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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
ab2aaa99-7126-41fe-9b38-80290269be7f | anda-a-novel-data-augmentation-technique | 1910.01256 | null | https://arxiv.org/abs/1910.01256v1 | https://arxiv.org/pdf/1910.01256v1.pdf | ANDA: A Novel Data Augmentation Technique Applied to Salient Object Detection | In this paper, we propose a novel data augmentation technique (ANDA) applied to the Salient Object Detection (SOD) context. Standard data augmentation techniques proposed in the literature, such as image cropping, rotation, flipping, and resizing, only generate variations of the existing examples, providing a limited g... | ['Bruno A. Krinski', 'Eduardo Todt', 'Daniel V. Ruiz'] | 2019-10-03 | null | null | null | null | ['image-cropping'] | ['computer-vision'] | [ 8.19587648e-01 3.52030218e-01 5.10958880e-02 1.47861764e-02
-1.85755238e-01 -3.42856556e-01 6.91986382e-01 4.30024683e-01
-5.81210852e-01 7.64021337e-01 1.88275352e-01 2.34177932e-02
2.79598206e-01 -7.45299995e-01 -9.97639894e-01 -9.10137475e-01
1.19496271e-01 1.32088372e-02 5.59400320e-01 -1.79297552... | [10.833586692810059, -0.9801509380340576] |
efeb7e50-0e82-434b-bf8c-5919d332bca0 | function-words-enhanced-attention-networks | 2204.12111 | null | https://arxiv.org/abs/2204.12111v1 | https://arxiv.org/pdf/2204.12111v1.pdf | Function-words Enhanced Attention Networks for Few-Shot Inverse Relation Classification | The relation classification is to identify semantic relations between two entities in a given text. While existing models perform well for classifying inverse relations with large datasets, their performance is significantly reduced for few-shot learning. In this paper, we propose a function words adaptively enhanced a... | ['Kewen Wang', 'Zhiyong Feng', 'Xiaowang Zhang', 'Shaojuan Wu', 'Chunliu Dou'] | 2022-04-26 | null | null | null | null | ['relation-classification'] | ['natural-language-processing'] | [ 1.36088133e-01 4.58449841e-01 -6.10464752e-01 -3.85286599e-01
-4.34599042e-01 1.07805748e-02 7.92092562e-01 5.11645734e-01
-3.43414277e-01 6.76786780e-01 3.25644612e-01 -5.04474863e-02
-4.79475290e-01 -1.17841649e+00 -5.34288943e-01 -3.41142118e-01
-1.45698518e-01 6.12756252e-01 2.94975251e-01 -8.64767432... | [9.254343032836914, 8.549166679382324] |
5f033c6c-b7af-47df-96b1-ac89a26b7038 | propagating-over-phrase-relations-for-one | null | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/3304_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123640579.pdf | Propagating Over Phrase Relations for One-Stage Visual Grounding | Phrase level visual grounding aims to locate in an image the corresponding visual regions referred to by multiple noun phrases in a given sentence. Its challenge comes not only from large variations in visual contents and unrestricted phrase descriptions but also from unambiguous referrals derived from phrase relationa... | ['Yizhou Yu', 'Sibei Yang', 'Guanbin Li'] | null | null | null | null | eccv-2020-8 | ['phrase-grounding'] | ['natural-language-processing'] | [ 1.73658449e-02 5.12117863e-01 -4.40529227e-01 -4.28369999e-01
-7.35930085e-01 -6.40429974e-01 4.61314380e-01 4.92785633e-01
-1.54834956e-01 4.15055096e-01 4.88126606e-01 -1.43867552e-01
8.20079166e-03 -7.68547535e-01 -7.16580033e-01 -4.84273165e-01
-4.28194217e-02 4.30332065e-01 3.21308881e-01 -1.41761467... | [10.493812561035156, 1.4765198230743408] |
fb5ea972-9b3a-4fd1-883b-e5cd3d00816f | chat-crowd-a-dialog-based-platform-for-visual | 1812.04081 | null | http://arxiv.org/abs/1812.04081v3 | http://arxiv.org/pdf/1812.04081v3.pdf | Chat-crowd: A Dialog-based Platform for Visual Layout Composition | In this paper we introduce Chat-crowd, an interactive environment for visual
layout composition via conversational interactions. Chat-crowd supports
multiple agents with two conversational roles: agents who play the role of a
designer are in charge of placing objects in an editable canvas according to
instructions or c... | ['Paola Cascante-Bonilla', 'Vicente Ordonez', 'Song Feng', 'Xuwang Yin'] | 2018-12-10 | chat-crowd-a-dialog-based-platform-for-visual-1 | https://aclanthology.org/N19-4024 | https://aclanthology.org/N19-4024.pdf | naacl-2019-6 | ['goal-oriented-dialog'] | ['natural-language-processing'] | [-2.88181961e-01 1.55091837e-01 5.95615923e-01 -4.00333285e-01
-2.42808491e-01 -1.17812085e+00 9.82616246e-01 3.51331204e-01
-3.81357461e-01 5.50347626e-01 2.60228723e-01 -2.88083255e-01
1.38437571e-02 -7.88110673e-01 5.76858781e-03 -3.66359025e-01
1.03436261e-01 9.78979826e-01 6.77725852e-01 -8.41515303... | [5.31419038772583, 0.2904457747936249] |
f9453a5c-8045-4375-b46d-8e5a52e559f2 | what-s-cracking-a-review-and-analysis-of-deep | 2202.03714 | null | https://arxiv.org/abs/2202.03714v1 | https://arxiv.org/pdf/2202.03714v1.pdf | What's Cracking? A Review and Analysis of Deep Learning Methods for Structural Crack Segmentation, Detection and Quantification | Surface cracks are a very common indicator of potential structural faults. Their early detection and monitoring is an important factor in structural health monitoring. Left untreated, they can grow in size over time and require expensive repairs or maintenance. With recent advances in computer vision and deep learning ... | ['Gordon Morison', 'Peter Barrie', 'Mike Mannion', 'Mark Jenkins', 'Jacob König'] | 2022-02-08 | null | null | null | null | ['crack-segmentation'] | ['computer-vision'] | [ 2.98663616e-01 -4.28706482e-02 -1.61450818e-01 -1.36903867e-01
-7.96469331e-01 -8.76291245e-02 -3.46021861e-01 6.44812047e-01
-2.35442385e-01 1.95863426e-01 -4.26780656e-02 -1.00538626e-01
-7.92188477e-03 -9.91589367e-01 -3.91428113e-01 -8.92524540e-01
-6.07932545e-02 4.03542489e-01 2.84032315e-01 -5.66903576... | [7.492422580718994, 1.5522392988204956] |
e135eca3-f474-4fb3-90ad-238e9f4a62ee | invariant-deep-compressible-covariance | 2011.05702 | null | https://arxiv.org/abs/2011.05702v1 | https://arxiv.org/pdf/2011.05702v1.pdf | Invariant Deep Compressible Covariance Pooling for Aerial Scene Categorization | Learning discriminative and invariant feature representation is the key to visual image categorization. In this article, we propose a novel invariant deep compressible covariance pooling (IDCCP) to solve nuisance variations in aerial scene categorization. We consider transforming the input image according to a finite t... | ['Ling Shao', 'Yu Guan', 'Gerard Parr', 'Yi Ren', 'Shidong Wang'] | 2020-11-11 | null | null | null | null | ['image-categorization'] | ['computer-vision'] | [ 1.89652860e-01 -4.09102023e-01 9.25201774e-02 -2.15403110e-01
-8.38251337e-02 -8.57752621e-01 3.67332846e-01 -5.39053380e-01
-2.34778181e-01 -7.84991831e-02 2.87560165e-01 -2.39696756e-01
-3.79135996e-01 -4.70427334e-01 -4.87881511e-01 -7.13996112e-01
-1.54460073e-01 -2.80157447e-01 -1.08371764e-01 -3.42473418... | [9.019206047058105, 2.2378528118133545] |
3c9c8d4f-a972-4ef8-bb3a-ae72e2c118d3 | automatic-annotation-of-semantic-term-types | null | null | https://aclanthology.org/L18-1586 | https://aclanthology.org/L18-1586.pdf | Automatic Annotation of Semantic Term Types in the Complete ACL Anthology Reference Corpus | null | ["H{\\'e}ctor Mart{\\'\\i}nez Alonso", 'Anne-Kathrin Schumann'] | 2018-05-01 | automatic-annotation-of-semantic-term-types-1 | https://aclanthology.org/L18-1586 | https://aclanthology.org/L18-1586.pdf | lrec-2018-5 | ['lexical-analysis'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
-2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00
-3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01
-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.269936561584473, 3.7788901329040527] |
41ed5ddf-204b-4175-a61b-e84e75a731fd | a-framework-for-bidirectional-decoding-case | 2305.1258 | null | https://arxiv.org/abs/2305.12580v1 | https://arxiv.org/pdf/2305.12580v1.pdf | A Framework for Bidirectional Decoding: Case Study in Morphological Inflection | Transformer-based encoder-decoder models that generate outputs in a left-to-right fashion have become standard for sequence-to-sequence tasks. In this paper, we propose a framework for decoding that produces sequences from the "outside-in": at each step, the model chooses to generate a token on the left, on the right, ... | ['Julia Hockenmaier', 'Marc E. Canby'] | 2023-05-21 | null | null | null | null | ['morphological-inflection'] | ['natural-language-processing'] | [ 6.25183165e-01 3.48714411e-01 -2.32431769e-01 -4.50678855e-01
-1.21519005e+00 -9.68081653e-01 9.14451122e-01 -1.58734366e-01
-3.25268090e-01 9.17918324e-01 7.73672462e-01 -8.27036858e-01
4.99183327e-01 -7.35079169e-01 -9.53731358e-01 -6.82731688e-01
3.24374586e-01 7.83150792e-01 -9.37035158e-02 -4.14977580... | [11.34865665435791, 9.185531616210938] |
87e58a1a-370e-46eb-8f92-455ec2c9c9e5 | learning-to-predict-3d-lane-shape-and-camera | 2112.15351 | null | https://arxiv.org/abs/2112.15351v1 | https://arxiv.org/pdf/2112.15351v1.pdf | Learning to Predict 3D Lane Shape and Camera Pose from a Single Image via Geometry Constraints | Detecting 3D lanes from the camera is a rising problem for autonomous vehicles. In this task, the correct camera pose is the key to generating accurate lanes, which can transform an image from perspective-view to the top-view. With this transformation, we can get rid of the perspective effects so that 3D lanes would lo... | ['Zejian yuan', 'Zhiliang Xiong', 'Tie Liu', 'Dapeng Chen', 'Ruijin Liu'] | 2021-12-31 | null | null | null | null | ['3d-lane-detection'] | ['computer-vision'] | [-2.28972808e-01 -1.09327145e-01 -3.59378219e-01 -4.95506495e-01
-8.08850229e-01 -6.14342093e-01 4.84264612e-01 -7.44693756e-01
-1.86295241e-01 1.69185400e-01 -1.28028482e-01 -5.22181988e-01
5.34565628e-01 -5.02711296e-01 -9.69896197e-01 -7.28066146e-01
5.36007643e-01 5.36568701e-01 5.90661883e-01 -2.00647444... | [8.005998611450195, -1.7175087928771973] |
d9f9bf7e-25b4-4430-a9df-ee447cd89ca4 | handling-noisy-labels-for-robustly-learning | 1903.12008 | null | http://arxiv.org/abs/1903.12008v1 | http://arxiv.org/pdf/1903.12008v1.pdf | Handling Noisy Labels for Robustly Learning from Self-Training Data for Low-Resource Sequence Labeling | In this paper, we address the problem of effectively self-training neural
networks in a low-resource setting. Self-training is frequently used to
automatically increase the amount of training data. However, in a low-resource
scenario, it is less effective due to unreliable annotations created using
self-labeling of unl... | ['Michael A. Hedderich', 'Mittul Singh', 'Debjit Paul', 'Dietrich Klakow'] | 2019-03-28 | handling-noisy-labels-for-robustly-learning-1 | https://aclanthology.org/N19-3005 | https://aclanthology.org/N19-3005.pdf | naacl-2019-6 | ['auxiliary-learning'] | ['methodology'] | [ 4.45143729e-02 4.41373438e-01 6.42286465e-02 -4.95069802e-01
-1.24056149e+00 -5.81416070e-01 5.18900871e-01 1.02371559e-01
-8.69340241e-01 1.00126398e+00 4.43833590e-01 -2.14847967e-01
5.89524329e-01 -5.68352997e-01 -8.19130480e-01 -5.89752138e-01
4.68059033e-01 2.82961518e-01 -2.12753154e-02 7.73967728... | [9.431920051574707, 4.05098819732666] |
520aa276-957b-4b78-96a4-80f87f86b6f6 | seeking-common-but-distinguishing-difference-1 | 2111.09634 | null | https://arxiv.org/abs/2111.09634v1 | https://arxiv.org/pdf/2111.09634v1.pdf | Seeking Common but Distinguishing Difference, A Joint Aspect-based Sentiment Analysis Model | Aspect-based sentiment analysis (ABSA) task consists of three typical subtasks: aspect term extraction, opinion term extraction, and sentiment polarity classification. These three subtasks are usually performed jointly to save resources and reduce the error propagation in the pipeline. However, most of the existing joi... | ['Shu Jiang', 'Hai Zhao', 'Zuchao Li', 'Hongjiang Jing'] | 2021-11-18 | seeking-common-but-distinguishing-difference | https://aclanthology.org/2021.emnlp-main.318 | https://aclanthology.org/2021.emnlp-main.318.pdf | emnlp-2021-11 | ['term-extraction', 'aspect-based-sentiment-analysis'] | ['natural-language-processing', 'natural-language-processing'] | [ 1.62842304e-01 -1.27733245e-01 -2.05391780e-01 -6.60909414e-01
-9.25276816e-01 -5.19186497e-01 4.82868284e-01 -3.06206550e-02
-2.22469732e-01 3.90250415e-01 4.29517090e-01 -3.56447071e-01
3.45699787e-01 -8.38541150e-01 -6.53900921e-01 -4.89692688e-01
2.64326572e-01 2.49606129e-02 1.71026677e-01 -1.69097036... | [11.529488563537598, 6.580546855926514] |
b38b310d-71f5-414a-824e-3a09c18d5844 | fundamental-limits-and-tradeoffs-in-invariant-1 | 2012.10713 | null | https://arxiv.org/abs/2012.10713v4 | https://arxiv.org/pdf/2012.10713v4.pdf | Fundamental Limits and Tradeoffs in Invariant Representation Learning | A wide range of machine learning applications such as privacy-preserving learning, algorithmic fairness, and domain adaptation/generalization among others, involve learning invariant representations of the data that aim to achieve two competing goals: (a) maximize information or accuracy with respect to a target respon... | ['Pradeep Ravikumar', 'Geoffrey J. Gordon', 'Tommi S. Jaakkola', 'Bryon Aragam', 'Chen Dan', 'Han Zhao'] | 2020-12-19 | fundamental-limits-and-tradeoffs-in-invariant | https://openreview.net/forum?id=9CG8RW_p3Y | https://openreview.net/pdf?id=9CG8RW_p3Y | null | ['information-plane'] | ['methodology'] | [ 6.42384648e-01 1.31718397e-01 -7.46377528e-01 -5.15423298e-01
-7.97185838e-01 -7.38553762e-01 3.28539103e-01 4.58113849e-01
-4.08685416e-01 7.30726779e-01 2.66937554e-01 -4.26694214e-01
-6.60533905e-01 -7.36424208e-01 -4.71251220e-01 -8.27198803e-01
-1.08422726e-01 1.12472959e-01 -5.46582282e-01 2.15798125... | [6.188374996185303, 6.732506275177002] |
6a08b505-0ec6-4994-b649-150822f443d3 | dip-dual-incongruity-perceiving-network-for | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Wen_DIP_Dual_Incongruity_Perceiving_Network_for_Sarcasm_Detection_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Wen_DIP_Dual_Incongruity_Perceiving_Network_for_Sarcasm_Detection_CVPR_2023_paper.pdf | DIP: Dual Incongruity Perceiving Network for Sarcasm Detection | Sarcasm indicates the literal meaning is contrary to the real attitude. Considering the popularity and complementarity of image-text data, we investigate the task of multi-modal sarcasm detection. Different from other multi-modal tasks, for the sarcastic data, there exists intrinsic incongruity between a pair of im... | ['Jufeng Yang', 'Guoli Jia', 'Changsong Wen'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['sarcasm-detection', 'semantic-textual-similarity', 'semantic-similarity'] | ['natural-language-processing', 'natural-language-processing', 'natural-language-processing'] | [-3.58598202e-01 -6.93275258e-02 8.89812782e-03 -5.46277046e-01
-5.21930695e-01 -2.75599211e-01 5.22445679e-01 9.90239233e-02
-5.93896329e-01 6.70520216e-02 6.30912840e-01 4.27531153e-01
3.15836191e-01 -4.61808294e-01 -4.29672331e-01 -6.67816937e-01
5.91013908e-01 3.10249060e-01 -2.02321306e-01 -3.98977965... | [13.076189994812012, 5.0307159423828125] |
