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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
65662f65-de60-4599-af2d-05a5c574f6dd | cross-document-temporal-relation-extraction | null | null | https://openreview.net/forum?id=h9FRrlcs8yo | https://openreview.net/pdf?id=h9FRrlcs8yo | Cross-Document Temporal Relation Extraction with Temporal Anchoring Events | Automatically extracting a timeline on a certain topic from multiple documents has been a challenge in natural language processing, partly due to the difficulty of collecting large amounts of training data. In this work, we collect a dataset for cross-document timeline extraction from online news that gives access to m... | ['Anonymous'] | 2021-11-16 | null | null | null | acl-arr-november-2021-11 | ['temporal-relation-extraction'] | ['natural-language-processing'] | [-5.99973761e-02 1.26621544e-01 -8.18355620e-01 -7.30839312e-01
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-3.54599208e-01 5.42481482e-01 4.02191073e-01 -1.08939610... | [9.124344825744629, 9.242755889892578] |
a13c57c6-e7c2-4d64-9e40-d187f938c4b5 | quotes-query-oriented-technical-summarization | 2306.11832 | null | https://arxiv.org/abs/2306.11832v1 | https://arxiv.org/pdf/2306.11832v1.pdf | QuOTeS: Query-Oriented Technical Summarization | Abstract. When writing an academic paper, researchers often spend considerable time reviewing and summarizing papers to extract relevant citations and data to compose the Introduction and Related Work sections. To address this problem, we propose QuOTeS, an interactive system designed to retrieve sentences related to a... | ['Evangelos Milios', 'Flavia P. Zanoto', 'Axel J. Soto', 'Ana Maguitman', 'Eduardo Xamena', 'Juan Ramirez-Orta'] | 2023-06-20 | null | null | null | null | ['retrieval', 'information-retrieval', 'extractive-summarization'] | ['methodology', 'natural-language-processing', 'natural-language-processing'] | [ 9.45996195e-02 -9.89191309e-02 -2.39859745e-01 -1.18308989e-02
-1.50738013e+00 -1.00465035e+00 6.10570252e-01 8.32347453e-01
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5.09173572e-01 1.40610993e-01 1.26921013e-01 1.42260656... | [12.375143051147461, 9.547451972961426] |
d4520950-4120-44c1-b3d5-216a77ace39c | automatic-truss-design-with-reinforcement | 2306.15182 | null | https://arxiv.org/abs/2306.15182v1 | https://arxiv.org/pdf/2306.15182v1.pdf | Automatic Truss Design with Reinforcement Learning | Truss layout design, namely finding a lightweight truss layout satisfying all the physical constraints, is a fundamental problem in the building industry. Generating the optimal layout is a challenging combinatorial optimization problem, which can be extremely expensive to solve by exhaustive search. Directly applying ... | ['Yi Wu', 'Xianzhong Zhao', 'Zhiyuan Liu', 'Ruifeng Luo', 'Siyang Wu', 'Zimeng Song', 'Xingcheng Yao', 'Chao Yu', 'Jinglun Zhao', 'Weihua Du'] | 2023-06-27 | null | null | null | null | ['layout-design', 'combinatorial-optimization'] | ['computer-vision', 'methodology'] | [-1.51258007e-01 -6.64519519e-02 1.07056379e-01 1.40953407e-01
-1.05200815e+00 -8.56103063e-01 -1.57732248e-01 -1.65208466e-02
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-3.62793684e-01 6.17468476e-01 -3.99900287e-01 -2.75983661... | [5.591645240783691, 3.1136295795440674] |
eebdb397-2a92-4e4d-a867-919baf5544e2 | shape-change-and-control-of-pressure-based | 2205.00467 | null | https://arxiv.org/abs/2205.00467v1 | https://arxiv.org/pdf/2205.00467v1.pdf | Shape Change and Control of Pressure-based Soft Agents | Biological agents possess bodies that are mostly of soft tissues. Researchers have resorted to soft bodies to investigate Artificial Life (ALife)-related questions; similarly, a new era of soft-bodied robots has just begun. Nevertheless, because of their infinite degrees of freedom, soft bodies pose unique challenges i... | ['Federico Pigozzi'] | 2022-05-01 | null | null | null | null | ['artificial-life'] | ['miscellaneous'] | [ 1.51122939e-02 6.90316617e-01 2.46899843e-01 4.94012505e-01
5.84047556e-01 -6.15809202e-01 6.49360120e-01 -2.46594369e-01
-3.25317323e-01 1.06729221e+00 -3.70163649e-01 4.15626436e-01
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-3.89642358e-01 7.65519977e-01 6.57315493e-01 -9.24874604... | [5.640406131744385, 3.998598098754883] |
a130f7af-ac8e-4678-88e8-3122bcb7c5c8 | pre-training-on-high-resource-speech | 1809.01431 | null | http://arxiv.org/abs/1809.01431v2 | http://arxiv.org/pdf/1809.01431v2.pdf | Pre-training on high-resource speech recognition improves low-resource speech-to-text translation | We present a simple approach to improve direct speech-to-text translation
(ST) when the source language is low-resource: we pre-train the model on a
high-resource automatic speech recognition (ASR) task, and then fine-tune its
parameters for ST. We demonstrate that our approach is effective by
pre-training on 300 hours... | ['Sharon Goldwater', 'Sameer Bansal', 'Karen Livescu', 'Adam Lopez', 'Herman Kamper'] | 2018-09-05 | pre-training-on-high-resource-speech-1 | https://aclanthology.org/N19-1006 | https://aclanthology.org/N19-1006.pdf | naacl-2019-6 | ['speech-to-text-translation'] | ['natural-language-processing'] | [ 3.70060772e-01 1.35566011e-01 -6.47498369e-02 -1.98790222e-01
-1.80624354e+00 -9.12781239e-01 6.18148327e-01 -3.15484285e-01
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3.88527960e-01 5.82158089e-01 -6.97625661e-03 -5.12913108... | [14.461509704589844, 7.08357048034668] |
62616794-c594-409f-ae7e-d6f71da2d109 | real-time-neural-style-transfer-for-videos | null | null | http://openaccess.thecvf.com/content_cvpr_2017/html/Huang_Real-Time_Neural_Style_CVPR_2017_paper.html | http://openaccess.thecvf.com/content_cvpr_2017/papers/Huang_Real-Time_Neural_Style_CVPR_2017_paper.pdf | Real-Time Neural Style Transfer for Videos | Recent research endeavors have shown the potential of using feed-forward convolutional neural networks to accomplish fast style transfer for images. In this work, we take one step further to explore the possibility of exploiting a feed-forward network to perform style transfer for videos and simultaneously maintain tem... | ['Hao-Zhi Huang', 'Xiaolong Zhu', 'Wenhan Luo', 'Lin Ma', 'Wei Liu', 'Hao Wang', 'Zhifeng Li', 'Wenhao Jiang'] | 2017-07-01 | null | null | null | cvpr-2017-7 | ['video-style-transfer'] | ['computer-vision'] | [ 3.96952540e-01 -5.49377836e-02 9.63583365e-02 -4.41108584e-01
-2.84959048e-01 -4.83009279e-01 6.80014193e-01 -2.85013199e-01
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4.05246496e-01 -2.09908217e-01 1.46875620e-01 -8.84538889... | [11.195060729980469, -0.7888762354850769] |
4f31924e-6313-4811-89b0-a4e18623fe34 | small-object-detection-using-context-and | 1912.06319 | null | https://arxiv.org/abs/1912.06319v2 | https://arxiv.org/pdf/1912.06319v2.pdf | Small Object Detection using Context and Attention | There are many limitations applying object detection algorithm on various environments. Especially detecting small objects is still challenging because they have low resolution and limited information. We propose an object detection method using context for improving accuracy of detecting small objects. The proposed me... | ['Seung-Ik Lee', 'Hyun-Jin Yoon', 'Jeong-Seon Lim', 'Marcella Astrid'] | 2019-12-13 | null | null | null | null | ['small-object-detection'] | ['computer-vision'] | [ 2.86659449e-01 -4.64811474e-01 1.21660724e-01 -4.30573136e-01
-4.40167725e-01 -2.81563371e-01 1.00107625e-01 1.57766864e-01
-7.96219528e-01 4.38149899e-01 6.03439112e-04 1.52583763e-01
1.32020757e-01 -7.83202291e-01 -5.20698369e-01 -6.44029319e-01
1.10320836e-01 -7.52481595e-02 1.38180232e+00 1.55761942... | [8.681130409240723, -0.5508219599723816] |
