] | ['computer-vision'] | [ 1.28573954e-01 7.57999066e-03 -5.69943860e-02 -5.44822693e-01
-4.69617844e-01 -7.34609604e-01 7.00910687e-01 6.13559902e-01
-7.16366768e-01 6.45208836e-01 -5.27651131e-01 -2.14859322e-01
-1.09581538e-01 -1.00495887e+00 -8.58576119e-01 -3.02795470e-01
-8.55973303e-01 8.69044185e-01 5.09325743e-01 -3.07250291... | [7.406903266906738, -2.065643548965454] |
9fb104af-d757-43a7-ac35-45b29ca6f03e | deep-directional-statistics-pose-estimation | 1805.03430 | null | http://arxiv.org/abs/1805.03430v1 | http://arxiv.org/pdf/1805.03430v1.pdf | Deep Directional Statistics: Pose Estimation with Uncertainty Quantification | Modern deep learning systems successfully solve many perception tasks such as
object pose estimation when the input image is of high quality. However, in
challenging imaging conditions such as on low-resolution images or when the
image is corrupted by imaging artifacts, current systems degrade considerably
in accuracy.... | ['Peter Gehler', 'Sergey Prokudin', 'Sebastian Nowozin'] | 2018-05-09 | deep-directional-statistics-pose-estimation-1 | http://openaccess.thecvf.com/content_ECCV_2018/html/Sergey_Prokudin_Deep_Directional_Statistics_ECCV_2018_paper.html | http://openaccess.thecvf.com/content_ECCV_2018/papers/Sergey_Prokudin_Deep_Directional_Statistics_ECCV_2018_paper.pdf | eccv-2018-9 | ['probabilistic-deep-learning'] | ['computer-vision'] | [ 2.01230153e-01 7.26073384e-02 6.88739121e-02 -5.74790716e-01
-1.20469964e+00 -5.10590613e-01 4.62817550e-01 -6.75677583e-02
-5.22520423e-01 7.08959341e-01 -1.35742813e-01 6.45696465e-03
-3.21379811e-01 -5.49196005e-01 -1.34025955e+00 -7.41714656e-01
8.41634721e-02 9.42067206e-01 3.05553466e-01 4.43191975... | [7.456586837768555, -1.4058055877685547] |
f846ad06-e30a-42f2-8711-0c54ffb43236 | lilt-a-simple-yet-effective-language | 2202.13669 | null | https://arxiv.org/abs/2202.13669v1 | https://arxiv.org/pdf/2202.13669v1.pdf | LiLT: A Simple yet Effective Language-Independent Layout Transformer for Structured Document Understanding | Structured document understanding has attracted considerable attention and made significant progress recently, owing to its crucial role in intelligent document processing. However, most existing related models can only deal with the document data of specific language(s) (typically English) included in the pre-training... | ['Kai Ding', 'Lianwen Jin', 'Jiapeng Wang'] | 2022-02-28 | null | https://aclanthology.org/2022.acl-long.534 | https://aclanthology.org/2022.acl-long.534.pdf | acl-2022-5 | ['document-image-classification', 'semantic-entity-labeling', 'key-information-extraction'] | ['computer-vision', 'natural-language-processing', 'natural-language-processing'] | [ 1.75031349e-01 -2.70447463e-01 -3.45118791e-01 -4.89987612e-01
-9.28101301e-01 -9.90708292e-01 6.82356894e-01 1.71737716e-01
-3.77451986e-01 4.90124166e-01 4.62766111e-01 -7.32056797e-01
1.04685768e-01 -6.14069462e-01 -6.43303633e-01 -2.93055832e-01
3.84420037e-01 6.45514250e-01 1.01380877e-01 -2.34392434... | [11.039701461791992, 8.693772315979004] |
974d41b4-b3bd-4325-a2bc-8e854036bcca | a-counterfactual-collaborative-session-based | 2301.13364 | null | https://arxiv.org/abs/2301.13364v3 | https://arxiv.org/pdf/2301.13364v3.pdf | A Counterfactual Collaborative Session-based Recommender System | Most session-based recommender systems (SBRSs) focus on extracting information from the observed items in the current session of a user to predict a next item, ignoring the causes outside the session (called outer-session causes, OSCs) that influence the user's selection of items. However, these causes widely exist in ... | ['Minghao Yin', 'Xueyan Liu', 'Kunpeng Liu', 'Yan Wang', 'Shoujin Wang', 'Wenzhuo Song'] | 2023-01-31 | null | null | null | null | ['counterfactual-inference'] | ['miscellaneous'] | [ 1.37589753e-01 8.30181409e-03 -6.55919135e-01 -4.96281743e-01
-3.55502740e-02 -3.06808144e-01 3.43310803e-01 -8.79614055e-02
4.91514988e-02 8.28373253e-01 7.04335213e-01 -4.26207691e-01
-5.94639540e-01 -9.14550900e-01 -1.01777184e+00 -4.70636874e-01
-2.64259189e-01 -1.71263572e-02 9.56601202e-02 -3.30790728... | [9.861831665039062, 5.5662617683410645] |
d0139ecc-a2d6-4a2d-86ad-ec52ab31e596 | audio-attacks-and-defenses-against-aed | 2106.07428 | null | https://arxiv.org/abs/2106.07428v4 | https://arxiv.org/pdf/2106.07428v4.pdf | Audio Attacks and Defenses against AED Systems -- A Practical Study | In this paper, we evaluate deep learning-enabled AED systems against evasion attacks based on adversarial examples. We test the robustness of multiple security critical AED tasks, implemented as CNNs classifiers, as well as existing third-party Nest devices, manufactured by Google, which run their own black-box deep le... | ['Shirin Nilizadeh', 'Rodrigo dos Santos'] | 2021-06-14 | null | null | null | null | ['audio-denoising'] | ['audio'] | [ 4.24646109e-01 1.87195778e-01 6.45101249e-01 3.03264648e-01
-8.06596398e-01 -1.06880641e+00 7.70652115e-01 -5.68444915e-02
-5.85447013e-01 7.50930369e-01 -2.48266295e-01 -5.71172118e-01
5.30235022e-02 -8.72338414e-01 -8.94399345e-01 -9.30042565e-01
-4.68135178e-01 -3.91689464e-02 1.51053369e-01 -3.86587888... | [5.5536675453186035, 7.766383647918701] |
e8ecae42-72a4-4a0c-a691-571197bffb94 | dual-temporal-memory-network-for-efficient | 2003.06125 | null | https://arxiv.org/abs/2003.06125v1 | https://arxiv.org/pdf/2003.06125v1.pdf | Dual Temporal Memory Network for Efficient Video Object Segmentation | Video Object Segmentation (VOS) is typically formulated in a semi-supervised setting. Given the ground-truth segmentation mask on the first frame, the task of VOS is to track and segment the single or multiple objects of interests in the rest frames of the video at the pixel level. One of the fundamental challenges in ... | ['Kaihua Zhang', 'Bo Liu', 'Zhu Li', 'Qingshan Liu', 'Long Wang', 'Dong Liu'] | 2020-03-13 | null | null | null | null | ['one-shot-visual-object-segmentation'] | ['computer-vision'] | [ 3.21140047e-03 -1.74490273e-01 -5.01496494e-01 -3.58719617e-01