c2ebc69d-a694-48f4-ba49-80bb625594f2 | codetrek-flexible-modeling-of-code-using-an | null | null | https://openreview.net/forum?id=WQc075jmBmf | https://openreview.net/pdf?id=WQc075jmBmf | CodeTrek: Flexible Modeling of Code using an Extensible Relational Representation | Designing a suitable representation for code-reasoning tasks is challenging in aspects such as the kinds of program information to model, how to combine them, and how much context to consider. We propose CodeTrek, a deep learning approach that addresses these challenges by representing codebases as databases that confo... | ['Mayur Naik', 'Petros Maniatis', 'Hanjun Dai', 'Yuepeng Wang', 'Aaditya Naik', 'Pardis Pashakhanloo'] | 2021-09-29 | null | null | null | iclr-2022-4 | ['variable-misuse', 'exception-type'] | ['computer-code', 'computer-code'] | [-2.81601608e-01 7.79276341e-02 -6.96380556e-01 -6.71655655e-01
-5.25257587e-01 -5.32330334e-01 3.20570230e-01 6.14482701e-01
-1.56806987e-02 2.43551999e-01 6.72694743e-02 -7.04890072e-01
-8.16400629e-03 -1.19610119e+00 -9.01534617e-01 1.72423616e-01
-2.84458827e-02 1.84035093e-01 5.41334093e-01 -3.38733107... | [7.621532440185547, 7.829018592834473] |
33531134-5b9b-4ddb-a625-86948ef7222c | improving-non-autoregressive-generation-with | 2110.11115 | null | https://arxiv.org/abs/2110.11115v1 | https://arxiv.org/pdf/2110.11115v1.pdf | Improving Non-autoregressive Generation with Mixup Training | While pre-trained language models have achieved great success on various natural language understanding tasks, how to effectively leverage them into non-autoregressive generation tasks remains a challenge. To solve this problem, we present a non-autoregressive generation model based on pre-trained transformer models. T... | ['Qi Zhang', 'Liangjie Zhang', 'Haizhen Huang', 'Furu Wei', 'Fuzhen Zhuang', 'Deqing Wang', 'Zihan Zhang', 'Shaohan Huang', 'Ting Jiang'] | 2021-10-21 | null | null | null | null | ['paraphrase-generation', 'paraphrase-generation'] | ['computer-code', 'natural-language-processing'] | [ 4.81725395e-01 4.24630016e-01 -5.95755987e-02 -3.53064865e-01
-1.29219544e+00 -4.47356373e-01 9.69088912e-01 -2.29055032e-01
-2.14902498e-02 8.03444028e-01 7.22795069e-01 -6.28437936e-01
3.82972091e-01 -9.13870752e-01 -7.68089712e-01 -3.61711204e-01
5.75483322e-01 7.43446350e-01 3.39522921e-02 -5.18024325... | [11.840886116027832, 9.084722518920898] |
d13a4d47-5e7b-41eb-8a7f-eb11f2f856e4 | emergent-resource-exchange-and-tolerated | 2307.01862 | null | https://arxiv.org/abs/2307.01862v1 | https://arxiv.org/pdf/2307.01862v1.pdf | Emergent Resource Exchange and Tolerated Theft Behavior using Multi-Agent Reinforcement Learning | For decades, the evolution of cooperation has piqued the interest of numerous academic disciplines such as game theory, economics, biology, and computer science. In this work, we demonstrate the emergence of a novel and effective resource exchange protocol formed by dropping and picking up resources in a foraging envir... | ['Jordan Pollack', 'Jack Garbus'] | 2023-07-04 | null | null | null | null | ['multi-agent-reinforcement-learning'] | ['methodology'] | [-7.80565292e-02 1.31403059e-01 4.39136356e-01 2.29885310e-01
4.54100549e-01 -9.34272647e-01 6.38829947e-01 1.82629600e-01
-9.73212481e-01 1.35316348e+00 -3.27497631e-01 -1.23027638e-01
-3.28496814e-01 -7.42489219e-01 -9.96243134e-02 -8.60699177e-01
-6.68383479e-01 3.19963992e-01 2.16026410e-01 -7.12496817... | [3.8922555446624756, 2.1788299083709717] |
6c7a0fb6-fb28-4dfa-bc39-3ca470e66613 | from-synthetic-to-real-image-dehazing | 2108.02934 | null | https://arxiv.org/abs/2108.02934v1 | https://arxiv.org/pdf/2108.02934v1.pdf | From Synthetic to Real: Image Dehazing Collaborating with Unlabeled Real Data | Single image dehazing is a challenging task, for which the domain shift between synthetic training data and real-world testing images usually leads to degradation of existing methods. To address this issue, we propose a novel image dehazing framework collaborating with unlabeled real data. First, we develop a disentang... | ['Wei Feng', 'Liang Wan', 'Qing Zhang', 'Jing Qin', 'Huazhu Fu', 'Shunda Pei', 'Lei Zhu', 'Ye Liu'] | 2021-08-06 | null | null | null | null | ['image-dehazing'] | ['computer-vision'] | [ 3.17004770e-01 1.57739922e-01 2.61647463e-01 -3.78001451e-01
-6.39202237e-01 -2.32077822e-01 7.93956101e-01 -4.49366897e-01
-1.17438756e-01 7.49029517e-01 -1.45883977e-01 -7.84777701e-02
-1.05074920e-01 -8.77933025e-01 -9.12093878e-01 -1.38551617e+00
2.27073461e-01 3.30467880e-01 4.93406236e-01 -3.10875803... | [10.943320274353027, -3.142702341079712] |
9e712718-6155-4925-8252-5a3579d54ee2 | roam-random-layer-mixup-for-semi-supervised | 2003.09439 | null | https://arxiv.org/abs/2003.09439v4 | https://arxiv.org/pdf/2003.09439v4.pdf | ROAM: Random Layer Mixup for Semi-Supervised Learning in Medical Imaging | Medical image segmentation is one of the major challenges addressed by machine learning methods. Yet, deep learning methods profoundly depend on a large amount of annotated data, which is time-consuming and costly. Though, semi-supervised learning methods approach this problem by leveraging an abundant amount of unlabe... | ['Shadi Albarqouni', 'Benedikt Wiestler', 'Tariq Bdair', 'Nassir Navab'] | 2020-03-20 | null | null | null | null | ['brain-image-segmentation'] | ['medical'] | [ 2.31434152e-01 3.64757210e-01 -4.04671103e-01 -7.07048118e-01
-9.31963086e-01 -2.42056012e-01 2.10868120e-01 4.68841977e-02
-9.72586811e-01 7.43031561e-01 -1.75370425e-01 -1.24281771e-01
2.68267214e-01 -6.13989770e-01 -7.71287501e-01 -9.27152395e-01
1.21952057e-01 6.33750916e-01 1.77124947e-01 1.24946877... | [14.621512413024902, -2.1378211975097656] |
6bb01454-2d03-4731-8a45-a3b61dad94d1 | tcgm-an-information-theoretic-framework-for | 2007.06793 | null | https://arxiv.org/abs/2007.06793v1 | https://arxiv.org/pdf/2007.06793v1.pdf | TCGM: An Information-Theoretic Framework for Semi-Supervised Multi-Modality Learning | Fusing data from multiple modalities provides more information to train machine learning systems. However, it is prohibitively expensive and time-consuming to label each modality with a large amount of data, which leads to a crucial problem of semi-supervised multi-modal learning. Existing methods suffer from either in... | ['Shanghang Zhang', 'Lingjing Hu', 'Xinwei Sun', 'Yilun Xu', 'Yuqing Kong', 'Peng Cao', 'Yizhou Wang'] | 2020-07-14 | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/6209_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123480171.pdf | eccv-2020-8 | ['news-classification'] | ['natural-language-processing'] | [ 3.70716095e-01 2.20260337e-01 -4.88602787e-01 -3.66956592e-01
-1.35173607e+00 -4.87591267e-01 4.19566810e-01 3.43900442e-01
-1.77042708e-01 8.83603692e-01 5.45912012e-02 1.50857434e-01
-2.89639890e-01 -5.07272720e-01 -8.02546382e-01 -1.21236646e+00
2.42110133e-01 3.71512085e-01 -6.46989467e-03 1.99596718... | [12.878022193908691, 4.872745037078857] |
c99fc710-1bde-4934-b23c-067fe8df19c9 | improving-eeg-decoding-via-clustering-based | 2012.06813 | null | https://arxiv.org/abs/2012.06813v1 | https://arxiv.org/pdf/2012.06813v1.pdf | Improving EEG Decoding via Clustering-based Multi-task Feature Learning | Accurate electroencephalogram (EEG) pattern decoding for specific mental tasks is one of the key steps for the development of brain-computer interface (BCI), which is quite challenging due to the considerably low signal-to-noise ratio of EEG collected at the brain scalp. Machine learning provides a promising technique ... | ['Andrzej Cichocki', 'Guoxu Zhou', 'Hongru Zhu', 'Hua Xie', 'Wei Wu', 'Tao Zhou', 'Yu Zhang'] | 2020-12-12 | null | null | null | null | ['eeg-decoding', 'eeg-decoding'] | ['medical', 'time-series'] | [ 5.38940191e-01 -5.79809070e-01 1.56493887e-01 -4.38232094e-01
-6.02127552e-01 -1.40179649e-01 -1.31973699e-01 5.18972389e-02
-7.50895739e-02 8.09655905e-01 -8.78043100e-02 -3.41821797e-02
-8.88415158e-01 -3.32358539e-01 -7.17180669e-01 -1.13909459e+00
-9.56225246e-02 4.28800464e-01 -2.27161348e-01 1.64075315... | [13.133993148803711, 3.468426465988159] |
2a090fd6-d756-41c7-93ee-a4c952901bb7 | action-localization-through-continual | 2003.12185 | null | https://arxiv.org/abs/2003.12185v1 | https://arxiv.org/pdf/2003.12185v1.pdf | Action Localization through Continual Predictive Learning | The problem of action recognition involves locating the action in the video, both over time and spatially in the image. The dominant current approaches use supervised learning to solve this problem, and require large amounts of annotated training data, in the form of frame-level bounding box annotations around the regi... | ['Sudeep Sarkar', 'Sathyanarayanan N. Aakur'] | 2020-03-26 | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/2129_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123590290.pdf | eccv-2020-8 | ['eye-tracking'] | ['computer-vision'] | [ 2.96405762e-01 1.61176056e-01 -4.45181102e-01 -5.82202852e-01
-6.03598714e-01 -1.51947737e-01 5.40035248e-01 2.42572606e-01
-6.70555711e-01 5.22856116e-01 3.89707178e-01 1.23821408e-01
1.69090420e-01 -5.46864033e-01 -1.10001564e+00 -5.75758457e-01
-2.53914922e-01 1.66137695e-01 6.89316928e-01 5.51034175... | [8.479859352111816, 0.5899574756622314] |
0d7c255b-80b6-4b1f-9b14-88aa2ba720f3 | sood-towards-semi-supervised-oriented-object | 2304.04515 | null | https://arxiv.org/abs/2304.04515v1 | https://arxiv.org/pdf/2304.04515v1.pdf | SOOD: Towards Semi-Supervised Oriented Object Detection | Semi-Supervised Object Detection (SSOD), aiming to explore unlabeled data for boosting object detectors, has become an active task in recent years. However, existing SSOD approaches mainly focus on horizontal objects, leaving multi-oriented objects that are common in aerial images unexplored. This paper proposes a nove... | ['Xiang Bai', 'Xiaoqing Ye', 'Zhikang Zou', 'Xiaolong Liu', 'Jingyu Li', 'Dingkang Liang', 'Wei Hua'] | 2023-04-10 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Hua_SOOD_Towards_Semi-Supervised_Oriented_Object_Detection_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Hua_SOOD_Towards_Semi-Supervised_Oriented_Object_Detection_CVPR_2023_paper.pdf | cvpr-2023-1 | ['semi-supervised-object-detection', 'pseudo-label'] | ['computer-vision', 'miscellaneous'] | [ 9.80517343e-02 6.60539139e-03 -4.44777429e-01 -5.64847648e-01
-5.61410725e-01 -4.19895887e-01 4.92998004e-01 2.26462781e-01
-2.22568259e-01 3.47693413e-01 -5.34703434e-02 -1.02754153e-01
-1.30089328e-01 -5.83717406e-01 -7.65349984e-01 -8.91169667e-01
-4.89419959e-02 2.04499811e-01 6.39847100e-01 -3.30672637... | [9.21463680267334, 1.13712477684021] |
72dff537-3221-43a0-8a34-d377656de9be | learning-rich-representation-of-keyphrases-1 | 2112.08547 | null | https://arxiv.org/abs/2112.08547v2 | https://arxiv.org/pdf/2112.08547v2.pdf | Learning Rich Representation of Keyphrases from Text | In this work, we explore how to train task-specific language models aimed towards learning rich representation of keyphrases from text documents. We experiment with different masking strategies for pre-training transformer language models (LMs) in discriminative as well as generative settings. In the discriminative set... | ['Rajarshi Bhowmik', 'Ravneet Arora', 'Debanjan Mahata', 'Mayank Kulkarni'] | 2021-12-16 | null | https://aclanthology.org/2022.findings-naacl.67 | https://aclanthology.org/2022.findings-naacl.67.pdf | findings-naacl-2022-7 | ['keyphrase-generation', 'keyphrase-extraction'] | ['natural-language-processing', 'natural-language-processing'] | [ 4.04317468e-01 5.16906202e-01 1.16250187e-01 1.77619427e-01
-1.62944973e+00 -7.52443910e-01 1.05863047e+00 5.34850836e-01
-7.50116646e-01 1.00506461e+00 7.52139568e-01 -3.53871852e-01
-1.89744443e-01 -8.06270719e-01 -9.46832538e-01 -5.28652847e-01
1.74774043e-02 5.24652123e-01 1.66694000e-01 -5.72932184... | [12.299161911010742, 9.030123710632324] |
458802f8-31bf-407e-a4e8-dcfeca0ec2a6 | learning-in-imperfect-environment-multi-label | 2304.10539 | null | https://arxiv.org/abs/2304.10539v1 | https://arxiv.org/pdf/2304.10539v1.pdf | Learning in Imperfect Environment: Multi-Label Classification with Long-Tailed Distribution and Partial Labels | Conventional multi-label classification (MLC) methods assume that all samples are fully labeled and identically distributed. Unfortunately, this assumption is unrealistic in large-scale MLC data that has long-tailed (LT) distribution and partial labels (PL). To address the problem, we introduce a novel task, Partial la... | ['Yueting Zhuang', 'Siliang Tang', 'Beng Chin Ooi', 'Lingze Zeng', 'Changshuo Liu', 'Wenqiao Zhang'] | 2023-04-20 | null | null | null | null | ['philosophy'] | ['miscellaneous'] | [ 3.41859370e-01 -2.27576613e-01 -4.85226482e-01 -8.32802474e-01
-1.27283084e+00 -5.43143690e-01 1.75440386e-01 3.08817536e-01
-4.65663821e-01 1.01566470e+00 -3.59055251e-01 -4.32393312e-01
-2.43099853e-01 -4.15392488e-01 -7.05980301e-01 -8.36859226e-01
3.68928343e-01 7.20966280e-01 1.69483960e-01 1.45331711... | [9.44477367401123, 4.152299404144287] |
0e2ed912-87a2-4ed8-95e0-0a913f5eb732 | a-simple-and-optimal-policy-design-for-online | 2206.02969 | null | https://arxiv.org/abs/2206.02969v5 | https://arxiv.org/pdf/2206.02969v5.pdf | A Simple and Optimal Policy Design with Safety against Heavy-tailed Risk for Stochastic Bandits | We study the stochastic multi-armed bandit problem and design new policies that enjoy both worst-case optimality for expected regret and light-tailed risk for regret distribution. Starting from the two-armed bandit setting with time horizon $T$, we propose a simple policy and prove that the policy (i) enjoys the worst-... | ['Feng Zhu', 'Zeyu Zheng', 'David Simchi-Levi'] | 2022-06-07 | null | null | null | null | ['thompson-sampling'] | ['methodology'] | [-1.04583383e-01 2.80028999e-01 -5.99101603e-01 -2.95395792e-01
-1.34659386e+00 -1.05576313e+00 -2.17415065e-01 1.02866665e-01
-8.39337170e-01 1.04903650e+00 -1.16850957e-01 -1.17489302e+00
-1.07140934e+00 -8.63621116e-01 -9.94648695e-01 -9.03739154e-01
-3.79747510e-01 4.37761962e-01 -3.25267673e-01 1.32758766... | [4.552914619445801, 3.3278088569641113] |
ab8e2377-228a-4076-8dff-d7051d9af571 | scheduling-techniques-for-liver-segmentation | 2202.06373 | null | https://arxiv.org/abs/2202.06373v1 | https://arxiv.org/pdf/2202.06373v1.pdf | Scheduling Techniques for Liver Segmentation: ReduceLRonPlateau Vs OneCycleLR | Machine learning and computer vision techniques have influenced many fields including the biomedical one. The aim of this paper is to investigate the important concept of schedulers in manipulating the learning rate (LR), for the liver segmentation task, throughout the training process, focusing on the newly devised On... | ['Sarada Prasad Dakua', 'Faycal Bensaali', 'Ayman Al-Kababji'] | 2022-02-13 | null | null | null | null | ['liver-segmentation'] | ['medical'] | [ 1.22999735e-02 2.52609730e-01 -2.12684095e-01 -2.00464830e-01
-6.62470639e-01 -3.23527515e-01 5.04909337e-01 3.94841135e-01
-6.90518975e-01 6.28289521e-01 -2.29568958e-01 -4.27821875e-01
-2.75612742e-01 -1.09622471e-01 -4.05755132e-01 -1.08909798e+00
-5.45633316e-01 3.65501195e-01 2.86596894e-01 2.30705068... | [14.56166934967041, -2.547041893005371] |