aa4d68ec-7a90-44f4-8ede-e556212872ef | cr-fiqa-face-image-quality-assessment-by | 2112.06592 | null | https://arxiv.org/abs/2112.06592v2 | https://arxiv.org/pdf/2112.06592v2.pdf | CR-FIQA: Face Image Quality Assessment by Learning Sample Relative Classifiability | The quality of face images significantly influences the performance of underlying face recognition algorithms. Face image quality assessment (FIQA) estimates the utility of the captured image in achieving reliable and accurate recognition performance. In this work, we propose a novel learning paradigm that learns inter... | ['Naser Damer', 'Biying Fu', 'Marcel Klemt', 'Meiling Fang', 'Fadi Boutros'] | 2021-12-13 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Boutros_CR-FIQA_Face_Image_Quality_Assessment_by_Learning_Sample_Relative_Classifiability_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Boutros_CR-FIQA_Face_Image_Quality_Assessment_by_Learning_Sample_Relative_Classifiability_CVPR_2023_paper.pdf | cvpr-2023-1 | ['face-quality-assessement', 'face-image-quality', 'face-image-quality-assessment'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 3.33560795e-01 -1.13676181e-02 -2.92546004e-01 -8.23817968e-01
-6.51516736e-01 -2.37605274e-01 3.95112127e-01 -3.86291176e-01
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-4.14991468e-01 -7.47461736e-01 -8.30893755e-01 -9.21766341e-01
-1.15195535e-01 4.31246877e-01 -5.87430894e-01 3.68430525... | [13.103196144104004, 0.7006977796554565] |
9f75f2af-db50-4cf7-874f-602a83485e6a | document-level-event-extraction-via-human | 2202.03092 | null | https://arxiv.org/abs/2202.03092v1 | https://arxiv.org/pdf/2202.03092v1.pdf | Document-Level Event Extraction via Human-Like Reading Process | Document-level Event Extraction (DEE) is particularly tricky due to the two challenges it poses: scattering-arguments and multi-events. The first challenge means that arguments of one event record could reside in different sentences in the document, while the second one reflects one document may simultaneously contain ... | ['Jinqiao Shi', 'Yucheng Wang', 'Tingwen Liu', 'Bowen Yu', 'Xin Cong', 'Shiyao Cui'] | 2022-02-07 | null | null | null | null | ['document-level-event-extraction'] | ['natural-language-processing'] | [ 7.38499463e-01 2.41833434e-01 -2.57527009e-02 -1.69063464e-01
-1.09654105e+00 -4.28727061e-01 7.06981063e-01 5.93202889e-01
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-1.56906620e-01 -7.98931062e-01 -4.20029551e-01 -5.79485655e-01
1.05322435e-01 3.89861763e-01 6.61569595e-01 -1.09868329... | [9.102408409118652, 9.204730987548828] |
cef131ea-148a-4a20-ba4c-a62c95764875 | device-depth-and-visual-concepts-aware | 2302.01540 | null | https://arxiv.org/abs/2302.01540v3 | https://arxiv.org/pdf/2302.01540v3.pdf | DEVICE: DEpth and VIsual ConcEpts Aware Transformer for TextCaps | Text-based image captioning is an important but under-explored task, aiming to generate descriptions containing visual objects and scene text. Recent studies have made encouraging progress, but they are still suffering from a lack of overall understanding of scenes and generating inaccurate captions. One possible reaso... | ['Feng Shuang', 'Yi Cai', 'Qingbao Huang', 'Dongsheng Xu'] | 2023-02-03 | null | null | null | null | ['optical-character-recognition', 'relational-reasoning'] | ['computer-vision', 'natural-language-processing'] | [ 2.57663697e-01 1.09169982e-01 7.85803497e-02 -5.03042400e-01
-5.62449574e-01 -5.58956683e-01 8.70274007e-01 6.72077835e-02
-8.99952129e-02 5.89078903e-01 3.77149910e-01 -4.10499163e-02
2.19194621e-01 -8.04243565e-01 -9.25718307e-01 -3.42598855e-01
7.97413528e-01 4.87634420e-01 4.73600000e-01 -3.20040226... | [10.544636726379395, 1.3133841753005981] |
7d11dabe-0587-46f2-ab4b-f813cd6cb942 | bistatic-ofdm-based-joint-radar-communication | 2305.15058 | null | https://arxiv.org/abs/2305.15058v1 | https://arxiv.org/pdf/2305.15058v1.pdf | Bistatic OFDM-based Joint Radar-Communication: Synchronization, Data Communication and Sensing | This article introduces a bistatic joint radar-communication (RadCom) system based on orthogonal frequency-division multiplexing (OFDM). In this context, the adopted OFDM frame structure is described and system model encompassing time, frequency, and sampling synchronization mismatches between the transmitter and recei... | ['Benjamin Nuss', 'Thomas Zwick', 'Laurent Schmalen', 'Charlotte Muth', 'Axel Diewald', 'David Brunner', 'Lucas Giroto de Oliveira'] | 2023-05-24 | null | null | null | null | ['joint-radar-communication'] | ['robots'] | [ 7.08057404e-01 -1.66747540e-01 -3.12186014e-02 -1.40572667e-01
-3.55446935e-01 -4.69592690e-01 8.25139523e-01 -1.55941680e-01
-3.27443779e-01 9.64036047e-01 -2.08538294e-01 -2.74341494e-01
-5.90498686e-01 -5.21587729e-01 8.72728005e-02 -8.13934624e-01
-5.31045496e-01 -2.47889921e-01 -4.69745278e-01 1.32188186... | [6.3840765953063965, 1.2439959049224854] |
ce726d0e-4a00-403a-8c02-dd4d3118e8b4 | fusionformer-exploiting-the-joint-motion | 2210.04006 | null | https://arxiv.org/abs/2210.04006v2 | https://arxiv.org/pdf/2210.04006v2.pdf | (Fusionformer):Exploiting the Joint Motion Synergy with Fusion Network Based On Transformer for 3D Human Pose Estimation | For the current 3D human pose estimation task, a group of methods mainly learn the rules of 2D-3D projection from spatial and temporal correlation. However, earlier methods model the global features of the entire body joint in the time domain, but ignore the motion trajectory of individual joint. The recent work [29] c... | ['Xiaohua Zhang', 'Xinwei Yu'] | 2022-10-08 | null | null | null | null | ['3d-human-pose-estimation'] | ['computer-vision'] | [-5.18623054e-01 -1.07358865e-01 -1.26566440e-01 -3.29496264e-01
-3.75409991e-01 -2.29869336e-01 5.29732049e-01 -2.98253804e-01
-5.56726515e-01 3.42613965e-01 5.29088497e-01 4.72565830e-01
-4.78774076e-03 -4.45412546e-01 -6.29810214e-01 -5.39906383e-01
-2.98579037e-01 6.19925797e-01 6.52796566e-01 -3.08553427... | [7.125755310058594, -0.6581653952598572] |
c6eadc0b-3de1-4d26-a363-d3c4664f47f5 | aspect-extraction-using-coreference | null | null | https://aclanthology.org/2020.aacl-srw.18 | https://aclanthology.org/2020.aacl-srw.18.pdf | Aspect Extraction Using Coreference Resolution and Unsupervised Filtering | Aspect extraction is a widely researched field of natural language processing in which aspects are identified from the text as a means for information. For example, in aspect-based sentiment analysis (ABSA), aspects need to be first identified. Previous studies have introduced various approaches to increasing accuracy,... | ['Wei Emma Zhang', 'Deon Mai'] | 2020-12-01 | null | null | null | asian-chapter-of-the-association-for | ['aspect-extraction'] | ['natural-language-processing'] | [ 5.01178026e-01 4.56999242e-01 -5.32773495e-01 -5.93702316e-01
-8.74027550e-01 -7.03572512e-01 9.46900904e-01 5.73012054e-01
-4.61795181e-01 7.22135723e-01 6.13833308e-01 -1.43308759e-01
-1.36458009e-01 -7.53034353e-01 -1.40897438e-01 -5.08307159e-01
2.82348037e-01 6.02154613e-01 1.56915888e-01 -3.15833926... | [11.274374961853027, 6.790992736816406] |
2c8d3e75-9a7b-4146-b0aa-2974e111c6f6 | segmentation-of-aortic-vessel-tree-in-ct | 2305.09833 | null | https://arxiv.org/abs/2305.09833v1 | https://arxiv.org/pdf/2305.09833v1.pdf | Segmentation of Aortic Vessel Tree in CT Scans with Deep Fully Convolutional Networks | Automatic and accurate segmentation of aortic vessel tree (AVT) in computed tomography (CT) scans is crucial for early detection, diagnosis and prognosis of aortic diseases, such as aneurysms, dissections and stenosis. However, this task remains challenges, due to the complexity of aortic vessel tree and amount of CT a... | ['Feng Yang', 'Shaofeng Yuan'] | 2023-05-16 | null | null | null | null | ['computed-tomography-ct'] | ['methodology'] | [-2.02019259e-01 6.29660189e-02 4.38503996e-02 -5.10797262e-01
