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
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9b0b5469-5fc6-4c8f-80b9-309c1c20df10 | real-time-3d-single-object-tracking-with | 2209.00860 | null | https://arxiv.org/abs/2209.00860v1 | https://arxiv.org/pdf/2209.00860v1.pdf | Real-time 3D Single Object Tracking with Transformer | LiDAR-based 3D single object tracking is a challenging issue in robotics and autonomous driving. Currently, existing approaches usually suffer from the problem that objects at long distance often have very sparse or partially-occluded point clouds, which makes the features extracted by the model ambiguous. Ambiguous fe... | ['Zheng Fang', 'Yubo Cui', 'Sifan Zhou', 'Jiayao Shan'] | 2022-09-02 | null | null | null | null | ['3d-single-object-tracking'] | ['computer-vision'] | [-3.63572955e-01 -3.41388792e-01 -1.16576077e-02 -1.42306358e-01
-5.42217553e-01 -4.38329458e-01 5.68730533e-01 -2.46300802e-01
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2.62399286e-01 5.07136345e-01 8.63923311e-01 -6.66382685... | [6.628447532653809, -2.350621223449707] |
5a531fd3-3bb8-4749-b47a-0af531c0832e | scalable-computation-of-prediction-intervals | 2205.03194 | null | https://arxiv.org/abs/2205.03194v1 | https://arxiv.org/pdf/2205.03194v1.pdf | Scalable computation of prediction intervals for neural networks via matrix sketching | Accounting for the uncertainty in the predictions of modern neural networks is a challenging and important task in many domains. Existing algorithms for uncertainty estimation require modifying the model architecture and training procedure (e.g., Bayesian neural networks) or dramatically increase the computational cost... | ['Maxim Panov', 'Alexander Fishkov'] | 2022-05-06 | null | null | null | null | ['prediction-intervals'] | ['miscellaneous'] | [ 2.1291353e-01 3.9699271e-01 -3.2483464e-01 -8.2654268e-01
-8.3083630e-01 -4.6054211e-01 5.3659457e-01 1.2099566e-01
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4.3313825e-01... | [7.376579284667969, 3.8427510261535645] |
7a95c47e-259f-4e87-be59-d0cca64ab667 | dp-lstm-differential-privacy-inspired-lstm | 1912.10806 | null | https://arxiv.org/abs/1912.10806v1 | https://arxiv.org/pdf/1912.10806v1.pdf | DP-LSTM: Differential Privacy-inspired LSTM for Stock Prediction Using Financial News | Stock price prediction is important for value investments in the stock market. In particular, short-term prediction that exploits financial news articles is promising in recent years. In this paper, we propose a novel deep neural network DP-LSTM for stock price prediction, which incorporates the news articles as hidden... | ['Xiao-Yang Liu', 'Yinchuan Li', 'Liuqing Yang', 'Hongyang Yang', 'Xinyi Li'] | 2019-12-20 | null | null | null | null | ['stock-price-prediction', 'stock-prediction'] | ['time-series', 'time-series'] | [-4.14801806e-01 -1.41098991e-01 -1.86567813e-01 -4.22463000e-01
-6.26116276e-01 -3.73385668e-01 3.77028227e-01 -2.50544548e-01
-4.45756257e-01 6.47040188e-01 5.72992682e-01 -3.59374493e-01
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6.64619170e-03 -1.31919965e-01 9.37753245e-02 -1.35258988... | [4.376539707183838, 4.27802848815918] |
b0211b7b-72af-4829-9559-16ea5ef83c56 | ynu-hpcc-at-semeval-2020-task-8-using-a | 2007.13968 | null | https://arxiv.org/abs/2007.13968v1 | https://arxiv.org/pdf/2007.13968v1.pdf | YNU-HPCC at SemEval-2020 Task 8: Using a Parallel-Channel Model for Memotion Analysis | In recent years, the growing ubiquity of Internet memes on social media platforms, such as Facebook, Instagram, and Twitter, has become a topic of immense interest. However, the classification and recognition of memes is much more complicated than that of social text since it involves visual cues and language understan... | ['Xue-jie Zhang', 'Li Yuan', 'Jin Wang'] | 2020-07-28 | null | https://aclanthology.org/2020.semeval-1.116 | https://aclanthology.org/2020.semeval-1.116.pdf | semeval-2020 | ['suggestion-mining'] | ['natural-language-processing'] | [-3.19614202e-01 -2.99789071e-01 8.57670903e-02 -4.29258585e-01
-4.75603789e-01 -5.49855888e-01 5.57320774e-01 1.00090533e-01
-4.82277751e-01 4.37731922e-01 3.03647339e-01 -1.91038847e-01
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2.71749556e-01 -1.61058649e-01 -4.70007025e-02 -3.80616993... | [8.489277839660645, 10.697379112243652] |
d28650c1-ac63-4891-9fc1-906ae4f90c19 | sagdre-sequence-aware-graph-based-document | null | null | https://openreview.net/forum?id=Vi9Cj61ZGsR | https://openreview.net/pdf?id=Vi9Cj61ZGsR | SagDRE: Sequence-Aware Graph-Based Document-Level Relation Extraction with Adaptive Margin Loss | Relation extraction (RE) is an important task for many natural language processing applications. Document-level relation extraction aims to extract the relations within a document and poses many challenges to the RE tasks as it requires reasoning across sentences and handling multiple relations expressed in the same do... | ['Anonymous'] | 2021-11-16 | null | null | null | acl-arr-november-2021-11 | ['document-level-relation-extraction'] | ['natural-language-processing'] | [ 2.37333179e-01 2.48953998e-01 -6.89077020e-01 -3.86492163e-01
-4.10933703e-01 -4.67647821e-01 6.01058960e-01 7.49337912e-01
-3.18166852e-01 8.35249543e-01 3.53243172e-01 -3.25748712e-01
-5.69992900e-01 -1.15332389e+00 -2.87914634e-01 -1.97599202e-01
-3.99056464e-01 3.96789044e-01 5.16757250e-01 -2.99789399... | [9.271812438964844, 8.625006675720215] |
1027d4f4-10eb-4ec4-9c9d-f71d82c29a58 | a-novel-memetic-strategy-for-optimized | 2305.07959 | null | https://arxiv.org/abs/2305.07959v1 | https://arxiv.org/pdf/2305.07959v1.pdf | A Novel Memetic Strategy for Optimized Learning of Classification Trees | Given the increasing interest in interpretable machine learning, classification trees have again attracted the attention of the scientific community because of their glass-box structure. These models are usually built using greedy procedures, solving subproblems to find cuts in the feature space that minimize some impu... | ['Tommaso Aldinucci'] | 2023-05-13 | null | null | null | null | ['interpretable-machine-learning'] | ['methodology'] | [ 3.08285266e-01 2.01423138e-01 -4.93917793e-01 -3.03709716e-01
-5.85061014e-01 -1.04992017e-01 1.60143584e-01 4.69369709e-01
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-9.56775010e-01 -1.05715418e+00 -4.14070934e-01 -7.58934915e-01
7.32867569e-02 1.04832458e+00 -2.20715523e-01 -5.83302937... | [6.077922344207764, 3.7749061584472656] |
5d94ab73-0493-4fd4-b8b9-d13b02e0dd0a | unsupervised-learning-of-sampling | 2302.01174 | null | https://arxiv.org/abs/2302.01174v1 | https://arxiv.org/pdf/2302.01174v1.pdf | Unsupervised Learning of Sampling Distributions for Particle Filters | Accurate estimation of the states of a nonlinear dynamical system is crucial for their design, synthesis, and analysis. Particle filters are estimators constructed by simulating trajectories from a sampling distribution and averaging them based on their importance weight. For particle filters to be computationally trac... | ['Santiago Segarra', 'Richard Baraniuk', 'Martin Sevilla', 'Nicolas Zilberstein', 'Fernando Gama'] | 2023-02-02 | null | null | null | null | ['design-synthesis'] | ['adversarial'] | [ 1.30030528e-01 -1.33679315e-01 -2.40485653e-01 -1.31901935e-01
-4.26576197e-01 -6.07088089e-01 8.85925174e-01 -1.87853262e-01
9.84402839e-03 1.02132678e+00 7.80746862e-02 -2.40003467e-01
-4.12733406e-01 -9.29148734e-01 -7.94822991e-01 -8.97735000e-01
-1.75213441e-01 6.47471666e-01 1.25369430e-01 1.92168698... | [6.582206726074219, 3.5873708724975586] |
e0601c4c-7ff8-45cf-b347-d8c3cd7d13f8 | geoqa-a-geometric-question-answering | 2105.14517 | null | https://arxiv.org/abs/2105.14517v3 | https://arxiv.org/pdf/2105.14517v3.pdf | GeoQA: A Geometric Question Answering Benchmark Towards Multimodal Numerical Reasoning | Automatic math problem solving has recently attracted increasing attention as a long-standing AI benchmark. In this paper, we focus on solving geometric problems, which requires a comprehensive understanding of textual descriptions, visual diagrams, and theorem knowledge. However, the existing methods were highly depen... | ['Liang Lin', 'Eric P. Xing', 'Lingbo Liu', 'Xiaodan Liang', 'Jinghui Qin', 'Jianheng Tang', 'Jiaqi Chen'] | 2021-05-30 | null | https://aclanthology.org/2021.findings-acl.46 | https://aclanthology.org/2021.findings-acl.46.pdf | findings-acl-2021-8 | ['mathematical-reasoning'] | ['natural-language-processing'] | [-1.26100689e-01 1.10908151e-01 2.76171993e-02 -6.22668564e-01
-8.81453693e-01 -7.97855914e-01 2.45203778e-01 9.66677219e-02
1.09718703e-01 4.55461591e-01 6.57028928e-02 -5.47746599e-01
-9.06950086e-02 -1.24942338e+00 -1.05557883e+00 -2.44360015e-01
2.11656824e-01 5.89762747e-01 6.32177573e-03 -3.92521471... | [9.470263481140137, 7.457009315490723] |
29e039bf-8b05-4652-bd7a-13067f029bc3 | focus-is-all-you-need-for-chinese-grammatical | 2210.12692 | null | https://arxiv.org/abs/2210.12692v3 | https://arxiv.org/pdf/2210.12692v3.pdf | Focus Is What You Need For Chinese Grammatical Error Correction | Chinese Grammatical Error Correction (CGEC) aims to automatically detect and correct grammatical errors contained in Chinese text. In the long term, researchers regard CGEC as a task with a certain degree of uncertainty, that is, an ungrammatical sentence may often have multiple references. However, we argue that even ... | ['Hai-Tao Zheng', 'Wei Wu', 'Rui Xie', 'Shirong Ma', 'Yinghui Li', 'Jingheng Ye'] | 2022-10-23 | null | null | null | null | ['grammatical-error-correction'] | ['natural-language-processing'] | [ 9.88101363e-02 3.27013433e-01 2.45479062e-01 -5.23189127e-01
-4.18206692e-01 -3.39349031e-01 5.81190065e-02 2.33765528e-01
-3.94163132e-01 8.98831725e-01 5.81166893e-03 -5.87056577e-01
2.10820464e-03 -8.02578628e-01 -7.97512054e-01 -4.27269638e-01
5.64578772e-01 2.72127867e-01 2.30665654e-01 -3.54987055... | [10.988863945007324, 10.773599624633789] |
38949582-12d4-4add-933c-75a29a269244 | ava-activespeaker-an-audio-visual-dataset-for | 1901.01342 | null | https://arxiv.org/abs/1901.01342v2 | https://arxiv.org/pdf/1901.01342v2.pdf | AVA-ActiveSpeaker: An Audio-Visual Dataset for Active Speaker Detection | Active speaker detection is an important component in video analysis algorithms for applications such as speaker diarization, video re-targeting for meetings, speech enhancement, and human-robot interaction. The absence of a large, carefully labeled audio-visual dataset for this task has constrained algorithm evaluatio... | ['Zhonghua Xi', 'Arkadiusz Stopczynski', 'Sourish Chaudhuri', 'Joseph Roth', 'Caroline Pantofaru', 'Sharadh Ramaswamy', 'Radhika Marvin', 'Cordelia Schmid', 'Andrew Gallagher', 'Ondrej Klejch', 'Liat Kaver'] | 2019-01-05 | null | null | null | null | ['audio-visual-active-speaker-detection'] | ['computer-vision'] | [ 3.78517807e-01 3.59772854e-02 -2.38044575e-01 -5.10730982e-01
-1.23505723e+00 -7.29946375e-01 6.66607916e-01 -6.82977140e-02
-2.27180481e-01 2.98032552e-01 5.30967653e-01 1.57126352e-01
1.37011781e-01 -2.20267158e-02 -3.28590125e-01 -9.36042070e-01
-3.39116603e-01 3.83751005e-01 2.52109319e-01 2.47616649... | [14.425010681152344, 5.195744037628174] |
db738991-b291-453f-b318-a20bd7ca7599 | detection-of-gan-synthesized-street-videos | 2109.04991 | null | https://arxiv.org/abs/2109.04991v2 | https://arxiv.org/pdf/2109.04991v2.pdf | Detection of GAN-synthesized street videos | Research on the detection of AI-generated videos has focused almost exclusively on face videos, usually referred to as deepfakes. Manipulations like face swapping, face reenactment and expression manipulation have been the subject of an intense research with the development of a number of efficient tools to distinguish... | ['Mauro Barni', 'Omran Alamayreh'] | 2021-09-10 | null | null | null | null | ['face-reenactment'] | ['computer-vision'] | [ 2.88950562e-01 1.77010596e-01 1.79689720e-01 -2.01465636e-01
-1.65800661e-01 -4.47897106e-01 8.09701502e-01 -5.16148567e-01
-2.18022764e-01 5.76635599e-01 -8.75543132e-02 1.79745868e-01
1.93610430e-01 -5.91451049e-01 -7.42386043e-01 -7.51530349e-01
-3.02405804e-01 2.55795956e-01 4.38676655e-01 -3.73546690... | [12.523754119873047, 1.0948313474655151] |
22553006-72c6-4b07-9e48-a5327a9cc2f4 | contrastive-prototypical-network-with | null | null | https://www.ecva.net/papers/eccv_2022/papers_ECCV/html/121_ECCV_2022_paper.php | https://www.ecva.net/papers/eccv_2022/papers_ECCV/papers/136790654.pdf | Contrastive Prototypical Network with Wasserstein Confidence Penalty | Unsupervised few-shot learning aims to learn the inductive bias from unlabeled dataset for solving the novel few-shot tasks. The existing unsupervised few-shot learning models and the contrastive learning models follow a unified paradigm. Therefore, we conduct empirical study under this paradigm and find that pairwise ... | ['Zhi-Hong Deng', 'Haoqing Wang'] | 2022-10-21 | null | null | null | european-conference-on-computer-vision-2022 | ['unsupervised-few-shot-learning', 'unsupervised-few-shot-image-classification'] | ['computer-vision', 'computer-vision'] | [ 1.13126367e-01 1.86697826e-01 -3.74095082e-01 -6.45190179e-01
-6.36766493e-01 7.63888359e-02 6.20192826e-01 -2.75305957e-01
-5.00289321e-01 7.17131317e-01 1.77091449e-01 2.49120623e-01
-2.41047367e-01 -6.62282526e-01 -6.70361936e-01 -7.65699804e-01
-5.22306748e-02 5.74635744e-01 5.92586517e-01 -1.60309717... | [9.980477333068848, 2.8856325149536133] |
6e4fc2cd-a66e-439e-a8ee-e9af017a45b9 | come-again-re-query-in-referring-expression | 2110.10206 | null | https://arxiv.org/abs/2110.10206v3 | https://arxiv.org/pdf/2110.10206v3.pdf | Evaluating and Improving Interactions with Hazy Oracles | Many AI systems integrate sensor inputs, world knowledge, and human-provided information to perform inference. While such systems often treat the human input as flawless, humans are better thought of as hazy oracles whose input may be ambiguous or outside of the AI system's understanding. In such situations it makes se... | ['Jason J. Corso', 'Stephan J. Lemmer'] | 2021-10-19 | null | null | null | null | ['video-object-tracking'] | ['computer-vision'] | [ 4.40414041e-01 4.70270663e-01 -1.10398023e-03 -6.85098886e-01