-4.04293120e-01 -2.16608554e-01 6.63518757e-02 -1.96308643e-01
-4.90711123e-01 3.01959187e-01 -8.65907595e-02 7.95081630e-02
1.93797022e-01 -6.77669406e-01 -1.02627134e+00 -6.23596668e-01
-2.60417819e-01 1.27739638e-01 1.07728910e+00 1.27237573... | [9.180350303649902, -0.07871927320957184] |
b206e803-24d8-4817-8d5f-d0ec54a2c532 | argoverse-2-next-generation-datasets-for-self-1 | 2301.00493 | null | https://arxiv.org/abs/2301.00493v1 | https://arxiv.org/pdf/2301.00493v1.pdf | Argoverse 2: Next Generation Datasets for Self-Driving Perception and Forecasting | We introduce Argoverse 2 (AV2) - a collection of three datasets for perception and forecasting research in the self-driving domain. The annotated Sensor Dataset contains 1,000 sequences of multimodal data, encompassing high-resolution imagery from seven ring cameras, and two stereo cameras in addition to lidar point cl... | ['James Hays', 'Peter Carr', 'Deva Ramanan', 'Jhony Kaesemodel Pontes', 'Andrew Hartnett', 'Ratnesh Kumar', 'Bowen Pan', 'Siddhesh Khandelwal', 'Jagjeet Singh', 'John Lambert', 'Tanmay Agarwal', 'William Qi', 'Benjamin Wilson'] | 2023-01-02 | argoverse-2-next-generation-datasets-for-self | https://datasets-benchmarks-proceedings.neurips.cc/paper/2021/hash/4734ba6f3de83d861c3176a6273cac6d-Abstract-round2.html | https://datasets-benchmarks-proceedings.neurips.cc/paper/2021/file/4734ba6f3de83d861c3176a6273cac6d-Paper-round2.pdf | proceedings-of-the-neural-information | ['motion-forecasting'] | ['computer-vision'] | [-1.37484923e-01 -2.61974800e-02 -5.49918473e-01 -9.18987989e-01
-7.43012846e-01 -7.70988762e-01 9.41890657e-01 2.35921726e-01
-3.49581182e-01 4.05818105e-01 1.63898796e-01 -2.79948503e-01
1.07105352e-01 -6.95284784e-01 -8.60799789e-01 -4.50661987e-01
-3.89916331e-01 1.02919734e+00 4.03523624e-01 -4.09471929... | [7.781923294067383, -1.9849220514297485] |
8c571041-9f02-4603-9c3d-a72cd527f50d | v4d4d-convolutional-neural-networks-for-video | 2002.07442 | null | https://arxiv.org/abs/2002.07442v1 | https://arxiv.org/pdf/2002.07442v1.pdf | V4D:4D Convolutional Neural Networks for Video-level Representation Learning | Most existing 3D CNNs for video representation learning are clip-based methods, and thus do not consider video-level temporal evolution of spatio-temporal features. In this paper, we propose Video-level 4D Convolutional Neural Networks, referred as V4D, to model the evolution of long-range spatio-temporal representatio... | ['Li-Min Wang', 'Weilin Huang', 'Sheng Guo', 'Matthew R. Scott', 'Shiwen Zhang'] | 2020-02-18 | null | null | null | null | ['long-range-modeling'] | ['natural-language-processing'] | [-2.94838697e-01 -4.20448005e-01 -4.83290672e-01 -1.42260075e-01
-1.18864290e-01 -2.95864254e-01 7.38769174e-01 -1.95144415e-01
-3.93762514e-02 1.57173738e-01 3.93648326e-01 -2.46375263e-01
9.26265586e-03 -6.66721761e-01 -1.03503239e+00 -5.49179256e-01
-5.12466431e-01 -2.56453902e-01 4.96245563e-01 -5.28283827... | [8.927339553833008, 0.42889800667762756] |
6f21ad73-ba0b-4dfc-90ad-34eaa69a0161 | optimal-estimation-of-low-rank-density | 1507.05131 | null | http://arxiv.org/abs/1507.05131v4 | http://arxiv.org/pdf/1507.05131v4.pdf | Optimal Estimation of Low Rank Density Matrices | The density matrices are positively semi-definite Hermitian matrices of unit
trace that describe the state of a quantum system. The goal of the paper is to
develop minimax lower bounds on error rates of estimation of low rank density
matrices in trace regression models used in quantum state tomography (in
particular, i... | ['Dong Xia', 'Vladimir Koltchinskii'] | 2015-07-17 | null | null | null | null | ['quantum-state-tomography'] | ['medical'] | [ 2.30567768e-01 3.31958950e-01 5.63987605e-02 -4.00655299e-01
-1.03098488e+00 -6.10770047e-01 3.08184117e-01 1.27131283e-01
-7.85045564e-01 8.89410734e-01 -2.80560348e-02 -3.65034312e-01
-7.76468217e-01 -6.66728497e-01 -4.28958833e-01 -8.59274685e-01
-4.56863225e-01 6.55344605e-01 -1.01663403e-01 -2.55055845... | [5.700873374938965, 4.8992204666137695] |
110064b7-f6da-4aab-898b-3f86087a9a6b | cvt-slr-contrastive-visual-textual | 2303.05725 | null | https://arxiv.org/abs/2303.05725v4 | https://arxiv.org/pdf/2303.05725v4.pdf | CVT-SLR: Contrastive Visual-Textual Transformation for Sign Language Recognition with Variational Alignment | Sign language recognition (SLR) is a weakly supervised task that annotates sign videos as textual glosses. Recent studies show that insufficient training caused by the lack of large-scale available sign datasets becomes the main bottleneck for SLR. Most SLR works thereby adopt pretrained visual modules and develop two ... | ['Stan Z. Li', 'Yidong Chen', 'Jun Xia', 'Ge Wang', 'Siyuan Li', 'Cheng Tan', 'Yile Wang', 'Jiangbin Zheng'] | 2023-03-10 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Zheng_CVT-SLR_Contrastive_Visual-Textual_Transformation_for_Sign_Language_Recognition_With_Variational_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Zheng_CVT-SLR_Contrastive_Visual-Textual_Transformation_for_Sign_Language_Recognition_With_Variational_CVPR_2023_paper.pdf | cvpr-2023-1 | ['sign-language-recognition'] | ['computer-vision'] | [ 2.74699837e-01 -5.74161887e-01 -5.19139349e-01 -3.63222152e-01
-9.43522215e-01 -3.64906788e-01 7.18608022e-01 -8.95191252e-01
-7.05730259e-01 3.43699306e-01 5.50374985e-01 -1.03131488e-01
-3.95917669e-02 -1.39450729e-01 -7.61302233e-01 -6.28141403e-01
5.84807098e-01 1.20964020e-01 2.14363575e-01 -4.27273735... | [9.217232704162598, -6.517561435699463] |
29107796-03ee-45db-94b0-bc40d2af8180 | in-situ-anomaly-detection-in-additive | 2305.02695 | null | https://arxiv.org/abs/2305.02695v1 | https://arxiv.org/pdf/2305.02695v1.pdf | In-situ Anomaly Detection in Additive Manufacturing with Graph Neural Networks | Transforming a design into a high-quality product is a challenge in metal additive manufacturing due to rare events which can cause defects to form. Detecting these events in-situ could, however, reduce inspection costs, enable corrective action, and is the first step towards a future of tailored material properties. I... | ['Paul A. Hooper', 'Sebastian Larsen'] | 2023-05-04 | null | null | null | null | ['defect-detection'] | ['computer-vision'] | [ 4.46452677e-01 2.63054222e-01 2.16416836e-01 -3.51989567e-01