-4.37810242e-01 -7.11709797e-01 4.24976982e-02 1.79558352e-01
-3.46890956e-01 5.38122177e-01 4.40621823e-02 -8.82090151e-01
-4.64308411e-02 -5.75852931e-01 -3.75246108e-01 -4.44827408e-01
-6.28897250e-01 9.43680227e-01 3.30302596e-01 1.23066515... | [14.29101848602295, -2.467310667037964] |
69b5fc7c-3645-4bf3-8d31-d1d7fc83308b | just-ask-for-calibration-strategies-for | 2305.14975 | null | https://arxiv.org/abs/2305.14975v1 | https://arxiv.org/pdf/2305.14975v1.pdf | Just Ask for Calibration: Strategies for Eliciting Calibrated Confidence Scores from Language Models Fine-Tuned with Human Feedback | A trustworthy real-world prediction system should be well-calibrated; that is, its confidence in an answer is indicative of the likelihood that the answer is correct, enabling deferral to a more expensive expert in cases of low-confidence predictions. While recent studies have shown that unsupervised pre-training produ... | ['Christopher D. Manning', 'Chelsea Finn', 'Huaxiu Yao', 'Rafael Rafailov', 'Archit Sharma', 'Allan Zhou', 'Eric Mitchell', 'Katherine Tian'] | 2023-05-24 | null | null | null | null | ['unsupervised-pre-training'] | ['methodology'] | [-4.60879095e-02 4.35375124e-01 -3.22275549e-01 -8.68796706e-01
-1.02593362e+00 -6.47254407e-01 3.94928008e-01 3.23948890e-01
-4.61359739e-01 1.04540157e+00 -9.20779479e-04 -6.50461912e-01
1.47575453e-01 -8.02151024e-01 -8.80134761e-01 -2.89351761e-01
2.73197681e-01 7.30787456e-01 4.66283828e-01 -1.36416733... | [9.939848899841309, 7.46804141998291] |
feaf396a-b900-4e2f-af8a-6b5c6b9235ae | fixed-neural-network-steganography-train-the | null | null | https://openreview.net/forum?id=hcMvApxGSzZ | https://openreview.net/pdf?id=hcMvApxGSzZ | Fixed Neural Network Steganography: Train the images, not the network | Recent attempts at image steganography make use of advances in deep learning to train an encoder-decoder network pair to hide and retrieve secret messages in images. These methods are able to hide large amounts of data, but also incur high decoding error rates (around 20\%). In this paper, we propose a novel algorithm ... | ['Kilian Q Weinberger', 'Boyi Li', 'Yan Wang', 'Xiangyu Chen', 'Varsha Kishore'] | 2021-09-29 | null | null | null | iclr-2022-4 | ['steganalysis', 'image-steganography'] | ['computer-vision', 'computer-vision'] | [ 1.00715888e+00 5.61542511e-01 5.33385538e-02 -4.98870052e-02
-7.04645038e-01 -4.80237395e-01 4.34811980e-01 -3.15230429e-01
-5.11953831e-01 7.21975982e-01 -1.98974878e-01 -7.63964236e-01
5.52585959e-01 -7.98809469e-01 -1.25695789e+00 -1.06899571e+00
-5.90477943e-01 1.79311130e-02 7.33825713e-02 -3.77331197... | [4.358722686767578, 8.035676002502441] |
3440fa0e-716f-43f9-9e2b-d83f762c7b4e | robust-direction-of-arrival-estimation-using | 2303.09053 | null | https://arxiv.org/abs/2303.09053v2 | https://arxiv.org/pdf/2303.09053v2.pdf | Robust Direction-of-Arrival Estimation using Array Feedback Beamforming in Low SNR Scenarios | A new spatial IIR beamformer based direction-of-arrival (DoA) estimation method is proposed in this paper. We propose a retransmission based spatial feedback method for an array of transmit and receive antennas that improves the performance parameters of a beamformer, viz. half-power beamwidth (HPBW), side-lobe suppres... | ['Rajbabu Velmurugan', 'Kumar Appaiah', 'Parth Mehta'] | 2023-03-16 | null | null | null | null | ['direction-of-arrival-estimation'] | ['audio'] | [ 2.03959331e-01 -3.30720633e-01 4.63402838e-01 1.50238527e-02
-8.51403236e-01 -9.44162846e-01 4.80403453e-01 -3.76752704e-01
-2.32101172e-01 7.97331989e-01 1.11076677e+00 -5.22509336e-01
-8.64858449e-01 -4.00040835e-01 -1.09760828e-01 -1.06256878e+00
-2.72469312e-01 -4.58272964e-01 -2.40427982e-02 -1.37557045... | [6.477575778961182, 1.3512932062149048] |
772facde-cbd1-4b10-bb54-e9a2a74a7351 | chinese-lexical-simplification | 2010.07048 | null | https://arxiv.org/abs/2010.07048v1 | https://arxiv.org/pdf/2010.07048v1.pdf | Chinese Lexical Simplification | Lexical simplification has attracted much attention in many languages, which is the process of replacing complex words in a given sentence with simpler alternatives of equivalent meaning. Although the richness of vocabulary in Chinese makes the text very difficult to read for children and non-native speakers, there is ... | ['Xindong Wu', 'Yang Shi', 'Yunhao Yuan', 'Yun Li', 'Xinyu Lu', 'Jipeng Qiang'] | 2020-10-14 | null | null | null | null | ['lexical-simplification'] | ['natural-language-processing'] | [ 1.16623089e-01 -9.65764523e-02 -7.28626475e-02 -3.82536799e-01
-4.93250042e-01 -2.77290732e-01 4.25687730e-01 3.95164818e-01
-9.11564052e-01 1.06734157e+00 5.62994778e-01 -2.59206116e-01
3.55173856e-01 -6.69933200e-01 -2.62434214e-01 -2.83899516e-01
7.12296486e-01 3.41468573e-01 3.25841933e-01 -5.52302897... | [10.86661434173584, 10.410893440246582] |
f152d8b6-21ef-48f3-b4e5-d50da37b3401 | metam-goal-oriented-data-discovery | 2304.09068 | null | https://arxiv.org/abs/2304.09068v1 | https://arxiv.org/pdf/2304.09068v1.pdf | METAM: Goal-Oriented Data Discovery | Data is a central component of machine learning and causal inference tasks. The availability of large amounts of data from sources such as open data repositories, data lakes and data marketplaces creates an opportunity to augment data and boost those tasks' performance. However, augmentation techniques rely on a user m... | ['Raul Castro Fernandez', 'Yue Gong', 'Sainyam Galhotra'] | 2023-04-18 | null | null | null | null | ['causal-inference', 'causal-inference'] | ['knowledge-base', 'miscellaneous'] | [ 2.78558403e-01 1.84575066e-01 -8.34764242e-01 -4.17698145e-01
-6.99274898e-01 -7.39996433e-01 7.53130376e-01 6.90346181e-01
-3.76322448e-01 8.41524005e-01 6.90017939e-01 -5.45169652e-01
-4.41149205e-01 -9.69618320e-01 -7.11092651e-01 -2.52813429e-01
-4.10014182e-01 7.52420783e-01 1.15508530e-02 9.88336653... | [7.876273155212402, 5.417774200439453] |
2876cd73-988f-423f-ad70-1b8ba034a3bf | atril-an-xml-visualization-system-for-corpus | null | null | https://aclanthology.org/2022.lrec-1.611 | https://aclanthology.org/2022.lrec-1.611.pdf | Atril: an XML Visualization System for Corpus Texts | This paper presents Atril, an XML visualization system for corpus texts, developed for, but not restricted to, the project Corpus de Audiências (CorAuDis), a corpus composed of transcripts of sessions of criminal proceedings recorded at the Coimbra Court. The main aim of the tool is to provide researchers with a web-ba... | ['Cornelia Plag', 'Conceição Carapinha', 'Andressa Rodrigues Gomide'] | null | null | null | null | lrec-2022-6 | ['word-alignment'] | ['natural-language-processing'] | [-1.26281828e-01 2.44402304e-01 4.93309170e-01 -1.10961221e-01
-2.71219075e-01 -7.08264768e-01 6.88077390e-01 5.68776309e-01
-1.50677517e-01 3.58177185e-01 3.95139396e-01 -8.71255875e-01
-3.84443343e-01 -3.98739010e-01 2.88543582e-01 -1.15449794e-01
-3.12214275e-03 6.33973360e-01 4.46005255e-01 -4.02974695... | [9.503018379211426, 9.15194034576416] |
7dc9adf1-39d2-44d5-bf10-9d5faaf6eadb | dfac-framework-factorizing-the-value-function | 2102.07936 | null | https://arxiv.org/abs/2102.07936v2 | https://arxiv.org/pdf/2102.07936v2.pdf | DFAC Framework: Factorizing the Value Function via Quantile Mixture for Multi-Agent Distributional Q-Learning | In fully cooperative multi-agent reinforcement learning (MARL) settings, the environments are highly stochastic due to the partial observability of each agent and the continuously changing policies of the other agents. To address the above issues, we integrate distributional RL and value function factorization methods ... | ['Chun-Yi Lee', 'Cheng-Kuang Lee', 'Wei-Fang Sun'] | 2021-02-16 | null | null | null | null | ['smac-1', 'smac'] | ['playing-games', 'playing-games'] | [-6.88505173e-01 2.55205631e-02 -2.18634114e-01 7.59868249e-02