a6904c00-d323-4d16-a897-c49428ed54b3 | temporal-dynamic-convolutional-neural-network | 2110.03213 | null | https://arxiv.org/abs/2110.03213v2 | https://arxiv.org/pdf/2110.03213v2.pdf | Temporal Dynamic Convolutional Neural Network for Text-Independent Speaker Verification and Phonemetic Analysis | In the field of text-independent speaker recognition, dynamic models that adapt along the time axis have been proposed to consider the phoneme-varying characteristics of speech. However, a detailed analysis of how dynamic models work depending on phonemes is insufficient. In this paper, we propose temporal dynamic CNN ... | ['Yong-Hwa Park', 'Hyeonuk Nam', 'Seong-Hu Kim'] | 2021-10-07 | null | null | null | null | ['text-independent-speaker-recognition', 'text-independent-speaker-verification'] | ['speech', 'speech'] | [-1.28799677e-01 -4.99798596e-01 1.59565628e-01 -6.33092821e-01
-4.61547792e-01 -6.44209087e-01 4.34317559e-01 -1.90001711e-01
-7.44200170e-01 3.05454731e-01 1.49417192e-01 -3.54394853e-01
-1.77411884e-01 -3.60051870e-01 -5.38235784e-01 -9.20455337e-01
-4.97389108e-01 2.52413660e-01 3.14213008e-01 -2.53158152... | [14.394279479980469, 6.107877254486084] |
e22b8ce6-e186-4d34-b539-7e8c0ef11554 | retrieval-as-attention-end-to-end-learning-of | 2212.02027 | null | https://arxiv.org/abs/2212.02027v1 | https://arxiv.org/pdf/2212.02027v1.pdf | Retrieval as Attention: End-to-end Learning of Retrieval and Reading within a Single Transformer | Systems for knowledge-intensive tasks such as open-domain question answering (QA) usually consist of two stages: efficient retrieval of relevant documents from a large corpus and detailed reading of the selected documents to generate answers. Retrievers and readers are usually modeled separately, which necessitates a c... | ['Graham Neubig', 'Jamie Callan', 'Zhiruo Wang', 'Haibo Ding', 'Jun Araki', 'Luyu Gao', 'Zhengbao Jiang'] | 2022-12-05 | null | null | null | null | ['passage-retrieval', 'open-domain-question-answering'] | ['natural-language-processing', 'natural-language-processing'] | [ 4.06329334e-02 3.37163150e-01 9.76833049e-03 -4.70639944e-01
-1.76587951e+00 -8.50269616e-01 6.90679610e-01 2.43399873e-01
-5.55454195e-01 5.01617432e-01 2.19693318e-01 -2.95717269e-01
-3.04816872e-01 -6.08303487e-01 -8.65653872e-01 -2.35026613e-01
4.77477044e-01 1.34850562e+00 4.98008788e-01 -5.02441168... | [11.345090866088867, 7.8913798332214355] |
661590e5-9f1d-42fb-aca1-b0cbe1d7483d | sampling-matters-an-empirical-study-of | null | null | https://aclanthology.org/D19-1128 | https://aclanthology.org/D19-1128.pdf | Sampling Matters! An Empirical Study of Negative Sampling Strategies for Learning of Matching Models in Retrieval-based Dialogue Systems | We study how to sample negative examples to automatically construct a training set for effective model learning in retrieval-based dialogue systems. Following an idea of dynamically adapting negative examples to matching models in learning, we consider four strategies including minimum sampling, maximum sampling, semi-... | ['Chongyang Tao', 'Wei Wu', 'Rui Yan', 'Dongyan Zhao', 'Yansong Feng', 'Jia Li'] | 2019-11-01 | null | null | null | ijcnlp-2019-11 | ['conversational-response-selection'] | ['natural-language-processing'] | [ 2.60706931e-01 3.56150657e-01 -5.54704249e-01 -3.93541068e-01
-1.10657585e+00 -5.42112112e-01 1.11000276e+00 1.74458757e-01
-8.56601417e-01 1.00859797e+00 6.38416186e-02 -3.88718903e-01
-1.39617827e-02 -6.12399280e-01 -6.91121519e-02 -5.14824986e-01
5.63041344e-02 1.17554712e+00 6.56498671e-01 -8.06209266... | [12.724601745605469, 8.103229522705078] |
621648d3-afc4-4f3a-bba2-686fbc9640bd | saliency-augmented-memory-completion-for | 2212.13242 | null | https://arxiv.org/abs/2212.13242v1 | https://arxiv.org/pdf/2212.13242v1.pdf | Saliency-Augmented Memory Completion for Continual Learning | Continual Learning is considered a key step toward next-generation Artificial Intelligence. Among various methods, replay-based approaches that maintain and replay a small episodic memory of previous samples are one of the most successful strategies against catastrophic forgetting. However, since forgetting is inevitab... | ['Liang Zhao', 'Yuyang Gao', 'Chen Ling', 'Guangji Bai'] | 2022-12-26 | null | null | null | null | ['bilevel-optimization'] | ['methodology'] | [ 3.00376445e-01 2.10734636e-01 -1.31119087e-01 -1.58655107e-01
-4.64344054e-01 2.60966029e-02 5.12463629e-01 2.31155664e-01
-5.34181178e-01 1.14634788e+00 1.13527328e-01 1.54040396e-01
-3.45224261e-01 -8.25812697e-01 -1.08415663e+00 -7.36402571e-01
1.46521956e-01 2.02221036e-01 2.75754213e-01 -7.45813921... | [9.845317840576172, 3.40586256980896] |
3a82e8b3-c8ce-4525-9281-ba4c12f4181e | gender-stereotyping-impact-in-facial | 2210.05332 | null | https://arxiv.org/abs/2210.05332v1 | https://arxiv.org/pdf/2210.05332v1.pdf | Gender Stereotyping Impact in Facial Expression Recognition | Facial Expression Recognition (FER) uses images of faces to identify the emotional state of users, allowing for a closer interaction between humans and autonomous systems. Unfortunately, as the images naturally integrate some demographic information, such as apparent age, gender, and race of the subject, these systems ... | ['Mikel Galar', 'Daniel Paternain', 'Iris Dominguez-Catena'] | 2022-10-11 | null | null | null | null | ['facial-expression-recognition'] | ['computer-vision'] | [ 1.56352166e-02 2.73715585e-01 -7.22115785e-02 -9.82892156e-01
6.12070225e-02 -4.98445094e-01 6.69253290e-01 4.40299846e-02
-5.51301241e-01 5.64977944e-01 3.75043154e-01 -5.29951714e-02
1.71154156e-01 -7.59359837e-01 -4.99221325e-01 -5.96514106e-01
6.68105707e-02 3.01139086e-01 -3.57111841e-01 -4.54045027... | [13.043142318725586, 1.3560817241668701] |
8bf7aca5-dcd0-4b7f-83f8-eb0e1f29c232 | uncertainty-inspired-open-set-learning-for | 2304.03981 | null | https://arxiv.org/abs/2304.03981v1 | https://arxiv.org/pdf/2304.03981v1.pdf | Uncertainty-inspired Open Set Learning for Retinal Anomaly Identification | Failure to recognize samples from the classes unseen during training is a major limit of artificial intelligence (AI) in real-world implementation of retinal anomaly classification. To resolve this obstacle, we propose an uncertainty-inspired open-set (UIOS) model which was trained with fundus images of 9 common retina... | ['Huazhu Fu', 'Haoyu Chen', 'Chi Pui Pang', 'Yong liu', 'Rick Siow Mong Goh', 'Daoqiang Zhang', 'Xinjian Chen', 'Changqing Zhang', 'Weifang Zhu', 'Mingzhi Zhang', 'Jianhong Lin', 'Junhong Chen', 'Zhiqun Wu', 'Guoyao Deng', 'Yiming Qian', 'Qingquan Meng', 'Yuanyuan Peng', 'Yi Zhou', 'Xinxing Xu', 'Ke Zou', 'Aidi Lin', '... | 2023-04-08 | null | null | null | null | ['anomaly-classification', 'open-set-learning'] | ['computer-vision', 'miscellaneous'] | [ 4.75892350e-02 5.07820189e-01 1.09598130e-01 -4.63781029e-01
-4.38099623e-01 -2.11009055e-01 1.92122281e-01 4.07491699e-02
-2.45236084e-01 1.09126377e+00 -3.73281419e-01 -2.23830879e-01
-4.95749801e-01 -6.57990813e-01 -6.24164402e-01 -6.38297796e-01
2.86942143e-02 4.55630898e-01 3.36012721e-01 3.78329068... | [15.785842895507812, -3.9077773094177246] |
55ad182d-6bde-4b89-b614-46c552705c8f | jointformer-single-frame-lifting-transformer | 2208.03704 | null | https://arxiv.org/abs/2208.03704v1 | https://arxiv.org/pdf/2208.03704v1.pdf | Jointformer: Single-Frame Lifting Transformer with Error Prediction and Refinement for 3D Human Pose Estimation | Monocular 3D human pose estimation technologies have the potential to greatly increase the availability of human movement data. The best-performing models for single-image 2D-3D lifting use graph convolutional networks (GCNs) that typically require some manual input to define the relationships between different body jo... | ['Aljosa Smolic', 'Ciaran Simms', 'Matthew Moynihan', 'Koustav Ghosal', 'Richard Blythman', 'Sebastian Lutz'] | 2022-08-07 | null | null | null | null | ['monocular-3d-human-pose-estimation'] | ['computer-vision'] | [-1.19610289e-02 2.49817282e-01 -1.81644157e-01 -3.54605049e-01
-6.16046846e-01 -3.00277680e-01 5.05784750e-01 -1.95785388e-01
-6.17919564e-01 5.42167306e-01 5.36869466e-01 -5.32102771e-02
1.79321989e-01 -3.27239960e-01 -1.08909297e+00 -1.40316889e-01
-3.26535016e-01 6.02352321e-01 4.72552717e-01 -4.07760382... | [7.000821113586426, -0.9118217825889587] |
38f4d85a-eb56-4532-b2d7-89727cf73b6f | closing-the-loop-testing-chatgpt-to-generate | 2306.05115 | null | https://arxiv.org/abs/2306.05115v1 | https://arxiv.org/pdf/2306.05115v1.pdf | Closing the Loop: Testing ChatGPT to Generate Model Explanations to Improve Human Labelling of Sponsored Content on Social Media | Regulatory bodies worldwide are intensifying their efforts to ensure transparency in influencer marketing on social media through instruments like the Unfair Commercial Practices Directive (UCPD) in the European Union, or Section 5 of the Federal Trade Commission Act. Yet enforcing these obligations has proven to be hi... | ['Adriana Iamnitchi', 'Gerasimos Spanakis', 'Catalina Goanta', 'Stefan Huber', 'Thales Bertaglia'] | 2023-06-08 | null | null | null | null | ['marketing'] | ['miscellaneous'] | [ 3.47881019e-01 5.77261925e-01 -4.96413499e-01 -5.89366198e-01
-9.82474029e-01 -8.50042045e-01 5.61216354e-01 4.50004429e-01
-4.00177598e-01 5.35314977e-01 4.91452843e-01 -4.44204569e-01
9.40856040e-02 -5.08097708e-01 -2.25442126e-01 -3.46070677e-01
4.13753927e-01 4.43527550e-01 2.07254916e-01 -1.40892729... | [10.025749206542969, 6.406435966491699] |
bf368426-7c13-455e-ad2b-27182dd961d0 | distributionally-robust-learning-for-2 | null | null | https://openreview.net/forum?id=qRdED5QjM9e | https://openreview.net/pdf?id=qRdED5QjM9e | Distributionally Robust Learning for Unsupervised Domain Adaptation | We propose a distributionally robust learning (DRL) method for unsupervised domain adaptation (UDA) that scales to modern computer-vision benchmarks. DRL can be naturally formulated as a competitive two-player game between a predictor and an adversary that is allowed to corrupt the labels, subject to certain const... | ['Anima Anandkumar', 'Yisong Yue', 'Zhiding Yu', 'Anqi Liu', 'Haoxuan Wang'] | 2020-09-28 | null | null | null | null | ['density-ratio-estimation'] | ['methodology'] | [ 3.83498847e-01 2.79086471e-01 -3.95853281e-01 -4.31265146e-01
-1.10464907e+00 -8.22952747e-01 6.69112921e-01 -3.06242588e-03
-7.62072921e-01 9.28990483e-01 -1.61881790e-01 -3.26743364e-01
6.92796484e-02 -7.61520505e-01 -1.02794135e+00 -9.50082481e-01
1.98383987e-01 8.22015882e-01 2.89936364e-01 2.41117164... | [10.295648574829102, 3.2252726554870605] |
48c7a5fd-6940-4f89-8714-cdab8bb1c6ac | mtfnet-mutual-transformer-fusion-network-for | 2112.01177 | null | https://arxiv.org/abs/2112.01177v3 | https://arxiv.org/pdf/2112.01177v3.pdf | MutualFormer: Multi-Modality Representation Learning via Cross-Diffusion Attention | Aggregating multi-modality data to obtain reliable data representation attracts more and more attention. Recent studies demonstrate that Transformer models usually work well for multi-modality tasks. Existing Transformers generally either adopt the Cross-Attention (CA) mechanism or simple concatenation to achieve the i... | ['Bin Luo', 'Jin Tang', 'Bo Jiang', 'Xiao Wang', 'Xixi Wang'] | 2021-12-02 | null | null | null | null | ['rgb-d-salient-object-detection'] | ['computer-vision'] | [-5.85849397e-02 -3.00345898e-01 -1.48144916e-01 -3.65311921e-01
-9.92158532e-01 -3.99906695e-01 5.89566469e-01 -2.02978905e-02
-3.94341528e-01 3.67432505e-01 5.53214431e-01 -5.44672161e-02
-3.19328487e-01 -6.87058568e-01 -4.99952674e-01 -9.93559837e-01
5.89734554e-01 2.77353246e-02 8.08460638e-02 -2.65934736... | [13.0941801071167, 4.965790748596191] |
59f4460d-088c-4497-bd63-9563657c283f | vast-the-valence-assessing-semantics-test-for | 2203.07504 | null | https://arxiv.org/abs/2203.07504v1 | https://arxiv.org/pdf/2203.07504v1.pdf | VAST: The Valence-Assessing Semantics Test for Contextualizing Language Models | VAST, the Valence-Assessing Semantics Test, is a novel intrinsic evaluation task for contextualized word embeddings (CWEs). VAST uses valence, the association of a word with pleasantness, to measure the correspondence of word-level LM semantics with widely used human judgments, and examines the effects of contextualiza... | ['Aylin Caliskan', 'Robert Wolfe'] | 2022-03-14 | null | null | null | null | ['word-similarity'] | ['natural-language-processing'] | [ 9.42451321e-03 -6.93787187e-02 -2.00872645e-01 -2.34732226e-01
-5.33987284e-01 -8.48981261e-01 6.26114130e-01 4.72674459e-01
-8.92166436e-01 2.02412114e-01 7.22755492e-01 -2.81638712e-01
6.59806058e-02 -6.32554114e-01 -4.79081511e-01 -5.21892786e-01
-2.38272667e-01 1.06067315e-01 -7.73461536e-02 -4.83364940... | [10.404618263244629, 8.959829330444336] |
6e908737-1765-4610-abfa-4829c47dda97 | understanding-dataset-design-choices-for | 1904.12106 | null | http://arxiv.org/abs/1904.12106v1 | http://arxiv.org/pdf/1904.12106v1.pdf | Understanding Dataset Design Choices for Multi-hop Reasoning | Learning multi-hop reasoning has been a key challenge for reading
comprehension models, leading to the design of datasets that explicitly focus
on it. Ideally, a model should not be able to perform well on a multi-hop
question answering task without doing multi-hop reasoning. In this paper, we
investigate two recently ... | ['Jifan Chen', 'Greg Durrett'] | 2019-04-27 | understanding-dataset-design-choices-for-1 | https://aclanthology.org/N19-1405 | https://aclanthology.org/N19-1405.pdf | naacl-2019-6 | ['multi-hop-question-answering'] | ['knowledge-base'] | [-1.70398932e-02 5.05921721e-01 1.63270459e-01 -2.81094253e-01
-1.26878464e+00 -9.09771442e-01 4.87615883e-01 2.76880056e-01
-5.88754177e-01 8.16995740e-01 4.70243424e-01 -8.16380441e-01
-6.05790436e-01 -9.40082490e-01 -7.99365938e-01 -1.65689453e-01
3.17549944e-01 8.93490255e-01 5.08561909e-01 -6.95769489... | [11.058050155639648, 7.993850231170654] |
db72d3bf-96b2-4453-8623-5c71215998ce | a-latent-feature-analysis-based-approach-for | 2208.07739 | null | https://arxiv.org/abs/2208.07739v1 | https://arxiv.org/pdf/2208.07739v1.pdf | A Latent Feature Analysis-based Approach for Spatio-Temporal Traffic Data Recovery | Missing data is an inevitable and common problem in data-driven intelligent transportation systems (ITS). In the past decade, scholars have done many research on the recovery of missing traffic data, however how to make full use of spatio-temporal traffic patterns to improve the recovery performance is still an open pr... | ['Di wu', 'Yuting Ding'] | 2022-08-16 | null | null | null | null | ['matrix-completion'] | ['methodology'] | [-3.62904556e-02 -6.71485543e-01 -4.59153265e-01 -4.04913694e-01
-5.77874064e-01 2.84686297e-01 2.97707260e-01 -5.02438247e-01
-2.37624310e-02 8.18818688e-01 7.10243881e-01 -3.17999333e-01
-6.75338805e-01 -8.17519069e-01 -3.30096960e-01 -8.30859721e-01
-1.93279739e-02 2.34572351e-01 1.59180671e-01 -2.84466594... | [6.55159854888916, 2.0862841606140137] |
7b356c16-c684-419c-9bba-b67fd24e213c | attentive-memory-networks-efficient-machine | 1712.07229 | null | http://arxiv.org/abs/1712.07229v1 | http://arxiv.org/pdf/1712.07229v1.pdf | Attentive Memory Networks: Efficient Machine Reading for Conversational Search | Recent advances in conversational systems have changed the search paradigm.