-7.56239414e-01 -8.39203537e-01 5.94019890e-01 3.35509360e-01
-6.69859707e-01 5.00369549e-01 -7.09273070e-02 -6.13243401e-01
-5.04875667e-02 -6.72776282e-01 -7.81094134e-01 -3.25852871e-01
5.26693642e-01 4.90999728e-01 2.95553833e-01 -5.95853887... | [10.747599601745605, 2.029576301574707] |
9b803d15-d8ba-4139-ba0b-53040867aea5 | csat-ftcn-a-fuzzy-oriented-model-with | null | null | https://link.springer.com/article/10.1007/s12559-023-10119-6 | https://link.springer.com/article/10.1007/s12559-023-10119-6 | CSAT‑FTCN: A Fuzzy‑Oriented Model with Contextual Self‑attention Network for Multimodal Emotion Recognition | Multimodal emotion analysis has become a hot trend because of its wide applications, such as the question-answering
system. However, in a real-world scenario, people usually have mixed or partial emotions about evaluating objects. In this
paper, we introduce a fuzzy temporal convolutional network based on contextual ... | ['Geng Tu', 'Runguo Wei', 'Hao liu', 'Dazhi Jiang'] | 2023-01-31 | null | null | null | cognitive-computation-2023-1 | ['multimodal-emotion-recognition', 'multimodal-emotion-recognition'] | ['computer-vision', 'speech'] | [-9.34945568e-02 -4.60094839e-01 1.92460701e-01 -7.16093838e-01
-3.59566867e-01 -1.74707949e-01 4.20059443e-01 1.37293324e-01
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-1.02840580e-01 -6.76402807e-01 -4.64119315e-01 -6.80777252e-01
2.41410822e-01 2.30719805e-01 -4.19329219e-02 -7.04527438... | [13.2105131149292, 5.12113094329834] |
e6856d6f-57bd-44e4-ac95-c5ba0201c26f | biodex-large-scale-biomedical-adverse-drug | 2305.13395 | null | https://arxiv.org/abs/2305.13395v1 | https://arxiv.org/pdf/2305.13395v1.pdf | BioDEX: Large-Scale Biomedical Adverse Drug Event Extraction for Real-World Pharmacovigilance | Timely and accurate extraction of Adverse Drug Events (ADE) from biomedical literature is paramount for public safety, but involves slow and costly manual labor. We set out to improve drug safety monitoring (pharmacovigilance, PV) through the use of Natural Language Processing (NLP). We introduce BioDEX, a large-scale ... | ['Christopher Potts', 'Jack Collins', 'Simon Ellershaw', 'Aneiss Ghodsi', 'Klim Zaporojets', 'Chris Develder', 'Thomas Demeester', 'Johannes Deleu', 'François Remy', "Karel D'Oosterlinck"] | 2023-05-22 | null | null | null | null | ['event-extraction'] | ['natural-language-processing'] | [ 1.10429540e-01 4.67302427e-02 -8.13101232e-01 -1.68349832e-01
-1.26438713e+00 -9.55615163e-01 4.36110765e-01 1.25183201e+00
-3.07353258e-01 1.00672877e+00 4.52196121e-01 -6.64692700e-01
-2.05710664e-01 -3.86727661e-01 -8.15602779e-01 -4.44035619e-01
-2.08760366e-01 2.42251605e-01 -6.32963061e-01 6.25170946... | [8.408272743225098, 8.685077667236328] |
40fe2335-cbed-47e3-b615-02250459cd0d | whats-new-identifying-the-unfolding-of-new | 2302.07748 | null | https://arxiv.org/abs/2302.07748v3 | https://arxiv.org/pdf/2302.07748v3.pdf | Whats New? Identifying the Unfolding of New Events in Narratives | Narratives include a rich source of events unfolding over time and context. Automatic understanding of these events provides a summarised comprehension of the narrative for further computation (such as reasoning). In this paper, we study the Information Status (IS) of the events and propose a novel challenging task: th... | ['Giuseppe Riccardi', 'Satoshi Nakamura', 'Koichiro Yoshino', 'Gabriel Roccabruna', 'Shohei Tanaka', 'Seyed Mahed Mousavi'] | 2023-02-15 | null | null | null | null | ['event-extraction'] | ['natural-language-processing'] | [ 7.28233874e-01 6.39466286e-01 -2.87861973e-01 -4.46241409e-01
-8.79080832e-01 -1.08209634e+00 1.25362968e+00 8.94376755e-01
-2.57632762e-01 9.09161806e-01 1.24423432e+00 -1.61198974e-01
-4.20063138e-02 -8.09875131e-01 -6.21209800e-01 -1.03386037e-01
-2.83887535e-02 3.90100241e-01 4.37197268e-01 -2.26719603... | [10.808202743530273, 8.930517196655273] |
04caf2bd-667b-4866-b3c9-aaa857433730 | shopping-in-the-multiverse-a-counterfactual | 2007.10087 | null | https://arxiv.org/abs/2007.10087v1 | https://arxiv.org/pdf/2007.10087v1.pdf | Shopping in the Multiverse: A Counterfactual Approach to In-Session Attribution | We tackle the challenge of in-session attribution for on-site search engines in eCommerce. We phrase the problem as a causal counterfactual inference, and contrast the approach with rule-based systems from industry settings and prediction models from the multi-touch attribution literature. We approach counterfactuals i... | ['Jacopo Tagliabue', 'Bingqing Yu'] | 2020-07-20 | null | null | null | null | ['counterfactual-inference'] | ['miscellaneous'] | [ 5.06318748e-01 7.89278209e-01 -6.46123886e-01 -4.83688235e-01
-4.07424182e-01 -6.45626426e-01 1.15358889e+00 -2.16451325e-02
-2.00622424e-01 8.60074878e-01 9.36569571e-01 -6.23711705e-01
-5.20196080e-01 -6.90879047e-01 -8.22960317e-01 -1.68627456e-01
-2.55280975e-02 6.58545673e-01 -6.28284752e-01 -9.66285542... | [8.515873908996582, 5.507641315460205] |
394b401c-ac95-4275-88da-fc3b4bb6b10e | semi-supervised-object-detection-for-sorghum | 2305.09810 | null | https://arxiv.org/abs/2305.09810v1 | https://arxiv.org/pdf/2305.09810v1.pdf | Semi-Supervised Object Detection for Sorghum Panicles in UAV Imagery | The sorghum panicle is an important trait related to grain yield and plant development. Detecting and counting sorghum panicles can provide significant information for plant phenotyping. Current deep-learning-based object detection methods for panicles require a large amount of training data. The data labeling is time-... | ['Edward J. Delp', 'Changye Yang', 'Jiaqi Guo', 'Enyu Cai'] | 2023-05-16 | null | null | null | null | ['plant-phenotyping', 'semi-supervised-object-detection'] | ['computer-vision', 'computer-vision'] | [ 1.80151999e-01 1.27253756e-01 -2.74951071e-01 -4.61648047e-01
-1.89128295e-01 -8.32531214e-01 -8.57538804e-02 4.22952801e-01
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2.21302837e-01 -1.35200441e+00 -1.52117819e-01 -6.99610651e-01
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c8d0d57f-ec16-4d8e-a25e-c7c522cd8493 | transformer-based-value-function | 2208.07298 | null | https://arxiv.org/abs/2208.07298v1 | https://arxiv.org/pdf/2208.07298v1.pdf | Transformer-based Value Function Decomposition for Cooperative Multi-agent Reinforcement Learning in StarCraft | The StarCraft II Multi-Agent Challenge (SMAC) was created to be a challenging benchmark problem for cooperative multi-agent reinforcement learning (MARL). SMAC focuses exclusively on the problem of StarCraft micromanagement and assumes that each unit is controlled individually by a learning agent that acts independentl... | ['Gita Sukthankar', 'Syed Hammad Ahmed', 'Muhammad Junaid Khan'] | 2022-08-15 | null | null | null | null | ['starcraft-ii', 'smac-1', 'starcraft', 'smac'] | ['playing-games', 'playing-games', 'playing-games', 'playing-games'] | [-4.36544448e-01 5.63534833e-02 -2.93488115e-01 1.58322856e-01
-8.55884314e-01 -8.11291993e-01 8.39440823e-01 1.55902326e-01
-8.31760108e-01 1.11032701e+00 1.09227620e-01 -6.11008704e-02
-1.78927168e-01 -5.56982338e-01 -5.75468361e-01 -8.82074475e-01
-7.06464589e-01 1.29063213e+00 4.61378843e-01 -7.83608794... | [3.699101448059082, 1.9575698375701904] |
7dd31304-7eb4-4d39-ba23-2b02a20b2f6e | on-the-advance-of-making-language-models | 2206.02336 | null | https://arxiv.org/abs/2206.02336v3 | https://arxiv.org/pdf/2206.02336v3.pdf | Making Large Language Models Better Reasoners with Step-Aware Verifier | Few-shot learning is a challenging task that requires language models to generalize from limited examples. Large language models like GPT-3 and PaLM have made impressive progress in this area, but they still face difficulties in reasoning tasks such as GSM8K, a benchmark for arithmetic problems. To improve their reason... | ['Weizhu Chen', 'Jian-Guang Lou', 'Bei Chen', 'Qiang Fu', 'Shizhuo Zhang', 'Zeqi Lin', 'Yifei Li'] | 2022-06-06 | null | null | null | null | ['gsm8k', 'arithmetic-reasoning'] | ['natural-language-processing', 'reasoning'] | [-6.82686120e-02 2.88895309e-01 4.34156768e-02 -3.85100782e-01
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-2.41935208e-01 -1.20377457e+00 -5.55953443e-01 -1.65080503e-01
2.00152412e-01 6.83559418e-01 6.95347667e-01 -7.31097758... | [9.742115020751953, 7.403118133544922] |
5b248580-ed82-46d8-bffe-9b713f7fb030 | a-deep-bayesian-bandits-approach-for | 2205.02944 | null | https://arxiv.org/abs/2205.02944v2 | https://arxiv.org/pdf/2205.02944v2.pdf | A Deep Bayesian Bandits Approach for Anticancer Therapy: Exploration via Functional Prior | Learning personalized cancer treatment with machine learning holds great promise to improve cancer patients' chance of survival. Despite recent advances in machine learning and precision oncology, this approach remains challenging as collecting data in preclinical/clinical studies for modeling multiple treatment effica... | ['Su-In Lee', 'Yifang Chen', 'Mingyu Lu'] | 2022-05-05 | null | null | null | null | ['drug-response-prediction'] | ['medical'] | [ 6.54715657e-01 -3.38654011e-01 -1.19943917e+00 -2.44491249e-01
-1.46851432e+00 -4.93378937e-01 3.45044643e-01 5.59098125e-01
-3.16782326e-01 1.34289050e+00 3.28938842e-01 -6.69126332e-01
-4.96223480e-01 -5.56513965e-01 -6.63121462e-01 -1.09160459e+00
4.14599746e-01 8.89021695e-01 -3.09087664e-01 4.10325766... | [5.70280647277832, 5.689138412475586] |
b7030216-b679-496e-972f-118baa686639 | gradient-matching-generative-networks-for | null | null | http://openaccess.thecvf.com/content_CVPR_2019/html/Sariyildiz_Gradient_Matching_Generative_Networks_for_Zero-Shot_Learning_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Sariyildiz_Gradient_Matching_Generative_Networks_for_Zero-Shot_Learning_CVPR_2019_paper.pdf | Gradient Matching Generative Networks for Zero-Shot Learning | Zero-shot learning (ZSL) is one of the most promising problems where substantial progress can potentially be achieved through unsupervised learning, due to distributional differences between supervised and zero-shot classes. For this reason, several works investigate the incorporation of discriminative domain adaptatio... | [' Ramazan Gokberk Cinbis', 'Mert Bulent Sariyildiz'] | 2019-06-01 | null | null | null | cvpr-2019-6 | ['generalized-zero-shot-learning-unseen'] | ['computer-vision'] | [ 5.03587484e-01 2.16479391e-01 -2.28553697e-01 -4.89674151e-01
-1.04418480e+00 -9.43939313e-02 8.26606214e-01 1.31078303e-01
-2.29850769e-01 7.54741728e-01 2.45181676e-02 2.45150283e-01
-3.54497842e-02 -1.09691191e+00 -6.93063617e-01 -8.90720487e-01
3.27744216e-01 4.58661228e-01 3.66458088e-01 -9.88527462... | [9.944303512573242, 2.950446367263794] |
3e9e6e6f-9ff7-4d0f-afcf-15208523697e | eico-improving-few-shot-text-classification | null | null | https://aclanthology.org/2022.findings-acl.283 | https://aclanthology.org/2022.findings-acl.283.pdf | EICO: Improving Few-Shot Text Classification via Explicit and Implicit Consistency Regularization | While the prompt-based fine-tuning methods had advanced few-shot natural language understanding tasks, self-training methods are also being explored. This work revisits the consistency regularization in self-training and presents explicit and implicit consistency regularization enhanced language model (EICO). By employ... | ['Cheng Yao', 'Lei Zhao'] | null | null | null | null | findings-acl-2022-5 | ['few-shot-text-classification'] | ['natural-language-processing'] | [ 2.50025511e-01 2.54651606e-01 -5.91115296e-01 -7.14399934e-01
-6.93215430e-01 1.18429825e-01 7.77591825e-01 6.66415468e-02
-3.77833694e-01 5.55307209e-01 6.14177465e-01 4.73360494e-02
2.31499657e-01 -6.79105341e-01 -6.46590590e-02 -2.72355735e-01
5.25720716e-01 7.54101276e-01 1.98019788e-01 -3.43866229... | [10.797394752502441, 7.584290504455566] |
3413af2d-42d1-469b-941b-5a20e1f6a04d | solving-the-steiner-tree-problem-with-few | 2011.04593 | null | https://arxiv.org/abs/2011.04593v1 | https://arxiv.org/pdf/2011.04593v1.pdf | Solving the Steiner Tree Problem with few Terminals | The Steiner tree problem is a well-known problem in network design, routing, and VLSI design. Given a graph, edge costs, and a set of dedicated vertices (terminals), the Steiner tree problem asks to output a sub-graph that connects all terminals at minimum cost. A state-of-the-art algorithm to solve the Steiner tree pr... | ['Andre Schidler', 'Markus Hecher', 'Johannes K. Fichte'] | 2020-11-09 | null | null | null | null | ['steiner-tree-problem'] | ['graphs'] | [ 2.99117744e-01 5.53641796e-01 -5.17588258e-01 -1.03141420e-01
-4.31022167e-01 -1.04968178e+00 -2.92558968e-01 1.34923771e-01
4.88832518e-02 8.08655262e-01 -3.14027101e-01 -8.05737197e-01
-7.01229215e-01 -9.56891894e-01 -6.39470458e-01 -6.50500059e-01
-2.83113688e-01 7.49475777e-01 4.32243019e-01 -1.88616097... | [5.237379550933838, 2.9395179748535156] |
f0289cfd-4993-4832-a2e1-696b7c1c8ad1 | identifying-risk-factors-for-heart-disease-in | null | null | https://aclanthology.org/W18-2303 | https://aclanthology.org/W18-2303.pdf | Identifying Risk Factors For Heart Disease in Electronic Medical Records: A Deep Learning Approach | Automatic identification of heart disease risk factors in clinical narratives can expedite disease progression modelling and support clinical decisions. Existing practical solutions for cardiovascular risk detection are mostly hybrid systems entailing the integration of knowledge-driven and data-driven methods, relying... | ['Hamed Hassanzadeh', 'Thanat Chokwijitkul', 'Siegfried Perez', 'Anthony Nguyen'] | 2018-07-01 | null | null | null | ws-2018-7 | ['clinical-concept-extraction'] | ['medical'] | [ 5.95114678e-02 3.83524030e-01 -4.49365765e-01 -3.27046782e-01
-1.04013205e+00 -1.77586347e-01 8.15111399e-01 8.84003937e-01
-4.42866057e-01 4.45995867e-01 7.50588655e-01 -6.85179353e-01
-8.30740571e-01 -7.10938752e-01 -2.24612672e-02 -4.74952906e-01
-3.67371291e-01 7.94436038e-01 -2.96625286e-01 -1.17685005... | [8.357229232788086, 8.312283515930176] |