-7.65216649e-01 -8.09033751e-01 6.41450942e-01 3.39852348e-02
-6.70360088e-01 1.32127357e+00 4.64433253e-01 -3.61968189e-01
-4.47167754e-01 -7.73882091e-01 -7.50552416e-01 -8.82939041e-01
-5.29410481e-01 8.58358979e-01 -1.91670924e-01 -5.66851199... | [3.709299325942993, 2.018826484680176] |
baae4fa7-9387-4000-a1ef-0ccf6336d277 | speech-denoising-without-clean-training-data | 2104.03838 | null | https://arxiv.org/abs/2104.03838v2 | https://arxiv.org/pdf/2104.03838v2.pdf | Speech Denoising Without Clean Training Data: A Noise2Noise Approach | This paper tackles the problem of the heavy dependence of clean speech data required by deep learning based audio-denoising methods by showing that it is possible to train deep speech denoising networks using only noisy speech samples. Conventional wisdom dictates that in order to achieve good speech denoising performa... | ['S Natarajan', 'Krishnamoorthy Manohara', 'Anuj Tambwekar', 'Madhav Mahesh Kashyap'] | 2021-04-08 | null | null | null | null | ['audio-denoising', 'speech-denoising'] | ['audio', 'speech'] | [ 3.13599259e-01 -1.09202422e-01 7.21040010e-01 -2.95401573e-01
-1.09811080e+00 -2.13059828e-01 3.66364598e-01 -4.39817868e-02
-7.69819140e-01 6.93543971e-01 2.14618310e-01 -3.46719742e-01
-5.67769744e-02 -7.30063975e-01 -6.24888957e-01 -9.56171095e-01
-1.11770049e-01 -1.58856064e-01 -1.30788118e-01 -6.11817777... | [15.085322380065918, 5.934104919433594] |
59c58e7d-b977-4852-802a-fa2f8021f5aa | kanerva-extending-the-kanerva-machine-with-1 | 2103.03905 | null | https://arxiv.org/abs/2103.03905v3 | https://arxiv.org/pdf/2103.03905v3.pdf | Kanerva++: extending The Kanerva Machine with differentiable, locally block allocated latent memory | Episodic and semantic memory are critical components of the human memory model. The theory of complementary learning systems (McClelland et al., 1995) suggests that the compressed representation produced by a serial event (episodic memory) is later restructured to build a more generalized form of reusable knowledge (se... | ['Alexandros Kalousis', 'Yan Wu', 'Jason Ramapuram'] | 2021-02-20 | kanerva-extending-the-kanerva-machine-with | https://openreview.net/forum?id=QoWatN-b8T | https://openreview.net/pdf?id=QoWatN-b8T | iclr-2021-1 | ['conditional-image-generation'] | ['computer-vision'] | [-2.04036776e-02 8.23706090e-02 -1.07279435e-01 -4.42061156e-01
-6.02584958e-01 -1.72655523e-01 9.90558684e-01 2.69775148e-02
-8.44832301e-01 1.09156406e+00 2.15463385e-01 -3.44922990e-01
-4.12861407e-01 -1.13615906e+00 -1.00523317e+00 -8.20434272e-01
-3.57016414e-01 6.78515077e-01 4.44992810e-01 3.55800897... | [7.460334300994873, 3.774923086166382] |
14817d71-743d-4e3f-9d34-0c2a6838b025 | concentric-spherical-gnn-for-3d-1 | 2103.10484 | null | https://arxiv.org/abs/2103.10484v1 | https://arxiv.org/pdf/2103.10484v1.pdf | Concentric Spherical GNN for 3D Representation Learning | Learning 3D representations that generalize well to arbitrarily oriented inputs is a challenge of practical importance in applications varying from computer vision to physics and chemistry. We propose a novel multi-resolution convolutional architecture for learning over concentric spherical feature maps, of which the s... | ['Le Song', 'Rampi Ramprasad', 'Sivasankaran Rajamanickam', 'Bo Zhao', 'James Fox'] | 2021-03-18 | concentric-spherical-gnn-for-3d | https://openreview.net/forum?id=OItp-Avs6Iy | https://openreview.net/pdf?id=OItp-Avs6Iy | null | ['3d-classification'] | ['computer-vision'] | [-9.33822766e-02 4.63143773e-02 4.19239253e-01 -2.96690524e-01
-6.60269439e-01 -7.84395695e-01 8.73020589e-01 1.81995943e-01
-2.84664094e-01 3.44186813e-01 -1.14313938e-01 -3.81458402e-01
-6.16621822e-02 -1.01340020e+00 -1.26044095e+00 -5.50592303e-01
-2.91100949e-01 7.71566153e-01 3.17709833e-01 -1.58480540... | [7.972278118133545, -3.6798954010009766] |
e2bd7222-ef8e-44da-86d2-519d512bf5b8 | peekaboo-text-to-image-diffusion-models-are | 2211.13224 | null | https://arxiv.org/abs/2211.13224v2 | https://arxiv.org/pdf/2211.13224v2.pdf | Peekaboo: Text to Image Diffusion Models are Zero-Shot Segmentors | Recently, text-to-image diffusion models have shown remarkable capabilities in creating realistic images from natural language prompts. However, few works have explored using these models for semantic localization or grounding. In this work, we explore how an off-the-shelf text-to-image diffusion model, trained without... | ['Michael S. Ryoo', 'Xiang Li', 'Kanchana Ranasinghe', 'Ryan Burgert'] | 2022-11-23 | null | null | null | null | ['unsupervised-semantic-segmentation'] | ['computer-vision'] | [ 4.13515389e-01 6.53108954e-01 1.10201567e-01 -4.01366472e-01
-8.94288421e-01 -6.75260305e-01 7.68107355e-01 -1.33900911e-01
-3.52581680e-01 4.25072581e-01 -5.44914231e-02 -2.84362465e-01
2.02471852e-01 -8.62934232e-01 -9.22484398e-01 -4.71867323e-01
4.73894238e-01 8.34375143e-01 5.91248035e-01 -2.42546394... | [9.887088775634766, 0.8136979937553406] |
3de832a9-b0d3-406f-b9cd-01f4cba39018 | anomaly-detection-with-variance-stabilized | 2306.00582 | null | https://arxiv.org/abs/2306.00582v1 | https://arxiv.org/pdf/2306.00582v1.pdf | Anomaly Detection with Variance Stabilized Density Estimation | Density estimation based anomaly detection schemes typically model anomalies as examples that reside in low-density regions. We propose a modified density estimation problem and demonstrate its effectiveness for anomaly detection. Specifically, we assume the density function of normal samples is uniform in some compact... | ['Ofir Lindenbaum', 'Lior Wolf', 'Henry Li', 'Barak Battash', 'Amit Rozner'] | 2023-06-01 | null | null | null | null | ['density-estimation'] | ['methodology'] | [-2.85336584e-01 -1.26543358e-01 -2.30061039e-01 -4.06289935e-01
-8.97670746e-01 -1.47887245e-01 7.01384068e-01 9.76454541e-02
-1.69532374e-01 6.60910070e-01 -6.06027730e-02 -3.42507929e-01
-8.57014954e-02 -7.95338333e-01 -6.63374782e-01 -6.48153007e-01
-3.10232073e-01 5.74898005e-01 3.64288121e-01 1.63213819... | [7.628641605377197, 2.486440896987915] |
fbc869eb-2592-4ed6-92d4-38fcec6e4ec9 | learning-to-follow-instructions-in-text-based | 2211.04591 | null | https://arxiv.org/abs/2211.04591v1 | https://arxiv.org/pdf/2211.04591v1.pdf | Learning to Follow Instructions in Text-Based Games | Text-based games present a unique class of sequential decision making problem in which agents interact with a partially observable, simulated environment via actions and observations conveyed through natural language. Such observations typically include instructions that, in a reinforcement learning (RL) setting, can d... | ['Sheila A. McIlraith', 'Scott Sanner', 'Toryn Q. Klassen', 'Pashootan Vaezipoor', 'Andrew C. Li', 'Mathieu Tuli'] | 2022-11-08 | null | null | null | null | ['text-based-games'] | ['playing-games'] | [ 2.25298241e-01 3.25111300e-01 -2.28835985e-01 -2.09134549e-01
-1.83987439e-01 -6.84748828e-01 1.18219244e+00 2.13760570e-01
-7.35026836e-01 9.46243227e-01 3.72204006e-01 -4.78313833e-01
-1.67749137e-01 -8.93830836e-01 -4.14147079e-01 -4.77967232e-01
-5.67085803e-01 5.42521238e-01 4.96956557e-01 -7.24414110... | [3.989110231399536, 1.5103503465652466] |
a1499035-b5a6-48b1-bf92-85f667335eae | learning-to-plan-chemical-syntheses | 1708.04202 | null | http://arxiv.org/abs/1708.04202v1 | http://arxiv.org/pdf/1708.04202v1.pdf | Learning to Plan Chemical Syntheses | From medicines to materials, small organic molecules are indispensable for