-6.17713153e-01 -1.18273489e-01 2.27925643e-01 6.28992319e-01
4.25301105e-01 3.84015828e-01 -3.77648205e-01 -5.41187413e-02
-2.54512161e-01 -9.75391686e-01 -7.07477391e-01 -6.00703537e-01
1.65061578e-01 5.35352468e-01 4.42466974e-01 -3.23377877... | [7.058096408843994, 2.237840175628662] |
83f3b8c5-5c0c-472e-82ba-26835c31c866 | fern-leveraging-graph-attention-networks-for | 2305.19153 | null | https://arxiv.org/abs/2305.19153v1 | https://arxiv.org/pdf/2305.19153v1.pdf | FERN: Leveraging Graph Attention Networks for Failure Evaluation and Robust Network Design | Robust network design, which aims to guarantee network availability under various failure scenarios while optimizing performance/cost objectives, has received significant attention. Existing approaches often rely on model-based mixed-integer optimization that is hard to scale or employ deep learning to solve specific e... | ['Qing Li', 'Mingwei Xu', 'Yuan Yang', 'Nan Geng', 'Tian Lan', 'Vaneet Aggarwal', 'Chenyi Liu'] | 2023-05-30 | null | null | null | null | ['graph-attention'] | ['graphs'] | [-1.60278276e-01 -1.87175155e-01 -6.06454194e-01 -3.69922549e-01
-4.22561139e-01 -4.80056703e-01 -2.08654970e-01 3.14931273e-02
2.83167899e-01 8.08292270e-01 -3.45689535e-01 -7.54330158e-01
-9.13201511e-01 -7.02692091e-01 -6.17973804e-01 -4.34087276e-01
-8.77379239e-01 6.57143414e-01 9.48914215e-02 -2.89985955... | [5.834531307220459, 1.7324328422546387] |
2994df02-6503-4e28-b511-4f69fd3b1900 | nose-eyes-and-ears-head-pose-estimation-by | 1812.00739 | null | http://arxiv.org/abs/1812.00739v1 | http://arxiv.org/pdf/1812.00739v1.pdf | Nose, eyes and ears: Head pose estimation by locating facial keypoints | Monocular head pose estimation requires learning a model that computes the
intrinsic Euler angles for pose (yaw, pitch, roll) from an input image of human
face. Annotating ground truth head pose angles for images in the wild is
difficult and requires ad-hoc fitting procedures (which provides only coarse
and approximate... | ['P. J. Narayanan', 'Vineet Gandhi', 'Aryaman Gupta', 'Kalpit Thakkar'] | 2018-12-03 | null | null | null | null | ['head-pose-estimation'] | ['computer-vision'] | [-2.39781052e-01 5.26549935e-01 2.93257535e-01 -9.05414343e-01
-7.33222008e-01 -4.93198484e-01 5.53101063e-01 -2.73461610e-01
-5.53369701e-01 5.21448672e-01 2.18118951e-01 3.00560206e-01
2.09529579e-01 -2.24737287e-01 -1.02926862e+00 -6.67418718e-01
3.12398828e-04 5.70651114e-01 -1.03421435e-01 -6.62619397... | [13.622071266174316, 0.27574923634529114] |
3ab96f68-273d-45a0-95ab-f126dd4aae18 | cross-modal-hierarchical-modelling-for-fine | 2007.15103 | null | https://arxiv.org/abs/2007.15103v2 | https://arxiv.org/pdf/2007.15103v2.pdf | Cross-Modal Hierarchical Modelling for Fine-Grained Sketch Based Image Retrieval | Sketch as an image search query is an ideal alternative to text in capturing the fine-grained visual details. Prior successes on fine-grained sketch-based image retrieval (FG-SBIR) have demonstrated the importance of tackling the unique traits of sketches as opposed to photos, e.g., temporal vs. static, strokes vs. pix... | ['Yi-Zhe Song', 'Tao Xiang', 'Ayan Kumar Bhunia', 'Aneeshan Sain', 'Yongxin Yang'] | 2020-07-29 | null | null | null | null | ['sketch-based-image-retrieval'] | ['computer-vision'] | [ 4.13549356e-02 -3.75529826e-01 -3.15495610e-01 -2.37062722e-01
-5.81307113e-01 -6.57169282e-01 9.48521435e-01 -2.52480246e-02
7.65353665e-02 4.15096998e-01 6.33651078e-01 1.67035788e-01
-2.88393408e-01 -7.92686462e-01 -5.63685715e-01 -5.63543975e-01
1.00745484e-01 2.67305933e-02 1.16126254e-01 -2.76352465... | [11.687507629394531, 0.5719165802001953] |
2488f3ef-b641-4f93-9f8a-70b72794cafb | learning-adversarial-semantic-embeddings-for | 2307.03416 | null | https://arxiv.org/abs/2307.03416v1 | https://arxiv.org/pdf/2307.03416v1.pdf | Learning Adversarial Semantic Embeddings for Zero-Shot Recognition in Open Worlds | Zero-Shot Learning (ZSL) focuses on classifying samples of unseen classes with only their side semantic information presented during training. It cannot handle real-life, open-world scenarios where there are test samples of unknown classes for which neither samples (e.g., images) nor their side semantic information is ... | ['Xin Ning', 'Lei Zhou', 'Jin Zheng', 'Xiao Bai', 'Guansong Pang', 'Tianqi Li'] | 2023-07-07 | null | null | null | null | ['zero-shot-learning', 'open-set-learning'] | ['methodology', 'miscellaneous'] | [ 5.58155298e-01 2.41378963e-01 -2.55642176e-01 -3.03403586e-01
-1.06544721e+00 -5.95613897e-01 5.40079951e-01 -1.21973440e-01
-1.39038429e-01 5.89387298e-01 -2.53284544e-01 -2.25568786e-01
-2.42386032e-02 -1.00050247e+00 -9.26527917e-01 -6.41832948e-01
2.40908727e-01 8.11040640e-01 3.24702978e-01 -2.71174729... | [9.759100914001465, 2.582951784133911] |
d5f64d5e-c0f7-435d-8e9a-f5e6896c7d9e | large-batch-neural-multi-objective-bayesian | 2306.01095 | null | https://arxiv.org/abs/2306.01095v2 | https://arxiv.org/pdf/2306.01095v2.pdf | Large-Batch, Neural Multi-Objective Bayesian Optimization | Bayesian optimization provides a powerful framework for global optimization of black-box, expensive-to-evaluate functions. However, it has a limited capacity in handling data-intensive problems, especially in multi-objective settings, due to the poor scalability of default Gaussian Process surrogates. We present a nove... | ['Vahid Babaei', 'Hans-Peter Seidel', 'Navid Ansari'] | 2023-06-01 | null | null | null | null | ['efficient-exploration', 'bayesian-optimization'] | ['methodology', 'methodology'] | [-8.09761658e-02 -2.43800297e-01 -1.95747375e-01 -3.21627349e-01