a5be6521-76a6-4f57-a3ba-3de068c1819a | stochastic-contextual-bandits-with-long | 2302.00814 | null | https://arxiv.org/abs/2302.00814v2 | https://arxiv.org/pdf/2302.00814v2.pdf | Stochastic Contextual Bandits with Long Horizon Rewards | The growing interest in complex decision-making and language modeling problems highlights the importance of sample-efficient learning over very long horizons. This work takes a step in this direction by investigating contextual linear bandits where the current reward depends on at most $s$ prior actions and contexts (n... | ['Samet Oymak', 'Maryam Fazel', 'Fabio Pasqualetti', 'Yingcong Li', 'Yuzhen Qin'] | 2023-02-02 | null | null | null | null | ['multi-armed-bandits'] | ['miscellaneous'] | [ 2.02035621e-01 8.92792046e-02 -6.13191783e-01 -5.99275865e-02
-9.98395562e-01 -8.33406627e-01 3.60191911e-01 2.17487857e-01
-6.60006702e-01 6.73845828e-01 -2.97336113e-02 -5.34139514e-01
-9.97065425e-01 -5.74869156e-01 -9.00904179e-01 -8.27767670e-01
-9.74675000e-01 3.44768941e-01 -2.06690326e-01 -1.83210298... | [4.644092559814453, 3.4112918376922607] |
925488a8-fe69-4643-8e2b-80b376da0e13 | single-stage-diffusion-nerf-a-unified | 2304.06714 | null | https://arxiv.org/abs/2304.06714v3 | https://arxiv.org/pdf/2304.06714v3.pdf | Single-Stage Diffusion NeRF: A Unified Approach to 3D Generation and Reconstruction | 3D-aware image synthesis encompasses a variety of tasks, such as scene generation and novel view synthesis from images. Despite numerous task-specific methods, developing a comprehensive model remains challenging. In this paper, we present SSDNeRF, a unified approach that employs an expressive diffusion model to learn ... | ['Hao Su', 'Lingjie Liu', 'Zhuowen Tu', 'Wei Tian', 'Anpei Chen', 'Jiatao Gu', 'Hansheng Chen'] | 2023-04-13 | null | null | null | null | ['scene-generation', '3d-aware-image-synthesis'] | ['computer-vision', 'computer-vision'] | [ 4.32691157e-01 2.28201021e-02 2.45398402e-01 -4.76830065e-01
-1.05520749e+00 -6.83457434e-01 9.74500299e-01 -6.34160697e-01
-6.54181605e-03 5.02708077e-01 4.54418808e-01 6.63172230e-02
1.37766302e-01 -7.05363095e-01 -9.56421673e-01 -6.99986279e-01
5.07853746e-01 5.62482059e-01 1.11906596e-01 -5.07647283... | [9.263154983520508, -3.1042797565460205] |
dd48db85-ecec-43d7-a293-e63887206bad | data-augmentation-for-robust-keyword-spotting | 1808.00563 | null | http://arxiv.org/abs/1808.00563v1 | http://arxiv.org/pdf/1808.00563v1.pdf | Data Augmentation for Robust Keyword Spotting under Playback Interference | Accurate on-device keyword spotting (KWS) with low false accept and false
reject rate is crucial to customer experience for far-field voice control of
conversational agents. It is particularly challenging to maintain low false
reject rate in real world conditions where there is (a) ambient noise from
external sources s... | ['Anirudh Raju', 'Nikko Strom', 'Arindam Mandal', 'Xing Liu', 'Sankaran Panchapagesan'] | 2018-08-01 | null | null | null | null | ['acoustic-echo-cancellation', 'acoustic-echo-cancellation'] | ['medical', 'speech'] | [ 5.68248808e-01 -2.80359268e-01 4.76268440e-01 -9.84515622e-02
-1.03376830e+00 -6.92578077e-01 3.95645231e-01 -1.13281988e-01
-2.99742848e-01 5.03903389e-01 1.97231710e-01 -4.09193039e-01
1.15215480e-01 -1.11812010e-01 -3.22896987e-01 -4.68444258e-01
-3.13412435e-02 1.76904470e-01 2.95725793e-01 -9.18667987... | [14.842238426208496, 5.976820945739746] |
14baff3f-d0f8-4d23-bd7f-7c1a81a74039 | learning-to-superoptimize-real-world-programs | 2109.13498 | null | https://arxiv.org/abs/2109.13498v2 | https://arxiv.org/pdf/2109.13498v2.pdf | Learning to Superoptimize Real-world Programs | Program optimization is the process of modifying software to execute more efficiently. Superoptimizers attempt to find the optimal program by employing significantly more expensive search and constraint solving techniques. Generally, these methods do not scale well to programs in real development scenarios, and as a re... | ['Graham Neubig', 'Edward Schwartz', 'Claire Le Goues', 'Jeremy Lacomis', 'Pengcheng Yin', 'Alex Shypula'] | 2021-09-28 | null | null | null | null | ['compiler-optimization'] | ['computer-code'] | [-7.78853372e-02 5.36421537e-02 -8.78312469e-01 -5.20656049e-01
-8.06988835e-01 -5.66336095e-01 1.70252860e-01 -1.48420185e-01
-3.89634728e-01 9.66426671e-01 1.59761578e-01 -9.40739751e-01
4.95680213e-01 -5.11696339e-01 -1.15586483e+00 -5.74154109e-02
-1.32898703e-01 3.01405489e-01 8.35552532e-03 -4.78613108... | [7.844346523284912, 7.540949821472168] |
818cd3a1-6c7b-4f23-b727-37fd66472802 | deep-fisher-kernels-end-to-end-learning-of | null | null | http://openaccess.thecvf.com/content_cvpr_2014/html/Sydorov_Deep_Fisher_Kernels_2014_CVPR_paper.html | http://openaccess.thecvf.com/content_cvpr_2014/papers/Sydorov_Deep_Fisher_Kernels_2014_CVPR_paper.pdf | Deep Fisher Kernels - End to End Learning of the Fisher Kernel GMM Parameters | Fisher Kernels and Deep Learning were two developments with significant impact on large-scale object categorization in the last years. Both approaches were shown to achieve state-of-the-art results on large-scale object categorization datasets, such as ImageNet. Conceptually, however, they are perceived as very differe... | ['Christoph H. Lampert', 'Vladyslav Sydorov', 'Mayu Sakurada'] | 2014-06-01 | null | null | null | cvpr-2014-6 | ['object-categorization'] | ['computer-vision'] | [ 1.91255268e-02 1.30940471e-02 -1.27277151e-01 -4.66255963e-01
-1.22438133e-01 -7.88878798e-01 8.34955990e-01 3.25694770e-01
-5.50837159e-01 2.47600615e-01 3.25920917e-02 -3.85390043e-01
-4.96371388e-01 -6.71526074e-01 -5.46475410e-01 -7.98786819e-01
-1.12039767e-01 2.78762668e-01 2.92642981e-01 -1.96090132... | [9.238531112670898, 2.8487422466278076] |
8698d938-1ef7-48a4-9bd0-b63ecaba546a | so-net-self-organizing-network-for-point | 1803.04249 | null | http://arxiv.org/abs/1803.04249v4 | http://arxiv.org/pdf/1803.04249v4.pdf | SO-Net: Self-Organizing Network for Point Cloud Analysis | This paper presents SO-Net, a permutation invariant architecture for deep
learning with orderless point clouds. The SO-Net models the spatial
distribution of point cloud by building a Self-Organizing Map (SOM). Based on
the SOM, SO-Net performs hierarchical feature extraction on individual points
and SOM nodes, and ult... | ['Ben M. Chen', 'Gim Hee Lee', 'Jiaxin Li'] | 2018-03-12 | so-net-self-organizing-network-for-point-1 | http://openaccess.thecvf.com/content_cvpr_2018/html/Li_SO-Net_Self-Organizing_Network_CVPR_2018_paper.html | http://openaccess.thecvf.com/content_cvpr_2018/papers/Li_SO-Net_Self-Organizing_Network_CVPR_2018_paper.pdf | cvpr-2018-6 | ['point-cloud-reconstruction', '3d-point-cloud-linear-classification', '3d-part-segmentation', 'unsupervised-3d-point-cloud-linear-evaluation'] | ['computer-vision', 'computer-vision', 'computer-vision', 'computer-vision'] | [-3.17054659e-01 -2.07469687e-01 -2.27333948e-01 -3.34394783e-01
-1.69286877e-01 -6.04832828e-01 4.73561466e-01 1.04292504e-01
-9.77985412e-02 2.55087435e-01 -1.73839614e-01 -1.99169576e-01
-4.98909354e-01 -1.12212753e+00 -1.07662642e+00 -7.12719202e-01
-1.10871516e-01 8.92766178e-01 3.84865314e-01 1.62211224... | [7.903643608093262, -3.6926395893096924] |
38a0b8e6-27dd-4a35-8fd2-4ffe416ec880 | curricular-contrastive-regularization-for | 2303.14218 | null | https://arxiv.org/abs/2303.14218v2 | https://arxiv.org/pdf/2303.14218v2.pdf | Curricular Contrastive Regularization for Physics-aware Single Image Dehazing | Considering the ill-posed nature, contrastive regularization has been developed for single image dehazing, introducing the information from negative images as a lower bound. However, the contrastive samples are nonconsensual, as the negatives are usually represented distantly from the clear (i.e., positive) image, leav... | ['Yong Du', 'Junyu Dong', 'Shengfeng He', 'Jiahui Zhan', 'Yu Zheng'] | 2023-03-24 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Zheng_Curricular_Contrastive_Regularization_for_Physics-Aware_Single_Image_Dehazing_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Zheng_Curricular_Contrastive_Regularization_for_Physics-Aware_Single_Image_Dehazing_CVPR_2023_paper.pdf | cvpr-2023-1 | ['image-dehazing'] | ['computer-vision'] | [ 2.08431154e-01 4.62331064e-02 3.61419886e-01 -3.20994198e-01
-5.16082406e-01 -1.80910572e-01 2.72823215e-01 -2.01409400e-01
-2.50913471e-01 5.68812668e-01 2.01610029e-01 -1.38428062e-01
-3.01007867e-01 -7.31547475e-01 -8.51922750e-01 -1.29387522e+00
1.87277630e-01 -4.62351069e-02 1.57520548e-01 -4.25269544... | [10.925012588500977, -3.0590970516204834] |
c985f7f2-f8d6-4915-9d87-4d258d3bc83c | fully-automatic-liver-attenuation-estimation | 1906.09549 | null | https://arxiv.org/abs/1906.09549v2 | https://arxiv.org/pdf/1906.09549v2.pdf | Fully Automatic Liver Attenuation Estimation Combing CNN Segmentation and Morphological Operations | Manually tracing regions of interest (ROIs) within the liver is the de facto standard method for measuring liver attenuation on computed tomography (CT) in diagnosing nonalcoholic fatty liver disease (NAFLD). However, manual tracing is resource intensive. To address these limitations and to expand the availability of a... | ['Yuankai Huo', 'J. Jeffery Carr', 'Barry I. Freedman', 'James G. Terry', 'Thomas A. Lasko', 'Sangeeta Nair', 'Jiachen Wang', 'Bennett A. Landman'] | 2019-06-23 | null | null | null | null | ['liver-segmentation'] | ['medical'] | [-2.00872794e-01 -3.64375144e-01 -1.80370227e-01 -3.90580893e-01
-7.78942883e-01 -5.18928230e-01 1.82051003e-01 4.54871386e-01
-2.97752470e-01 4.62378353e-01 2.42561832e-01 -7.64110506e-01
2.83288807e-01 -9.47452009e-01 -3.97937894e-01 -6.68303668e-01
-7.33858109e-01 6.47661269e-01 1.94914386e-01 5.47136307... | [14.488167762756348, -2.7024292945861816] |
46763a75-c3ca-431e-8796-06425065709d | sparse-distillation-speeding-up-text | 2110.08536 | null | https://arxiv.org/abs/2110.08536v2 | https://arxiv.org/pdf/2110.08536v2.pdf | Sparse Distillation: Speeding Up Text Classification by Using Bigger Student Models | Distilling state-of-the-art transformer models into lightweight student models is an effective way to reduce computation cost at inference time. The student models are typically compact transformers with fewer parameters, while expensive operations such as self-attention persist. Therefore, the improved inference speed... | ['Aaron Jaech', 'Xiang Ren', 'Sinong Wang', 'Mike Lewis', 'Madian Khabsa', 'Qinyuan Ye'] | 2021-10-16 | sparse-distillation-speeding-up-text-1 | https://aclanthology.org/2022.naacl-main.169 | https://aclanthology.org/2022.naacl-main.169.pdf | naacl-2022-7 | ['sentence-pair-classification'] | ['natural-language-processing'] | [-6.78958222e-02 1.30741060e-01 -2.58982390e-01 -4.82847482e-01
-1.06692898e+00 -5.30514956e-01 3.78485054e-01 3.42888296e-01
-3.53355825e-01 5.24633288e-01 1.09058172e-01 -9.42621410e-01
2.22298622e-01 -8.62902999e-01 -6.94402814e-01 -5.61297715e-01
4.31189150e-01 9.83727515e-01 2.88426489e-01 -2.39078999... | [8.76987075805664, 3.660040855407715] |
14ba173e-2014-4ecd-9022-2d6616249983 | mdpose-real-time-multi-person-pose-estimation | 2302.08751 | null | https://arxiv.org/abs/2302.08751v2 | https://arxiv.org/pdf/2302.08751v2.pdf | MDPose: Real-Time Multi-Person Pose Estimation via Mixture Density Model | One of the major challenges in multi-person pose estimation is instance-aware keypoint estimation. Previous methods address this problem by leveraging an off-the-shelf detector, heuristic post-grouping process or explicit instance identification process, hindering further improvements in the inference speed which is an... | ['Nojun Kwak', 'Jihye Hwang', 'Jaeyoung Yoo', 'Seunghyeon Seo'] | 2023-02-17 | null | null | null | null | ['multi-person-pose-estimation'] | ['computer-vision'] | [-4.11285877e-01 -2.18631566e-01 -1.51225492e-01 -1.88516498e-01
-9.17298734e-01 -3.37270141e-01 6.16813779e-01 9.28091854e-02
-8.34714472e-01 5.80114007e-01 -1.16667580e-02 3.10463846e-01
-4.43082713e-02 -6.07136369e-01 -9.95654225e-01 -5.41338325e-01
-1.60982590e-02 9.45942342e-01 2.06965357e-01 2.41343081... | [7.131261825561523, -0.8102759718894958] |
c645062c-3835-43e2-b4e8-387baee4b3d8 | supsiam-non-contrastive-auxiliary-loss-for | 2302.07754 | null | https://arxiv.org/abs/2302.07754v1 | https://arxiv.org/pdf/2302.07754v1.pdf | SupSiam: Non-contrastive Auxiliary Loss for Learning from Molecular Conformers | We investigate Siamese networks for learning related embeddings for augmented samples of molecular conformers. We find that a non-contrastive (positive-pair only) auxiliary task aids in supervised training of Euclidean neural networks (E3NNs) and increases manifold smoothness (MS) around point-cloud geometries. We demo... | ['Andrew Watkins', 'Nathan C. Frey', 'Jae Hyeon Lee', 'Joshua Yao-Yu Lin', 'Ji Won Park', 'Michael Maser'] | 2023-02-15 | null | null | null | null | ['activity-prediction', 'activity-prediction'] | ['computer-vision', 'time-series'] | [ 2.04796448e-01 -3.80838402e-02 -2.86624581e-01 -4.57282633e-01
-8.51146221e-01 -4.97610778e-01 4.86755311e-01 6.33884728e-01
-6.16980672e-01 7.31589735e-01 8.99329260e-02 -5.28443217e-01
-5.69311380e-01 -4.67047513e-01 -7.81704485e-01 -8.56446683e-01
-7.39007354e-01 4.44028467e-01 2.19171103e-02 -1.74731418... | [5.106529712677002, 5.67194938659668] |
8f9d4ed3-272f-4475-8cda-02efd7d6f499 | hybrid-reinforced-medical-report-generation | 2210.13729 | null | https://arxiv.org/abs/2210.13729v1 | https://arxiv.org/pdf/2210.13729v1.pdf | Hybrid Reinforced Medical Report Generation with M-Linear Attention and Repetition Penalty | To reduce doctors' workload, deep-learning-based automatic medical report generation has recently attracted more and more research efforts, where deep convolutional neural networks (CNNs) are employed to encode the input images, and recurrent neural networks (RNNs) are used to decode the visual features into medical re... | ['Thomas Lukasiewicz', 'Chang Qi', 'Junyang Chen', 'Zhenghua Xu', 'Wenting Xu'] | 2022-10-14 | null | null | null | null | ['medical-report-generation'] | ['medical'] | [ 1.05697721e-01 3.47954750e-01 -1.82685152e-01 -3.56703401e-01
-1.06957424e+00 6.21828958e-02 5.20208657e-01 9.93170738e-02
-3.34799886e-01 5.33341229e-01 5.15650153e-01 -2.32264087e-01
-3.42411011e-01 -7.17857599e-01 -5.13226628e-01 -5.43526471e-01
-8.14224184e-02 2.98061103e-01 -8.24428126e-02 -1.41730681... | [15.041853904724121, -1.4162007570266724] |