human well-being. To plan their syntheses, chemists employ a problem solving
technique called retrosynthesis. In retrosynthesis, target molecules are
recursively transformed into increasingly simpler precursor compounds until a
set of readily av... | ['Mark P. Waller', 'Mike Preuss', 'Marwin H. S. Segler'] | 2017-08-14 | null | null | null | null | ['retrosynthesis'] | ['medical'] | [ 3.25768232e-01 1.52456909e-01 -5.41210711e-01 2.70767789e-02
-5.73548853e-01 -1.13708663e+00 5.26186705e-01 4.53427076e-01
-5.04905283e-01 1.34204841e+00 1.26538292e-01 -7.53098071e-01
2.60716885e-01 -9.18014526e-01 -6.36801720e-01 -5.23001373e-01
-5.63357249e-02 9.10403728e-01 -7.36200139e-02 -3.56931150... | [4.513131141662598, 6.08503532409668] |
d4424e6f-ea72-46e4-93d8-d39149289771 | speech-data-augmentation-for-improving | null | null | https://aclanthology.org/2022.rapid-1.8 | https://aclanthology.org/2022.rapid-1.8.pdf | Speech Data Augmentation for Improving Phoneme Transcriptions of Aphasic Speech Using Wav2Vec 2.0 for the PSST Challenge | As part of the PSST challenge, we explore how data augmentations, data sources, and model size affect phoneme transcription accuracy on speech produced by individuals with aphasia. We evaluate model performance in terms of feature error rate (FER) and phoneme error rate (PER). We find that data augmentations techniques... | ['Jonas Beskow', 'Joakim Gustafson', 'Harm Lameris', 'Ambika Kirkland', 'Shivam Mehta', 'Jim O’Regan', 'Birger Moell'] | null | null | null | null | rapid-lrec-2022-6 | ['room-impulse-response'] | ['audio'] | [ 2.25529745e-01 1.63974568e-01 1.04549557e-01 -1.65740177e-01
-1.09600699e+00 -2.96097845e-01 4.56815302e-01 9.06277522e-02
-7.68656790e-01 4.56062406e-01 1.38911259e+00 -3.22573751e-01
3.22197825e-01 -1.76601544e-01 -9.65254381e-02 -1.92497253e-01
3.97231989e-02 3.42567742e-01 -3.09547901e-01 -3.46777380... | [14.456958770751953, 6.373517036437988] |
0258a8ff-087e-4f08-85ef-b2a46874daa6 | decorrelated-adversarial-learning-for-age | 1904.04972 | null | http://arxiv.org/abs/1904.04972v1 | http://arxiv.org/pdf/1904.04972v1.pdf | Decorrelated Adversarial Learning for Age-Invariant Face Recognition | There has been an increasing research interest in age-invariant face
recognition. However, matching faces with big age gaps remains a challenging
problem, primarily due to the significant discrepancy of face appearances
caused by aging. To reduce such a discrepancy, in this paper we propose a novel
algorithm to remove ... | ['Wei Liu', 'Hao Wang', 'Zhifeng Li', 'Dihong Gong'] | 2019-04-10 | decorrelated-adversarial-learning-for-age-1 | http://openaccess.thecvf.com/content_CVPR_2019/html/Wang_Decorrelated_Adversarial_Learning_for_Age-Invariant_Face_Recognition_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Wang_Decorrelated_Adversarial_Learning_for_Age-Invariant_Face_Recognition_CVPR_2019_paper.pdf | cvpr-2019-6 | ['age-invariant-face-recognition'] | ['computer-vision'] | [ 6.18759245e-02 -1.94298625e-01 1.54894054e-01 -5.00984192e-01
-1.03100859e-01 -1.23713866e-01 2.76345998e-01 -3.44036460e-01
-2.25694686e-01 6.49412513e-01 1.63702890e-01 2.33204678e-01
-3.13879997e-02 -8.22457314e-01 -4.63347673e-01 -9.95479405e-01
-7.74141699e-02 -9.17070135e-02 -2.99548507e-01 -2.31522992... | [13.223690032958984, 0.5382445454597473] |
db3be938-06ea-4df2-bb1c-e15445cb2484 | occlusions-motion-and-depth-boundaries-with-a | 1808.01838 | null | http://arxiv.org/abs/1808.01838v2 | http://arxiv.org/pdf/1808.01838v2.pdf | Occlusions, Motion and Depth Boundaries with a Generic Network for Disparity, Optical Flow or Scene Flow Estimation | Occlusions play an important role in disparity and optical flow estimation,
since matching costs are not available in occluded areas and occlusions
indicate depth or motion boundaries. Moreover, occlusions are relevant for
motion segmentation and scene flow estimation. In this paper, we present an
efficient learning-ba... | ['Thomas Brox', 'Margret Keuper', 'Eddy Ilg', 'Tonmoy Saikia'] | 2018-08-06 | occlusions-motion-and-depth-boundaries-with-a-1 | http://openaccess.thecvf.com/content_ECCV_2018/html/Eddy_Ilg_Occlusions_Motion_and_ECCV_2018_paper.html | http://openaccess.thecvf.com/content_ECCV_2018/papers/Eddy_Ilg_Occlusions_Motion_and_ECCV_2018_paper.pdf | eccv-2018-9 | ['scene-flow-estimation'] | ['computer-vision'] | [-6.98799640e-02 -4.24055934e-01 -5.69988012e-01 -3.15025359e-01
-3.70301336e-01 -4.91196573e-01 2.68276662e-01 -2.03694910e-01
-5.85911751e-01 9.36613381e-01 1.27009556e-01 -8.36626515e-02
2.93428063e-01 -7.05564797e-01 -4.19040769e-01 -5.52366734e-01
-1.93211511e-01 1.03055328e-01 8.17902088e-01 1.24647751... | [8.757762908935547, -1.8818602561950684] |
7224a21c-ee63-4746-b2d7-2b1cfc0443e1 | hopfield-networks-is-all-you-need | 2008.02217 | null | https://arxiv.org/abs/2008.02217v3 | https://arxiv.org/pdf/2008.02217v3.pdf | Hopfield Networks is All You Need | We introduce a modern Hopfield network with continuous states and a corresponding update rule. The new Hopfield network can store exponentially (with the dimension of the associative space) many patterns, retrieves the pattern with one update, and has exponentially small retrieval errors. It has three types of energy m... | ['Thomas Adler', 'Sepp Hochreiter', 'Günter Klambauer', 'Milena Pavlović', 'Markus Holzleitner', 'Bernhard Schäfl', 'Victor Greiff', 'Michael Widrich', 'Michael Kopp', 'Johannes Brandstetter', 'Hubert Ramsauer', 'Lukas Gruber', 'Philipp Seidl', 'Johannes Lehner', 'Geir Kjetil Sandve', 'David Kreil'] | 2020-07-16 | null | https://openreview.net/forum?id=tL89RnzIiCd | https://openreview.net/pdf?id=tL89RnzIiCd | iclr-2021-1 | ['immune-repertoire-classification'] | ['medical'] | [-1.00704193e-01 -1.24212034e-01 -1.74330696e-02 -7.29412064e-02
-2.46898830e-01 -4.70274240e-01 7.01700628e-01 2.51313329e-01
-6.95414186e-01 8.16184580e-01 9.03387964e-02 3.17505449e-02
-3.88958603e-01 -9.72419560e-01 -9.82375979e-01 -1.05832887e+00
-5.82911849e-01 7.38969386e-01 4.70876634e-01 -2.49984488... | [8.7337007522583, 3.2767155170440674] |
f11054ed-40ef-4014-8903-547373bc2f9f | recursive-tree-attention-improving-semantic | null | null | https://openreview.net/forum?id=NH-gIZJ8mOD | https://openreview.net/pdf?id=NH-gIZJ8mOD | Recursive Tree Attention: Improving Semantic Representations with Syntactic Tree Structured Attention Mechanism | Attention mechanism has shown its effectiveness in state-of-the-art methods on various tasks in natural language processing (NLP). However, these methods are still using attention mechanism in plain, linear topological structures while the syntactic structures of natural languages are known to be hierarchical. Modeling... | ['Anonymous'] | 2021-06-04 | null | null | null | null | ['constituency-parsing'] | ['natural-language-processing'] | [-6.73711896e-02 6.40363038e-01 -1.55801192e-01 -4.19018775e-01
-2.76117772e-01 -2.20209673e-01 2.07987368e-01 5.46180665e-01
-2.15688318e-01 5.50569594e-01 8.89250875e-01 -3.91554892e-01
-7.62148798e-02 -1.03553212e+00 -5.20228863e-01 -4.42120790e-01
-1.06061764e-01 4.43253040e-01 3.64155412e-01 -2.90207714... | [10.652423858642578, 9.22823429107666] |
6a363c1e-b334-48e0-aafc-101152a9e980 | a-sketch-based-3d-shape-retrieval-approach | 1903.00117 | null | http://arxiv.org/abs/1903.00117v2 | http://arxiv.org/pdf/1903.00117v2.pdf | A Sketch Based 3D Shape Retrieval Approach Based on Efficient Deep Point-to-Subspace Metric Learning | A sketch based 3D shape retrieval | ['Lingqiao Liu', 'Pingping Zhang', 'Zijun Ma', 'Yinjie Lei', 'Ziqin Zhou', 'Yulan Guo'] | 2019-03-01 | null | null | null | null | ['3d-shape-retrieval'] | ['computer-vision'] | [-4.98136103e-01 -4.64488864e-01 -1.02306657e-01 -3.32421422e-01