Traditionally, a user poses a query to a search engine that returns an answer
based on its index, possibly leveraging external knowledge bases and
conditioning the response on earlier interactions in the search session. In a
natural conversatio... | ['Maarten de Rijke', 'Tom Kenter'] | 2017-12-19 | null | null | null | null | ['conversational-search'] | ['natural-language-processing'] | [ 6.87916756e-01 6.82487130e-01 -1.33751750e-01 -4.88411993e-01
-9.37212586e-01 -8.55190575e-01 1.00787044e+00 3.35337162e-01
-5.84667563e-01 6.09429598e-01 5.22969365e-01 -6.77736521e-01
-1.81035623e-01 -8.66824269e-01 -4.83558178e-01 -2.86121517e-01
2.20523119e-01 1.13097703e+00 2.11945325e-01 -6.15077615... | [12.174091339111328, 7.840701580047607] |
f6f13b06-8753-4522-8707-b36ec5f9fbb7 | protecting-the-intellectual-properties-of | 2104.09203 | null | https://arxiv.org/abs/2104.09203v1 | https://arxiv.org/pdf/2104.09203v1.pdf | Protecting the Intellectual Properties of Deep Neural Networks with an Additional Class and Steganographic Images | Recently, the research on protecting the intellectual properties (IP) of deep neural networks (DNN) has attracted serious concerns. A number of DNN copyright protection methods have been proposed. However, most of the existing watermarking methods focus on verifying the copyright of the model, which do not support the ... | ['Weiqiang Liu', 'Jian Wang', 'Mingfu Xue', 'Shichang Sun'] | 2021-04-19 | null | null | null | null | ['image-steganography'] | ['computer-vision'] | [ 6.51650190e-01 -3.26225579e-01 -5.12374461e-01 1.44843921e-01
8.09487998e-02 -5.99984109e-01 2.91213304e-01 -3.00012112e-01
-6.65303290e-01 6.51073635e-01 -3.22204411e-01 -5.02523422e-01
1.18316654e-02 -8.62082243e-01 -6.17883980e-01 -6.98555410e-01
1.37620538e-01 -3.96836251e-01 6.82957351e-01 -1.90557949... | [5.339414596557617, 7.86707878112793] |
cca4b9d9-6fe2-4685-8955-a37e70bbffb8 | denoising-bottleneck-with-mutual-information | 2305.14652 | null | https://arxiv.org/abs/2305.14652v3 | https://arxiv.org/pdf/2305.14652v3.pdf | Denoising Bottleneck with Mutual Information Maximization for Video Multimodal Fusion | Video multimodal fusion aims to integrate multimodal signals in videos, such as visual, audio and text, to make a complementary prediction with multiple modalities contents. However, unlike other image-text multimodal tasks, video has longer multimodal sequences with more redundancy and noise in both visual and audio m... | ['Shaoxiang Wu', 'Zhifang Sui', 'Yunbo Cao', 'Binghuai Lin', 'Tianyu Liu', 'Ziwei Qin', 'Damai Dai'] | 2023-05-24 | null | null | null | null | ['multimodal-sentiment-analysis', 'sentiment-analysis', 'multimodal-sentiment-analysis'] | ['computer-vision', 'natural-language-processing', 'natural-language-processing'] | [ 2.33661950e-01 -3.28028381e-01 -1.61811598e-02 -1.78867102e-01
-1.22426748e+00 -4.37993377e-01 4.66783792e-01 1.51859179e-01
-4.00054544e-01 5.48997462e-01 9.16253924e-01 2.34804705e-01
2.38101214e-01 -1.73490882e-01 -8.26704621e-01 -8.43864202e-01
3.60767037e-01 -4.18975353e-01 4.09485102e-02 -3.12376976... | [13.546647071838379, 4.764918804168701] |
ad7068d5-f7cc-4d14-b9e8-fc601480c5b5 | employing-weak-annotations-for-medical-image | 1708.06297 | null | http://arxiv.org/abs/1708.06297v1 | http://arxiv.org/pdf/1708.06297v1.pdf | Employing Weak Annotations for Medical Image Analysis Problems | To efficiently establish training databases for machine learning methods,
collaborative and crowdsourcing platforms have been investigated to
collectively tackle the annotation effort. However, when this concept is ported
to the medical imaging domain, reading expertise will have a direct impact on
the annotation accur... | ['Kensaku MORI', 'Kazunari Misawa', 'Jonathan Passerat-Palmbach', 'Christian Ledig', 'Daniel Rueckert', 'Martin Rajchl', 'Lisa M. Koch'] | 2017-08-21 | null | null | null | null | ['liver-segmentation'] | ['medical'] | [-1.74146250e-03 6.50736809e-01 2.07481131e-01 -3.08154881e-01
-1.14400399e+00 -6.33485436e-01 2.90551960e-01 8.26542377e-01
-9.31289971e-01 8.11734974e-01 -2.06010304e-02 -2.07862779e-01
5.66164441e-02 -3.94755512e-01 -6.60936892e-01 -5.52533984e-01
8.07826743e-02 8.09355795e-01 5.68007112e-01 8.50096643... | [14.863517761230469, -2.502560615539551] |
d4d22155-a2a1-4051-9628-65499dae3cef | generalized-lstm-based-end-to-end-text | 2011.04896 | null | https://arxiv.org/abs/2011.04896v4 | https://arxiv.org/pdf/2011.04896v4.pdf | An Empirical Study on Text-Independent Speaker Verification based on the GE2E Method | While many researchers in the speaker recognition area have started to replace the former classical state-of-the-art methods with deep learning techniques, some of the traditional i-vector-based methods are still state-of-the-art in the context of text-independent speaker verification. Google's Generalized End-to-End L... | ['Soroosh Tayebi Arasteh'] | 2020-11-10 | null | null | null | null | ['text-independent-speaker-verification'] | ['speech'] | [-1.01522394e-01 -4.94593143e-01 1.32406633e-02 -8.68742824e-01
-1.05763829e+00 -3.43327522e-01 4.66686040e-01 -7.34274387e-02
-4.81875360e-01 3.78220558e-01 2.60934770e-01 -6.26302421e-01
9.63614658e-02 -6.19437173e-02 -4.37286139e-01 -7.28974640e-01
-1.40436172e-01 3.12048405e-01 -5.42401485e-02 -1.06138304... | [14.321465492248535, 6.084474563598633] |
9a8fb870-fbfd-4887-a302-8eb5999e10a7 | semi-supervised-learning-with-normalizing-1 | 1912.13025 | null | https://arxiv.org/abs/1912.13025v1 | https://arxiv.org/pdf/1912.13025v1.pdf | Semi-Supervised Learning with Normalizing Flows | Normalizing flows transform a latent distribution through an invertible neural network for a flexible and pleasingly simple approach to generative modelling, while preserving an exact likelihood. We propose FlowGMM, an end-to-end approach to generative semi supervised learning with normalizing flows, using a latent Gau... | ['Pavel Izmailov', 'Andrew Gordon Wilson', 'Marc Finzi', 'Polina Kirichenko'] | 2019-12-30 | null | https://proceedings.icml.cc/static/paper_files/icml/2020/3378-Paper.pdf | https://proceedings.icml.cc/static/paper_files/icml/2020/3378-Paper.pdf | icml-2020-1 | ['semi-supervised-text-classification-1'] | ['natural-language-processing'] | [ 9.69952568e-02 2.33048841e-01 -3.68661553e-01 -8.14655662e-01
-7.14941204e-01 -7.04360306e-01 9.33395028e-01 -4.84301507e-01
1.50213838e-01 6.27024531e-01 6.54282212e-01 -3.76236349e-01
-4.31799352e-01 -6.60653234e-01 -4.22629297e-01 -7.85024822e-01
1.34604096e-01 1.15486681e+00 -4.72955376e-01 1.61468431... | [11.369890213012695, -0.0861077532172203] |
b4a0df00-264a-434c-9d68-d4f8755bd3d8 | visual-depth-mapping-from-monocular-images | 1812.04082 | null | http://arxiv.org/abs/1812.04082v1 | http://arxiv.org/pdf/1812.04082v1.pdf | Visual Depth Mapping from Monocular Images using Recurrent Convolutional Neural Networks | A reliable sense-and-avoid system is critical to enabling safe autonomous
operation of unmanned aircraft. Existing sense-and-avoid methods often require
specialized sensors that are too large or power intensive for use on small
unmanned vehicles. This paper presents a method to estimate object distances
based on visual... | ['Rachael E. Tompa', 'John Mern', 'Mykel J. Kochenderfer', 'Kyle Julian'] | 2018-12-10 | null | null | null | null | ['depth-and-camera-motion'] | ['computer-vision'] | [ 5.88234551e-02 -5.53602651e-02 2.15267837e-01 -3.67073536e-01
-5.09537637e-01 -7.39539623e-01 5.19348145e-01 -3.65193337e-01
-7.27060020e-01 6.24316931e-01 -3.76468480e-01 -6.37518525e-01
1.90139577e-01 -7.61094630e-01 -8.34878922e-01 -3.16350937e-01
-3.38242650e-01 3.32250893e-01 5.13388515e-01 -4.75654334... | [4.893213272094727, 0.7395444512367249] |
60a8c495-b466-484f-8e68-b7dd22110462 | receptive-field-regularized-cnns-for-music | 2007.13503 | null | https://arxiv.org/abs/2007.13503v1 | https://arxiv.org/pdf/2007.13503v1.pdf | Receptive-Field Regularized CNNs for Music Classification and Tagging | Convolutional Neural Networks (CNNs) have been successfully used in various Music Information Retrieval (MIR) tasks, both as end-to-end models and as feature extractors for more complex systems. However, the MIR field is still dominated by the classical VGG-based CNN architecture variants, often in combination with mor... | ['Gerhard Widmer', 'Hamid Eghbal-zadeh', 'Paul Primus', 'Khaled Koutini', 'Shreyan Chowdhury', 'Verena Haunschmid'] | 2020-07-27 | null | null | null | null | ['music-classification'] | ['music'] | [ 1.16954610e-01 -3.09971366e-02 4.88202460e-02 -2.84692086e-02
-5.71447432e-01 -3.98738474e-01 6.63093746e-01 -5.54105081e-02
-6.61979675e-01 2.99074113e-01 3.96234363e-01 6.79169893e-02
-4.25035566e-01 -6.21098518e-01 -7.47698545e-01 -4.84906077e-01
-3.82028073e-02 3.60176474e-01 1.25262722e-01 -5.90080380... | [15.683359146118164, 5.2280049324035645] |
620c2020-f8dd-4b96-8b81-ee05ba679662 | turning-to-a-teacher-for-timestamp-supervised | 2207.00712 | null | https://arxiv.org/abs/2207.00712v1 | https://arxiv.org/pdf/2207.00712v1.pdf | Turning to a Teacher for Timestamp Supervised Temporal Action Segmentation | Temporal action segmentation in videos has drawn much attention recently. Timestamp supervision is a cost-effective way for this task. To obtain more information to optimize the model, the existing method generated pseudo frame-wise labels iteratively based on the output of a segmentation model and the timestamp annota... | ['Yan Song', 'Yang Zhao'] | 2022-07-02 | null | null | null | null | ['action-segmentation'] | ['computer-vision'] | [ 3.85687768e-01 8.57105702e-02 -4.64992255e-01 -6.20158494e-01
-6.53540552e-01 -2.17604294e-01 4.57950562e-01 5.02187200e-03
-4.45945084e-01 5.15536547e-01 1.78103477e-01 1.75340936e-01
2.38647938e-01 -3.48168343e-01 -6.37240827e-01 -9.13976550e-01
2.00289309e-01 1.97729632e-01 8.52551818e-01 3.27895522... | [8.480764389038086, 0.652700662612915] |
53d339f4-1f8f-400a-aa8f-c374ae5ff2a4 | the-theory-of-artificial-immutability | 2205.01166 | null | https://arxiv.org/abs/2205.01166v1 | https://arxiv.org/pdf/2205.01166v1.pdf | The Theory of Artificial Immutability: Protecting Algorithmic Groups Under Anti-Discrimination Law | Artificial Intelligence (AI) is increasingly used to make important decisions about people. While issues of AI bias and proxy discrimination are well explored, less focus has been paid to the harms created by profiling based on groups that do not map to or correlate with legally protected groups such as sex or ethnicit... | ['Sandra Wachter'] | 2022-05-02 | null | null | null | null | ['jurisprudence'] | ['miscellaneous'] | [ 5.96429765e-01 8.63997638e-01 -6.76320314e-01 -5.18876731e-01
-4.69802320e-02 -4.84798461e-01 7.15243220e-01 2.27696270e-01
-7.50129163e-01 8.55925620e-01 9.98547912e-01 -7.66072631e-01
-4.99843150e-01 -7.72127151e-01 -1.34739146e-01 -4.72784013e-01
4.29027826e-01 5.33143103e-01 -6.06796980e-01 -4.32626903... | [8.968252182006836, 5.7589569091796875] |
ab1e4101-f99a-4993-a465-9791fc3d9fc4 | tackling-provably-hard-representative | 2205.10403 | null | https://arxiv.org/abs/2205.10403v1 | https://arxiv.org/pdf/2205.10403v1.pdf | Tackling Provably Hard Representative Selection via Graph Neural Networks | Representative selection (RS) is the problem of finding a small subset of exemplars from an unlabeled dataset, and has numerous applications in summarization, active learning, data compression and many other domains. In this paper, we focus on finding representatives that optimize the accuracy of a model trained on the... | ['Vahab Mirrokni', 'Bryan Perozzi', 'Deepak Ramachandran', 'Mohammadhossein Bateni', 'Hossein Esfandiari', 'Anton Tsitsulin', 'Seyed Mehran Kazemi'] | 2022-05-20 | null | null | null | null | ['data-compression'] | ['time-series'] | [ 7.50046372e-01 8.09648216e-01 -7.74145782e-01 -2.95739233e-01
-1.10033178e+00 -4.26910698e-01 2.42014125e-01 8.58636260e-01
2.42277049e-02 7.22966909e-01 2.98527092e-01 -1.60403088e-01
-7.56885886e-01 -9.89823520e-01 -9.87970114e-01 -5.37796199e-01
-7.10654736e-01 9.73467708e-01 -1.45678684e-01 -1.65929615... | [7.117372989654541, 6.101532459259033] |
06272b4a-426c-4207-bc8f-03ac65180eb6 | towards-addressing-training-data-scarcity | 2304.1248 | null | https://arxiv.org/abs/2304.12480v1 | https://arxiv.org/pdf/2304.12480v1.pdf | Towards Addressing Training Data Scarcity Challenge in Emerging Radio Access Networks: A Survey and Framework | The future of cellular networks is contingent on artificial intelligence (AI) based automation, particularly for radio access network (RAN) operation, optimization, and troubleshooting. To achieve such zero-touch automation, a myriad of AI-based solutions are being proposed in literature for modeling and optimizing net... | ['Ali Imran', 'Ali Rizwan', 'Per Karlsson', 'Shruti Bothe', 'Maxime Bouton', 'Julien Forgeat', 'Hasan Farooq', 'Syed Muhammad Asad Zaidi', 'Marvin Manalastas', 'Usama Masood', 'Haneya Naeem Qureshi'] | 2023-04-24 | null | null | null | null | ['matrix-completion'] | ['methodology'] | [ 6.33227453e-02 -8.08718354e-02 -9.70933288e-02 7.60346800e-02
-1.05400562e-01 -4.57012296e-01 1.34488627e-01 -4.23812509e-01
-9.21389181e-03 1.42120719e+00 -3.03145677e-01 -6.62657678e-01
-6.64983213e-01 -1.06854761e+00 -2.70413697e-01 -7.93892860e-01
-7.35275924e-01 6.72527194e-01 -3.73340845e-01 -6.96146011... | [6.031755447387695, 1.6166057586669922] |