-1.12254798e+00 -5.02057731e-01 2.10249230e-01 -2.36958321e-02
-3.34888548e-01 9.41099286e-01 1.42888166e-02 -5.07355750e-01
-5.79825461e-01 -8.82389903e-01 -7.28541672e-01 -8.69166136e-01
-6.26058653e-02 8.73578668e-01 -7.02040677e-04 -1.63492292... | [6.322909832000732, 3.8473360538482666] |
89d43c9f-7f8d-4f07-a8ed-ec4f7d8da13a | shared-tasks-of-the-2015-workshop-on-noisy | null | null | https://aclanthology.org/W15-4319 | https://aclanthology.org/W15-4319.pdf | Shared Tasks of the 2015 Workshop on Noisy User-generated Text: Twitter Lexical Normalization and Named Entity Recognition | null | ['Young-Bum Kim', 'Wei Xu', 'Timothy Baldwin', 'Bo Han', 'Alan Ritter', 'Marie Catherine de Marneffe'] | 2015-07-01 | null | null | null | ws-2015-7 | ['lexical-normalization'] | ['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... | [-7.325130462646484, 3.717496156692505] |
992420b3-b99e-4cab-8153-720f6af19db8 | driver-hand-localization-and-grasp-analysis-a | 1802.07854 | null | http://arxiv.org/abs/1802.07854v1 | http://arxiv.org/pdf/1802.07854v1.pdf | Driver Hand Localization and Grasp Analysis: A Vision-based Real-time Approach | Extracting hand regions and their grasp information from images robustly in
real-time is critical for occupants' safety and in-vehicular infotainment
applications. It must however, be noted that naturalistic driving scenes suffer
from rapidly changing illumination and occlusion. This is aggravated by the
fact that hand... | ['Eshed Ohn-Bar', 'Akshay Rangesh', 'Siddharth', 'Mohan M. Trivedi'] | 2018-02-22 | null | null | null | null | ['hand-detection'] | ['computer-vision'] | [ 2.42598012e-01 -1.96121722e-01 -1.06637768e-01 -3.08435291e-01
-3.84622723e-01 -7.38449991e-01 2.51834124e-01 -4.17655617e-01
-7.34571517e-01 2.89475501e-01 -3.53135288e-01 -3.16057265e-01
2.04811886e-01 -4.50713545e-01 -6.54465914e-01 -1.00479698e+00
2.28355750e-01 2.24486828e-01 7.54377186e-01 -1.41358569... | [6.596848964691162, -0.6113993525505066] |
a284fffc-0eb1-4453-b08e-c6ebc6e1b5bf | pixel-level-segmentation-based-drivable-road | null | null | https://ieeexplore.ieee.org/document/9646953 | https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9646953 | Pixel Level Segmentation Based Drivable Road Region Detection and Steering Angle Estimation Method for Autonomous Driving on Unstructured Roads | With the recent emergence of deep learning, computer vision-based applications have demonstrated better applicability in accomplishing driving tasks including drivable road region detection, lane keeping and steering control in self-driving cars. Till recently, numerous lane-marking detection based steering control and... | ['Muhammad Akram', 'Adel Sulaiman', 'Faisal Riaz', 'Muhammad Atif Butt', 'Marya Rasib'] | 2021-12-10 | null | null | null | ieee-access-2021-12 | ['steering-control'] | ['computer-vision'] | [ 1.10545412e-01 2.23533437e-01 -2.79214203e-01 -5.35844445e-01
-5.16717672e-01 -5.37120044e-01 6.98768497e-01 -3.04466903e-01
-4.05327290e-01 5.66369474e-01 -2.21885607e-01 -8.71123016e-01
1.39620453e-01 -9.88433123e-01 -5.81717253e-01 -4.91222382e-01
1.77953586e-01 2.12068960e-01 8.05540383e-01 -6.78394556... | [8.066669464111328, -1.5444262027740479] |
9eaf0dcc-c58b-4fe0-9e21-f2eb51181822 | sentence-representation-learning-with | 2210.08474 | null | https://arxiv.org/abs/2210.08474v2 | https://arxiv.org/pdf/2210.08474v2.pdf | Sentence Representation Learning with Generative Objective rather than Contrastive Objective | Though offering amazing contextualized token-level representations, current pre-trained language models take less attention on accurately acquiring sentence-level representation during their self-supervised pre-training. However, contrastive objectives which dominate the current sentence representation learning bring l... | ['Hai Zhao', 'Bohong Wu'] | 2022-10-16 | null | null | null | null | ['semantic-retrieval'] | ['natural-language-processing'] | [ 2.43160486e-01 4.09964472e-01 -4.48799312e-01 -6.01807058e-01
-1.35629535e+00 -4.01681304e-01 8.04559112e-01 2.32145742e-01
-3.28748912e-01 8.38659823e-01 8.27419102e-01 -3.65487456e-01
5.06350771e-02 -8.85287881e-01 -6.70841217e-01 -5.26678741e-01
4.02844310e-01 6.58100903e-01 -8.80777910e-02 -3.67665470... | [10.973440170288086, 8.776704788208008] |
d003363e-1bbf-4b43-a6f4-4b70133230f9 | a-black-box-approach-for-non-stationary-multi | 2306.07465 | null | https://arxiv.org/abs/2306.07465v1 | https://arxiv.org/pdf/2306.07465v1.pdf | A Black-box Approach for Non-stationary Multi-agent Reinforcement Learning | We investigate learning the equilibria in non-stationary multi-agent systems and address the challenges that differentiate multi-agent learning from single-agent learning. Specifically, we focus on games with bandit feedback, where testing an equilibrium can result in substantial regret even when the gap to be tested i... | ['Simon S. Du', 'Maryam Fazel', 'Zhihan Xiong', 'Qiwen Cui', 'Haozhe Jiang'] | 2023-06-12 | null | null | null | null | ['multi-agent-reinforcement-learning'] | ['methodology'] | [-1.27858311e-01 1.14423648e-01 -1.50795847e-01 2.74704754e-01
-1.13436699e+00 -9.01830494e-01 -2.50759840e-01 2.95889433e-02
-7.19365716e-01 1.23142815e+00 -4.16005820e-01 -5.94297290e-01
-9.86683846e-01 -8.67292225e-01 -9.29552555e-01 -1.07607222e+00
-6.35652959e-01 5.36180377e-01 1.25620496e-02 -2.74674088... | [4.347800254821777, 3.131152629852295] |
20a202f3-a1bb-4076-ab97-a5a9c7ece3f2 | siamese-detr | 2303.18144 | null | https://arxiv.org/abs/2303.18144v1 | https://arxiv.org/pdf/2303.18144v1.pdf | Siamese DETR | Recent self-supervised methods are mainly designed for representation learning with the base model, e.g., ResNets or ViTs. They cannot be easily transferred to DETR, with task-specific Transformer modules. In this work, we present Siamese DETR, a Siamese self-supervised pretraining approach for the Transformer architec... | ['Lu Sheng', 'Chen Change Loy', 'Jing Shao', 'Kun Wang', 'Jianing Teng', 'Wei Li', 'Gengshi Huang', 'Zeren Chen'] | 2023-03-31 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Huang_Siamese_DETR_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Huang_Siamese_DETR_CVPR_2023_paper.pdf | cvpr-2023-1 | ['multi-view-learning'] | ['computer-vision'] | [ 8.76246989e-02 -2.07389399e-01 -2.32737452e-01 -2.38672510e-01