-6.04498982e-01 -1.06826818e+00 8.76795828e-01 -4.52027321e-01
5.32107115e-01 -3.03859189e-02 2.60196388e-01 -1.75194085e-01
-1.96995437e-01 -1.22423029e+00 -3.53060305e-01 -1.10791497e-01
9.63878557e-02 1.04821622e+00 4.22664970e-01 -2.55159736... | [8.44394588470459, -3.688795566558838] |
97ce9a1e-0376-472c-913c-b0f2a7306bb9 | assessing-differentially-private-deep | 1912.11328 | null | https://arxiv.org/abs/1912.11328v4 | https://arxiv.org/pdf/1912.11328v4.pdf | Assessing differentially private deep learning with Membership Inference | Attacks that aim to identify the training data of public neural networks represent a severe threat to the privacy of individuals participating in the training data set. A possible protection is offered by anonymization of the training data or training function with differential privacy. However, data scientists can cho... | ['Philip-William Grassal', 'Florian Kerschbaum', 'Jonas Robl', 'Daniel Bernau'] | 2019-12-24 | null | null | null | null | ['membership-inference-attack'] | ['computer-vision'] | [ 9.30254534e-02 2.41130710e-01 7.26186903e-03 -6.76585495e-01
-6.44572377e-01 -1.22404087e+00 3.70322406e-01 2.02565044e-01
-9.39389229e-01 7.81522691e-01 -2.13870645e-01 -9.13523912e-01
-3.26695919e-01 -8.86655152e-01 -8.32448602e-01 -8.91069710e-01
-2.84067988e-01 1.57226861e-01 -4.16359901e-01 3.15220833... | [5.920676231384277, 6.9641432762146] |
3c9f5f39-ae84-4dcf-854e-b8c248c07d91 | intra-and-inter-epoch-temporal-context | 1902.06562 | null | https://arxiv.org/abs/1902.06562v2 | https://arxiv.org/pdf/1902.06562v2.pdf | Intra- and Inter-epoch Temporal Context Network (IITNet) Using Sub-epoch Features for Automatic Sleep Scoring on Raw Single-channel EEG | A deep learning model, named IITNet, is proposed to learn intra- and inter-epoch temporal contexts from raw single-channel EEG for automatic sleep scoring. To classify the sleep stage from half-minute EEG, called an epoch, sleep experts investigate sleep-related events and consider the transition rules between the foun... | ['Kyoobin Lee', 'Seongju Lee', 'Seunghyeok Back', 'Deokhwan Park', 'Tae Kim', 'Hogeon Seo'] | 2019-02-18 | null | null | null | null | ['sleep-stage-detection'] | ['medical'] | [-1.41383614e-02 -2.56164849e-01 -1.52421361e-02 -4.29117590e-01
-6.05266273e-01 -2.00206056e-01 1.87848017e-01 -6.74867332e-02
-8.68810058e-01 1.13744283e+00 5.27452603e-02 -2.60660984e-02
-3.41273159e-01 -4.00099963e-01 -5.25394380e-01 -7.26005435e-01
-4.06717688e-01 -3.13740999e-01 2.22879216e-01 -6.77956790... | [13.490825653076172, 3.507603883743286] |
a2d29af7-e57c-4b17-a7ef-6c028934eaa5 | hierarchical-gnns-for-large-graph-generation | 2306.11412 | null | https://arxiv.org/abs/2306.11412v1 | https://arxiv.org/pdf/2306.11412v1.pdf | Hierarchical GNNs for Large Graph Generation | Large graphs are present in a variety of domains, including social networks, civil infrastructure, and the physical sciences to name a few. Graph generation is similarly widespread, with applications in drug discovery, network analysis and synthetic datasets among others. While GNN (Graph Neural Network) models have be... | ['Telmo M. Silva Filho', 'Nirav S. Ajmeri', 'Alex O. Davies'] | 2023-06-20 | null | null | null | null | ['drug-discovery'] | ['medical'] | [ 1.54216975e-01 1.00558019e+00 -8.93560946e-02 2.80620847e-02
-3.28941762e-01 -6.14938796e-01 9.45841670e-01 3.21217120e-01
1.67267427e-01 1.13241708e+00 9.51202214e-02 -5.33198237e-01
-3.88667405e-01 -1.60230470e+00 -8.25668812e-01 -2.98570186e-01
-7.13568747e-01 1.02069867e+00 4.17350680e-01 -4.02659357... | [6.932538986206055, 6.012482166290283] |
6a45b812-6e83-4370-b300-f520b30a154e | a-stepwise-label-based-approach-for-improving | null | null | https://dl.acm.org/doi/abs/10.1145/3347449.3357482 | https://dl.acm.org/doi/abs/10.1145/3347449.3357482 | A Stepwise, Label-based Approach for Improving the Adversarial Training in Unsupervised Video Summarization | In this paper we present our work on improving the efficiency of adversarial training for unsupervised video summarization. Our starting point is the SUM-GAN model, which creates a representative summary based on the intuition that such a summary should make it possible to reconstruct a video that is indistinguishable ... | ['Ioannis Patras', 'Vasileios Mezaris', 'Eleni Adamantidou', 'Alexandros I. Metsai', 'Evlampios Apostolidis'] | 2019-10-21 | null | null | null | ai4tv-2019-10 | ['unsupervised-video-summarization'] | ['computer-vision'] | [ 4.89090085e-01 4.56079274e-01 1.37782514e-01 -4.68138307e-02
-7.73106039e-01 -6.11979902e-01 8.51836085e-01 7.68805156e-04
-5.55524468e-01 6.92845225e-01 3.60709667e-01 -2.85429627e-01
7.29177892e-02 -6.12806380e-01 -1.20182049e+00 -6.77902579e-01
-2.57959783e-01 3.98790598e-01 3.24439585e-01 -2.90859073... | [10.498174667358398, 0.33130115270614624] |
5bbe047d-b6b5-4bff-bb66-7f909facbfb7 | real-world-font-recognition-using-deep | 1504.00028 | null | http://arxiv.org/abs/1504.00028v1 | http://arxiv.org/pdf/1504.00028v1.pdf | Real-World Font Recognition Using Deep Network and Domain Adaptation | We address a challenging fine-grain classification problem: recognizing a
font style from an image of text. In this task, it is very easy to generate
lots of rendered font examples but very hard to obtain real-world labeled
images. This real-to-synthetic domain gap caused poor generalization to new
real data in previou... | ['Jianchao Yang', 'Aseem Agarwala', 'Zhangyang Wang', 'Thomas S. Huang', 'Hailin Jin', 'Eli Shechtman', 'Jonathan Brandt'] | 2015-03-31 | null | null | null | null | ['font-recognition'] | ['computer-vision'] | [ 6.35572851e-01 -9.82040092e-02 3.04806978e-01 -5.64223528e-01
-5.77583730e-01 -8.49481046e-01 6.42284274e-01 -4.03394133e-01
-2.23715305e-01 1.00661731e+00 -1.93983659e-01 -3.08960825e-01
3.92668933e-01 -9.02171314e-01 -1.13751042e+00 -2.12486610e-01
5.47455728e-01 4.46700543e-01 4.58329171e-02 -2.58468121... | [11.901555061340332, 1.8166049718856812] |
ea3acaec-2e1a-435e-9dce-b523a8e25905 | spectral-reconstruction-from-dispersive-blur | null | null | http://openaccess.thecvf.com/content_CVPR_2019/html/Zhao_Spectral_Reconstruction_From_Dispersive_Blur_A_Novel_Light_Efficient_Spectral_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Zhao_Spectral_Reconstruction_From_Dispersive_Blur_A_Novel_Light_Efficient_Spectral_CVPR_2019_paper.pdf | Spectral Reconstruction From Dispersive Blur: A Novel Light Efficient Spectral Imager | Developing high light efficiency imaging techniques to retrieve high dimensional optical signal is a long-term goal in computational photography. Multispectral imaging, which captures images of different wavelengths and boosting the abilities for revealing scene properties, has developed rapidly in the last few decades... | [' Xun Cao', ' Tao Yue', ' Zhan Ma', ' Hui Guo', ' Xuemei Hu', 'Yuanyuan Zhao'] | 2019-06-01 | null | null | null | cvpr-2019-6 | ['spectral-reconstruction'] | ['computer-vision'] | [ 5.76788723e-01 -1.04959345e+00 5.00955462e-01 -1.04564041e-01
-2.56934434e-01 -5.75699508e-01 3.15397620e-01 -8.70209813e-01
-2.62905687e-01 7.90724337e-01 1.60367355e-01 -3.89846824e-02
-6.81661606e-01 -4.63695139e-01 -2.65099287e-01 -1.14396012e+00
2.12546825e-01 -3.93783033e-01 2.50518769e-01 3.35593149... | [10.857457160949707, -2.6165482997894287] |