97be1198-0927-4e68-b967-0ecbeff4222c | piano-timbre-development-analysis-using | 2112.03214 | null | https://arxiv.org/abs/2112.03214v2 | https://arxiv.org/pdf/2112.03214v2.pdf | Piano Timbre Development Analysis using Machine Learning | A data set of recorded single played tones of a concert grand piano is investigated using Machine Learning (ML) on psychoacoustic timbre features. The examined instrument has been recorded at two stages: firstly right after manufacture and secondly after being played in a concert hall for one year. A previous study [Pl... | ['Rolf Bader', 'Niko Plath'] | 2021-12-06 | null | null | null | null | ['music-information-retrieval'] | ['music'] | [-1.04531497e-01 -1.96716383e-01 4.63098884e-01 2.50474513e-02
-5.04831553e-01 -6.82921588e-01 6.23011231e-01 5.77863157e-01
-3.94772112e-01 1.95181519e-01 3.85000497e-01 9.29778740e-02
-7.54007697e-01 -6.16922498e-01 1.30489632e-01 -9.18622851e-01
-4.75957602e-01 3.69510680e-01 3.68125975e-01 -4.86447632... | [15.805678367614746, 5.375743389129639] |
867b12dc-e47c-473a-aafa-32fb877fe6c4 | identifying-supporting-facts-for-multi-hop | 1910.00290 | null | https://arxiv.org/abs/1910.00290v1 | https://arxiv.org/pdf/1910.00290v1.pdf | Identifying Supporting Facts for Multi-hop Question Answering with Document Graph Networks | Recent advances in reading comprehension have resulted in models that surpass human performance when the answer is contained in a single, continuous passage of text. However, complex Question Answering (QA) typically requires multi-hop reasoning - i.e. the integration of supporting facts from different sources, to infe... | ['Viktor Schlegel', 'Mokanarangan Thayaparan', 'Marco Valentino', 'Andre Freitas'] | 2019-10-01 | identifying-supporting-facts-for-multi-hop-1 | https://aclanthology.org/D19-5306 | https://aclanthology.org/D19-5306.pdf | ws-2019-11 | ['multi-hop-question-answering'] | ['knowledge-base'] | [ 2.56460607e-01 9.17692125e-01 1.13471560e-01 -3.98653179e-01
-1.13876724e+00 -6.89636469e-01 7.18071342e-01 1.30835986e+00
-2.10814878e-01 8.16873014e-01 8.42154741e-01 -8.55554283e-01
-4.56150234e-01 -1.07235038e+00 -8.23012173e-01 4.09147501e-01
8.94644186e-02 8.14760804e-01 7.38801777e-01 -8.38719785... | [10.998550415039062, 7.94274377822876] |
dc0564d3-5b2c-497c-af04-58f54d67cd05 | enabling-a-network-ai-gym-for-autonomous | 2304.01366 | null | https://arxiv.org/abs/2304.01366v1 | https://arxiv.org/pdf/2304.01366v1.pdf | Enabling A Network AI Gym for Autonomous Cyber Agents | This work aims to enable autonomous agents for network cyber operations (CyOps) by applying reinforcement and deep reinforcement learning (RL/DRL). The required RL training environment is particularly challenging, as it must balance the need for high-fidelity, best achieved through real network emulation, with the need... | ['Thomas Kunz', 'James Hailing Rao', 'Adrian Taylor', 'Jean-Pierre S. El Rami', 'Li Li'] | 2023-04-03 | null | null | null | null | ['offline-rl'] | ['playing-games'] | [-2.60561228e-01 6.88922703e-01 2.65107393e-01 1.16228275e-01
-5.67963570e-02 -6.41721547e-01 7.99262822e-01 -3.22373420e-01
-6.85310364e-01 1.15108418e+00 -7.19911754e-01 -6.90165818e-01
-2.17284128e-01 -1.04507363e+00 -6.72530293e-01 -5.09511948e-01
-6.39702439e-01 9.05789793e-01 1.38213128e-01 -7.29220390... | [4.0331244468688965, 1.538980484008789] |
9cc54934-5966-43c4-92a7-e1bd68554563 | effective-few-shot-classification-with | null | null | https://aclanthology.org/2020.coling-main.92 | https://aclanthology.org/2020.coling-main.92.pdf | Effective Few-Shot Classification with Transfer Learning | Few-shot learning addresses the the problem of learning based on a small amount of training data. Although more well-studied in the domain of computer vision, recent work has adapted the Amazon Review Sentiment Classification (ARSC) text dataset for use in the few-shot setting. In this work, we use the ARSC dataset to ... | ["Neil O{'}Hare", 'Kapil Thadani', 'Aakriti Gupta'] | 2020-12-01 | null | null | null | coling-2020-8 | ['few-shot-text-classification'] | ['natural-language-processing'] | [ 3.37555140e-01 -1.14150971e-01 -4.57876921e-01 -7.77512729e-01
-9.34512675e-01 -3.49454790e-01 1.00597262e+00 4.24082339e-01
-7.11753964e-01 3.69886339e-01 9.42597017e-02 -3.99975963e-02
-2.25978158e-03 -7.83548713e-01 -4.08612221e-01 -5.36071420e-01
4.30500269e-01 5.48691928e-01 5.51445782e-01 -3.75513792... | [10.073862075805664, 3.219714641571045] |
1c38a390-b31c-4ede-9c85-a8fa488bf445 | causal-inference-out-of-control-estimating | 2302.04989 | null | https://arxiv.org/abs/2302.04989v1 | https://arxiv.org/pdf/2302.04989v1.pdf | Causal Inference out of Control: Estimating the Steerability of Consumption | Regulators and academics are increasingly interested in the causal effect that algorithmic actions of a digital platform have on consumption. We introduce a general causal inference problem we call the steerability of consumption that abstracts many settings of interest. Focusing on observational designs and exploiting... | ['Celestine Mendler-Dünner', 'Moritz Hardt', 'Gary Cheng'] | 2023-02-10 | null | null | null | null | ['econometrics'] | ['miscellaneous'] | [ 1.10224932e-01 2.10936651e-01 -8.64519417e-01 2.56552368e-01
-1.61415011e-01 -9.93835032e-01 9.08220589e-01 1.00123242e-01
-3.37462835e-02 4.75551039e-01 7.49031126e-01 -7.08289146e-01
-5.78290999e-01 -7.05205560e-01 -8.14414680e-01 -7.13635743e-01
-2.42826447e-01 -2.72165388e-01 -3.81843925e-01 1.11933947... | [7.950804710388184, 5.314448833465576] |
b17ab42f-3f67-4dcf-814c-facd99874c6e | representation-learning-with-autoencoders-for | 1908.09174 | null | https://arxiv.org/abs/1908.09174v2 | https://arxiv.org/pdf/1908.09174v2.pdf | Representation Learning with Autoencoders for Electronic Health Records: A Comparative Study | Increasing volume of Electronic Health Records (EHR) in recent years provides great opportunities for data scientists to collaborate on different aspects of healthcare research by applying advanced analytics to these EHR clinical data. A key requirement however is obtaining meaningful insights from high dimensional, sp... | ['Dongxiao Zhu', 'Najibesadat Sadati', 'Milad Zafar Nezhad', 'Ratna Babu Chinnam'] | 2019-08-24 | null | null | null | null | ['small-data'] | ['computer-vision'] | [-1.20169692e-01 1.28266707e-01 3.67953372e-03 -3.86202872e-01
-7.33812511e-01 1.22595094e-02 4.92764980e-01 5.72778106e-01
-2.58436024e-01 5.76407909e-01 7.10838020e-01 -1.49946541e-01
-3.05619329e-01 -7.64712334e-01 -5.94618022e-01 -6.18236125e-01
-2.07659870e-01 5.95157027e-01 -4.23828363e-01 -2.99426258... | [7.7754034996032715, 6.507485866546631] |
410f246d-49d9-42e7-a55b-d50f4445b6c7 | locoop-few-shot-out-of-distribution-detection | 2306.01293 | null | https://arxiv.org/abs/2306.01293v2 | https://arxiv.org/pdf/2306.01293v2.pdf | LoCoOp: Few-Shot Out-of-Distribution Detection via Prompt Learning | We present a novel vision-language prompt learning approach for few-shot out-of-distribution (OOD) detection. Few-shot OOD detection aims to detect OOD images from classes that are unseen during training using only a few labeled in-distribution (ID) images. While prompt learning methods such as CoOp have shown effectiv... | ['Kiyoharu Aizawa', 'Go Irie', 'Qing Yu', 'Atsuyuki Miyai'] | 2023-06-02 | null | null | null | null | ['prompt-engineering'] | ['natural-language-processing'] | [ 5.85880093e-02 2.26148982e-02 -4.43804175e-01 -1.87203571e-01
-9.09758270e-01 -2.34585181e-01 6.95269346e-01 1.93843603e-01
-1.49256006e-01 9.57051143e-02 3.43871236e-01 1.04697138e-01
2.82229304e-01 -3.74927551e-01 -6.85467482e-01 -8.11181068e-01
1.94111511e-01 2.34367713e-01 4.68477935e-01 1.39893010... | [9.470002174377441, 1.5120694637298584] |
2323ede0-f07e-413f-9c90-0a2e5ba4f84c | semeval-2017-task-12-clinical-tempeval | null | null | https://aclanthology.org/S17-2093 | https://aclanthology.org/S17-2093.pdf | SemEval-2017 Task 12: Clinical TempEval | Clinical TempEval 2017 aimed to answer the question: how well do systems trained on annotated timelines for one medical condition (colon cancer) perform in predicting timelines on another medical condition (brain cancer)? Nine sub-tasks were included, covering problems in time expression identification, event expressio... | ['Guergana Savova', 'Steven Bethard', 'Martha Palmer', 'James Pustejovsky'] | 2017-08-01 | null | null | null | semeval-2017-8 | ['temporal-information-extraction'] | ['natural-language-processing'] | [ 1.70638959e-04 3.91675293e-01 -5.91664791e-01 -5.22924364e-01
-8.75534236e-01 -6.27071142e-01 6.47399306e-01 8.56053829e-01
-7.10173547e-01 8.47459495e-01 3.66601169e-01 -5.34490705e-01
-5.40113270e-01 -3.17223847e-01 9.63162258e-02 -6.24344528e-01
-7.30852067e-01 5.88134885e-01 -3.76128078e-01 9.30501521... | [8.50344467163086, 9.007991790771484] |
322fed48-0ee7-433f-a938-f89e879b9795 | robo-gym-an-open-source-toolkit-for | 2007.02753 | null | https://arxiv.org/abs/2007.02753v2 | https://arxiv.org/pdf/2007.02753v2.pdf | robo-gym -- An Open Source Toolkit for Distributed Deep Reinforcement Learning on Real and Simulated Robots | Applying Deep Reinforcement Learning (DRL) to complex tasks in the field of robotics has proven to be very successful in the recent years. However, most of the publications focus either on applying it to a task in simulation or to a task in a real world setup. Although there are great examples of combining the two worl... | ['Stephan Mühlbacher-Karrer', 'Matteo Lucchi', 'Friedemann Zindler', 'Horst Pichler'] | 2020-07-06 | null | null | null | null | ['industrial-robots'] | ['robots'] | [-3.05024028e-01 5.29897623e-02 3.16104591e-01 -2.22831637e-01
-1.97783664e-01 -4.06565934e-01 5.55819809e-01 -1.76058054e-01
-7.13851273e-01 7.76289761e-01 -5.38503826e-01 -4.75529164e-01
-3.89459610e-01 -9.96642947e-01 -1.00853968e+00 -5.76696575e-01
-3.43305618e-01 9.17098165e-01 7.13395178e-01 -7.32810497... | [4.523762226104736, 1.0241694450378418] |
2f7169bc-24c6-4997-86d5-859735723b6a | discourse-representation-structure-parsing-1 | null | null | https://aclanthology.org/W19-1203 | https://aclanthology.org/W19-1203.pdf | Discourse Representation Structure Parsing with Recurrent Neural Networks and the Transformer Model | We describe the systems we developed for Discourse Representation Structure (DRS) parsing as part of the IWCS-2019 Shared Task of DRS Parsing.1 Our systems are based on sequence-to-sequence modeling. To implement our model, we use the open-source neural machine translation system implemented in PyTorch, OpenNMT-py. We ... | ['Mirella Lapata', 'Jiangming Liu', 'Shay B. Cohen'] | 2019-05-01 | null | null | null | ws-2019-5 | ['drs-parsing'] | ['natural-language-processing'] | [ 5.28063476e-01 8.31993878e-01 -3.42071474e-01 -4.18473989e-01
-1.36803567e+00 -5.11207223e-01 3.74204874e-01 -1.40299499e-01
-3.05096745e-01 7.12107062e-01 9.70685124e-01 -1.13089859e+00
7.31679738e-01 -6.57481134e-01 -6.87250137e-01 -2.71737184e-02
2.56837308e-01 5.93897939e-01 2.07194194e-01 -8.10938001... | [10.7086820602417, 9.377432823181152] |
3fa0e55c-f25c-4ac3-9f47-31ac0abee70c | gen-nerf-efficient-and-generalizable-neural | 2304.11842 | null | https://arxiv.org/abs/2304.11842v2 | https://arxiv.org/pdf/2304.11842v2.pdf | Gen-NeRF: Efficient and Generalizable Neural Radiance Fields via Algorithm-Hardware Co-Design | Novel view synthesis is an essential functionality for enabling immersive experiences in various Augmented- and Virtual-Reality (AR/VR) applications, for which generalizable Neural Radiance Fields (NeRFs) have gained increasing popularity thanks to their cross-scene generalization capability. Despite their promise, the... | ['Yingyan Lin', 'Haoran You', 'Sixu Li', 'Shunyao Zhang', 'Jiayi Yuan', 'Zhifan Ye', 'Yonggan Fu'] | 2023-04-24 | null | null | null | null | ['novel-view-synthesis'] | ['computer-vision'] | [ 2.04869092e-01 -2.62144983e-01 7.95569047e-02 -2.71242231e-01
-3.36796910e-01 -3.89428258e-01 3.24610561e-01 6.78004324e-02
-3.76744211e-01 3.07667464e-01 1.10831201e-01 -4.61056113e-01
-7.19592199e-02 -1.14930713e+00 -8.22828531e-01 -5.36823273e-01
9.56012383e-02 -2.01320872e-01 2.57616401e-01 -3.87803495... | [9.465231895446777, -2.7519092559814453] |
1b9d0aa3-bba2-46a3-843c-5426d21b7a3c | robust-dialogue-utterance-rewriting-as | 2012.14535 | null | https://arxiv.org/abs/2012.14535v1 | https://arxiv.org/pdf/2012.14535v1.pdf | Robust Dialogue Utterance Rewriting as Sequence Tagging | The task of dialogue rewriting aims to reconstruct the latest dialogue utterance by copying the missing content from the dialogue context. Until now, the existing models for this task suffer from the robustness issue, i.e., performances drop dramatically when testing on a different domain. We address this robustness is... | ['Dong Yu', 'Zhaopeng Tu', 'Kun Xu', 'LiWei Wang', 'Linfeng Song', 'Jie Hao'] | 2020-12-29 | null | null | null | null | ['dialogue-rewriting'] | ['natural-language-processing'] | [ 4.67218816e-01 7.41368532e-01 4.85816449e-02 -4.32241023e-01
-9.86919940e-01 -6.80794358e-01 8.58916640e-01 -2.41457492e-01
-2.24050775e-01 1.25529420e+00 7.54285753e-01 -2.54073262e-01
5.88803172e-01 -5.67340553e-01 -6.06187940e-01 -2.21395090e-01
4.97083843e-01 7.30020821e-01 3.06292385e-01 -8.22321177... | [12.514610290527344, 8.369741439819336] |
d95c5378-a15d-4421-88ad-18db501e11f5 | omnimvs-end-to-end-learning-for | 1908.06257 | null | https://arxiv.org/abs/1908.06257v1 | https://arxiv.org/pdf/1908.06257v1.pdf | OmniMVS: End-to-End Learning for Omnidirectional Stereo Matching | In this paper, we propose a novel end-to-end deep neural network model for omnidirectional depth estimation from a wide-baseline multi-view stereo setup. The images captured with ultra wide field-of-view (FOV) cameras on an omnidirectional rig are processed by the feature extraction module, and then the deep feature ma... | ['Changhee Won', 'Jongwoo Lim', 'Jongbin Ryu'] | 2019-08-17 | omnimvs-end-to-end-learning-for-1 | http://openaccess.thecvf.com/content_ICCV_2019/html/Won_OmniMVS_End-to-End_Learning_for_Omnidirectional_Stereo_Matching_ICCV_2019_paper.html | http://openaccess.thecvf.com/content_ICCV_2019/papers/Won_OmniMVS_End-to-End_Learning_for_Omnidirectional_Stereo_Matching_ICCV_2019_paper.pdf | iccv-2019-10 | ['stereo-matching'] | ['computer-vision'] | [-8.33149627e-02 -2.17522651e-01 4.01191086e-01 -7.67254174e-01
-4.84697640e-01 -4.81882125e-01 6.42230213e-01 -1.17655003e+00