-1.31258273e+00 -7.61016548e-01 6.95677996e-01 -2.36965165e-01
-4.99272972e-01 3.79716754e-01 4.78275493e-02 4.70524132e-02
4.74715114e-01 -5.09425044e-01 -9.07689035e-01 -7.10572004e-01
3.36398304e-01 5.61635375e-01 4.84110385e-01 -2.74244249... | [9.733621597290039, 1.8356924057006836] |
1092ca7f-6c3c-4de3-80b1-c79c0d09492d | differentiable-instruction-optimization-for | 2306.10098 | null | https://arxiv.org/abs/2306.10098v1 | https://arxiv.org/pdf/2306.10098v1.pdf | Differentiable Instruction Optimization for Cross-Task Generalization | Instruction tuning has been attracting much attention to achieve generalization ability across a wide variety of tasks. Although various types of instructions have been manually created for instruction tuning, it is still unclear what kind of instruction is optimal to obtain cross-task generalization ability. This work... | ['Ichiro Sakata', 'Junichiro Mori', 'Masaru Isonuma'] | 2023-06-16 | null | null | null | null | ['bilevel-optimization'] | ['methodology'] | [ 9.37934443e-02 -2.79513627e-01 -5.04356802e-01 -7.85759568e-01
-5.51619053e-01 -6.82619691e-01 1.48310393e-01 1.37854934e-01
-6.29394531e-01 6.60444975e-01 1.44878447e-01 -6.42348707e-01
-8.42312351e-02 -6.50215089e-01 -6.04243457e-01 -3.47523540e-01
-1.01136580e-01 2.46990934e-01 1.15244314e-01 -3.42744172... | [10.652546882629395, 8.352051734924316] |
945e2a78-c1dc-4afe-91a3-886511b5bcd0 | background-aware-3d-point-cloud | 2111.07248 | null | https://arxiv.org/abs/2111.07248v1 | https://arxiv.org/pdf/2111.07248v1.pdf | Background-Aware 3D Point Cloud Segmentationwith Dynamic Point Feature Aggregation | With the proliferation of Lidar sensors and 3D vision cameras, 3D point cloud analysis has attracted significant attention in recent years. After the success of the pioneer work PointNet, deep learning-based methods have been increasingly applied to various tasks, including 3D point cloud segmentation and 3D object cla... | ['Senem Velipasalar', 'Burak Kakillioglu', 'Jiajing Chen'] | 2021-11-14 | null | null | null | null | ['3d-object-classification', 'point-cloud-segmentation'] | ['computer-vision', 'computer-vision'] | [-1.08120635e-01 -2.65659332e-01 -1.01186015e-01 -4.36925650e-01
-4.12794530e-01 -2.83271670e-01 5.00863492e-01 2.15225443e-01
-3.34722757e-01 -8.30316916e-03 -3.12595546e-01 -8.26490298e-02
5.66304429e-03 -9.06885505e-01 -8.02777231e-01 -5.88755071e-01
1.25998229e-01 4.70178545e-01 7.11712062e-01 1.40296608... | [7.95474910736084, -3.357167959213257] |
e67e7117-97a4-487c-b7c1-7dde6b4072a5 | language-model-pre-training-with-sparse | 2210.12582 | null | https://arxiv.org/abs/2210.12582v2 | https://arxiv.org/pdf/2210.12582v2.pdf | Language Model Pre-Training with Sparse Latent Typing | Modern large-scale Pre-trained Language Models (PLMs) have achieved tremendous success on a wide range of downstream tasks. However, most of the LM pre-training objectives only focus on text reconstruction, but have not sought to learn latent-level interpretable representations of sentences. In this paper, we manage to... | ['Heng Ji', 'ChengXiang Zhai', 'Clare R. Voss', 'Han Wang', 'Zixuan Zhang', 'Liliang Ren'] | 2022-10-23 | null | null | null | null | ['few-shot-ner'] | ['natural-language-processing'] | [ 2.16689408e-01 5.36073208e-01 -6.82266712e-01 -4.58095312e-01
-1.04329967e+00 -4.28923905e-01 6.56227171e-01 1.91383690e-01
-1.69077978e-01 5.32948732e-01 7.41720557e-01 -4.75605726e-01
2.53969818e-01 -5.82713723e-01 -7.39295423e-01 -3.56252313e-01
3.03503394e-01 4.66033518e-01 -2.94499755e-01 8.90213624... | [10.987235069274902, 8.824398040771484] |
24999f9b-8623-45f0-af4c-85c8ec761fcd | automatic-speech-recognition-of-non-native | 2306.16710 | null | https://arxiv.org/abs/2306.16710v1 | https://arxiv.org/pdf/2306.16710v1.pdf | Automatic Speech Recognition of Non-Native Child Speech for Language Learning Applications | Voicebots have provided a new avenue for supporting the development of language skills, particularly within the context of second language learning. Voicebots, though, have largely been geared towards native adult speakers. We sought to assess the performance of two state-of-the-art ASR systems, Wav2Vec2.0 and Whisper ... | ['Helmer Strik', 'Catia Cucchiarini', 'Cristian Tejedor-Garcia', 'Yu Bai', 'Simone Wills'] | 2023-06-29 | null | null | null | null | ['speech-recognition'] | ['speech'] | [-1.80757791e-01 2.84062445e-01 -1.40060067e-01 -3.65748972e-01
-8.91539574e-01 -8.92785847e-01 4.73801702e-01 2.26552591e-01
-6.13020957e-01 1.60929531e-01 8.23420882e-01 -6.01710618e-01
3.13144803e-01 -3.55599999e-01 -4.11215007e-01 -9.87567380e-02
4.02518809e-01 4.88000512e-01 4.18655351e-02 -6.35863900... | [14.308941841125488, 6.820274353027344] |
7b3ca433-5a2e-4437-8a92-489b6f2c8f7d | efficient-bayesian-inference-using-physics | 2304.12541 | null | https://arxiv.org/abs/2304.12541v2 | https://arxiv.org/pdf/2304.12541v2.pdf | Efficient Bayesian inference using physics-informed invertible neural networks for inverse problems | In the paper, we propose a novel approach for solving Bayesian inverse problems with physics-informed invertible neural networks (PI-INN). The architecture of PI-INN consists of two sub-networks: an invertible neural network (INN) and a neural basis network (NB-Net). The invertible map between the parametric input and ... | ['Hao Wu', 'Xintong Wang', 'Xiaofei Guan'] | 2023-04-25 | null | null | null | null | ['bayesian-inference'] | ['methodology'] | [ 4.31819819e-02 2.75642842e-01 2.43910566e-01 -3.81538093e-01
-5.25939763e-01 2.46648461e-01 4.05964196e-01 -7.39903331e-01
-3.78686309e-01 9.50255990e-01 6.85140043e-02 -1.93668634e-01