3e48ad7e-5e02-4ff2-88b3-914e6abd128f | graphing-the-future-activity-and-next-active | 2209.05194 | null | https://arxiv.org/abs/2209.05194v1 | https://arxiv.org/pdf/2209.05194v1.pdf | Graphing the Future: Activity and Next Active Object Prediction using Graph-based Activity Representations | We present a novel approach for the visual prediction of human-object interactions in videos. Rather than forecasting the human and object motion or the future hand-object contact points, we aim at predicting (a)the class of the on-going human-object interaction and (b) the class(es) of the next active object(s) (NAOs)... | ['Antonis Argyros', 'Konstantinos Papoutsakis', 'Victoria Manousaki'] | 2022-09-12 | null | null | null | null | ['human-object-interaction-detection', 'graph-matching'] | ['computer-vision', 'graphs'] | [ 3.98141116e-01 -1.72236003e-02 -1.08968116e-01 -1.87165588e-01
2.20512435e-01 -1.92504182e-01 7.19184101e-01 2.83078700e-01
-1.08766973e-01 4.18996453e-01 -4.40872572e-02 1.15375882e-02
-2.89367169e-01 -5.45103192e-01 -4.71377581e-01 -4.67265666e-01
-4.89165366e-01 7.14018404e-01 7.38698006e-01 -3.02536525... | [8.428596496582031, 0.40439894795417786] |
be5f6cc8-d7f2-47ba-b1f8-0424f3de3499 | generalization-bounds-with-data-dependent | 2302.02766 | null | https://arxiv.org/abs/2302.02766v2 | https://arxiv.org/pdf/2302.02766v2.pdf | Generalization Bounds with Data-dependent Fractal Dimensions | Providing generalization guarantees for modern neural networks has been a crucial task in statistical learning. Recently, several studies have attempted to analyze the generalization error in such settings by using tools from fractal geometry. While these works have successfully introduced new mathematical tools to app... | ['Umut Şimşekli', 'George Deligiannidis', 'Benjamin Dupuis'] | 2023-02-06 | null | null | null | null | ['topological-data-analysis'] | ['graphs'] | [ 4.49976772e-02 1.00413516e-01 9.98220295e-02 -2.51884729e-01
-9.40774381e-02 -5.40633321e-01 3.85289222e-01 5.20587921e-01
-5.21700144e-01 7.88843751e-01 -3.03377777e-01 -3.85841250e-01
-6.93025649e-01 -9.38443482e-01 -7.06578612e-01 -1.05190170e+00
-3.17117542e-01 -8.84297416e-02 2.87188292e-01 -3.81754339... | [7.605167388916016, 3.940443277359009] |
8762b5d9-1f00-4edd-afff-8c1be4d850d1 | for-the-underrepresented-in-gender-bias | 2302.00419 | null | https://arxiv.org/abs/2302.00419v1 | https://arxiv.org/pdf/2302.00419v1.pdf | For the Underrepresented in Gender Bias Research: Chinese Name Gender Prediction with Heterogeneous Graph Attention Network | Achieving gender equality is an important pillar for humankind's sustainable future. Pioneering data-driven gender bias research is based on large-scale public records such as scientific papers, patents, and company registrations, covering female researchers, inventors and entrepreneurs, and so on. Since gender informa... | ['Haipeng Zhang', 'Shuai Ling', 'Kai Peng', 'Zihao Pan'] | 2023-02-01 | null | null | null | null | ['gender-prediction'] | ['computer-vision'] | [-2.56283224e-01 4.22134064e-02 -8.20372224e-01 -3.92286956e-01
-1.70992717e-01 -5.89091063e-01 7.06899762e-01 3.74322474e-01
-4.75481689e-01 6.69458389e-01 6.25893533e-01 -6.40560448e-01
1.19905032e-01 -8.60678434e-01 -3.58644336e-01 -4.34439212e-01
6.15448534e-01 5.53152680e-01 -2.95229286e-01 -3.48574370... | [9.388835906982422, 10.261147499084473] |
5195a86e-6ee5-4c06-b907-bf5653317e79 | 2305-14984 | 2305.14984 | null | https://arxiv.org/abs/2305.14984v1 | https://arxiv.org/pdf/2305.14984v1.pdf | Adversarial robustness of amortized Bayesian inference | Bayesian inference usually requires running potentially costly inference procedures separately for every new observation. In contrast, the idea of amortized Bayesian inference is to initially invest computational cost in training an inference network on simulated data, which can subsequently be used to rapidly perform ... | ['Jakob H. Macke', 'Michael Deistler', 'Manuel Glöckler'] | 2023-05-24 | null | null | null | null | ['bayesian-inference'] | ['methodology'] | [ 4.72296327e-01 1.31991580e-01 4.94204760e-01 -4.06157643e-01
-9.42539096e-01 -6.12942517e-01 6.33759558e-01 -5.12730144e-02
-6.61313474e-01 1.11783290e+00 -2.15365335e-01 -3.13717753e-01
-1.49517432e-01 -6.94988072e-01 -1.31078875e+00 -9.00137246e-01
-1.52010083e-01 6.21205270e-01 9.01679881e-03 3.52921695... | [6.963768005371094, 3.893561601638794] |
421fd8f4-8013-49d7-8656-9ba6d6f83291 | v2c-visual-voice-cloning | 2111.1289 | null | https://arxiv.org/abs/2111.12890v1 | https://arxiv.org/pdf/2111.12890v1.pdf | V2C: Visual Voice Cloning | Existing Voice Cloning (VC) tasks aim to convert a paragraph text to a speech with desired voice specified by a reference audio. This has significantly boosted the development of artificial speech applications. However, there also exist many scenarios that cannot be well reflected by these VC tasks, such as movie dubbi... | ['Qi Wu', 'Mingkui Tan', 'Jiaqiu Zhou', 'Yuankai Qi', 'Yuanqing Li', 'Qi Chen'] | 2021-11-25 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Chen_V2C_Visual_Voice_Cloning_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Chen_V2C_Visual_Voice_Cloning_CVPR_2022_paper.pdf | cvpr-2022-1 | ['voice-cloning'] | ['speech'] | [ 3.53262983e-02 -1.08285420e-01 1.03790581e-01 -4.45732832e-01
-6.65055037e-01 -6.40401304e-01 6.81969464e-01 -6.37981653e-01
8.72727185e-02 4.57811087e-01 6.15558565e-01 -1.60931647e-01
6.21521413e-01 -3.86417210e-01 -5.50918043e-01 -5.60819983e-01
3.67356002e-01 -3.22434977e-02 5.01880720e-02 -3.44567955... | [14.478862762451172, 5.943192005157471] |
fa27c2b7-bab0-49e6-80a1-19fb192af3c0 | image-harmonization-with-region-wise | 2205.14058 | null | https://arxiv.org/abs/2205.14058v2 | https://arxiv.org/pdf/2205.14058v2.pdf | Image Harmonization with Region-wise Contrastive Learning | Image harmonization task aims at harmonizing different composite foreground regions according to specific background image. Previous methods would rather focus on improving the reconstruction ability of the generator by some internal enhancements such as attention, adaptive normalization and light adjustment, $etc.$. H... | ['Chi-Man Pun', 'Jingtang Liang'] | 2022-05-27 | null | null | null | null | ['image-harmonization'] | ['computer-vision'] | [ 4.77724969e-01 -3.00263315e-01 -1.57035850e-02 -2.24989235e-01
-7.26707935e-01 -2.89758056e-01 4.50323731e-01 -4.30591434e-01
-3.12955797e-01 6.17125750e-01 9.51592475e-02 2.42734909e-01
1.96545869e-01 -9.17364955e-01 -7.60988176e-01 -1.06933188e+00
8.36001992e-01 -1.85399526e-03 1.98963046e-01 -3.74660820... | [11.21369457244873, -1.1563202142715454] |
9ccd1d31-2b36-4919-a706-77874597edff | sequentialpointnet-a-strong-parallelized | 2111.08492 | null | https://arxiv.org/abs/2111.08492v2 | https://arxiv.org/pdf/2111.08492v2.pdf | SequentialPointNet: A strong frame-level parallel point cloud sequence network for 3D action recognition | The point cloud sequence of 3D human actions consists of a set of ordered point cloud frames. Compared to static point clouds, point cloud sequences have huge data sizes proportional to the time dimension. Therefore, developing an efficient and lightweight point cloud sequence model is pivotal for 3D action recognition... | ['Tianjin Yang', 'Zhenjie Hou', 'Zhijian Wang', 'Qian Huang', 'Xing Li'] | 2021-11-16 | null | null | null | null | ['3d-human-action-recognition'] | ['computer-vision'] | [ 1.67946257e-02 -6.86368227e-01 -3.93032581e-01 3.58767994e-02
-3.07123244e-01 -2.81592399e-01 4.64083463e-01 -1.83157131e-01
-4.18597668e-01 1.41145766e-01 -1.49235517e-01 -1.99004769e-01
2.39295185e-01 -7.82504022e-01 -6.27374470e-01 -4.93599832e-01
-4.14442122e-02 3.68560284e-01 8.54739845e-01 -1.84543088... | [8.189865112304688, 0.10553991794586182] |
d7c07c0a-ab46-4637-bd7f-46b236a2fee4 | automatic-pulmonary-nodule-detection-in-ct | 1904.05956 | null | https://arxiv.org/abs/1904.05956v2 | https://arxiv.org/pdf/1904.05956v2.pdf | Automatic Pulmonary Nodule Detection in CT Scans Using Convolutional Neural Networks Based on Maximum Intensity Projection | Accurate pulmonary nodule detection is a crucial step in lung cancer screening. Computer-aided detection (CAD) systems are not routinely used by radiologists for pulmonary nodule detection in clinical practice despite their potential benefits. Maximum intensity projection (MIP) images improve the detection of pulmonary... | ['Sunyi Zheng', 'Raymond N. J. Veldhuis', 'Peter M. A. van Ooijen', 'Matthijs Oudkerk', 'Jiapan Guo', 'Xiaonan Cui'] | 2019-04-11 | null | null | null | null | ['lung-nodule-detection'] | ['medical'] | [ 2.63848990e-01 4.26534474e-01 -3.70407909e-01 1.05371997e-01
-6.33451641e-01 -3.63177001e-01 2.82278836e-01 -1.32623464e-01
-5.63637733e-01 3.44474167e-01 1.52113438e-01 -9.82053280e-01
-2.22714677e-01 -1.05464709e+00 -3.20101678e-01 -5.76527297e-01
-1.85148582e-01 6.31293833e-01 7.94827700e-01 3.74171376... | [15.359626770019531, -2.1555259227752686] |
dd14a0a2-8aee-4d8d-bc04-6ab9b26e5dba | modeling-hierarchical-syntax-structure-with-1 | null | null | https://aclanthology.org/2022.acl-long.37 | https://aclanthology.org/2022.acl-long.37.pdf | Modeling Hierarchical Syntax Structure with Triplet Position for Source Code Summarization | Automatic code summarization, which aims to describe the source code in natural language, has become an essential task in software maintenance. Our fellow researchers have attempted to achieve such a purpose through various machine learning-based approaches. One key challenge keeping these approaches from being practic... | ['Pingyi Zhou', 'Li Li', 'Yao Wan', 'Jin Liu', 'Juncai Guo'] | null | null | null | null | acl-2022-5 | ['code-summarization'] | ['computer-code'] | [ 2.60583252e-01 1.98542252e-01 -3.77891272e-01 -2.02547684e-01
-5.96110582e-01 -4.47963953e-01 4.09542859e-01 5.09163380e-01
3.54843408e-01 2.37197146e-01 5.94320774e-01 -5.23697078e-01
2.36988097e-01 -5.54151475e-01 -6.82252705e-01 -1.83021545e-01
-6.08050898e-02 -4.29057717e-01 4.56859469e-01 -2.09582657... | [7.614256381988525, 7.937649726867676] |
2917cbf6-b8a5-4a87-ad06-842d8d108535 | edict-exact-diffusion-inversion-via-coupled | 2211.12446 | null | https://arxiv.org/abs/2211.12446v2 | https://arxiv.org/pdf/2211.12446v2.pdf | EDICT: Exact Diffusion Inversion via Coupled Transformations | Finding an initial noise vector that produces an input image when fed into the diffusion process (known as inversion) is an important problem in denoising diffusion models (DDMs), with applications for real image editing. The state-of-the-art approach for real image editing with inversion uses denoising diffusion impli... | ['Nikhil Naik', 'Akash Gokul', 'Bram Wallace'] | 2022-11-22 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Wallace_EDICT_Exact_Diffusion_Inversion_via_Coupled_Transformations_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Wallace_EDICT_Exact_Diffusion_Inversion_via_Coupled_Transformations_CVPR_2023_paper.pdf | cvpr-2023-1 | ['text-based-image-editing', 'text-guided-image-editing', 'image-stylization'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 5.84183931e-01 1.37852058e-01 2.24226952e-01 -1.04317747e-01
-5.39303958e-01 -6.51624084e-01 8.74860764e-01 -4.02599007e-01
-3.73375207e-01 3.55434716e-01 1.51052266e-01 -2.59033442e-01
7.13332891e-02 -7.00485885e-01 -9.35525358e-01 -6.10669971e-01
4.82942581e-01 3.15771103e-01 -7.80859217e-03 -2.90747106... | [11.454667091369629, -0.4751112461090088] |
0f15642d-30c1-4606-be6c-52cff4348691 | unsupervised-hdr-image-and-video-tone-mapping | 2303.07327 | null | https://arxiv.org/abs/2303.07327v2 | https://arxiv.org/pdf/2303.07327v2.pdf | Unsupervised HDR Image and Video Tone Mapping via Contrastive Learning | Capturing high dynamic range (HDR) images (videos) is attractive because it can reveal the details in both dark and bright regions. Since the mainstream screens only support low dynamic range (LDR) content, tone mapping algorithm is required to compress the dynamic range of HDR images (videos). Although image tone mapp... | ['Jingyu Yang', 'Xin Liu', 'Huanjing Yue', 'Cong Cao'] | 2023-03-13 | null | null | null | null | ['tone-mapping'] | ['computer-vision'] | [ 3.79118204e-01 -6.33463979e-01 -3.33909571e-01 -3.09974819e-01
-6.10449553e-01 -2.10853070e-01 3.59054387e-01 -7.65062690e-01
-2.10106716e-01 5.82587540e-01 1.53107971e-01 -6.21908270e-02
-1.23422192e-02 -9.11630452e-01 -7.50061393e-01 -8.25017154e-01
1.81425780e-01 -3.12455386e-01 4.19433206e-01 -2.59689987... | [10.941662788391113, -2.1558117866516113] |
1d26f207-4eeb-40e8-a6f3-e024ca00daae | inverse-consistency-by-construction-for | 2305.00087 | null | https://arxiv.org/abs/2305.00087v1 | https://arxiv.org/pdf/2305.00087v1.pdf | Inverse Consistency by Construction for Multistep Deep Registration | Inverse consistency is a desirable property for image registration. We propose a simple technique to make a neural registration network inverse consistent by construction, as a consequence of its structure, as long as it parameterizes its output transform by a Lie group. We extend this technique to multi-step neural re... | ['Marc Niethammer', 'Richard Rushmore', 'Raul San Jose Estepar', 'Sylvain Bouix', 'Roland Kwitt', 'Francois-Xavier Vialard', 'Lin Tian', 'Hastings Greer'] | 2023-04-28 | null | null | null | null | ['image-registration', 'medical-image-registration'] | ['computer-vision', 'medical'] | [ 2.65422940e-01 1.88688844e-01 -8.93636644e-02 -6.77898228e-01
-7.13187456e-01 -4.80615944e-01 7.67472923e-01 -2.40841046e-01
-5.93088269e-01 4.60578412e-01 3.50784272e-01 8.15976709e-02
-3.37048769e-01 -8.07420135e-01 -7.16633499e-01 -5.85093737e-01
-1.48070008e-01 5.89176118e-01 1.16452150e-01 -5.68792820... | [13.938958168029785, -2.5451152324676514] |