-4.64757293e-01 6.16498828e-01 6.55041456e-01 9.45692733e-02
-1.76343679e-01 -7.06241310e-01 -8.83537948e-01 -7.74833322e-01
2.09722281e-01 4.19463664e-01 -8.56257882e-03 8.19755718... | [8.740814208984375, -2.428908586502075] |
c662c102-186e-4b94-9d5b-495c887b6378 | depth-pooling-based-large-scale-3d-action | 1804.01194 | null | http://arxiv.org/abs/1804.01194v2 | http://arxiv.org/pdf/1804.01194v2.pdf | Depth Pooling Based Large-scale 3D Action Recognition with Convolutional Neural Networks | This paper proposes three simple, compact yet effective representations of
depth sequences, referred to respectively as Dynamic Depth Images (DDI),
Dynamic Depth Normal Images (DDNI) and Dynamic Depth Motion Normal Images
(DDMNI), for both isolated and continuous action recognition. These dynamic
images are constructed... | ['Philip Ogunbona', 'Zhimin Gao', 'Chang Tang', 'Wanqing Li', 'Pichao Wang'] | 2018-03-17 | null | null | null | null | ['3d-human-action-recognition'] | ['computer-vision'] | [ 5.67477524e-01 -2.53139466e-01 -2.10161939e-01 -4.31546748e-01
-4.77959275e-01 -4.15066600e-01 8.13686371e-01 -5.70602357e-01
-8.07677507e-01 5.20949066e-01 3.97538215e-01 2.04619870e-01
-2.46182337e-01 -7.23089755e-01 -5.39403081e-01 -8.69526029e-01
-3.29981089e-01 2.69096583e-01 4.78815019e-01 2.25384906... | [7.816109657287598, 0.4071153700351715] |
8c2ab131-e716-4df3-a47b-c0c981b3793e | stochastic-second-order-methods-provably-beat | 2205.12856 | null | https://arxiv.org/abs/2205.12856v2 | https://arxiv.org/pdf/2205.12856v2.pdf | Stochastic Second-Order Methods Improve Best-Known Sample Complexity of SGD for Gradient-Dominated Function | We study the performance of Stochastic Cubic Regularized Newton (SCRN) on a class of functions satisfying gradient dominance property with $1\le\alpha\le2$ which holds in a wide range of applications in machine learning and signal processing. This condition ensures that any first-order stationary point is a global opti... | ['Patrick Thiran', 'Negar Kiyavash', 'Niao He', 'Saber Salehkaleybar', 'Saeed Masiha'] | 2022-05-25 | null | null | null | null | ['policy-gradient-methods'] | ['methodology'] | [ 1.65264383e-02 5.79777807e-02 -1.59730747e-01 -7.21062422e-02
-8.90526891e-01 -3.67780268e-01 -1.54673606e-01 2.43528888e-01
-1.18880165e+00 1.16893744e+00 -6.23351932e-01 -6.97833180e-01
-5.38864911e-01 -6.94673300e-01 -8.30128968e-01 -9.97895122e-01
-8.04176807e-01 1.58414036e-01 3.30774218e-01 -6.49406791... | [4.3201494216918945, 2.7656474113464355] |
f2d159ae-f58a-4bde-8e67-7f82a9fc8c2f | scribble-supervised-cell-segmentation-using | 2306.14136 | null | https://arxiv.org/abs/2306.14136v1 | https://arxiv.org/pdf/2306.14136v1.pdf | Scribble-supervised Cell Segmentation Using Multiscale Contrastive Regularization | Current state-of-the-art supervised deep learning-based segmentation approaches have demonstrated superior performance in medical image segmentation tasks. However, such supervised approaches require fully annotated pixel-level ground-truth labels, which are labor-intensive and time-consuming to acquire. Recently, Scri... | ['Won-Ki Jeong', 'Kanggeun Lee', 'Hyun-Jic Oh'] | 2023-06-25 | null | null | null | null | ['self-supervised-learning', 'medical-image-segmentation', 'cell-segmentation'] | ['computer-vision', 'medical', 'medical'] | [ 3.39550465e-01 2.26614013e-01 -1.62093148e-01 -3.78510416e-01
-1.06478751e+00 -4.00350243e-01 9.48174968e-02 3.12665612e-01
-5.66202044e-01 7.78698862e-01 -2.97952324e-01 -2.14547634e-01
1.99472979e-01 -6.57129169e-01 -7.88434386e-01 -9.24286664e-01
1.75554320e-01 1.78945765e-01 5.38358450e-01 6.85479343... | [14.590603828430176, -2.1804354190826416] |
87dc3e39-4f7d-4b7b-bec7-fa0da68ee38f | multi-task-self-supervised-learning-for-1 | 2001.09239 | null | https://arxiv.org/abs/2001.09239v2 | https://arxiv.org/pdf/2001.09239v2.pdf | Multi-task self-supervised learning for Robust Speech Recognition | Despite the growing interest in unsupervised learning, extracting meaningful knowledge from unlabelled audio remains an open challenge. To take a step in this direction, we recently proposed a problem-agnostic speech encoder (PASE), that combines a convolutional encoder followed by multiple neural networks, called work... | ['Pawel Swietojanski', 'Mirco Ravanelli', 'Jianyuan Zhong', 'Jan Trmal', 'Santiago Pascual', 'Joao Monteiro', 'Yoshua Bengio'] | 2020-01-25 | null | null | null | null | ['robust-speech-recognition'] | ['speech'] | [ 1.14046402e-01 2.84958661e-01 3.79651576e-01 -4.10979122e-01
-9.96604681e-01 -4.59838480e-01 4.51967895e-01 -2.71418989e-01
-3.05851519e-01 6.64011776e-01 5.31877816e-01 -7.81882331e-02
-1.86888084e-01 -2.86284208e-01 -9.13346946e-01 -7.06087768e-01
-5.74171320e-02 2.55180568e-01 5.65739162e-02 -3.68368298... | [14.734098434448242, 6.197501182556152] |
fd678add-7607-439f-8bc2-b7de97cce99a | palmtree-learning-an-assembly-language-model | 2103.03809 | null | https://arxiv.org/abs/2103.03809v3 | https://arxiv.org/pdf/2103.03809v3.pdf | PalmTree: Learning an Assembly Language Model for Instruction Embedding | Deep learning has demonstrated its strengths in numerous binary analysis tasks, including function boundary detection, binary code search, function prototype inference, value set analysis, etc. When applying deep learning to binary analysis tasks, we need to decide what input should be fed into the neural network model... | ['Heng Yin', 'Qu Yu', 'Xuezixiang Li'] | 2021-01-21 | null | null | null | null | ['code-search', 'code-search'] | ['computer-code', 'computer-vision'] | [-2.92961323e-03 -3.57492238e-01 -7.46688247e-01 -5.00988722e-01
-5.00460386e-01 -5.23170054e-01 2.16345832e-01 3.03219199e-01
-4.14142430e-01 3.97449553e-01 2.39424884e-01 -8.44480038e-01
3.01150829e-01 -8.51757586e-01 -7.23644435e-01 -5.44214368e-01
2.99989013e-03 1.01494156e-01 1.73480988e-01 -3.36376131... | [7.362212657928467, 7.820347309112549] |
efcdd478-40e6-4a9d-a568-118ea9335021 | multitask-learning-and-benchmarking-with | 1703.07771 | null | https://arxiv.org/abs/1703.07771v3 | https://arxiv.org/pdf/1703.07771v3.pdf | Multitask learning and benchmarking with clinical time series data | Health care is one of the most exciting frontiers in data mining and machine learning. Successful adoption of electronic health records (EHRs) created an explosion in digital clinical data available for analysis, but progress in machine learning for healthcare research has been difficult to measure because of the absen... | ['Hrant Khachatrian', 'Aram Galstyan', 'Hrayr Harutyunyan', 'Greg Ver Steeg', 'David C. Kale'] | 2017-03-22 | null | null | null | null | ['computational-phenotyping', 'length-of-stay-prediction', 'phenotype-classification'] | ['medical', 'medical', 'medical'] | [ 3.13054800e-01 -5.78374118e-02 -3.89048904e-01 -6.68176532e-01
-1.02479899e+00 -3.02353084e-01 2.27289483e-01 9.57562804e-01
-5.22778749e-01 6.20725811e-01 4.46601182e-01 -7.14599609e-01
-2.35350326e-01 -4.18598890e-01 -4.94892567e-01 -3.44416469e-01
-3.59107137e-01 6.25527263e-01 -2.94809610e-01 3.08194548... | [7.965457439422607, 6.277404308319092] |
d236f824-abe9-4279-ae71-f4c34f59ef50 | graph-based-sinogram-denoising-for | 1603.04203 | null | http://arxiv.org/abs/1603.04203v1 | http://arxiv.org/pdf/1603.04203v1.pdf | Graph Based Sinogram Denoising for Tomographic Reconstructions | Limited data and low dose constraints are common problems in a variety of
tomographic reconstruction paradigms which lead to noisy and incomplete data.
Over the past few years sinogram denoising has become an essential
pre-processing step for low dose Computed Tomographic (CT) reconstructions. We
propose a novel sinogr... | ['Pierre Vandergheynst', 'Faisal Mahmood', 'Ulf Skoglund', 'Nauman Shahid'] | 2016-03-14 | null | null | null | null | ['tomographic-reconstructions'] | ['medical'] | [ 4.37101692e-01 1.10035948e-01 1.86424181e-01 -2.96444505e-01
-5.19031823e-01 -2.39282444e-01 4.34116602e-01 3.36209625e-01
-4.17780012e-01 9.07504857e-01 5.36116302e-01 -9.29493923e-03
-2.87694216e-01 -8.26563418e-01 -4.33979034e-01 -8.55786979e-01
-2.50411421e-01 5.91711581e-01 5.47823131e-01 -2.78522342... | [13.043591499328613, -2.6726508140563965] |
8bfb99bf-7ecf-40c9-8e98-8d328dc2aaca | polarity-in-the-classroom-a-case-study | 2108.10068 | null | https://arxiv.org/abs/2108.10068v1 | https://arxiv.org/pdf/2108.10068v1.pdf | Polarity in the Classroom: A Case Study Leveraging Peer Sentiment Toward Scalable Assessment | Accurately grading open-ended assignments in large or massive open online courses (MOOCs) is non-trivial. Peer review is a promising solution but can be unreliable due to few reviewers and an unevaluated review form. To date, no work has 1) leveraged sentiment analysis in the peer-review process to inform or validate g... | ['Paul Rosen', 'Les A. Piegl', 'Zachariah J. Beasley'] | 2021-08-02 | null | null | null | null | ['aspect-extraction'] | ['natural-language-processing'] | [ 7.18312338e-02 2.47653350e-01 -2.93502659e-01 -7.29991138e-01
-1.07223904e+00 -1.07557631e+00 9.88842174e-02 8.38700771e-01
-1.26306891e-01 7.50788808e-01 1.32718131e-01 -1.03228819e+00
-1.89717591e-01 -7.10531175e-01 -4.63848144e-01 2.72875112e-02
7.89570212e-01 1.36999786e-01 2.65414029e-01 -5.56090951... | [11.26439094543457, 9.242026329040527] |
6d433e65-166d-49df-a665-b9a893bf41ca | contrastive-clustering | 2009.09687 | null | https://arxiv.org/abs/2009.09687v1 | https://arxiv.org/pdf/2009.09687v1.pdf | Contrastive Clustering | In this paper, we propose a one-stage online clustering method called Contrastive Clustering (CC) which explicitly performs the instance- and cluster-level contrastive learning. To be specific, for a given dataset, the positive and negative instance pairs are constructed through data augmentations and then projected in... | ['Dezhong Peng', 'Zitao Liu', 'Xi Peng', 'Joey Tianyi Zhou', 'Yunfan Li', 'Peng Hu'] | 2020-09-21 | null | null | null | null | ['image-clustering', 'online-clustering'] | ['computer-vision', 'computer-vision'] | [ 3.29879038e-02 -2.96619087e-02 -1.89743266e-01 -5.84190607e-01
-9.90371287e-01 -2.54212767e-01 6.82735324e-01 3.12576592e-01
-4.83415186e-01 2.80889690e-01 -1.80975839e-01 1.05141446e-01
-8.59274939e-02 -3.57182473e-01 -7.51422763e-01 -1.15900159e+00
-3.09474409e-01 5.50232053e-01 -4.54791248e-01 3.23203743... | [9.236263275146484, 3.3084630966186523] |
12aad737-e746-4dbc-86dd-31547bd2de01 | graphxnet-chest-x-ray-classification-under | 1907.10085 | null | https://arxiv.org/abs/1907.10085v3 | https://arxiv.org/pdf/1907.10085v3.pdf | GraphX$^{NET}-$ Chest X-Ray Classification Under Extreme Minimal Supervision | The task of classifying X-ray data is a problem of both theoretical and clinical interest. Whilst supervised deep learning methods rely upon huge amounts of labelled data, the critical problem of achieving a good classification accuracy when an extremely small amount of labelled data is available has yet to be tackled.... | ['Carola-Bibiane Schönlieb', 'Robby T. Tan', 'Philip Sellars', 'Ruoteng Li', 'Angelica I. Aviles-Rivero', 'Qingnan Fan', 'Nicolas Papadakis'] | 2019-07-23 | null | null | null | null | ['semi-supervised-medical-image-classification'] | ['medical'] | [ 5.31913102e-01 5.10418117e-01 -2.40150064e-01 -6.19402230e-01
-1.05375338e+00 -2.59146214e-01 5.56335270e-01 7.00562298e-01
-5.78905344e-01 7.48893440e-01 -1.25661373e-01 -5.41284323e-01
-4.23035860e-01 -6.27608001e-01 -6.10442877e-01 -7.13959813e-01
-2.35379606e-01 7.35068858e-01 2.19186187e-01 -1.08190969... | [14.701715469360352, -2.2577481269836426] |
fa67a126-9b04-47da-b66b-1ec0acb33f3b | on-leveraging-variational-graph-embeddings | 2204.11848 | null | https://arxiv.org/abs/2204.11848v1 | https://arxiv.org/pdf/2204.11848v1.pdf | On Leveraging Variational Graph Embeddings for Open World Compositional Zero-Shot Learning | Humans are able to identify and categorize novel compositions of known concepts. The task in Compositional Zero-Shot learning (CZSL) is to learn composition of primitive concepts, i.e. objects and states, in such a way that even their novel compositions can be zero-shot classified. In this work, we do not assume any pr... | ['Martin Kleinsteuber', 'Zhihui Pan', 'Muhammad Umer Anwaar'] | 2022-04-23 | null | null | null | null | ['compositional-zero-shot-learning'] | ['computer-vision'] | [ 1.12066895e-01 2.85179883e-01 9.30109397e-02 -5.27986921e-02
-3.83418888e-01 -6.73145354e-01 8.32822442e-01 2.33743012e-01
-3.48775864e-01 3.05805564e-01 7.28939772e-02 -1.67706296e-01
-2.15912282e-01 -1.14989758e+00 -9.12852168e-01 -7.23780751e-01
-8.28543305e-02 7.99906492e-01 5.51592521e-02 -3.94758463... | [10.247488021850586, 2.2955851554870605] |
bc8a9025-f4f1-4317-b002-50d73d6b6433 | a-detection-and-segmentation-architecture-for | 1809.03917 | null | http://arxiv.org/abs/1809.03917v2 | http://arxiv.org/pdf/1809.03917v2.pdf | A Detection and Segmentation Architecture for Skin Lesion Segmentation on Dermoscopy Images | This report summarises our method and validation results for the ISIC
Challenge 2018 - Skin Lesion Analysis Towards Melanoma Detection - Task 1:
Lesion Segmentation. We present a two-stage method for lesion segmentation with
optimised training method and ensemble post-process. Our method achieves
state-of-the-art perfo... | ['Hao Jiang', 'Ting Liu', 'Chengyao Qian', 'Pengfei Wang', 'Biao Sun', 'Zhe Wang', 'Mingxin Guan'] | 2018-09-11 | null | null | null | null | ['skin-lesion-segmentation'] | ['medical'] | [ 1.08581007e+00 1.63686141e-01 -5.12194753e-01 1.59970112e-02
-1.46818924e+00 -5.74655294e-01 7.67838001e-01 1.67114377e-01
-7.72495627e-01 3.14571559e-01 1.26652420e-01 -7.49161959e-01
1.21628530e-01 -1.41823232e-01 -9.21643451e-02 -8.86513650e-01
4.65510748e-02 -1.14114977e-04 6.14711106e-01 7.43431896... | [15.774272918701172, -3.041229248046875] |
5552d977-9bac-4bcd-88c2-5b72fa5e36a1 | controlling-the-risk-of-conversational-search | 2101.06327 | null | https://arxiv.org/abs/2101.06327v1 | https://arxiv.org/pdf/2101.06327v1.pdf | Controlling the Risk of Conversational Search via Reinforcement Learning | Users often formulate their search queries with immature language without well-developed keywords and complete structures. Such queries fail to express their true information needs and raise ambiguity as fragmental language often yield various interpretations and aspects. This gives search engines a hard time processin... | ['Qingyao Ai', 'Zhenduo Wang'] | 2021-01-15 | null | null | null | null | ['conversational-search'] | ['natural-language-processing'] | [ 2.59413663e-02 5.96728802e-01 -3.13584328e-01 -5.95427454e-01