319d9fed-a099-45c6-8619-61e2838c2ba6 | steering-distortions-to-preserve-classes-and | null | null | http://proceedings.neurips.cc/paper/2020/hash/99607461cdb9c26e2bd5f31b12dcf27a-Abstract.html | http://proceedings.neurips.cc/paper/2020/file/99607461cdb9c26e2bd5f31b12dcf27a-Paper.pdf | Steering Distortions to Preserve Classes and Neighbors in Supervised Dimensionality Reduction | Nonlinear dimensionality reduction of high-dimensional data is challenging as the low-dimensional embedding will necessarily contain distortions, and it can be hard to determine which distortions are the most important to avoid. When annotation of data into known relevant classes is available, it can be used to guide t... | ['Sylvain Lespinats', 'Denys Dutykh', 'Michael Aupetit', 'Jaakko Peltonen', 'Benoît Colange'] | 2020-12-01 | null | null | null | neurips-2020-12 | ['supervised-dimensionality-reduction'] | ['computer-vision'] | [ 1.25655636e-01 3.39074343e-01 -2.65504599e-01 -2.93370545e-01
-4.31622356e-01 -8.03744614e-01 8.01307142e-01 5.25028586e-01
-3.86906475e-01 6.31933749e-01 4.07546610e-01 1.01010136e-01
-5.92319429e-01 -9.86244678e-01 -2.29040846e-01 -9.18240786e-01
-2.15070602e-02 6.15041673e-01 4.36939508e-01 -1.45585716... | [7.991508483886719, 4.15956449508667] |
16825b43-9e89-4f1c-a864-245be1f20723 | densehybrid-hybrid-anomaly-detection-for | 2207.02606 | null | https://arxiv.org/abs/2207.02606v1 | https://arxiv.org/pdf/2207.02606v1.pdf | DenseHybrid: Hybrid Anomaly Detection for Dense Open-set Recognition | Anomaly detection can be conceived either through generative modelling of regular training data or by discriminating with respect to negative training data. These two approaches exhibit different failure modes. Consequently, hybrid algorithms present an attractive research goal. Unfortunately, dense anomaly detection r... | ['Siniša Šegvić', 'Petra Bevandić', 'Matej Grcić'] | 2022-07-06 | null | null | null | null | ['scene-segmentation', 'open-set-learning'] | ['computer-vision', 'miscellaneous'] | [ 3.44065696e-01 3.16706806e-01 1.92571431e-01 -5.32876194e-01
-1.04596698e+00 -7.24877059e-01 7.05537140e-01 1.50431722e-01
-5.84626675e-01 4.93894368e-01 -3.74209940e-01 -2.91724473e-01
-8.78021866e-02 -9.55500841e-01 -9.92737710e-01 -9.68926787e-01
-1.61594942e-01 8.42737317e-01 4.73879039e-01 -1.16349466... | [7.6944899559021, 2.170311450958252] |
682cee23-4ed9-4790-90c5-f97860770c59 | finding-optimal-combination-of-kernels-using | 1604.02376 | null | http://arxiv.org/abs/1604.02376v2 | http://arxiv.org/pdf/1604.02376v2.pdf | Finding Optimal Combination of Kernels using Genetic Programming | In Computer Vision, problem of identifying or classifying the objects present
in an image is called Object Categorization. It is a challenging problem,
especially when the images have clutter background, occlusions or different
lighting conditions. Many vision features have been proposed which aid object
categorization... | ['Jyothi Korra'] | 2016-04-08 | null | null | null | null | ['object-categorization'] | ['computer-vision'] | [ 1.7088161e-01 -7.7588302e-01 4.3847892e-02 -6.5394366e-01
-2.3726138e-01 -7.0498741e-01 4.4198465e-01 4.0633157e-01
-4.0655929e-01 5.4157388e-01 -3.4098497e-01 -1.3104512e-01
-6.4565367e-01 -6.8003160e-01 -2.3922956e-01 -6.8609303e-01
-2.3351288e-01 1.7246567e-01 4.9912140e-01 -1.8809801e-02
8.0673367e-01... | [8.214740753173828, 3.930480718612671] |
68ec89d7-e547-48ab-881f-5c5c8225cd1d | improving-cross-modal-alignment-in-vision | 2104.09580 | null | https://arxiv.org/abs/2104.09580v1 | https://arxiv.org/pdf/2104.09580v1.pdf | Improving Cross-Modal Alignment in Vision Language Navigation via Syntactic Information | Vision language navigation is the task that requires an agent to navigate through a 3D environment based on natural language instructions. One key challenge in this task is to ground instructions with the current visual information that the agent perceives. Most of the existing work employs soft attention over individu... | ['Mohit Bansal', 'Hao Tan', 'Jialu Li'] | 2021-04-19 | null | https://aclanthology.org/2021.naacl-main.82 | https://aclanthology.org/2021.naacl-main.82.pdf | naacl-2021-4 | ['vision-language-navigation'] | ['computer-vision'] | [-9.73544121e-02 -1.18056491e-01 7.86610246e-02 -6.93320334e-01
-2.88330734e-01 -6.99247956e-01 5.18639147e-01 1.62913680e-01
-6.21702552e-01 3.36019307e-01 8.25179696e-01 -6.93262994e-01
3.68676990e-01 -4.77953762e-01 -6.30919099e-01 -6.13697350e-01
3.66149366e-01 2.72434294e-01 1.87785476e-02 -5.97766101... | [4.4437055587768555, 0.44788965582847595] |
1689216b-f329-42e4-9751-9b1c5277dc37 | focusing-on-context-is-nice-improving | 2210.06164 | null | https://arxiv.org/abs/2210.06164v1 | https://arxiv.org/pdf/2210.06164v1.pdf | Focusing on Context is NICE: Improving Overshadowed Entity Disambiguation | Entity disambiguation (ED) is the task of mapping an ambiguous entity mention to the corresponding entry in a structured knowledge base. Previous research showed that entity overshadowing is a significant challenge for existing ED models: when presented with an ambiguous entity mention, the models are much more likely ... | ['Evangelos Kanoulas', 'Roberto Navigli', 'Svitlana Vakulenko', 'Simone Tedeschi', 'Vera Provatorova'] | 2022-10-12 | null | null | null | null | ['entity-disambiguation'] | ['natural-language-processing'] | [-4.28294957e-01 5.51218987e-01 -5.19813895e-01 -2.00257018e-01
-8.83639991e-01 -7.96613455e-01 4.78151530e-01 7.56387949e-01
-8.32477629e-01 1.09076273e+00 4.32275116e-01 -2.37943128e-01
-9.24713835e-02 -8.21192384e-01 -6.43583775e-01 -9.16233659e-02
-3.70780438e-01 8.42985392e-01 6.89618647e-01 -2.35264331... | [9.423666000366211, 8.843281745910645] |
b2ef01e4-f749-46a3-833f-3128c0071d68 | reaction-network-analysis-of-metabolic | 2204.04614 | null | https://arxiv.org/abs/2204.04614v3 | https://arxiv.org/pdf/2204.04614v3.pdf | Reaction Network Analysis of Metabolic Insulin Signaling | Absolute concentration robustness (ACR) and concordance are novel concepts in the theory of robustness and stability within Chemical Reaction Network Theory. In this paper, we have extended Shinar and Feinberg's reaction network analysis approach to the insulin signaling system based on recent advances in decomposing r... | ['Angelyn R. Lao', 'Eduardo R. Mendoza', 'Patrick Vincent N. Lubenia'] | 2022-04-10 | null | null | null | null | ['novel-concepts'] | ['reasoning'] | [ 4.36538517e-01 1.41196504e-01 -9.81364325e-02 2.95115858e-01
2.82008410e-01 -1.09092009e+00 3.52885127e-01 3.17142874e-01
-1.23433210e-01 1.15685976e+00 4.33158614e-02 -4.52588707e-01
-7.66242445e-01 -6.12456977e-01 -5.28844357e-01 -9.57430243e-01
-6.20452285e-01 4.55917567e-01 1.80460885e-01 -6.23162866... | [6.351328372955322, 4.755594253540039] |
c9a91991-c02d-4fd2-800e-91fe5651e089 | efficient-estimation-of-weighted-cumulative | 2305.02373 | null | https://arxiv.org/abs/2305.02373v1 | https://arxiv.org/pdf/2305.02373v1.pdf | Efficient estimation of weighted cumulative treatment effects by double/debiased machine learning | In empirical studies with time-to-event outcomes, investigators often leverage observational data to conduct causal inference on the effect of exposure when randomized controlled trial data is unavailable. Model misspecification and lack of overlap are common issues in observational studies, and they often lead to inco... | ['Zach Shahn', 'Ioanna Tzoulaki', 'Kenney Ng', 'Roy Welsch', 'Stan Finkelstein', 'Bowen Su', 'Bang Zheng', 'Shenbo Xu'] | 2023-05-03 | null | null | null | null | ['causal-inference', 'causal-inference'] | ['knowledge-base', 'miscellaneous'] | [ 2.74933606e-01 -6.26225844e-02 -1.42120337e+00 -3.38172942e-01
-1.02951658e+00 -2.76288569e-01 1.38806805e-01 4.88620043e-01
-3.95808399e-01 1.11233187e+00 3.00296009e-01 -9.24466252e-01
-7.71235585e-01 -5.00839174e-01 -9.04538393e-01 -5.76912224e-01
-5.59088528e-01 4.62156713e-01 -5.10419011e-01 6.61263287... | [8.007328987121582, 5.268938064575195] |