-6.10412896e-01 -1.10167789e+00 -9.08429563e-01 -1.11739624e+00
-9.05144215e-02 7.83084929e-01 2.03948930e-01 1.68618351... | [6.953646183013916, 3.590203046798706] |
abef3298-ecee-4839-b448-4af57e4dcb67 | implicit-ray-transformers-for-multi-view | 2303.08401 | null | https://arxiv.org/abs/2303.08401v1 | https://arxiv.org/pdf/2303.08401v1.pdf | Implicit Ray-Transformers for Multi-view Remote Sensing Image Segmentation | The mainstream CNN-based remote sensing (RS) image semantic segmentation approaches typically rely on massive labeled training data. Such a paradigm struggles with the problem of RS multi-view scene segmentation with limited labeled views due to the lack of considering 3D information within the scene. In this paper, we... | ['Zhengxia Zou', 'Zhenwei Shi', 'Chenyang Liu', 'Hao Chen', 'Zipeng Qi'] | 2023-03-15 | null | null | null | null | ['scene-segmentation'] | ['computer-vision'] | [ 4.41457987e-01 -8.05715621e-02 -6.05394430e-02 -6.70543909e-01
-8.97378385e-01 -6.72519743e-01 4.49269205e-01 -1.93919957e-01
-2.59173781e-01 3.01105052e-01 -2.27371417e-02 -1.26901716e-01
-2.63127804e-01 -1.21272027e+00 -9.25832629e-01 -6.08905733e-01
2.88062572e-01 4.71107811e-01 4.16123092e-01 -1.21336646... | [8.889982223510742, -2.831312894821167] |
076b913d-ac5e-4e96-bb7a-bb411606c791 | nlg-evaluation-metrics-beyond-correlation | 2305.08566 | null | https://arxiv.org/abs/2305.08566v4 | https://arxiv.org/pdf/2305.08566v4.pdf | NLG Evaluation Metrics Beyond Correlation Analysis: An Empirical Metric Preference Checklist | In this study, we analyze automatic evaluation metrics for Natural Language Generation (NLG), specifically task-agnostic metrics and human-aligned metrics. Task-agnostic metrics, such as Perplexity, BLEU, BERTScore, are cost-effective and highly adaptable to diverse NLG tasks, yet they have a weak correlation with huma... | ['Mykola Pechenizkiy', 'Vlado Menkovski', 'Meng Fang', "Iftitahu Ni'mah"] | 2023-05-15 | null | null | null | null | ['dialogue-generation', 'response-generation', 'text-summarization', 'controllable-language-modelling', 'dialogue-generation'] | ['natural-language-processing', 'natural-language-processing', 'natural-language-processing', 'natural-language-processing', 'speech'] | [ 1.49422780e-01 2.71084726e-01 1.23101035e-02 -3.17146063e-01
-1.11263180e+00 -1.01423073e+00 1.07759356e+00 5.12475967e-01
-5.38041115e-01 1.02207398e+00 5.53568363e-01 -2.71787345e-01
-3.50553304e-01 -3.49890888e-01 7.74356797e-02 -2.90902317e-01
1.57899484e-01 7.21249938e-01 -6.26035705e-02 -6.10865831... | [11.84183406829834, 9.033109664916992] |
edc6a116-ff78-4d00-964d-0ce5fbd85312 | from-wide-to-deep-dimension-lifting-network | 2303.12816 | null | https://arxiv.org/abs/2303.12816v2 | https://arxiv.org/pdf/2303.12816v2.pdf | From Wide to Deep: Dimension Lifting Network for Parameter-efficient Knowledge Graph Embedding | Knowledge graph embedding (KGE) that maps entities and relations into vector representations is essential for downstream tasks. Conventional KGE methods require relatively high-dimensional entity representations to preserve the structural information of knowledge graph, but lead to oversized model parameters. Recent me... | ['Tom Luan', 'Jiong Jin', 'He Zhang', 'Di wu', 'Longxiang Gao', 'Yong Xiang', 'Borui Cai'] | 2023-03-22 | null | null | null | null | ['knowledge-graph-embedding'] | ['graphs'] | [-2.83304900e-01 4.20184553e-01 -4.65870649e-01 -1.00647867e-01
-1.61713079e-01 -4.40213501e-01 3.82032692e-01 2.33463660e-01
-4.46039706e-01 5.01493752e-01 3.98670137e-01 -4.22423780e-01
-3.25712293e-01 -1.29883838e+00 -7.09276438e-01 -3.15790445e-01
-2.27989897e-01 2.65641898e-01 2.39465848e-01 -1.88892528... | [8.734603881835938, 7.870014190673828] |
c012cf84-617d-4eee-a475-4da92fc14e07 | text2struct-a-machine-learning-pipeline-for | 2212.09044 | null | https://arxiv.org/abs/2212.09044v3 | https://arxiv.org/pdf/2212.09044v3.pdf | Text2Struct: A Machine Learning Pipeline for Mining Structured Data from Text | Many analysis and prediction tasks require the extraction of structured data from unstructured texts. However, an annotation scheme and a training dataset have not been available for training machine learning models to mine structured data from text without special templates and patterns. To solve it, this paper presen... | ['Bo Yang', 'Chaochao Zhou'] | 2022-12-18 | null | null | null | null | ['text-annotation'] | ['natural-language-processing'] | [ 2.74203598e-01 7.23828197e-01 -1.28260955e-01 -6.09015405e-01
-8.67027402e-01 -5.22688210e-01 3.75678599e-01 8.27110052e-01
-4.86309528e-01 8.14772904e-01 1.68986440e-01 -4.42551911e-01
-1.85357794e-01 -6.99783146e-01 -3.89736772e-01 -2.27934912e-01
8.41910392e-02 9.31327403e-01 5.66692725e-02 2.06196606... | [8.71609878540039, 8.554275512695312] |
6e03daa1-9ccc-45c3-9d29-2f850d1b849b | causal-identification-with-subjective | 2212.14622 | null | https://arxiv.org/abs/2212.14622v2 | https://arxiv.org/pdf/2212.14622v2.pdf | Causal identification with subjective outcomes | Survey questions often elicit responses on ordered scales for which the definitions of the categories are subjective, possibly varying by individual. This paper clarifies what is learned when these subjective reports are used as an outcome in regression-based causal inference. When a continuous treatment variable is st... | ['Leonard Goff'] | 2022-12-30 | null | null | null | null | ['unity', 'causal-identification'] | ['computer-vision', 'reasoning'] | [ 2.96298325e-01 6.36563301e-02 -1.20962954e+00 -6.21309757e-01
-9.41414714e-01 -9.13859129e-01 6.46423876e-01 3.24395746e-01
-4.45585608e-01 1.11302519e+00 1.02024698e+00 -5.61419010e-01
-4.48004335e-01 -8.19372654e-01 -4.72131580e-01 -5.51099062e-01
-2.20566499e-03 2.23697439e-01 -2.98127174e-01 3.23137522... | [8.003931045532227, 5.275940418243408] |