-1.42109573e+00 -1.00629842e+00 6.75012946e-01 6.61771670e-02
-3.79313678e-01 7.68286049e-01 4.36223805e-01 -7.16749191e-01
-4.24322896e-02 -5.17074466e-01 -3.29754651e-01 3.27943265e-02
5.34349382e-01 1.20421708e+00 2.48778298e-01 -7.65088141... | [12.175833702087402, 7.777220249176025] |
f0f9aac6-e546-4f19-83b5-738a2e53b81f | video-instance-shadow-detection | 2211.12827 | null | https://arxiv.org/abs/2211.12827v1 | https://arxiv.org/pdf/2211.12827v1.pdf | Video Instance Shadow Detection | Video instance shadow detection aims to simultaneously detect, segment, associate, and track paired shadow-object associations in videos. This work has three key contributions to the task. First, we design SSIS-Track, a new framework to extract shadow-object associations in videos with paired tracking and without categ... | ['Pheng-Ann Heng', 'Chi-Wing Fu', 'Haoran Wu', 'Xiaowei Hu', 'Tianyu Wang', 'Zhenghao Xing'] | 2022-11-23 | null | null | null | null | ['shadow-detection'] | ['computer-vision'] | [ 2.90512234e-01 -2.18398273e-01 -5.90537786e-01 -3.15165430e-01
-7.36630321e-01 -7.21659660e-01 3.11891854e-01 -5.46367824e-01
-6.43472224e-02 6.97120368e-01 -3.74891721e-02 -6.89863190e-02
1.19003139e-01 1.00918218e-01 -1.18031049e+00 -6.93811119e-01
-5.56818545e-01 -6.49825484e-02 1.09849846e+00 3.77998322... | [9.181081771850586, -0.12969030439853668] |
a9427190-9333-4cdf-a6d0-403d05fc071b | autocount-unsupervised-segmentation-and | 2007.09178 | null | https://arxiv.org/abs/2007.09178v1 | https://arxiv.org/pdf/2007.09178v1.pdf | AutoCount: Unsupervised Segmentation and Counting of Organs in Field Images | Counting plant organs such as heads or tassels from outdoor imagery is a popular benchmark computer vision task in plant phenotyping, which has been previously investigated in the literature using state-of-the-art supervised deep learning techniques. However, the annotation of organs in field images is time-consuming a... | ['Steve Shirtliffe', 'Tewodros Ayalew', 'Ian Stavness', 'Curtis Pozniak', 'Jordan Ubbens', 'Anique Josuttes'] | 2020-07-17 | null | null | null | null | ['plant-phenotyping'] | ['computer-vision'] | [ 3.58137935e-01 -1.28690064e-01 1.30612999e-01 -3.00528795e-01
-1.70967609e-01 -1.05271149e+00 2.64473230e-01 7.34576821e-01
-4.73783493e-01 5.67744434e-01 -7.94257402e-01 -6.70834839e-01
2.81458031e-02 -8.39257121e-01 -4.79562610e-01 -5.83587766e-01
5.18006645e-02 6.06665969e-01 3.47774535e-01 1.48822486... | [9.130760192871094, -1.5355734825134277] |
6fa2c6a2-b48f-4c7b-b355-5deb46c7a8a7 | zeroc-a-neuro-symbolic-model-for-zero-shot | 2206.15049 | null | https://arxiv.org/abs/2206.15049v3 | https://arxiv.org/pdf/2206.15049v3.pdf | ZeroC: A Neuro-Symbolic Model for Zero-shot Concept Recognition and Acquisition at Inference Time | Humans have the remarkable ability to recognize and acquire novel visual concepts in a zero-shot manner. Given a high-level, symbolic description of a novel concept in terms of previously learned visual concepts and their relations, humans can recognize novel concepts without seeing any examples. Moreover, they can acq... | ['Jure Leskovec', 'Rok Sosič', 'Kevin Liu', 'Xuelin Yang', 'Zhengxuan Wu', 'Megan Tjandrasuwita', 'Tailin Wu'] | 2022-06-30 | null | null | null | null | ['novel-concepts'] | ['reasoning'] | [ 4.89560783e-01 3.01347792e-01 -5.16250283e-02 -4.48448539e-01
-3.84841524e-02 -6.65282965e-01 9.03126240e-01 6.17974520e-01
-1.64049938e-01 4.57680315e-01 -4.09373760e-01 -3.96359861e-01
-1.76549867e-01 -1.35894966e+00 -9.38985348e-01 -4.21458602e-01
-5.41914821e-01 6.60621047e-01 3.52816433e-01 -3.75256062... | [10.269198417663574, 2.3374013900756836] |
194ed220-b624-4301-b07a-ec4fcd26edc8 | overcoming-catastrophic-forgetting-in | 2111.01549 | null | https://arxiv.org/abs/2111.01549v2 | https://arxiv.org/pdf/2111.01549v2.pdf | Overcoming Catastrophic Forgetting in Incremental Few-Shot Learning by Finding Flat Minima | This paper considers incremental few-shot learning, which requires a model to continually recognize new categories with only a few examples provided. Our study shows that existing methods severely suffer from catastrophic forgetting, a well-known problem in incremental learning, which is aggravated due to data scarcity... | ['Xiao-Ming Wu', 'Li-Ming Zhan', 'Wenlong Zhang', 'Jiaxin Chen', 'Guangyuan Shi'] | 2021-10-30 | null | http://proceedings.neurips.cc/paper/2021/hash/357cfba15668cc2e1e73111e09d54383-Abstract.html | http://proceedings.neurips.cc/paper/2021/file/357cfba15668cc2e1e73111e09d54383-Paper.pdf | neurips-2021-12 | ['few-shot-class-incremental-learning'] | ['methodology'] | [ 2.37371534e-01 1.49897709e-01 -3.65795463e-01 -2.89106131e-01
-7.13392973e-01 -1.13656051e-01 4.21677470e-01 9.25671980e-02
-5.85390210e-01 1.04028034e+00 -6.18277155e-02 1.76649429e-02
-2.46252581e-01 -5.98436177e-01 -7.85291612e-01 -7.96810687e-01
1.11014888e-01 5.76579392e-01 6.87318385e-01 -1.22189857... | [9.869529724121094, 3.3439793586730957] |
0e56bec9-c9b5-46ac-a085-aadf9a3a7ea7 | escape-from-cells-deep-kd-networks-for-the | 1704.01222 | null | http://arxiv.org/abs/1704.01222v2 | http://arxiv.org/pdf/1704.01222v2.pdf | Escape from Cells: Deep Kd-Networks for the Recognition of 3D Point Cloud Models | We present a new deep learning architecture (called Kd-network) that is
designed for 3D model recognition tasks and works with unstructured point
clouds. The new architecture performs multiplicative transformations and share
parameters of these transformations according to the subdivisions of the point
clouds imposed o... | ['Roman Klokov', 'Victor Lempitsky'] | 2017-04-04 | escape-from-cells-deep-kd-networks-for-the-1 | http://openaccess.thecvf.com/content_iccv_2017/html/Klokov_Escape_From_Cells_ICCV_2017_paper.html | http://openaccess.thecvf.com/content_ICCV_2017/papers/Klokov_Escape_From_Cells_ICCV_2017_paper.pdf | iccv-2017-10 | ['3d-part-segmentation'] | ['computer-vision'] | [-1.38606429e-01 2.57565011e-03 6.62004128e-02 -5.21697342e-01
-2.72151977e-01 -7.83548057e-01 8.11451137e-01 2.31057126e-02
-3.75692219e-01 1.38636753e-01 -2.33222961e-01 -5.30429304e-01
-1.25569925e-01 -1.29282582e+00 -9.00191128e-01 -4.39442873e-01
-1.34131953e-01 1.17717123e+00 4.99694258e-01 -5.34416549... | [8.06166934967041, -3.713047742843628] |
8d0a524d-56f5-4e2f-a025-0dc31404a53a | perspective-flow-aggregation-for-data-limited | 2203.09836 | null | https://arxiv.org/abs/2203.09836v2 | https://arxiv.org/pdf/2203.09836v2.pdf | Perspective Flow Aggregation for Data-Limited 6D Object Pose Estimation | Most recent 6D object pose estimation methods, including unsupervised ones, require many real training images. Unfortunately, for some applications, such as those in space or deep under water, acquiring real images, even unannotated, is virtually impossible. In this paper, we propose a method that can be trained solely... | ['Mathieu Salzmann', 'Pascal Fua', 'Yinlin Hu'] | 2022-03-18 | null | null | null | null | ['6d-pose-estimation'] | ['computer-vision'] | [ 4.43062007e-01 5.86791754e-01 2.76165873e-01 -4.14872259e-01
-4.83502030e-01 -6.82339370e-01 6.90013528e-01 -2.10370757e-02
-7.41677463e-01 7.37305045e-01 -3.19987595e-01 -8.69907439e-02
1.10138506e-01 -6.32778227e-01 -1.11762524e+00 -4.38474149e-01
5.54630682e-02 1.18666518e+00 4.88911778e-01 -2.57318854... | [7.880552291870117, -2.533768892288208] |
94296feb-a401-438a-8b33-8c254acc1dce | estimation-of-low-rank-density-matrices-by | 1610.04811 | null | http://arxiv.org/abs/1610.04811v2 | http://arxiv.org/pdf/1610.04811v2.pdf | Estimation of low rank density matrices by Pauli measurements | Density matrices are positively semi-definite Hermitian matrices with unit
trace that describe the states of quantum systems. Many quantum systems of
physical interest can be represented as high-dimensional low rank density
matrices. A popular problem in {\it quantum state tomography} (QST) is to
estimate the unknown l... | ['Dong Xia'] | 2016-10-16 | null | null | null | null | ['quantum-state-tomography'] | ['medical'] | [ 1.55580878e-01 1.58620939e-01 5.48221841e-02 -6.96118250e-02
-7.43828654e-01 -3.05782795e-01 1.89525425e-01 -3.82943928e-01
-7.54816115e-01 1.18295848e+00 -4.02327120e-01 -1.90811902e-01
-6.27351820e-01 -9.05090094e-01 -6.73323750e-01 -1.09722996e+00
-2.84631878e-01 6.21017754e-01 -4.72546294e-02 -1.00932002... | [5.804417133331299, 4.876912593841553] |
3553cf61-1ede-4be0-a5d7-441506d6e9d6 | on-the-transferability-of-visual-features-in | 2211.12494 | null | https://arxiv.org/abs/2211.12494v1 | https://arxiv.org/pdf/2211.12494v1.pdf | On the Transferability of Visual Features in Generalized Zero-Shot Learning | Generalized Zero-Shot Learning (GZSL) aims to train a classifier that can generalize to unseen classes, using a set of attributes as auxiliary information, and the visual features extracted from a pre-trained convolutional neural network. While recent GZSL methods have explored various techniques to leverage the capaci... | ['Vicente Ordonez', 'Yanjun Qi', 'James Seale Smith', 'Leonid Karlinsky', 'Paola Cascante-Bonilla'] | 2022-11-22 | null | null | null | null | ['generalized-zero-shot-learning', 'generalized-zero-shot-learning'] | ['computer-vision', 'methodology'] | [ 2.44572341e-01 2.08321642e-02 -2.66950309e-01 -3.89587253e-01
-9.76327002e-01 -4.91120666e-01 8.86441827e-01 3.50569263e-02
-1.67718768e-01 4.95296538e-01 3.58945817e-01 -7.19400272e-02
-2.81053811e-01 -9.75299418e-01 -8.13660443e-01 -4.82146740e-01
-1.02597862e-01 2.96872318e-01 1.33295864e-01 -4.00796294... | [9.842458724975586, 2.82704496383667] |
1282ee4d-9a61-4978-99bd-855a79bcf8ef | why-and-how-to-pay-different-attention-to | 1604.06896 | null | http://arxiv.org/abs/1604.06896v2 | http://arxiv.org/pdf/1604.06896v2.pdf | Why and How to Pay Different Attention to Phrase Alignments of Different Intensities | This work studies comparatively two typical sentence pair classification
tasks: textual entailment (TE) and answer selection (AS), observing that phrase
alignments of different intensities contribute differently in these tasks. We
address the problems of identifying phrase alignments of flexible granularity
and pooling... | ['Hinrich Schütze', 'Wenpeng Yin'] | 2016-04-23 | null | null | null | null | ['sentence-pair-classification'] | ['natural-language-processing'] | [ 6.56487823e-01 -3.05421483e-02 -1.39647692e-01 -4.72320646e-01
-1.02787399e+00 -5.76204777e-01 5.33032060e-01 5.76243460e-01
-4.93456095e-01 8.19273889e-01 6.11250877e-01 -6.59387112e-01
-1.29245341e-01 -6.61565900e-01 -4.82322365e-01 -4.20296967e-01
5.27089760e-02 4.73212510e-01 3.24581623e-01 -5.67448497... | [11.01756763458252, 8.973421096801758] |
3289f217-e438-4747-b09d-4401c6e416c9 | object-recognition-system-on-a-tactile-device | 2307.02211 | null | https://arxiv.org/abs/2307.02211v1 | https://arxiv.org/pdf/2307.02211v1.pdf | Object Recognition System on a Tactile Device for Visually Impaired | People with visual impairments face numerous challenges when interacting with their environment. Our objective is to develop a device that facilitates communication between individuals with visual impairments and their surroundings. The device will convert visual information into auditory feedback, enabling users to un... | ['Slimane Larabi', 'Mokretar Kraroubi Abderrahmene', 'Souayah Abdelkader'] | 2023-07-05 | null | null | null | null | ['object-recognition', 'object-detection', 'scene-understanding'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 2.34596997e-01 -2.92712241e-01 1.21975794e-01 2.08932593e-01
-1.32250696e-01 -3.32111329e-01 -1.98431686e-01 3.19898009e-01
-3.87235403e-01 5.08901417e-01 2.06902415e-01 -1.85202509e-01
-1.01712495e-01 -6.30217671e-01 -1.66216165e-01 -3.08269888e-01
1.69243962e-01 2.48750243e-02 4.57198352e-01 -1.07508143... | [6.540733814239502, -0.17228522896766663] |
dda541c9-2952-4bde-9bac-62434aae076e | sketchformer-transformer-based-representation | 2002.10381 | null | https://arxiv.org/abs/2002.10381v1 | https://arxiv.org/pdf/2002.10381v1.pdf | Sketchformer: Transformer-based Representation for Sketched Structure | Sketchformer is a novel transformer-based representation for encoding free-hand sketches input in a vector form, i.e. as a sequence of strokes. Sketchformer effectively addresses multiple tasks: sketch classification, sketch based image retrieval (SBIR), and the reconstruction and interpolation of sketches. We report s... | ['John Collomosse', 'Moacir Ponti', 'Tu Bui', 'Leo Sampaio Ferraz Ribeiro'] | 2020-02-24 | sketchformer-transformer-based-representation-1 | http://openaccess.thecvf.com/content_CVPR_2020/html/Ribeiro_Sketchformer_Transformer-Based_Representation_for_Sketched_Structure_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Ribeiro_Sketchformer_Transformer-Based_Representation_for_Sketched_Structure_CVPR_2020_paper.pdf | cvpr-2020-6 | ['sketch-based-image-retrieval'] | ['computer-vision'] | [ 2.70709872e-01 -4.37438339e-01 -5.55167973e-01 -2.09271178e-01
-6.31431222e-01 -8.54470134e-01 1.22571349e+00 -3.37338209e-01
-3.01595122e-01 3.65894675e-01 4.98975098e-01 -2.32394487e-01
1.03409952e-02 -7.64301300e-01 -6.56364918e-01 -4.64036047e-01
1.95873693e-01 4.40290093e-01 -3.16602170e-01 -1.79511234... | [11.728300094604492, 0.4135324954986572] |
bc8df28a-c450-4b1d-9214-b637b714e6d7 | 190107454 | 1901.07454 | null | http://arxiv.org/abs/1901.07454v1 | http://arxiv.org/pdf/1901.07454v1.pdf | Arbor -- a morphologically-detailed neural network simulation library for contemporary high-performance computing architectures | We introduce Arbor, a performance portable library for simulation of large
networks of multi-compartment neurons on HPC systems. Arbor is open source
software, developed under the auspices of the HBP. The performance portability
is by virtue of back-end specific optimizations for x86 multicore, Intel KNL,