80cf2c73-de9d-42a1-8b2f-40e2dae90bcd | deep-hdr-imaging-via-a-non-local-network | null | null | https://ieeexplore.ieee.org/abstract/document/8989959 | https://ieeexplore.ieee.org/abstract/document/8989959 | Deep HDR Imaging via A Non-Local Network | One of the most challenging problems in reconstructing a high dynamic range (HDR) image from multiple low dynamic range (LDR) inputs is the ghosting artifacts caused by the object motion across different inputs. When the object motion is slight, most existing methods can well suppress the ghosting artifacts through ali... | ['Q. Yan and L. Zhang and Y. Liu and Y. Zhu and J. Sun and Q. Shi and Y. Zhang'] | 2020-02-10 | null | null | null | null | ['hdr-reconstruction'] | ['computer-vision'] | [ 9.19405296e-02 -5.85230768e-01 1.15717329e-01 -6.59567714e-02
-4.00810540e-01 -2.07258448e-01 4.59625185e-01 -4.88853216e-01
-2.43927956e-01 6.71414196e-01 3.16202521e-01 3.82616878e-01
-2.14595869e-01 -7.07671165e-01 -5.51130474e-01 -1.06857312e+00
2.82459706e-01 -1.33952081e-01 3.29449415e-01 -1.71665460... | [10.95154094696045, -1.91062593460083] |
77a5d721-782b-43fd-9a6b-35716e054a6c | block-bilinear-superdiagonal-fusion-for | 1902.00038 | null | http://arxiv.org/abs/1902.00038v2 | http://arxiv.org/pdf/1902.00038v2.pdf | BLOCK: Bilinear Superdiagonal Fusion for Visual Question Answering and Visual Relationship Detection | Multimodal representation learning is gaining more and more interest within
the deep learning community. While bilinear models provide an interesting
framework to find subtle combination of modalities, their number of parameters
grows quadratically with the input dimensions, making their practical
implementation within... | ['Rémi Cadene', 'Hedi Ben-Younes', 'Matthieu Cord', 'Nicolas Thome'] | 2019-01-31 | null | null | null | null | ['visual-relationship-detection'] | ['computer-vision'] | [-2.38181978e-01 -3.28163326e-01 -3.13208662e-02 -4.69689578e-01
-1.17333841e+00 -8.57502937e-01 8.07749033e-01 2.90814489e-01
-2.32992351e-01 2.30190679e-01 6.06479585e-01 -3.69289458e-01
-1.95212334e-01 -4.47552502e-01 -6.48166597e-01 -6.49733067e-01
-2.78660059e-01 3.46629143e-01 -1.26471937e-01 -4.03230667... | [10.755874633789062, 1.54763925075531] |
96f0aeef-6f16-473b-8df6-bc928a8dc4b9 | stochastic-pitch-prediction-improves-the | 2305.17724 | null | https://arxiv.org/abs/2305.17724v1 | https://arxiv.org/pdf/2305.17724v1.pdf | Stochastic Pitch Prediction Improves the Diversity and Naturalness of Speech in Glow-TTS | Flow-based generative models are widely used in text-to-speech (TTS) systems to learn the distribution of audio features (e.g., Mel-spectrograms) given the input tokens and to sample from this distribution to generate diverse utterances. However, in the zero-shot multi-speaker TTS scenario, the generated utterances lac... | ['Emmanuel Vincent', 'Vincent Colotte', 'Sewade Ogun'] | 2023-05-28 | null | null | null | null | ['zero-shot-multi-speaker-tts'] | ['audio'] | [ 2.22466290e-01 1.11581467e-01 5.36511913e-02 -3.57497543e-01
-1.01940119e+00 -4.36698139e-01 6.44228458e-01 -6.22736476e-02
-6.16157707e-03 6.47718787e-01 6.37579918e-01 -1.89808980e-01
1.94210902e-01 -6.10539079e-01 -4.81049567e-01 -9.66383159e-01
1.88736152e-02 3.90644044e-01 6.34191707e-02 -2.66558677... | [15.205803871154785, 6.348029136657715] |
61a7f2fa-ab0e-4267-989d-f902c353dd01 | meshwalker-deep-mesh-understanding-by-random | 2006.05353 | null | https://arxiv.org/abs/2006.05353v3 | https://arxiv.org/pdf/2006.05353v3.pdf | MeshWalker: Deep Mesh Understanding by Random Walks | Most attempts to represent 3D shapes for deep learning have focused on volumetric grids, multi-view images and point clouds. In this paper we look at the most popular representation of 3D shapes in computer graphics - a triangular mesh - and ask how it can be utilized within deep learning. The few attempts to answer th... | ['Alon Lahav', 'Ayellet Tal'] | 2020-06-09 | null | null | null | null | ['3d-object-recognition', '3d-classification'] | ['computer-vision', 'computer-vision'] | [-7.61202946e-02 2.70718962e-01 2.30785072e-01 -2.68844843e-01
-3.66953462e-01 -6.29638553e-01 5.58633566e-01 3.38860840e-01
-1.67725384e-01 2.10713610e-01 -2.56619602e-01 -2.83818990e-01
1.06209978e-01 -1.33033776e+00 -1.03925288e+00 -6.43380105e-01
-1.64137945e-01 1.00427830e+00 2.45802104e-01 -1.96477637... | [8.267056465148926, -3.6855616569519043] |
0e38564a-5998-475a-bb5c-e14fc88265ae | an-end-to-end-network-for-co-saliency | 1910.11819 | null | https://arxiv.org/abs/1910.11819v2 | https://arxiv.org/pdf/1910.11819v2.pdf | An End-to-End Network for Co-Saliency Detection in One Single Image | Co-saliency detection within a single image is a common vision problem that has received little attention and has not yet been well addressed. Existing methods often used a bottom-up strategy to infer co-saliency in an image in which salient regions are firstly detected using visual primitives such as color and shape a... | ['Song Wang', 'Zhongyuan Wang', 'Qian Wang', 'Yuanhao Yue', 'Qin Zou', 'Hongkai Yu'] | 2019-10-25 | null | null | null | null | ['co-saliency-detection'] | ['computer-vision'] | [ 5.25849283e-01 1.32031664e-01 2.83838660e-02 -5.24704635e-01
-4.70264703e-01 -1.00859590e-01 4.22295600e-01 1.71195790e-01
-3.63715529e-01 4.10140544e-01 7.10508367e-03 1.13299772e-01
2.14498237e-01 -5.08197665e-01 -8.84172916e-01 -4.02369887e-01
-3.92546281e-02 4.98869382e-02 1.16605604e+00 -2.12549359... | [9.794486999511719, -0.3374933898448944] |
963d0afa-d26d-4645-a60b-73f73ce20b8f | rethinking-the-learning-paradigm-for-facial | 2209.15402 | null | https://arxiv.org/abs/2209.15402v1 | https://arxiv.org/pdf/2209.15402v1.pdf | Rethinking the Learning Paradigm for Facial Expression Recognition | Due to the subjective crowdsourcing annotations and the inherent inter-class similarity of facial expressions, the real-world Facial Expression Recognition (FER) datasets usually exhibit ambiguous annotation. To simplify the learning paradigm, most previous methods convert ambiguous annotation results into precise one-... | ['Bruno Lepri', 'Nicu Sebe', 'Weijie Wang'] | 2022-09-30 | null | null | null | null | ['facial-expression-recognition'] | ['computer-vision'] | [ 1.80397406e-01 3.08027655e-01 -2.67286181e-01 -9.21252668e-01
-7.26188958e-01 -6.79675817e-01 1.95954323e-01 -3.83585691e-01
-5.03038168e-01 1.03309631e+00 -2.98272632e-02 9.05419812e-02
3.72905344e-01 -1.40771449e-01 -3.56310427e-01 -4.68027532e-01
2.29817584e-01 4.66813862e-01 2.76645750e-01 -4.02542561... | [13.593207359313965, 1.677939534187317] |
6ec2fcad-256c-4528-9672-e0adbd4b139a | how-asynchronous-events-encode-video | 2206.04341 | null | https://arxiv.org/abs/2206.04341v1 | https://arxiv.org/pdf/2206.04341v1.pdf | How Asynchronous Events Encode Video | As event-based sensing gains in popularity, theoretical understanding is needed to harness this technology's potential. Instead of recording video by capturing frames, event-based cameras have sensors that emit events when their inputs change, thus encoding information in the timing of events. This creates new challeng... | ['Martin Vetterli', 'Adam Scholefield', 'Karen Adam'] | 2022-06-09 | null | null | null | null | ['event-based-vision'] | ['computer-vision'] | [ 8.29122186e-01 -2.74582207e-01 4.75829728e-02 -1.60277411e-01
-5.80378532e-01 -6.90828443e-01 4.96319115e-01 2.32195687e-02
-6.29124820e-01 7.02176511e-01 5.14066406e-02 1.15943896e-02
-2.61787735e-02 -8.68429720e-01 -8.31435084e-01 -6.35432899e-01
-4.02097136e-01 -1.54523656e-01 5.54419518e-01 3.94629091... | [8.697234153747559, -1.3519951105117798] |
182ac7d4-c1fd-4481-aee9-6f5690073d7c | concept-oriented-deep-learning-with-large | 2306.17089 | null | https://arxiv.org/abs/2306.17089v1 | https://arxiv.org/pdf/2306.17089v1.pdf | Concept-Oriented Deep Learning with Large Language Models | Large Language Models (LLMs) have been successfully used in many natural-language tasks and applications including text generation and AI chatbots. They also are a promising new technology for concept-oriented deep learning (CODL). However, the prerequisite is that LLMs understand concepts and ensure conceptual consist... | ['Daniel T. Chang'] | 2023-06-29 | null | null | null | null | ['text-generation'] | ['natural-language-processing'] | [ 7.01467544e-02 5.10283351e-01 -1.45933717e-01 -1.17931046e-01
-5.08339286e-01 -7.11316168e-01 1.09441864e+00 6.61541998e-01
-4.28029805e-01 6.23883009e-01 -8.40508789e-02 -2.28717536e-01
-4.44718227e-02 -9.06619608e-01 -5.24521589e-01 -4.45745498e-01
-7.94136431e-03 6.21836901e-01 1.28726035e-01 -4.55938041... | [10.59089469909668, 1.8899989128112793] |
2bf207a8-1161-4b7d-a307-247e48f74723 | evaluation-of-deep-segmentation-models-for | 2006.02662 | null | https://arxiv.org/abs/2006.02662v2 | https://arxiv.org/pdf/2006.02662v2.pdf | Exploiting the Transferability of Deep Learning Systems Across Multi-modal Retinal Scans for Extracting Retinopathy Lesions | Retinal lesions play a vital role in the accurate classification of retinal abnormalities. Many researchers have proposed deep lesion-aware screening systems that analyze and grade the progression of retinopathy. However, to the best of our knowledge, no literature exploits the tendency of these systems to generalize a... | ['Naoufel Werghi', 'Taimur Hassan', 'Muhammad Usman Akram'] | 2020-06-04 | null | null | null | null | ['scene-parsing'] | ['computer-vision'] | [ 1.82818752e-02 -1.12899147e-01 1.40214413e-01 -4.81556267e-01
-5.21107674e-01 -4.95737225e-01 1.37024105e-01 2.43706815e-02
-2.07165256e-01 7.78707445e-01 1.22346118e-01 -5.62920153e-01
-4.08854276e-01 -6.67639911e-01 -1.91931486e-01 -5.02904594e-01
-1.22156203e-01 -9.22798812e-02 2.89056540e-01 3.48654896... | [15.819971084594727, -3.995516300201416] |
839abc65-045e-4021-9c15-4bf679a3d224 | link-prediction-without-graph-neural-networks | 2305.13656 | null | https://arxiv.org/abs/2305.13656v1 | https://arxiv.org/pdf/2305.13656v1.pdf | Link Prediction without Graph Neural Networks | Link prediction, which consists of predicting edges based on graph features, is a fundamental task in many graph applications. As for several related problems, Graph Neural Networks (GNNs), which are based on an attribute-centric message-passing paradigm, have become the predominant framework for link prediction. GNNs ... | ['Ambuj Singh', 'Arlei Silva', 'Mert Kosan', 'Zexi Huang'] | 2023-05-23 | null | null | null | null | ['link-prediction'] | ['graphs'] | [ 4.26372327e-02 3.89276773e-01 -8.13996613e-01 -1.92783728e-01
-3.87893856e-01 -2.34651461e-01 2.48998225e-01 7.20210969e-01
-3.59027162e-02 9.55336750e-01 -1.95379317e-01 -7.16308832e-01
-6.29358947e-01 -1.39315248e+00 -9.05177116e-01 -2.54157394e-01
-8.20868254e-01 1.19354689e+00 4.41380918e-01 -3.10678035... | [7.04879093170166, 6.193018436431885] |
61519088-dd04-4a14-b701-9da19a198ed3 | deep-learning-on-implicit-neural-datasets | 2206.01178 | null | https://arxiv.org/abs/2206.01178v3 | https://arxiv.org/pdf/2206.01178v3.pdf | Discretization Invariant Learning on Neural Fields | While neural fields have emerged as powerful representations of continuous data, there is a need for neural networks that can perform inference on such data without being sensitive to how the field is sampled, a property called discretization invariance. We develop DI-Net, a framework for learning discretization invari... | ['Polina Golland', 'Clinton J. Wang'] | 2022-06-02 | null | null | null | null | ['numerical-integration'] | ['miscellaneous'] | [ 1.78911939e-01 1.38137594e-01 -1.85523391e-01 -6.92853391e-01
-4.24998134e-01 -5.95507801e-01 3.83876741e-01 -1.62861440e-02
-3.05911869e-01 8.14296842e-01 -2.03125954e-01 -3.76660228e-01
-2.95019716e-01 -1.13579416e+00 -1.09549654e+00 -6.06176555e-01
-4.81028289e-01 3.19322884e-01 2.40620658e-01 -1.81484938... | [7.694057464599609, 3.5835254192352295] |
9f7c205a-f4c7-49b3-9de4-4e2d4d5450fb | convert-efficient-and-accurate-conversational | 1911.03688 | null | https://arxiv.org/abs/1911.03688v2 | https://arxiv.org/pdf/1911.03688v2.pdf | ConveRT: Efficient and Accurate Conversational Representations from Transformers | General-purpose pretrained sentence encoders such as BERT are not ideal for real-world conversational AI applications; they are computationally heavy, slow, and expensive to train. We propose ConveRT (Conversational Representations from Transformers), a pretraining framework for conversational tasks satisfying all the ... | ['Ivan Vulić', 'Tsung-Hsien Wen', 'Pei-Hao Su', 'Nikola Mrkšić', 'Iñigo Casanueva', 'Matthew Henderson'] | 2019-11-09 | null | https://aclanthology.org/2020.findings-emnlp.196 | https://aclanthology.org/2020.findings-emnlp.196.pdf | findings-of-the-association-for-computational | ['conversational-response-selection'] | ['natural-language-processing'] | [ 3.42046529e-01 1.26579776e-01 -3.31650555e-01 -7.32770324e-01
-1.14245808e+00 -5.17303169e-01 7.55223513e-01 4.40470837e-02
-4.94582355e-01 8.44311774e-01 7.64994979e-01 -4.43650931e-01
2.02134758e-01 -7.03438163e-01 -5.37842453e-01 -3.63444477e-01
6.20458648e-02 9.51059937e-01 3.38377245e-02 -7.16398656... | [12.304620742797852, 7.805327892303467] |
09f8570c-6300-4835-9db0-4493f5724fa7 | named-entity-recognition-for-social-media | 2010.15458 | null | https://arxiv.org/abs/2010.15458v1 | https://arxiv.org/pdf/2010.15458v1.pdf | Named Entity Recognition for Social Media Texts with Semantic Augmentation | Existing approaches for named entity recognition suffer from data sparsity problems when conducted on short and informal texts, especially user-generated social media content. Semantic augmentation is a potential way to alleviate this problem. Given that rich semantic information is implicitly preserved in pre-trained ... | ['Bo Dai', 'Yan Song', 'Xiang Wan', 'Yuanhe Tian', 'Yuyang Nie'] | 2020-10-29 | null | https://aclanthology.org/2020.emnlp-main.107 | https://aclanthology.org/2020.emnlp-main.107.pdf | emnlp-2020-11 | ['chinese-named-entity-recognition'] | ['natural-language-processing'] | [ 2.39992663e-01 2.58945227e-01 -3.71680379e-01 -5.58814526e-01
-4.34317440e-01 -2.79384941e-01 6.66979313e-01 5.25823116e-01