2714194b-6c0a-4f31-b17b-604cf462a6eb | machine-translation-by-projecting-text-into | 2305.12371 | null | https://arxiv.org/abs/2305.12371v1 | https://arxiv.org/pdf/2305.12371v1.pdf | Machine Translation by Projecting Text into the Same Phonetic-Orthographic Space Using a Common Encoding | The use of subword embedding has proved to be a major innovation in Neural Machine Translation (NMT). It helps NMT to learn better context vectors for Low Resource Languages (LRLs) so as to predict the target words by better modelling the morphologies of the two languages and also the morphosyntax transfer. Even so, th... | ['Anil Kumar Singh', 'Ajay Pratap', 'Shantipriya Parida', 'Amit Kumar'] | 2023-05-21 | null | null | null | null | ['nmt'] | ['computer-code'] | [-4.04002406e-02 -3.53491217e-01 -2.71687925e-01 -2.35706806e-01
-9.01040494e-01 -8.33215952e-01 7.74516404e-01 8.75455290e-02
-5.99246144e-01 1.01301527e+00 4.61119890e-01 -7.46955752e-01
1.53530553e-01 -8.39379907e-01 -7.55987525e-01 -5.71068347e-01
3.06749612e-01 7.95695722e-01 -1.10565729e-01 -6.54647827... | [11.282066345214844, 10.221285820007324] |
4f2aa9ce-df66-487e-aba1-7ae5c65f7c1f | saliency-based-multi-view-mixed-language | null | null | https://aclanthology.org/2021.findings-emnlp.55 | https://aclanthology.org/2021.findings-emnlp.55.pdf | Saliency-based Multi-View Mixed Language Training for Zero-shot Cross-lingual Classification | Recent multilingual pre-trained models, like XLM-RoBERTa (XLM-R), have been demonstrated effective in many cross-lingual tasks. However, there are still gaps between the contextualized representations of similar words in different languages. To solve this problem, we propose a novel framework named Multi-View Mixed Lan... | ['Jian Liu', 'Jinan Xu', 'Yufeng Chen', 'Dong Jing', 'Hui Huang', 'Siyu Lai'] | null | null | null | null | findings-emnlp-2021-11 | ['multi-view-learning'] | ['computer-vision'] | [-2.79877841e-01 -2.42232054e-01 -7.61257529e-01 -4.74673033e-01
-1.06353593e+00 -7.14637697e-01 8.47893059e-01 -3.83897088e-02
-3.76310706e-01 4.23222125e-01 8.59968483e-01 -2.65127450e-01
6.28764093e-01 -8.38373136e-03 -4.71237957e-01 -2.57905185e-01
3.56826276e-01 2.81709522e-01 8.56467187e-02 -8.00755084... | [11.273503303527832, 9.758744239807129] |
80fd57fc-41a6-4d49-9a0e-3ccecc0b4f91 | probabilistic-3d-surface-reconstruction-from | 2010.02041 | null | https://arxiv.org/abs/2010.02041v1 | https://arxiv.org/pdf/2010.02041v1.pdf | Probabilistic 3D surface reconstruction from sparse MRI information | Surface reconstruction from magnetic resonance (MR) imaging data is indispensable in medical image analysis and clinical research. A reliable and effective reconstruction tool should: be fast in prediction of accurate well localised and high resolution models, evaluate prediction uncertainty, work with as little input ... | ['Ender Konukoglu', 'Marc Pollefeys', 'Andrew King', 'Esther Puyol-Antón', 'Matthew Lee', 'Sarah Parisot', 'Katarína Tóthová'] | 2020-10-05 | null | null | null | null | ['probabilistic-deep-learning'] | ['computer-vision'] | [ 2.93552428e-01 4.55508143e-01 2.77066499e-01 -5.51953197e-01
-1.36365807e+00 -3.88763435e-02 5.52826583e-01 2.19327614e-01
-2.36929134e-01 5.37000418e-01 2.04272121e-01 -1.17352381e-01
-5.65607250e-01 -5.32393456e-01 -7.73794115e-01 -7.71357656e-01
-3.52203429e-01 1.63459623e+00 3.40574890e-01 2.54676402... | [13.796443939208984, -2.3009612560272217] |
497cc863-1c6b-4d61-ae9e-53c27c341168 | extreme-acceleration-of-graph-neural-network | 2211.13853 | null | https://arxiv.org/abs/2211.13853v1 | https://arxiv.org/pdf/2211.13853v1.pdf | Extreme Acceleration of Graph Neural Network-based Prediction Models for Quantum Chemistry | Molecular property calculations are the bedrock of chemical physics. High-fidelity \textit{ab initio} modeling techniques for computing the molecular properties can be prohibitively expensive, and motivate the development of machine-learning models that make the same predictions more efficiently. Training graph neural ... | ['Sutanay Choudhury', 'Sotiris Xantheas', 'Ang Li', 'Tom Murray', 'Mario Michael Krell', 'Jenna Bilbrey', 'Jesun Firoz', 'Hatem Helal'] | 2022-11-25 | null | null | null | null | ['molecular-property-prediction'] | ['miscellaneous'] | [ 4.03299987e-01 1.59856811e-01 -4.50985610e-01 -4.39885259e-01
-3.23932737e-01 -4.67760175e-01 2.47244053e-02 1.06470346e+00
-3.35820228e-01 8.79423440e-01 -3.68727058e-01 -1.05468166e+00
-2.32322961e-01 -1.38237536e+00 -1.15297616e+00 -4.87408876e-01
-7.07256138e-01 7.05872536e-01 2.31172591e-01 -5.45580983... | [5.494052886962891, 5.7152581214904785] |
04a09f61-582f-47b2-9be0-844f0aaa4a13 | multi-lingual-wikipedia-summarization-and | null | null | https://aclanthology.org/W19-8904 | https://aclanthology.org/W19-8904.pdf | Multi-lingual Wikipedia Summarization and Title Generation On Low Resource Corpus | MultiLing 2019 Headline Generation Task on Wikipedia Corpus raised a critical and practical problem: multilingual task on low resource corpus. In this paper we proposed QDAS extractive summarization model enhanced by sentence2vec and try to apply transfer learning based on large multilingual pre-trained language model ... | ['Lei LI', 'Zuying Huang', 'Yinan Liu', 'Wei Liu'] | 2019-09-01 | null | null | null | ranlp-2019-9 | ['headline-generation'] | ['natural-language-processing'] | [-2.51138825e-02 5.54362416e-01 -2.56823540e-01 -4.54703160e-03
-1.42368352e+00 -6.22705460e-01 7.97371030e-01 6.16989173e-02
-8.06761384e-01 1.68422246e+00 1.13511872e+00 -1.79677442e-01
4.51809466e-01 -7.10212946e-01 -8.20163012e-01 -3.29351351e-02
1.43002898e-01 6.17941201e-01 9.24332999e-03 -1.04636896... | [12.29757022857666, 9.474344253540039] |