and NVIDIA GP... | ['Alexander Peyser', 'Anne Küsters', 'Vasileios Karakasis', 'Wouter Klijn', 'Nora Abi Akar', 'Ben Cumming', 'Stuart Yates'] | 2019-01-17 | null | null | null | null | ['neural-network-simulation'] | ['computer-code'] | [-5.79615891e-01 -5.70592344e-01 3.96086931e-01 -2.34783031e-02
6.34618178e-02 -5.32002032e-01 4.30730313e-01 1.98237792e-01
-8.69038999e-01 9.22500491e-01 -1.69913322e-01 -5.00349700e-01
9.29948911e-02 -7.17467427e-01 -4.43165213e-01 -1.00081444e+00
-2.48467252e-01 6.27596140e-01 5.98102391e-01 -4.19230200... | [8.156537055969238, 2.669367790222168] |
f44ef6a8-bbe4-4a98-bf58-001547ab80a8 | teleidoscopic-imaging-system-for-microscale | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Kawahara_Teleidoscopic_Imaging_System_for_Microscale_3D_Shape_Reconstruction_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Kawahara_Teleidoscopic_Imaging_System_for_Microscale_3D_Shape_Reconstruction_CVPR_2023_paper.pdf | Teleidoscopic Imaging System for Microscale 3D Shape Reconstruction | This paper proposes a practical method of microscale 3D shape capturing by a teleidoscopic imaging system. The main challenge in microscale 3D shape reconstruction is to capture the target from multiple viewpoints with a large enough depth-of-field. Our idea is to employ a teleidoscopic measurement system consistin... | ['Shohei Nobuhara', 'Meng-Yu Jennifer Kuo', 'Ryo Kawahara'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['3d-shape-reconstruction'] | ['computer-vision'] | [ 1.51511490e-01 -1.47128589e-02 6.45508885e-01 -2.97137231e-01
-1.70971721e-03 -6.56110525e-01 6.31896853e-01 -1.18715525e+00
-2.41630957e-01 3.56016219e-01 -5.22442646e-02 -2.02825293e-01
-6.10498749e-02 -7.74532795e-01 -7.32578337e-01 -6.64116919e-01
6.27070904e-01 8.91340733e-01 2.38063231e-01 3.07360757... | [9.678053855895996, -2.8785977363586426] |
acd62b87-c2a3-4faf-a051-1157db6eb94e | multistylegan-multiple-one-shot-face | 2210.04120 | null | https://arxiv.org/abs/2210.04120v2 | https://arxiv.org/pdf/2210.04120v2.pdf | MultiStyleGAN: Multiple One-shot Image Stylizations using a Single GAN | Image stylization aims at applying a reference style to arbitrary input images. A common scenario is one-shot stylization, where only one example is available for each reference style. Recent approaches for one-shot stylization such as JoJoGAN fine-tune a pre-trained StyleGAN2 generator on a single style reference imag... | ['Svetlana Lazebnik', 'Sudharsan Krishnakumar Anitha', 'Ayush Sarkar', 'Viraj Shah'] | 2022-10-08 | null | null | null | null | ['image-stylization', 'one-shot-face-stylization'] | ['computer-vision', 'computer-vision'] | [ 4.72844690e-01 2.10315689e-01 -1.35496184e-01 -1.96052432e-01
-8.96801651e-01 -9.08198655e-01 7.29670942e-01 -6.07204556e-01
-2.09479704e-01 7.46159375e-01 3.25986266e-01 -1.81874290e-01
5.80880463e-01 -8.26840401e-01 -9.58798945e-01 -5.02653301e-01
7.97575891e-01 3.61098498e-01 -2.77913958e-01 -3.22387338... | [11.65623950958252, -0.41302478313446045] |
409311af-85e7-41d7-8216-5cc024875335 | leveraging-pseudo-labeled-data-to-improve | 2205.08993 | null | https://arxiv.org/abs/2205.08993v1 | https://arxiv.org/pdf/2205.08993v1.pdf | Leveraging Pseudo-labeled Data to Improve Direct Speech-to-Speech Translation | Direct Speech-to-speech translation (S2ST) has drawn more and more attention recently. The task is very challenging due to data scarcity and complex speech-to-speech mapping. In this paper, we report our recent achievements in S2ST. Firstly, we build a S2ST Transformer baseline which outperforms the original Translatot... | ['Yu Zhang', 'Qibing Bai', 'Mingxuan Wang', 'Tom Ko', 'Fengpeng Yue', 'Qianqian Dong'] | 2022-05-18 | null | null | null | null | ['speech-to-speech-translation'] | ['speech'] | [ 1.67126045e-01 -1.96485016e-02 -2.15747237e-01 -5.30037403e-01
-1.61842000e+00 -7.55141556e-01 6.67062879e-01 -3.25502217e-01
-3.82396877e-01 8.10515344e-01 3.82914811e-01 -6.17681026e-01
5.87839782e-01 -2.57183552e-01 -6.15539134e-01 -4.41030651e-01
5.78232408e-01 7.30583787e-01 2.91145205e-01 -4.97437835... | [14.49433422088623, 7.207890033721924] |
1daf6d17-8655-4259-b7ff-7058a16fd1a4 | gait-identification-under-surveillance | 2111.11720 | null | https://arxiv.org/abs/2111.11720v2 | https://arxiv.org/pdf/2111.11720v2.pdf | Gait Identification under Surveillance Environment based on Human Skeleton | As an emerging biological identification technology, vision-based gait identification is an important research content in biometrics. Most existing gait identification methods extract features from gait videos and identify a probe sample by a query in the gallery. However, video data contains redundant information and ... | ['Tanfeng Sun', 'Xinghao Jiang', 'Ke Xu', 'Xirui Li', 'Xingkai Zheng'] | 2021-11-23 | null | null | null | null | ['gait-identification'] | ['computer-vision'] | [ 6.61853254e-02 -5.95346451e-01 -2.11967528e-01 -8.10417831e-02
-1.58224985e-01 -3.16588849e-01 2.30057880e-01 -3.02694350e-01
-4.50854778e-01 5.13235986e-01 6.92163929e-02 5.34814060e-01
1.14035852e-01 -7.46744633e-01 -3.84801984e-01 -8.71023178e-01
-3.38040203e-01 3.08778226e-01 3.41459155e-01 5.41758239... | [14.25181770324707, 1.4353035688400269] |
17fc121c-be52-4899-a88a-f2f83a364cfd | a-security-steganography-scheme-based-on-hdr | 1902.10943 | null | http://arxiv.org/abs/1902.10943v1 | http://arxiv.org/pdf/1902.10943v1.pdf | A security steganography scheme based on hdr image | It is widely recognized that the image format is crucial to steganography for
that each individual format has its unique properities. Nowadays, the most
famous approach of digital image steganography is to combine a well-defined
distortion function with efficient practical codes such as STC. And numerous
researches are... | ['Wei Gao', 'Yongqing Huo', 'Yan Qiao'] | 2019-02-28 | null | null | null | null | ['image-steganography'] | ['computer-vision'] | [ 5.72010338e-01 -2.23737672e-01 -1.00607648e-02 1.32041052e-01
-7.52799809e-02 -3.18754613e-01 2.63549119e-01 -3.43900830e-01
-3.71433407e-01 8.26554239e-01 4.16974491e-03 -7.09794760e-01
8.07566643e-02 -1.01650584e+00 -4.06108618e-01 -7.36031711e-01
-1.47105932e-01 -4.14013207e-01 4.41542804e-01 -5.16177714... | [4.296730041503906, 8.054306030273438] |
944456ca-2407-4968-99ef-a6a81900a86c | radio-astronomical-images-object-detection | 2303.04506 | null | https://arxiv.org/abs/2303.04506v2 | https://arxiv.org/pdf/2303.04506v2.pdf | Radio astronomical images object detection and segmentation: A benchmark on deep learning methods | In recent years, deep learning has been successfully applied in various scientific domains. Following these promising results and performances, it has recently also started being evaluated in the domain of radio astronomy. In particular, since radio astronomy is entering the Big Data era, with the advent of the largest... | ['Carmelo Pino', 'Cristobal Bordiu', 'Francesco Schillirò', 'Filomena Bufano', 'Andrew M. Hopkins', 'Concetto Spampinato', 'Andrea DeMarco', 'Simone Riggi', 'Eva Sciacca', 'Giuseppe Fiameni', 'Daniel Magro', 'Renato Sortino'] | 2023-03-08 | null | null | null | null | ['astronomy'] | ['miscellaneous'] | [ 2.23388031e-01 -4.68110591e-02 4.71915789e-02 -7.14642182e-02
-4.41516250e-01 -4.71566081e-01 8.51173818e-01 -6.87271133e-02
-5.06740689e-01 4.57833171e-01 -2.48070851e-01 -3.86247575e-01
-3.25055689e-01 -8.38382602e-01 -2.51531690e-01 -6.97384655e-01
-9.06383172e-02 7.24741280e-01 1.44601300e-01 2.11847514... | [7.676526069641113, 3.021493434906006] |
d7bf1359-b688-430b-8435-1b203c0a0e8e | local-object-crop-collision-network-for | 2304.09439 | null | https://arxiv.org/abs/2304.09439v2 | https://arxiv.org/pdf/2304.09439v2.pdf | Local object crop collision network for efficient simulation of non-convex objects in GPU-based simulators | Our goal is to develop an efficient contact detection algorithm for large-scale GPU-based simulation of non-convex objects. Current GPU-based simulators such as IsaacGym and Brax must trade-off speed with fidelity, generality, or both when simulating non-convex objects. Their main issue lies in contact detection (CD): ... | ['Beomjoon Kim', 'Dongwon Son'] | 2023-04-19 | null | null | null | null | ['contact-detection'] | ['robots'] | [-1.48247883e-01 -3.23261887e-01 3.10790807e-01 1.17717884e-01
-7.45916009e-01 -6.06271088e-01 3.20524067e-01 2.82604933e-01
-5.73032975e-01 6.11959457e-01 -2.32192591e-01 -5.28214216e-01
1.80401690e-02 -1.05668366e+00 -1.07572615e+00 -3.98397923e-01
-3.81077260e-01 9.92959440e-01 6.01953626e-01 -4.30097789... | [8.111599922180176, -3.197916030883789] |
46a67e75-5e47-4b6f-9edd-e0b76f4898a1 | material-classification-in-the-wild-do | 1711.03874 | null | http://arxiv.org/abs/1711.03874v1 | http://arxiv.org/pdf/1711.03874v1.pdf | Material Classification in the Wild: Do Synthesized Training Data Generalise Better than Real-World Training Data? | We question the dominant role of real-world training images in the field of
material classification by investigating whether synthesized data can
generalise more effectively than real-world data. Experimental results on three
challenging real-world material databases show that the best performing
pre-trained convolutio... | ['Klaus D. McDonald-Maier', 'Anca Sticlaru', 'Shoaib Ehsan', 'Grigorios Kalliatakis', 'Juergen Gall', 'Ales Leonardis', 'George Stamatiadis'] | 2017-11-09 | null | null | null | null | ['material-classification'] | ['computer-vision'] | [ 5.31830549e-01 -1.16766676e-01 -1.88787654e-01 -2.24029243e-01
-8.27313364e-01 -4.66838926e-01 6.74910188e-01 9.02899802e-02
-5.03785849e-01 6.32742941e-01 -1.58490941e-01 -5.76878525e-02
-3.14928144e-01 -1.19683039e+00 -1.41553056e+00 -4.99722332e-01
-2.09751055e-01 2.17385456e-01 3.13029855e-01 -2.33159646... | [10.202317237854004, -0.15614552795886993] |
2c4ee1d9-7926-4771-9b01-95e30e52be08 | forecasting-the-2016-2017-central-apennines | 2301.09948 | null | https://arxiv.org/abs/2301.09948v2 | https://arxiv.org/pdf/2301.09948v2.pdf | Forecasting the 2016-2017 Central Apennines Earthquake Sequence with a Neural Point Process | Point processes have been dominant in modeling the evolution of seismicity for decades, with the Epidemic Type Aftershock Sequence (ETAS) model being most popular. Recent advances in machine learning have constructed highly flexible point process models using neural networks to improve upon existing parametric models. ... | ['Maxmilian J. Werner', 'Daniel J. Lawson', 'Samuel Stockman'] | 2023-01-24 | null | null | null | null | ['point-processes'] | ['methodology'] | [ 7.17195496e-02 -2.60035396e-02 1.39557347e-01 7.42847621e-02
-7.30905831e-01 -6.63210392e-01 9.29808676e-01 1.99672014e-01
-4.88678187e-01 8.22265208e-01 3.98034632e-01 -6.77693844e-01
-4.69490409e-01 -9.38391507e-01 -6.25092745e-01 -9.64220941e-01
-6.57717764e-01 7.36763299e-01 3.67661655e-01 -4.86660868... | [6.7379469871521, 3.06388258934021] |
982bab17-eacf-4cb6-8206-34ce170d560a | evolving-boxes-for-fast-vehicle-detection | 1702.00254 | null | http://arxiv.org/abs/1702.00254v3 | http://arxiv.org/pdf/1702.00254v3.pdf | Evolving Boxes for Fast Vehicle Detection | We perform fast vehicle detection from traffic surveillance cameras. A novel
deep learning framework, namely Evolving Boxes, is developed that proposes and
refines the object boxes under different feature representations. Specifically,
our framework is embedded with a light-weight proposal network to generate
initial a... | ['xiangyang xue', 'Hong Wang', 'Yingbin Zheng', 'Yao Lu', 'Li Wang', 'Hao Ye'] | 2017-02-01 | null | null | null | null | ['fast-vehicle-detection'] | ['computer-vision'] | [ 1.0877198e-01 -1.0721744e-01 -7.5332597e-02 -3.6448750e-01
-1.0638630e+00 -5.1060563e-01 6.9497579e-01 -1.3824601e-01
-6.1454237e-01 3.7199870e-01 -1.0576873e-01 -2.5844264e-01
5.2997416e-01 -4.4495657e-01 -8.9390343e-01 -6.7404658e-01
-1.7920251e-01 2.7300057e-01 1.0298342e+00 -5.5461377e-02
7.5046785e-02... | [8.752557754516602, -0.15226198732852936] |
979b3d67-249e-4e8a-962e-c8f5907ccafb | beyond-bounding-box-convex-hull-feature | null | null | http://openaccess.thecvf.com//content/CVPR2021/html/Guo_Beyond_Bounding-Box_Convex-Hull_Feature_Adaptation_for_Oriented_and_Densely_Packed_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Guo_Beyond_Bounding-Box_Convex-Hull_Feature_Adaptation_for_Oriented_and_Densely_Packed_CVPR_2021_paper.pdf | Beyond Bounding-Box: Convex-Hull Feature Adaptation for Oriented and Densely Packed Object Detection | Detecting oriented and densely packed objects remains challenging for spatial feature aliasing caused by the intersection of reception fields between objects. In this paper, we propose a convex-hull feature adaptation (CFA) approach for configuring convolutional features in accordance with oriented and densely pack... | ['Qixiang Ye', 'Xiangyang Ji', 'Jianbin Jiao', 'Xiaosong Zhang', 'Chang Liu', 'Zonghao Guo'] | 2021-06-19 | null | null | null | cvpr-2021-1 | ['object-detection-in-aerial-images'] | ['computer-vision'] | [-9.97256637e-02 -4.85288128e-02 2.49375060e-01 -4.24113601e-01
-4.35561299e-01 -8.23076844e-01 3.76955032e-01 5.13566613e-01
-2.92623103e-01 6.26229635e-03 -7.82031566e-02 1.27768800e-01
-2.83397824e-01 -6.80059791e-01 -9.17379797e-01 -4.80440587e-01
-7.01577365e-01 3.37265551e-01 7.42789209e-01 -5.27702942... | [8.952756881713867, 0.0574321411550045] |
323006a1-2897-4487-a1df-d2f5768ea5d9 | source-identification-a-self-supervision-task | 2307.02238 | null | https://arxiv.org/abs/2307.02238v1 | https://arxiv.org/pdf/2307.02238v1.pdf | Source Identification: A Self-Supervision Task for Dense Prediction | The paradigm of self-supervision focuses on representation learning from raw data without the need of labor-consuming annotations, which is the main bottleneck of current data-driven methods. Self-supervision tasks are often used to pre-train a neural network with a large amount of unlabeled data and extract generic fe... | ['Marleen de Bruijne', 'Subhradeep Kayal', 'Shuai Chen'] | 2023-07-05 | null | null | null | null | ['super-resolution', 'tumor-segmentation', 'medical-image-segmentation', 'brain-tumor-segmentation', 'representation-learning'] | ['computer-vision', 'computer-vision', 'medical', 'medical', 'methodology'] | [ 1.03860033e+00 4.91794080e-01 -1.83156699e-01 -5.88888943e-01
-9.52350855e-01 -1.63111165e-01 4.17302072e-01 -1.58685483e-02
-5.09641647e-01 9.23607945e-01 3.27105045e-01 8.64297301e-02