-1.12296748e+00 7.58487761e-01 9.78982270e-01 9.91526097e-02
3.21632922e-01 -9.01074350e-01 -3.49804252e-01 -1.85498714e-01
2.36190110e-01 2.26557910e-01 3.17164436e-02 -5.39433897... | [9.77266788482666, 9.464421272277832] |
c68a62b7-2b49-4e65-9475-eef01684bf65 | gshard-scaling-giant-models-with-conditional | 2006.16668 | null | https://arxiv.org/abs/2006.16668v1 | https://arxiv.org/pdf/2006.16668v1.pdf | GShard: Scaling Giant Models with Conditional Computation and Automatic Sharding | Neural network scaling has been critical for improving the model quality in many real-world machine learning applications with vast amounts of training data and compute. Although this trend of scaling is affirmed to be a sure-fire approach for better model quality, there are challenges on the path such as the computati... | ['Maxim Krikun', 'HyoukJoong Lee', 'Noam Shazeer', 'Dehao Chen', 'Yuanzhong Xu', 'Orhan Firat', 'Zhifeng Chen', 'Yanping Huang', 'Dmitry Lepikhin'] | 2020-06-30 | null | https://openreview.net/forum?id=qrwe7XHTmYb | https://openreview.net/pdf?id=qrwe7XHTmYb | iclr-2021-1 | ['2048'] | ['playing-games'] | [ 1.69259440e-02 -1.96778178e-02 -5.28417528e-01 -6.40313327e-01
-1.09249496e+00 -4.15366411e-01 6.01287901e-01 -2.89397955e-01
-5.88363171e-01 5.68621933e-01 4.02590409e-02 -1.03410971e+00
4.94822115e-01 -5.78613579e-01 -9.79783535e-01 -5.68949044e-01
3.94889146e-01 1.18600214e+00 -3.91587242e-03 -3.76648188... | [8.664346694946289, 3.5126850605010986] |
10238f4f-5cf3-4e4f-93d6-128c24a13286 | uncertainty-aware-distillation-for-semi | 2301.09964 | null | https://arxiv.org/abs/2301.09964v1 | https://arxiv.org/pdf/2301.09964v1.pdf | Uncertainty-Aware Distillation for Semi-Supervised Few-Shot Class-Incremental Learning | Given a model well-trained with a large-scale base dataset, Few-Shot Class-Incremental Learning (FSCIL) aims at incrementally learning novel classes from a few labeled samples by avoiding overfitting, without catastrophically forgetting all encountered classes previously. Currently, semi-supervised learning technique t... | ['Li Liu', 'Haoyu Chen', 'Wanxia Deng', 'Yawen Cui'] | 2023-01-24 | null | null | null | null | ['class-incremental-learning', 'few-shot-class-incremental-learning'] | ['computer-vision', 'methodology'] | [ 4.16115493e-01 4.65440214e-01 -3.84941101e-01 -3.68218541e-01
-7.13628292e-01 -4.15375680e-01 6.96013331e-01 5.10933772e-02
-4.26136345e-01 1.04929769e+00 -2.51523107e-01 -1.35826478e-02
-7.42759481e-02 -6.66114807e-01 -6.69748545e-01 -7.74146736e-01
3.21632922e-01 6.98179245e-01 5.76569855e-01 5.95951788... | [9.899123191833496, 3.2209882736206055] |
f0f65a90-19c1-4e3b-b41f-891cd30bb8bf | stylecarigan-caricature-generation-via | 2107.04331 | null | https://arxiv.org/abs/2107.04331v1 | https://arxiv.org/pdf/2107.04331v1.pdf | StyleCariGAN: Caricature Generation via StyleGAN Feature Map Modulation | We present a caricature generation framework based on shape and style manipulation using StyleGAN. Our framework, dubbed StyleCariGAN, automatically creates a realistic and detailed caricature from an input photo with optional controls on shape exaggeration degree and color stylization type. The key component of our me... | ['Seungyong Lee', 'Xin Tong', 'Jiaolong Yang', 'Yucheol Jung', 'Gwangjin Ju', 'Wonjong Jang'] | 2021-07-09 | null | null | null | null | ['caricature'] | ['computer-vision'] | [ 5.95193624e-01 4.67036843e-01 4.02613491e-01 -4.21456099e-01
-1.78364128e-01 -8.50684762e-01 6.13898158e-01 -8.96332622e-01
1.60179198e-01 6.22343719e-01 -6.80474862e-02 -6.20387271e-02
6.21405125e-01 -1.02524316e+00 -9.51370060e-01 -6.16660953e-01
5.57034254e-01 1.30629152e-01 -2.72367388e-01 -4.24015909... | [11.976268768310547, -0.4398401975631714] |
79f202da-3e4d-48a6-a0e1-2b441e5a3dd5 | dash-semi-supervised-learning-with-dynamic | 2109.0065 | null | https://arxiv.org/abs/2109.00650v1 | https://arxiv.org/pdf/2109.00650v1.pdf | Dash: Semi-Supervised Learning with Dynamic Thresholding | While semi-supervised learning (SSL) has received tremendous attentions in many machine learning tasks due to its successful use of unlabeled data, existing SSL algorithms use either all unlabeled examples or the unlabeled examples with a fixed high-confidence prediction during the training progress. However, it is pos... | ['Rong Jin', 'Hao Li', 'Baigui Sun', 'Yu-Feng Li', 'Qi Qian', 'Jinxing Ye', 'Lei Shang', 'Yi Xu'] | 2021-09-01 | null | null | null | null | ['semi-supervised-image-classification'] | ['computer-vision'] | [ 2.30678588e-01 9.85750556e-02 -5.30590773e-01 -6.57380939e-01
-8.05898309e-01 -3.88435453e-01 2.53843904e-01 3.69704992e-01
-6.76863194e-01 9.67401028e-01 -2.38734320e-01 -1.03371657e-01
-1.41284734e-01 -5.28544128e-01 -6.16691232e-01 -8.80773902e-01
2.49825493e-01 6.48840606e-01 2.51520157e-01 2.09547117... | [9.296937942504883, 3.9765195846557617] |
5d10c330-484a-4ada-b69e-0a701ad5484f | leveraging-text-data-for-causal-inference | 2307.03687 | null | https://arxiv.org/abs/2307.03687v1 | https://arxiv.org/pdf/2307.03687v1.pdf | Leveraging text data for causal inference using electronic health records | Text is a ubiquitous component of medical data, containing valuable information about patient characteristics and care that are often missing from structured chart data. Despite this richness, it is rarely used in clinical research, owing partly to its complexity. Using a large database of patient records and treatment... | ['Luke Miratrix', 'Leo A. Celi', 'Aaron R. Kaufman', 'Reagan Mozer'] | 2023-06-09 | null | null | null | null | ['imputation', 'causal-inference', 'imputation', 'causal-inference', 'imputation'] | ['computer-vision', 'knowledge-base', 'miscellaneous', 'miscellaneous', 'time-series'] | [ 5.64653218e-01 2.12909237e-01 -8.84407938e-01 -5.46452582e-01
-7.29170620e-01 -5.09084642e-01 9.58299264e-02 1.00017035e+00
-4.26909447e-01 1.01447856e+00 1.13121200e+00 -7.79714465e-01
-5.16647518e-01 -8.26232851e-01 -6.66106522e-01 -2.24631041e-01
-4.90247719e-02 4.14610147e-01 -4.26711977e-01 2.90700078... | [7.975508689880371, 5.528327465057373] |
64a76bf7-0170-493c-8391-3ada58f0c0a3 | brain-structure-ages-a-new-biomarker-for | 2304.06591 | null | https://arxiv.org/abs/2304.06591v1 | https://arxiv.org/pdf/2304.06591v1.pdf | Brain Structure Ages -- A new biomarker for multi-disease classification | Age is an important variable to describe the expected brain's anatomy status across the normal aging trajectory. The deviation from that normative aging trajectory may provide some insights into neurological diseases. In neuroimaging, predicted brain age is widely used to analyze different diseases. However, using only... | ['Pierrick Coupé', 'Boris Mansencal', 'Michaël Clément', 'Huy-Dung Nguyen'] | 2023-04-13 | null | null | null | null | ['age-estimation', 'anatomy', 'age-estimation'] | ['computer-vision', 'miscellaneous', 'miscellaneous'] | [-6.00848682e-02 6.82344735e-02 9.80818719e-02 -5.51208854e-01
-3.30089748e-01 -6.81700855e-02 3.55733961e-01 6.34916902e-01
-6.27447307e-01 7.34421194e-01 -6.81549534e-02 -1.91815242e-01
6.88230433e-03 -8.37106466e-01 -3.21134210e-01 -8.95038962e-01
-3.12487394e-01 6.63462758e-01 1.55610949e-01 1.23640083... | [14.079485893249512, -1.572855830192566] |
d36ca94a-eae7-4c80-af5e-5e4b554443fc | generative-prompt-tuning-for-relation-1 | 2210.12435 | null | https://arxiv.org/abs/2210.12435v1 | https://arxiv.org/pdf/2210.12435v1.pdf | Generative Prompt Tuning for Relation Classification | Using prompts to explore the knowledge contained within pre-trained language models for downstream tasks has now become an active topic. Current prompt tuning methods mostly convert the downstream tasks to masked language modeling problems by adding cloze-style phrases and mapping all labels to verbalizations with fixe... | ['Wei Lu', 'Shengkun Ma', 'Bo Cheng', 'Shuai Zhao', 'Jiale Han'] | 2022-10-22 | null | null | null | null | ['relation-classification', 'text-infilling'] | ['natural-language-processing', 'natural-language-processing'] | [ 4.75784123e-01 6.19872451e-01 -6.09974384e-01 -6.24595284e-01
-9.23913121e-01 -7.71497786e-01 7.05575049e-01 2.13115692e-01
-2.56356984e-01 8.54563534e-01 5.71288407e-01 -6.95073366e-01
-5.93346842e-02 -7.84788430e-01 -4.61880535e-01 -2.45650679e-01
1.70567572e-01 8.57758284e-01 -9.47915167e-02 -1.52503937... | [10.153142929077148, 8.572461128234863] |
4c45723d-4418-4be6-b483-6e42a25106ce | uncertainty-aware-cascaded-dilation-filtering | 2201.02366 | null | https://arxiv.org/abs/2201.02366v1 | https://arxiv.org/pdf/2201.02366v1.pdf | Uncertainty-Aware Cascaded Dilation Filtering for High-Efficiency Deraining | Deraining is a significant and fundamental computer vision task, aiming to remove the rain streaks and accumulations in an image or video captured under a rainy day. Existing deraining methods usually make heuristic assumptions of the rain model, which compels them to employ complex optimization or iterative refinement... | ['Song Wang', 'Wei Feng', 'Di Lin', 'Lei Ma', 'Felix Juefei-Xu', 'Jingyang Sun', 'Qing Guo'] | 2022-01-07 | null | null | null | null | ['single-image-deraining'] | ['computer-vision'] | [ 3.44395190e-02 -4.36767578e-01 4.41209465e-01 -4.39792752e-01
-6.25292838e-01 -2.62551427e-01 2.97036976e-01 -5.97099125e-01
-1.35518789e-01 7.61452198e-01 5.23075042e-03 -1.88129485e-01
-6.84713051e-02 -8.35021555e-01 -8.39741528e-01 -1.10021925e+00
1.69411704e-01 -7.12304488e-02 3.81602526e-01 -2.72867471... | [10.88329792022705, -3.2488625049591064] |
c77da1c9-599b-4938-a4d2-786c36ec11ff | pipeline-coreference-resolution-model-for | null | null | https://aclanthology.org/2022.codi-crac.3 | https://aclanthology.org/2022.codi-crac.3.pdf | Pipeline Coreference Resolution Model for Anaphoric Identity in Dialogues | CODI-CRAC 2022 Shared Task in Dialogues consists of three sub-tasks: Sub-task 1 is the resolution of anaphoric identity, sub-task 2 is the resolution of bridging references, and sub-task 3 is the resolution of discourse deixis/abstract anaphora. Anaphora resolution is the task of detecting mentions from input documents... | ['Harksoo Kim', 'Mirae Han', 'Seongsik Park', 'Damrin Kim'] | null | null | null | null | coling-codi-crac-2022-10 | ['coreference-resolution'] | ['natural-language-processing'] | [-1.92046165e-02 6.22448206e-01 -2.85429507e-01 -3.12171906e-01
-1.04324210e+00 -5.34698963e-01 6.27613425e-01 2.77902126e-01
-5.58369040e-01 8.92912626e-01 6.33864641e-01 2.04862058e-02
-3.23617965e-01 -7.43582547e-01 -3.58094335e-01 -3.24272096e-01
2.02715963e-01 1.10849762e+00 5.70042372e-01 -4.81306881... | [9.341130256652832, 9.53217887878418] |
df280b00-1b36-4990-b6cf-767137bd03bc | int-fp-qsim-mixed-precision-and-formats-for | 2307.03712 | null | https://arxiv.org/abs/2307.03712v1 | https://arxiv.org/pdf/2307.03712v1.pdf | INT-FP-QSim: Mixed Precision and Formats For Large Language Models and Vision Transformers | The recent rise of large language models (LLMs) has resulted in increased efforts towards running LLMs at reduced precision. Running LLMs at lower precision supports resource constraints and furthers their democratization, enabling users to run billion-parameter LLMs on their personal devices. To supplement this ongoin... | ['Darius Bunandar', 'Ayon Basumallik', 'Craig Chan', 'Arulselvan Madhavan', 'Mikhail Bernadskiy', 'Lakshmi Nair'] | 2023-07-07 | null | null | null | null | ['quantization'] | ['methodology'] | [-4.09263045e-01 -7.29153991e-01 -5.12445867e-01 -3.09561193e-01
-8.95520687e-01 -5.81106365e-01 5.11811495e-01 2.24870786e-01
-4.98408943e-01 1.65260673e-01 2.87305295e-01 -8.83591413e-01
2.33348355e-01 -8.49290073e-01 -4.82543468e-01 -2.54214872e-02
-4.15106773e-01 8.94522741e-02 3.71280164e-01 -4.93326604... | [8.642549514770508, 3.4407434463500977] |
3701846f-37d1-4960-a3a5-538f88185986 | acorn-adaptive-coordinate-networks-for-neural | 2105.02788 | null | https://arxiv.org/abs/2105.02788v1 | https://arxiv.org/pdf/2105.02788v1.pdf | ACORN: Adaptive Coordinate Networks for Neural Scene Representation | Neural representations have emerged as a new paradigm for applications in rendering, imaging, geometric modeling, and simulation. Compared to traditional representations such as meshes, point clouds, or volumes they can be flexibly incorporated into differentiable learning-based pipelines. While recent improvements to ... | ['Gordon Wetzstein', 'Marco Monteiro', 'Eric R. Chan', 'Connor Z. Lin', 'David B. Lindell', 'Julien N. P. Martel'] | 2021-05-06 | null | null | null | null | ['3d-shape-representation'] | ['computer-vision'] | [ 3.08099002e-01 3.46580185e-02 3.55172962e-01 -2.91364014e-01
-9.34246182e-01 -1.72374517e-01 7.38177061e-01 2.52492756e-01
-2.98264176e-01 6.58676147e-01 -1.82102650e-01 -2.71654278e-01
-1.63937174e-02 -1.03064072e+00 -8.88002932e-01 -4.90655810e-01
-4.19451505e-01 6.35771573e-01 2.83726335e-01 -3.37439366... | [9.259081840515137, -3.157470703125] |
4d957c74-ea89-4d27-9fce-923a594cd3b7 | personalization-in-goal-oriented-dialog | 1706.07503 | null | http://arxiv.org/abs/1706.07503v3 | http://arxiv.org/pdf/1706.07503v3.pdf | Personalization in Goal-Oriented Dialog | The main goal of modeling human conversation is to create agents which can
interact with people in both open-ended and goal-oriented scenarios. End-to-end
trained neural dialog systems are an important line of research for such
generalized dialog models as they do not resort to any situation-specific
handcrafting of ru... | ['Fei Mi', 'Boi Faltings', 'Chaitanya K. Joshi'] | 2017-06-22 | null | null | null | null | ['goal-oriented-dialog'] | ['natural-language-processing'] | [-2.55510569e-01 6.66391611e-01 -5.29309409e-03 -9.53820407e-01
-2.08965942e-01 -6.26740575e-01 1.00336397e+00 -2.86261350e-01
-6.33162618e-01 9.74149525e-01 8.19505990e-01 -1.50086746e-01