84205f9a-5c3e-47a0-a6f9-d75e4a0c3f19 | capture-learning-and-synthesis-of-3d-speaking | 1905.03079 | null | https://arxiv.org/abs/1905.03079v1 | https://arxiv.org/pdf/1905.03079v1.pdf | Capture, Learning, and Synthesis of 3D Speaking Styles | Audio-driven 3D facial animation has been widely explored, but achieving realistic, human-like performance is still unsolved. This is due to the lack of available 3D datasets, models, and standard evaluation metrics. To address this, we introduce a unique 4D face dataset with about 29 minutes of 4D scans captured at 60... | ['Daniel Cudeiro', 'Michael J. Black', 'Cassidy Laidlaw', 'Anurag Ranjan', 'Timo Bolkart'] | 2019-05-08 | capture-learning-and-synthesis-of-3d-speaking-1 | http://openaccess.thecvf.com/content_CVPR_2019/html/Cudeiro_Capture_Learning_and_Synthesis_of_3D_Speaking_Styles_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Cudeiro_Capture_Learning_and_Synthesis_of_3D_Speaking_Styles_CVPR_2019_paper.pdf | cvpr-2019-6 | ['3d-face-animation', 'talking-face-generation'] | ['computer-vision', 'computer-vision'] | [-6.98942021e-02 1.66251794e-01 5.88009283e-02 -3.89026105e-01
-6.65110648e-01 -6.12317264e-01 4.43180889e-01 -6.64758027e-01
-1.71628013e-01 1.92760631e-01 1.25048637e-01 -1.14790410e-01
5.09398460e-01 -1.54931769e-01 -6.07315183e-01 -4.55017745e-01
-3.76113266e-01 5.66671252e-01 -1.12040155e-01 -2.29287446... | [13.166370391845703, -0.40191134810447693] |
32e2adab-dad2-478c-9789-1243982067ba | self-organizing-intelligent-matter-a-1 | 2101.07627 | null | https://arxiv.org/abs/2101.07627v1 | https://arxiv.org/pdf/2101.07627v1.pdf | Self-Organizing Intelligent Matter: A blueprint for an AI generating algorithm | We propose an artificial life framework aimed at facilitating the emergence of intelligent organisms. In this framework there is no explicit notion of an agent: instead there is an environment made of atomic elements. These elements contain neural operations and interact through exchanges of information and through phy... | ['Frederic Besse', 'Karol Gregor'] | 2021-01-19 | self-organizing-intelligent-matter-a | https://openreview.net/forum?id=160xFQdp7HR | https://openreview.net/pdf?id=160xFQdp7HR | null | ['artificial-life'] | ['miscellaneous'] | [ 1.54140681e-01 4.07525122e-01 4.96399999e-01 5.95710415e-04
7.53086925e-01 -4.04522270e-01 1.16593230e+00 6.48316042e-03
-4.03298885e-01 1.02043164e+00 -1.51706571e-02 -1.37220874e-01
-8.24905038e-02 -1.34563732e+00 -7.17379093e-01 -8.96927357e-01
-3.60153139e-01 5.89080870e-01 3.59664887e-01 -8.93282235... | [5.582370758056641, 4.135457515716553] |
b59de970-a8d0-4de2-995f-f062c2015dce | 2d-driven-3d-object-detection-in-rgb-d-images | null | null | http://openaccess.thecvf.com/content_iccv_2017/html/Lahoud_2D-Driven_3D_Object_ICCV_2017_paper.html | http://openaccess.thecvf.com/content_ICCV_2017/papers/Lahoud_2D-Driven_3D_Object_ICCV_2017_paper.pdf | 2D-Driven 3D Object Detection in RGB-D Images | In this paper, we present a technique that places 3D bounding boxes around objects in an RGB-D scene. Our approach makes best use of the 2D information to quickly reduce the search space in 3D, benefiting from state-of-the-art 2D object detection techniques. We then use the 3D information to orient, place, and score bo... | ['Jean Lahoud', 'Bernard Ghanem'] | 2017-10-01 | null | null | null | iccv-2017-10 | ['object-detection-in-indoor-scenes'] | ['computer-vision'] | [ 2.12000504e-01 4.55553271e-02 1.57253057e-01 -2.33995497e-01
-3.06312561e-01 -5.77535152e-01 5.70907652e-01 4.53641832e-01
-7.52047181e-01 -7.47619867e-02 -3.65679562e-01 -3.62879038e-01
8.45322087e-02 -7.38231957e-01 -7.72493184e-01 -5.43721259e-01
-3.04470062e-01 7.84591377e-01 1.03810644e+00 3.17065567... | [7.655828475952148, -2.611741304397583] |
451eecc4-587c-4a22-a3c3-48e4b5da24e5 | wavelet-based-unsupervised-label-to-image-1 | 2305.09647 | null | https://arxiv.org/abs/2305.09647v1 | https://arxiv.org/pdf/2305.09647v1.pdf | Wavelet-based Unsupervised Label-to-Image Translation | Semantic Image Synthesis (SIS) is a subclass of image-to-image translation where a semantic layout is used to generate a photorealistic image. State-of-the-art conditional Generative Adversarial Networks (GANs) need a huge amount of paired data to accomplish this task while generic unpaired image-to-image translation f... | ['Bin Yang', 'Shuai Zhang', 'Karim Armanious', 'Mohamed Abdelsamad', 'George Eskandar'] | 2023-05-16 | wavelet-based-unsupervised-label-to-image | https://ieeexplore.ieee.org/document/9746759 | https://arxiv.org/pdf/2109.14715.pdf | ieee-international-conference-on-acoustics-14 | ['unsupervised-image-to-image-translation', 'image-to-image-translation', 'multimodal-unsupervised-image-to-image', 'image-to-image-translation'] | ['computer-vision', 'computer-vision', 'computer-vision', 'miscellaneous'] | [ 1.05743206e+00 3.67765665e-01 1.30433708e-01 -3.61742646e-01
-9.14825261e-01 -8.63859117e-01 8.55870068e-01 -3.39453638e-01
-2.39362732e-01 7.22004533e-01 -1.63390011e-01 -2.48432644e-02
1.42037392e-01 -1.09284222e+00 -1.05923676e+00 -7.67946005e-01
5.16451776e-01 5.75689495e-01 1.35126993e-01 -3.77405912... | [11.697796821594238, -0.46125540137290955] |
5ec2ff1c-aca3-41ac-842a-e2fa855539dc | robust-neural-circuit-reconstruction-from | 1811.11356 | null | https://arxiv.org/abs/1811.11356v4 | https://arxiv.org/pdf/1811.11356v4.pdf | Robust neural circuit reconstruction from serial electron microscopy with convolutional recurrent networks | Recent successes in deep learning have started to impact neuroscience. Of particular significance are claims that current segmentation algorithms achieve "super-human" accuracy in an area known as connectomics. However, as we will show, these algorithms do not effectively generalize beyond the particular source and bra... | ['David Berson', 'Junkyung Kim', 'Drew Linsley', 'Thomas Serre'] | 2018-11-28 | null | null | null | null | ['contour-detection'] | ['computer-vision'] | [ 2.15802312e-01 9.53949690e-02 7.12057576e-02 -3.22947472e-01
-5.11069834e-01 -5.88877320e-01 3.56959671e-01 9.28518772e-02
-6.49847627e-01 5.82598269e-01 -2.73406208e-02 -3.53816867e-01
1.13577796e-02 -2.73897827e-01 -7.91685581e-01 -5.39349914e-01
-1.30872890e-01 5.12345135e-01 1.31530121e-01 -1.34130001... | [14.250567436218262, -2.9973933696746826] |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.