1.05302185e-01 -4.11150813e-01 -8.41406882e-01 -9.33257520e-01
2.95819581e-01 5.54723382e-01 1.07504047e-01 -1.10037297... | [14.517945289611816, -2.1573853492736816] |
49ccb369-f4ea-4e27-b2b1-16098cae7722 | sketching-a-linguistically-driven-reasoning | null | null | https://aclanthology.org/2022.acl-srw.14 | https://aclanthology.org/2022.acl-srw.14.pdf | Sketching a Linguistically-Driven Reasoning Dialog Model for Social Talk | The capability of holding social talk (or casual conversation) and making sense of conversational content requires context-sensitive natural language understanding and reasoning, which cannot be handled efficiently by the current popular open-domain dialog systems and chatbots. Heavily relying on corpus-based machine l... | ['Alex Lưu'] | null | null | null | null | acl-2022-5 | ['open-domain-dialog'] | ['natural-language-processing'] | [-3.63945402e-02 7.21670568e-01 -1.48791656e-01 -5.94940186e-01
-4.10185605e-01 -7.56152987e-01 7.25464463e-01 2.20470220e-01
-1.79723784e-01 8.58174324e-01 7.05664515e-01 -5.99190652e-01
-1.22105673e-01 -8.62404823e-01 3.68928611e-02 -3.63162816e-01
4.85832756e-03 8.58427823e-01 4.23890382e-01 -8.12884450... | [12.584440231323242, 7.939801216125488] |
2e8305b2-28c6-4642-b00b-d1ef8d960a62 | image-generation-from-layout | 1811.11389 | null | https://arxiv.org/abs/1811.11389v3 | https://arxiv.org/pdf/1811.11389v3.pdf | Image Generation from Layout | Despite significant recent progress on generative models, controlled generation of images depicting multiple and complex object layouts is still a difficult problem. Among the core challenges are the diversity of appearance a given object may possess and, as a result, exponential set of images consistent with a specifi... | ['Weidong Yin', 'Bo Zhao', 'Lili Meng', 'Leonid Sigal'] | 2018-11-28 | image-generation-from-layout-1 | http://openaccess.thecvf.com/content_CVPR_2019/html/Zhao_Image_Generation_From_Layout_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Zhao_Image_Generation_From_Layout_CVPR_2019_paper.pdf | cvpr-2019-6 | ['layout-to-image-generation'] | ['computer-vision'] | [ 4.87789869e-01 6.71318844e-02 1.31445482e-01 -2.69004196e-01
-5.77171683e-01 -6.89705670e-01 7.19712496e-01 -1.02288008e-01
8.86155199e-03 8.60527694e-01 5.63489348e-02 1.22497529e-01
5.43389395e-02 -8.51363003e-01 -1.11137271e+00 -8.88498008e-01
2.37491325e-01 6.17574453e-01 -1.44147217e-01 1.62675142... | [11.506095886230469, -0.39867591857910156] |
2a4b3b53-fe7e-4f25-8e70-7075e7286e4b | context-dependent-semantic-parsing-for | 2112.00894 | null | https://arxiv.org/abs/2112.00894v1 | https://arxiv.org/pdf/2112.00894v1.pdf | Context-Dependent Semantic Parsing for Temporal Relation Extraction | Extracting temporal relations among events from unstructured text has extensive applications, such as temporal reasoning and question answering. While it is difficult, recent development of Neural-symbolic methods has shown promising results on solving similar tasks. Current temporal relation extraction methods usually... | ['Jane Yung-jen Hsu', 'Kuan-Yin Lai', 'Shang-Ling Hsu', 'Bo-Ying Su'] | 2021-12-02 | null | null | null | null | ['temporal-relation-extraction'] | ['natural-language-processing'] | [ 1.39675602e-01 4.05179918e-01 -5.50736308e-01 -6.77960515e-01
-2.54698694e-01 -5.31282663e-01 6.22651875e-01 4.50410455e-01
-1.34973302e-02 7.96280742e-01 1.31899506e-01 -5.16456842e-01
-1.57067850e-01 -1.15412676e+00 -6.13339961e-01 -1.22191265e-01
-2.87451625e-01 4.76583004e-01 6.81197822e-01 -2.15156108... | [9.129121780395508, 7.773336887359619] |
3dfc545e-bf62-4af9-bf01-7b6a7ea86223 | self-supervised-learning-to-guide | 2204.09854 | null | https://arxiv.org/abs/2204.09854v1 | https://arxiv.org/pdf/2204.09854v1.pdf | Self-Supervised Learning to Guide Scientifically Relevant Categorization of Martian Terrain Images | Automatic terrain recognition in Mars rover images is an important problem not just for navigation, but for scientists interested in studying rock types, and by extension, conditions of the ancient Martian paleoclimate and habitability. Existing approaches to label Martian terrain either involve the use of non-expert a... | ['Mario Parente', 'Erik Learned-Miller', 'Ralph Milliken', 'Melissa Meyer', 'Deep Chakraborty', 'Tejas Panambur'] | 2022-04-21 | null | null | null | null | ['texture-classification'] | ['computer-vision'] | [ 5.70382252e-02 4.38394509e-02 -1.94132440e-02 -5.87555051e-01
-4.17366385e-01 -4.96462554e-01 7.23768651e-01 5.11754990e-01
-1.73625112e-01 7.91315436e-01 3.03906828e-01 -5.92483222e-01
-2.57469177e-01 -1.19285989e+00 -3.54766577e-01 -5.93491137e-01
-4.11857814e-01 8.05539727e-01 3.98032457e-01 -6.31619573... | [7.162048816680908, 2.1786768436431885] |
faf75c74-b163-4901-98d1-658a8b483346 | test-time-training-on-nearest-neighbors-for | 2305.18466 | null | https://arxiv.org/abs/2305.18466v2 | https://arxiv.org/pdf/2305.18466v2.pdf | Test-Time Training on Nearest Neighbors for Large Language Models | Many recent efforts aim to augment language models with relevant information retrieved from a database at test time. We avoid the need for prompt engineering by directly fine-tuning the model on data retrieved at test time using its standard training setup. For this purpose, we build a large-scale distributed nearest n... | ['Yu Sun', 'Moritz Hardt'] | 2023-05-29 | null | null | null | null | ['prompt-engineering'] | ['natural-language-processing'] | [-9.97532010e-02 -1.77224725e-01 -4.53250915e-01 -3.98553133e-01
-1.37549806e+00 -7.68137038e-01 7.62342811e-01 5.51196337e-01
-8.72761130e-01 4.56474841e-01 4.53280360e-01 -5.32786369e-01
-1.60824284e-01 -7.78680801e-01 -8.57704043e-01 -2.55117446e-01
6.36136830e-02 1.23416209e+00 5.09204447e-01 -4.23312843... | [11.238118171691895, 7.8508381843566895] |
10175bf3-4e40-4d85-af60-2eea4579eaf5 | audio-denoising-for-robust-audio | 2212.11277 | null | https://arxiv.org/abs/2212.11277v1 | https://arxiv.org/pdf/2212.11277v1.pdf | Audio Denoising for Robust Audio Fingerprinting | Music discovery services let users identify songs from short mobile recordings. These solutions are often based on Audio Fingerprinting, and rely more specifically on the extraction of spectral peaks in order to be robust to a number of distortions. Few works have been done to study the robustness of these algorithms t... | ['Kamil Akesbi'] | 2022-12-21 | null | null | null | null | ['audio-denoising'] | ['audio'] | [ 3.77370566e-01 2.06440762e-02 3.04641575e-01 7.42529258e-02
-7.33955741e-01 -4.80834693e-01 5.82592785e-01 2.92124271e-01
-5.26584566e-01 5.00670969e-01 6.41795024e-02 1.30085528e-01
-3.75244558e-01 -8.83418858e-01 -8.17212462e-01 -7.93560088e-01
1.56140346e-02 3.79457235e-01 4.49188024e-01 -8.37661326... | [15.5432767868042, 5.507719993591309] |
dd0fecc8-4127-4206-959f-be0e83e5ed19 | illumination-based-data-augmentation-for | 1910.08470 | null | https://arxiv.org/abs/1910.08470v1 | https://arxiv.org/pdf/1910.08470v1.pdf | Illumination-Based Data Augmentation for Robust Background Subtraction | A core challenge in background subtraction (BGS) is handling videos with sudden illumination changes in consecutive frames. In this paper, we tackle the problem from a data point-of-view using data augmentation. Our method performs data augmentation that not only creates endless data on the fly, but also features seman... | ['Edmond S. L. Ho', 'Hubert P. H. Shum', 'Dimitrios Sakkos'] | 2019-10-18 | null | null | null | null | ['video-background-subtraction', 'foreground-segmentation'] | ['computer-vision', 'computer-vision'] | [ 4.60967571e-01 -1.89341158e-01 5.78753531e-01 -3.81665587e-01
-3.47886175e-01 -6.30818963e-01 7.53472447e-01 -4.47752327e-01
-4.28835571e-01 7.38034606e-01 2.68810447e-02 -2.15092033e-01
6.47508860e-01 -6.74190044e-01 -9.84296322e-01 -9.46855843e-01
1.65208295e-01 -1.66133702e-01 3.64532918e-01 -1.89427897... | [9.8147611618042, -1.2683175802230835] |
42ba4a17-174b-40c6-baf5-af0f3d927596 | multi-modal-transformers-excel-at-class | 2111.11430 | null | https://arxiv.org/abs/2111.11430v6 | https://arxiv.org/pdf/2111.11430v6.pdf | Class-agnostic Object Detection with Multi-modal Transformer | What constitutes an object? This has been a long-standing question in computer vision. Towards this goal, numerous learning-free and learning-based approaches have been developed to score objectness. However, they generally do not scale well across new domains and novel objects. In this paper, we advocate that existing... | ['Ming-Hsuan Yang', 'Rao Muhammad Anwer', 'Fahad Shahbaz Khan', 'Salman Khan', 'Hanoona Rasheed', 'Muhammad Maaz'] | 2021-11-22 | null | null | null | null | ['object-proposal-generation', 'class-agnostic-object-detection', 'open-world-object-detection'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 2.25955069e-01 -2.66958982e-01 -1.96956575e-01 -4.06410575e-01
-1.08585501e+00 -5.80481708e-01 7.13228405e-01 7.91540965e-02
-5.02562225e-01 3.50047797e-01 -6.61621764e-02 -4.13432419e-02
-4.13494967e-02 -3.78873050e-01 -9.13568854e-01 -5.18237054e-01
2.47681946e-01 3.62063050e-01 7.24001288e-01 -2.67435670... | [9.913325309753418, 1.4793370962142944] |
20d25d57-272b-412c-b710-564a142a4bc5 | a-corpus-and-evaluation-framework-for-deeper | 1604.01696 | null | http://arxiv.org/abs/1604.01696v1 | http://arxiv.org/pdf/1604.01696v1.pdf | A Corpus and Evaluation Framework for Deeper Understanding of Commonsense Stories | Representation and learning of commonsense knowledge is one of the
foundational problems in the quest to enable deep language understanding. This
issue is particularly challenging for understanding casual and correlational
relationships between events. While this topic has received a lot of interest
in the NLP communit... | ['James Allen', 'Nasrin Mostafazadeh', 'Xiaodong He', 'Nathanael Chambers', 'Lucy Vanderwende', 'Dhruv Batra', 'Pushmeet Kohli', 'Devi Parikh'] | 2016-04-06 | null | null | null | null | ['cloze-test'] | ['natural-language-processing'] | [ 4.40964431e-01 4.66137156e-02 -1.96131945e-01 -6.06682777e-01
-9.24405336e-01 -7.05946743e-01 1.11235535e+00 3.43444794e-01
-2.52246767e-01 1.05031717e+00 1.03068340e+00 -3.56092781e-01
-4.30048853e-02 -8.36778164e-01 -6.15359724e-01 -9.02536288e-02
7.24671185e-02 6.12713456e-01 2.49096885e-01 -6.38994753... | [11.130915641784668, 8.82497501373291] |
ddc0d5f2-e134-42be-9c19-9fc8ec056c67 | comparing-different-criteria-for-vietnamese | null | null | https://aclanthology.org/W12-5005 | https://aclanthology.org/W12-5005.pdf | Comparing Different Criteria for Vietnamese Word Segmentation | null | ['Yusuke Miyao', 'Ngan L.T. Nguyen', 'Quy T. Nguyen'] | 2012-12-01 | comparing-different-criteria-for-vietnamese-1 | https://aclanthology.org/W12-5005 | https://aclanthology.org/W12-5005.pdf | ws-2012-12 | ['vietnamese-word-segmentation'] | ['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... | [-7.40680456161499, 3.687720775604248] |
b55dbbcd-46dd-47dd-a266-d5d92a1c8f0a | autonomous-robotic-drilling-system-for-mice | 2303.12265 | null | https://arxiv.org/abs/2303.12265v1 | https://arxiv.org/pdf/2303.12265v1.pdf | Autonomous Robotic Drilling System for Mice Cranial Window Creation: An Evaluation with an Egg Model | Robotic assistance for experimental manipulation in the life sciences is expected to enable precise manipulation of valuable samples, regardless of the skill of the scientist. Experimental specimens in the life sciences are subject to individual variability and deformation, and therefore require autonomous robotic cont... | ['Kanako Harada', 'Murilo M. Marinho', 'Enduo Zhao'] | 2023-03-22 | null | null | null | null | ['trajectory-planning'] | ['robots'] | [-4.93670285e-01 -2.93863509e-02 2.41075650e-01 3.09308805e-02
3.08367342e-01 -4.51119363e-01 -1.33104309e-01 1.38770640e-01
-7.04866886e-01 3.98646474e-01 -4.41656083e-01 6.39041066e-02
-2.95543671e-01 -4.91335273e-01 -8.24799836e-01 -8.45373869e-01
-1.68887973e-01 6.21524811e-01 3.27841073e-01 4.00580168... | [5.9580488204956055, -0.7077503800392151] |
ebc0cdf9-e48f-4436-b04b-cce018763b98 | token-level-supervised-contrastive-learning | 2107.09099 | null | https://arxiv.org/abs/2107.09099v3 | https://arxiv.org/pdf/2107.09099v3.pdf | Token-Level Supervised Contrastive Learning for Punctuation Restoration | Punctuation is critical in understanding natural language text. Currently, most automatic speech recognition (ASR) systems do not generate punctuation, which affects the performance of downstream tasks, such as intent detection and slot filling. This gives rise to the need for punctuation restoration. Recent work in pu... | ['Bo Wu', 'Xubo Liu', 'H Lilian Tang', 'Tom Ko', 'Qiushi Huang'] | 2021-07-19 | null | null | null | null | ['punctuation-restoration'] | ['natural-language-processing'] | [ 3.55843604e-01 1.57270551e-01 -3.52404237e-01 -2.95558482e-01
-8.04623842e-01 -3.66327733e-01 3.53402883e-01 7.64394403e-01
-6.64144993e-01 7.07690299e-01 6.55012548e-01 -6.83955669e-01
1.95701823e-01 -3.75736326e-01 -4.43842441e-01 -4.18978572e-01
1.68115556e-01 4.92271669e-02 3.98712568e-02 -1.92370713... | [14.22718334197998, 7.124086856842041] |
d3d4e6c9-1d76-480a-a1d0-1fdf0eef7121 | probabilistic-partition-of-unity-networks | 2107.03066 | null | https://arxiv.org/abs/2107.03066v1 | https://arxiv.org/pdf/2107.03066v1.pdf | Probabilistic partition of unity networks: clustering based deep approximation | Partition of unity networks (POU-Nets) have been shown capable of realizing algebraic convergence rates for regression and solution of PDEs, but require empirical tuning of training parameters. We enrich POU-Nets with a Gaussian noise model to obtain a probabilistic generalization amenable to gradient-based minimizatio... | ['Kookjin Lee', 'Andy Huang', 'Mamikon Gulian', 'Nat Trask'] | 2021-07-07 | null | null | null | null | ['probabilistic-deep-learning'] | ['computer-vision'] | [ 1.94591749e-02 1.53485641e-01 2.24393476e-02 -2.17011318e-01
-1.28716969e+00 -4.74572629e-01 6.52117848e-01 6.83818385e-02
-3.62354726e-01 9.06641066e-01 -1.07963629e-01 -2.02784687e-01
-5.93148828e-01 -8.14967573e-01 -7.10951626e-01 -1.05304873e+00
-2.17862114e-01 9.17773724e-01 1.33033648e-01 2.33933419... | [7.083524703979492, 3.8291358947753906] |
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