-1.46654680e-01 -5.56459427e-01 7.15035126e-02 -3.63936871e-01
1.50153279e-01 1.40290546e+00 3.43853980e-01 -9.80547845... | [12.785675048828125, 8.020492553710938] |
00c35ae9-dda7-45a9-b868-fa76a3679968 | cross3dvg-baseline-and-dataset-for-cross | 2305.13876 | null | https://arxiv.org/abs/2305.13876v1 | https://arxiv.org/pdf/2305.13876v1.pdf | Cross3DVG: Baseline and Dataset for Cross-Dataset 3D Visual Grounding on Different RGB-D Scans | We present Cross3DVG, a novel task for cross-dataset visual grounding in 3D scenes, revealing the limitations of existing 3D visual grounding models using restricted 3D resources and thus easily overfit to a specific 3D dataset. To facilitate Cross3DVG, we have created a large-scale 3D visual grounding dataset containi... | ['Motoki Kawanabe', 'Shuhei Kurita', 'Daichi Azuma', 'Taiki Miyanishi'] | 2023-05-23 | null | null | null | null | ['visual-grounding', '3d-reconstruction'] | ['computer-vision', 'computer-vision'] | [-1.18143909e-01 2.25254968e-01 -9.94250104e-02 -5.72502494e-01
-1.10941303e+00 -1.12997258e+00 5.59716284e-01 3.97758543e-01
1.28966402e-02 6.02613911e-02 2.06584454e-01 -4.66512442e-01
4.75453623e-02 -8.21782410e-01 -1.13745844e+00 -2.21330151e-02
-1.89564049e-01 6.64577723e-01 4.24335390e-01 -1.39590949... | [8.069695472717285, -3.0801408290863037] |
8950000b-673d-4e9e-845a-f99fa848cb84 | deep-learning-for-real-time-gravitational-1 | 1711.03121 | null | http://arxiv.org/abs/1711.03121v1 | http://arxiv.org/pdf/1711.03121v1.pdf | Deep Learning for Real-time Gravitational Wave Detection and Parameter Estimation: Results with Advanced LIGO Data | The recent Nobel-prize-winning detections of gravitational waves from merging
black holes and the subsequent detection of the collision of two neutron stars
in coincidence with electromagnetic observations have inaugurated a new era of
multimessenger astrophysics. To enhance the scope of this emergent field of
science,... | ['E. A. Huerta', 'Daniel George'] | 2017-11-08 | null | null | null | null | ['gravitational-wave-detection'] | ['miscellaneous'] | [-4.14244533e-01 -2.98208714e-01 5.65558672e-01 -8.26998726e-02
-5.59040189e-01 -6.28269076e-01 1.15851450e+00 -3.16671312e-01
-5.57363153e-01 3.04430038e-01 -8.72582048e-02 -6.65207744e-01
-3.09677213e-01 -1.06806946e+00 -4.32706773e-01 -8.47948492e-01
-5.69281816e-01 8.70765746e-01 3.36004823e-01 -2.39822879... | [7.562434673309326, 3.1218490600585938] |
5f59112c-8adb-407b-bd53-1f114f345fd6 | model-agnostic-few-shot-open-set-recognition | 2206.09236 | null | https://arxiv.org/abs/2206.09236v1 | https://arxiv.org/pdf/2206.09236v1.pdf | Model-Agnostic Few-Shot Open-Set Recognition | We tackle the Few-Shot Open-Set Recognition (FSOSR) problem, i.e. classifying instances among a set of classes for which we only have few labeled samples, while simultaneously detecting instances that do not belong to any known class. Departing from existing literature, we focus on developing model-agnostic inference m... | ['Ismail Ben Ayed', 'Pablo Piantanida', 'Antoine Toubhans', 'Celine Hudelot', 'Myriam Tami', 'Etienne Bennequin', 'Malik Boudiaf'] | 2022-06-18 | null | null | null | null | ['open-set-learning'] | ['miscellaneous'] | [ 5.50733507e-01 3.11988026e-01 -4.66649055e-01 -2.07332835e-01
-9.62971091e-01 -6.59183741e-01 7.05160737e-01 1.47672549e-01
-1.59944668e-01 6.88545465e-01 -1.08522199e-01 -1.61300614e-01
-4.36703503e-01 -7.95876026e-01 -6.56917870e-01 -6.88662291e-01
-9.50945467e-02 7.75272191e-01 8.16028863e-02 -3.39623928... | [9.780423164367676, 2.9995365142822266] |
5ffc6fc4-918e-432d-9845-973b5ae7289c | ppg-based-heart-rate-estimation-with | 2303.13636 | null | https://arxiv.org/abs/2303.13636v1 | https://arxiv.org/pdf/2303.13636v1.pdf | PPG-based Heart Rate Estimation with Efficient Sensor Sampling and Learning Models | Recent studies showed that Photoplethysmography (PPG) sensors embedded in wearable devices can estimate heart rate (HR) with high accuracy. However, despite of prior research efforts, applying PPG sensor based HR estimation to embedded devices still faces challenges due to the energy-intensive high-frequency PPG sampli... | ['Dakai Zhu', 'Jing Wang', 'Keying Ye', 'Wei Wang', 'Mimi Xie', 'Jingye Xu', 'Yuntong Zhang'] | 2023-03-23 | null | null | null | null | ['photoplethysmography-ppg', 'heart-rate-estimation'] | ['medical', 'medical'] | [ 2.35193938e-01 -1.10733412e-01 -3.98166329e-01 -1.88801229e-01
-2.72570044e-01 -7.14599863e-02 -3.98272216e-01 1.93865895e-02
-1.88746989e-01 8.81190300e-01 -8.34919419e-03 -2.96100110e-01
-1.74256209e-02 -7.72540867e-01 -6.16401806e-02 -5.77765465e-01
-2.01856300e-01 -5.01424909e-01 -1.92141309e-01 3.87434542... | [13.924928665161133, 3.050413131713867] |
7d4b9959-1036-4e84-a39a-1dec10ad95a1 | fusing-multimodal-signals-on-hyper-complex | 2306.13968 | null | https://arxiv.org/abs/2306.13968v1 | https://arxiv.org/pdf/2306.13968v1.pdf | Fusing Multimodal Signals on Hyper-complex Space for Extreme Abstractive Text Summarization (TL;DR) of Scientific Contents | The realm of scientific text summarization has experienced remarkable progress due to the availability of annotated brief summaries and ample data. However, the utilization of multiple input modalities, such as videos and audio, has yet to be thoroughly explored. At present, scientific multimodal-input-based text summa... | ['Tanmoy Chakraborty', 'Vikram Goyal', 'Yash Kumar Atri'] | 2023-06-24 | null | null | null | null | ['abstractive-text-summarization', 'text-summarization'] | ['natural-language-processing', 'natural-language-processing'] | [ 2.23623663e-01 6.71175122e-02 7.29071274e-02 -1.30896941e-01
-1.41755593e+00 -6.41889334e-01 8.15450490e-01 1.33068994e-01
-1.98195204e-02 6.92460954e-01 1.02875757e+00 1.76993594e-01
-3.94529104e-02 -2.07228824e-01 -6.15056932e-01 -5.74512601e-01
8.37254003e-02 2.60794401e-01 -3.60871494e-01 -1.51543155... | [10.656126976013184, 0.6710078716278076] |
e3d13657-7f93-4dbe-9dd4-861b9a87a323 | an-interpretable-machine-vision-approach-to | 1812.00668 | null | http://arxiv.org/abs/1812.00668v1 | http://arxiv.org/pdf/1812.00668v1.pdf | An Interpretable Machine Vision Approach to Human Activity Recognition using Photoplethysmograph Sensor Data | The current gold standard for human activity recognition (HAR) is based on
the use of cameras. However, the poor scalability of camera systems renders
them impractical in pursuit of the goal of wider adoption of HAR in mobile
computing contexts. Consequently, researchers instead rely on wearable sensors
and in particul... | ['José Juan Dominguez Veiga', 'Eoin Brophy', 'Zhengwei Wang', 'Tomas E. Ward', 'Alan F. Smeaton'] | 2018-12-03 | null | null | null | null | ['2048'] | ['playing-games'] | [ 4.88211691e-01 6.02531573e-03 -5.09311929e-02 -7.16938302e-02
-3.17024350e-01 -4.81970727e-01 1.75674468e-01 1.88371558e-02
-7.42270470e-01 6.69219911e-01 1.02395721e-01 -3.89156550e-01
4.30665351e-02 -5.31888366e-01 -4.21446413e-01 -6.60952330e-01
1.65536076e-01 -3.44528645e-01 -2.40183473e-01 1.79427460... | [13.67502212524414, 2.9760146141052246] |
1a92db05-b0b1-4f0d-9edb-285839d0659a | super-resolution-of-bvoc-emission-maps-via | 2306.12796 | null | https://arxiv.org/abs/2306.12796v1 | https://arxiv.org/pdf/2306.12796v1.pdf | Super-Resolution of BVOC Emission Maps Via Domain Adaptation | Enhancing the resolution of Biogenic Volatile Organic Compound (BVOC) emission maps is a critical task in remote sensing. Recently, some Super-Resolution (SR) methods based on Deep Learning (DL) have been proposed, leveraging data from numerical simulations for their training process. However, when dealing with data de... | ['Stefano Tubaro', 'Marco Marcon', 'Paolo Bestagini', 'Sara Mandelli', 'Antonio Giganti'] | 2023-06-22 | null | null | null | null | ['super-resolution'] | ['computer-vision'] | [ 3.77031505e-01 -4.48238820e-01 1.94402367e-01 -1.75910696e-01
-7.86698818e-01 -5.19064784e-01 8.56680095e-01 5.56149334e-02
-4.82202619e-01 1.29556429e+00 1.58246100e-01 -3.57759267e-01
-4.76603240e-01 -1.14469743e+00 -4.87092793e-01 -1.03932202e+00
-1.86490715e-01 3.65298897e-01 3.25058430e-01 -4.80047673... | [9.77794361114502, -1.6997723579406738] |
3b228ba3-faf6-477c-9e2e-7d11e39b68bf | a-simple-and-robust-convolutional-attention | 1904.01375 | null | https://arxiv.org/abs/1904.01375v5 | https://arxiv.org/pdf/1904.01375v5.pdf | A Holistic Representation Guided Attention Network for Scene Text Recognition | Reading irregular scene text of arbitrary shape in natural images is still a challenging problem, despite the progress made recently. Many existing approaches incorporate sophisticated network structures to handle various shapes, use extra annotations for stronger supervision, or employ hard-to-train recurrent neural n... | ['Yanning Zhang', 'Zhen Li', 'Hui Li', 'Peng Wang', 'Fan Dang', 'Lu Yang'] | 2019-04-02 | null | null | null | null | ['irregular-text-recognition'] | ['computer-vision'] | [ 8.45542669e-01 -1.88126788e-01 -2.70281043e-02 -4.34864312e-01
-7.05677986e-01 -3.12818140e-01 8.33806396e-01 4.87948023e-03
-6.26013041e-01 2.39224598e-01 2.96949565e-01 -4.99594778e-01
5.81373513e-01 -6.22553766e-01 -8.59207749e-01 -6.24803424e-01
7.04925656e-01 1.71528414e-01 2.91985512e-01 -2.24767998... | [11.900084495544434, 2.2219715118408203] |
e79b9710-3f34-448c-aa6c-11ab4aa643a3 | a-new-android-malware-detection-approach | 1608.00848 | null | http://arxiv.org/abs/1608.00848v1 | http://arxiv.org/pdf/1608.00848v1.pdf | A New Android Malware Detection Approach Using Bayesian Classification | Mobile malware has been growing in scale and complexity as smartphone usage
continues to rise. Android has surpassed other mobile platforms as the most
popular whilst also witnessing a dramatic increase in malware targeting the
platform. A worrying trend that is emerging is the increasing sophistication of
Android malw... | ['Gavin McWilliams', 'Suleiman Y. Yerima', 'Sakir Sezer', 'Igor Muttik'] | 2016-08-02 | null | null | null | null | ['android-malware-detection'] | ['miscellaneous'] | [ 4.06961173e-01 -2.22606152e-01 -4.17803258e-01 5.70161715e-02
-5.19099832e-01 -9.45794702e-01 7.27688849e-01 2.95724291e-02
-2.07070664e-01 6.71887577e-01 -2.66447932e-01 -8.26972306e-01
1.85937330e-01 -6.02904499e-01 -5.18150985e-01 -4.08986986e-01
-2.46389642e-01 1.23249702e-01 6.17487788e-01 1.11629479... | [14.422342300415039, 9.679590225219727] |
cf181fae-34f3-4b8d-ad2b-f6527f97c0c3 | wasserstein-gaussianization-and-efficient | 2305.14746 | null | https://arxiv.org/abs/2305.14746v1 | https://arxiv.org/pdf/2305.14746v1.pdf | Wasserstein Gaussianization and Efficient Variational Bayes for Robust Bayesian Synthetic Likelihood | The Bayesian Synthetic Likelihood (BSL) method is a widely-used tool for likelihood-free Bayesian inference. This method assumes that some summary statistics are normally distributed, which can be incorrect in many applications. We propose a transformation, called the Wasserstein Gaussianization transformation, that us... | ['David Nott', 'Christopher Drovandi', 'Minh-Ngoc Tran', 'Nhat-Minh Nguyen'] | 2023-05-24 | null | null | null | null | ['bayesian-inference'] | ['methodology'] | [-6.30290955e-02 -1.49750367e-01 3.80322337e-02 -6.88088059e-01
-1.22358000e+00 -2.90859550e-01 7.15882361e-01 -9.56215113e-02
-2.62943834e-01 1.18402958e+00 9.04242173e-02 -2.15215564e-01
-1.73232064e-01 -7.29437411e-01 -5.41082978e-01 -8.06437671e-01
2.59317696e-01 4.98408794e-01 2.80373603e-01 3.93194169... | [7.0150933265686035, 3.9851067066192627] |
bb677445-8c6f-4f7e-875d-8c19068e560c | a-robust-predictive-model-for-stock-price | 1912.077 | null | https://arxiv.org/abs/1912.07700v1 | https://arxiv.org/pdf/1912.07700v1.pdf | A Robust Predictive Model for Stock Price Prediction Using Deep Learning and Natural Language Processing | Prediction of future movement of stock prices has been a subject matter of many research work. There is a gamut of literature of technical analysis of stock prices where the objective is to identify patterns in stock price movements and derive profit from it. Improving the prediction accuracy remains the single most ch... | ['Sidra Mehtab', 'Jaydip Sen'] | 2019-12-09 | null | null | null | null | ['stock-price-prediction'] | ['time-series'] | [-7.47520924e-01 -5.11615515e-01 -3.17637205e-01 -3.12229156e-01
-2.17029184e-01 -6.40425801e-01 5.88811696e-01 8.83250684e-02
-5.94534934e-01 9.28519547e-01 3.14036340e-01 -5.90870976e-01
-1.10389784e-01 -1.57814205e+00 -5.59016466e-01 -4.97572303e-01
-2.82727808e-01 2.81727970e-01 7.59491250e-02 -8.25577676... | [4.449123382568359, 4.246147632598877] |
9fca886b-a416-4718-abf0-1af63ae2903c | quantifying-the-intrinsic-usefulness-of | 2305.15961 | null | https://arxiv.org/abs/2305.15961v1 | https://arxiv.org/pdf/2305.15961v1.pdf | Quantifying the Intrinsic Usefulness of Attributional Explanations for Graph Neural Networks with Artificial Simulatability Studies | Despite the increasing relevance of explainable AI, assessing the quality of explanations remains a challenging issue. Due to the high costs associated with human-subject experiments, various proxy metrics are often used to approximately quantify explanation quality. Generally, one possible interpretation of the qualit... | ['Pascal Friederich', 'Luca Torresi', 'Jonas Teufel'] | 2023-05-25 | null | null | null | null | ['graph-classification'] | ['graphs'] | [ 3.81714165e-01 8.70394349e-01 -3.69264692e-01 -4.20788884e-01
4.42615300e-02 -5.26804745e-01 8.02109480e-01 6.38631344e-01
-1.52618244e-01 5.67451358e-01 7.41030350e-02 -8.11092913e-01
-7.22542942e-01 -8.71349573e-01 -7.34611213e-01 -3.02872568e-01
-5.57828322e-02 2.39672884e-01 -1.54273286e-01 -1.61288515... | [8.611167907714844, 5.94423246383667] |
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