fbffca20-895c-4fdd-9af6-07a9db3fa513 | a-deep-learning-based-bayesian-approach-to | 2001.04567 | null | https://arxiv.org/abs/2001.04567v2 | https://arxiv.org/pdf/2001.04567v2.pdf | A deep-learning based Bayesian approach to seismic imaging and uncertainty quantification | Uncertainty quantification is essential when dealing with ill-conditioned inverse problems due to the inherent nonuniqueness of the solution. Bayesian approaches allow us to determine how likely an estimation of the unknown parameters is via formulating the posterior distribution. Unfortunately, it is often not possibl... | ['Felix J. Herrmann', 'Ali Siahkoohi', 'Gabrio Rizzuti'] | 2020-01-13 | null | null | null | null | ['seismic-imaging'] | ['miscellaneous'] | [ 3.97902459e-01 1.92772731e-01 4.37586039e-01 -3.37346107e-01
-6.43393576e-01 -2.91026086e-01 5.51327586e-01 -2.98623234e-01
-6.29231215e-01 1.03810740e+00 2.05927327e-01 1.50044352e-01
-4.77471918e-01 -6.18703902e-01 -7.44941533e-01 -9.72813368e-01
3.64669532e-01 3.91421437e-01 -5.84094860e-02 2.55726933... | [6.840970993041992, 3.548114776611328] |
6c284a1d-979f-4966-8a8c-fad9b506910b | pose-randomization-for-weakly-paired-image | 2011.00301 | null | https://arxiv.org/abs/2011.00301v2 | https://arxiv.org/pdf/2011.00301v2.pdf | PREGAN: Pose Randomization and Estimation for Weakly Paired Image Style Translation | Utilizing the trained model under different conditions without data annotation is attractive for robot applications. Towards this goal, one class of methods is to translate the image style from another environment to the one on which models are trained. In this paper, we propose a weakly-paired setting for the style tr... | ['Rong Xiong', 'Yue Wang', 'Yunkai Wang', 'Xuecheng Xu', 'Jiaxin Guo', 'Zexi Chen'] | 2020-10-31 | null | null | null | null | ['road-segementation'] | ['computer-vision'] | [ 4.47333694e-01 3.55975956e-01 3.04785520e-02 -6.02123976e-01
-6.61144078e-01 -8.46795380e-01 5.66986144e-01 -6.93962932e-01
-3.33898038e-01 7.31086969e-01 -2.88056999e-01 -2.92192977e-02
2.74370164e-01 -7.93046832e-01 -1.33540213e+00 -8.01356256e-01
4.28544641e-01 8.11308742e-01 -4.58979085e-02 -1.67887002... | [8.389904022216797, -2.2223429679870605] |
ac2251b7-e130-4499-b548-22b2f6b99aac | fine-tuning-self-organizing-maps-for-sentinel | null | null | https://www.mdpi.com/2072-4292/12/12/1923 | https://www.mdpi.com/2072-4292/12/12/1923/htm | Fine-Tuning Self-Organizing Maps for Sentinel-2 Imagery: Separating Clouds from Bright Surfaces | Removal of cloud interference is a crucial step for the exploitation of the spectral information stored in optical satellite images. Several cloud masking approaches have been developed through time, based on direct interpretation of the spectral and temporal properties of clouds through thresholds. The problem has als... | ['Vassilia Karathanassi', 'Viktoria Kristollari'] | 2020-06-14 | null | null | null | null | ['cloud-detection'] | ['computer-vision'] | [ 3.25951606e-01 -3.81224394e-01 4.54227060e-01 -6.17412962e-02
-1.49581820e-01 -8.43787253e-01 5.61448216e-01 3.22118253e-01
-5.78711510e-01 6.00605607e-01 -3.74373555e-01 -4.53192145e-01
-4.84340549e-01 -9.48167741e-01 -2.03758955e-01 -1.19790196e+00
-2.39885956e-01 5.98152876e-01 4.55911219e-01 -2.28378251... | [9.720248222351074, -1.751252293586731] |
e1638a77-a1af-42e5-adc9-73b1594e4563 | improving-transformer-based-end-to-end | 2303.01192 | null | https://arxiv.org/abs/2303.01192v1 | https://arxiv.org/pdf/2303.01192v1.pdf | Improving Transformer-based End-to-End Speaker Diarization by Assigning Auxiliary Losses to Attention Heads | Transformer-based end-to-end neural speaker diarization (EEND) models utilize the multi-head self-attention (SA) mechanism to enable accurate speaker label prediction in overlapped speech regions. In this study, to enhance the training effectiveness of SA-EEND models, we propose the use of auxiliary losses for the SA h... | ['Joon-Hyuk Chang', 'Jeong-Hwan Choi', 'Joon-Young Yang', 'Ye-Rin Jeoung'] | 2023-03-02 | null | null | null | null | ['activity-detection'] | ['computer-vision'] | [ 2.19327435e-01 3.06462735e-01 1.33745968e-01 -5.29623687e-01
-6.42718136e-01 -3.06622654e-01 2.48968899e-01 -3.20448816e-01
-2.41721958e-01 3.27463657e-01 3.63185644e-01 -2.92683452e-01
-2.49950718e-02 -1.93791270e-01 -3.99849176e-01 -9.08965051e-01
-7.38765392e-03 2.93068826e-01 -2.29505301e-02 -6.91801012... | [14.5946044921875, 6.10465145111084] |
ba01dd81-3453-4751-8367-ba86f29fea71 | malaria-likelihood-prediction-by-effectively | 1711.09223 | null | http://arxiv.org/abs/1711.09223v1 | http://arxiv.org/pdf/1711.09223v1.pdf | Malaria Likelihood Prediction By Effectively Surveying Households Using Deep Reinforcement Learning | We build a deep reinforcement learning (RL) agent that can predict the
likelihood of an individual testing positive for malaria by asking questions
about their household. The RL agent learns to determine which survey question
to ask next and when to stop to make a prediction about their likelihood of
malaria based on t... | ['Vinaya Polamreddi', 'Anusha Balakrishnan', 'Pranav Rajpurkar'] | 2017-11-25 | null | null | null | null | ['holdout-set'] | ['computer-vision'] | [ 3.57847333e-01 4.63568836e-01 -2.13695079e-01 -8.23090315e-01
-7.66401052e-01 -4.15434837e-01 3.22836697e-01 3.22187930e-01
-8.30066442e-01 9.83027577e-01 1.79239467e-01 -6.64771855e-01
-1.25742853e-01 -1.36739945e+00 -7.38150656e-01 -5.60867310e-01
-2.23160803e-01 1.05967891e+00 -3.91290598e-02 -1.44674582... | [4.06635856628418, 1.8331241607666016] |
150f1ea7-0cab-42f5-9d7f-ea2170920f29 | improving-solvability-for-procedurally | 1810.01926 | null | https://arxiv.org/abs/1810.01926v3 | https://arxiv.org/pdf/1810.01926v3.pdf | Improving Solvability for Procedurally Generated Challenges in Physical Solitaire Games Through Entangled Components | Challenges for physical solitaire puzzle games are typically designed in advance by humans and limited in number. Alternatively, some games incorporate rules for stochastic setup, where the human solver randomly sets up the game board before solving the challenge. These setup rules greatly increase the number of possib... | ['Mark Goadrich', 'James Droscha'] | 2018-10-03 | null | null | null | null | ['solitaire'] | ['playing-games'] | [ 2.29201138e-01 -3.15231644e-02 3.24522346e-01 2.69610703e-01
-8.34153235e-01 -1.25915718e+00 3.66762578e-01 -5.21629266e-02
-4.21045572e-01 8.92190695e-01 2.27972969e-01 -4.52582419e-01
-6.38064265e-01 -1.15460563e+00 -4.24416840e-01 -3.83897185e-01
1.18326485e-01 5.91642022e-01 1.42558604e-01 -3.16221565... | [3.521829843521118, 1.5244169235229492] |
21f6a1b0-639f-45de-93ae-cb956021286e | robots-enact-malignant-stereotypes | 2207.11569 | null | https://arxiv.org/abs/2207.11569v1 | https://arxiv.org/pdf/2207.11569v1.pdf | Robots Enact Malignant Stereotypes | Stereotypes, bias, and discrimination have been extensively documented in Machine Learning (ML) methods such as Computer Vision (CV) [18, 80], Natural Language Processing (NLP) [6], or both, in the case of large image and caption models such as OpenAI CLIP [14]. In this paper, we evaluate how ML bias manifests in robot... | ['Matthew Gombolay', 'Severin Kacianka', 'Vicky Zeng', 'William Agnew', 'Andrew Hundt'] | 2022-07-23 | null | null | null | null | ['gender-bias-detection', 'gender-bias-detection', 'stereotypical-bias-analysis', 'robotic-grasping'] | ['miscellaneous', 'natural-language-processing', 'natural-language-processing', 'robots'] | [ 2.82945991e-01 7.02204645e-01 -4.02457118e-01 -2.27510426e-02
-2.22563148e-01 -6.64885223e-01 7.02604234e-01 9.95023474e-02
-4.90804553e-01 6.33653879e-01 5.40484667e-01 -3.58404398e-01
-3.36656690e-01 -6.11351669e-01 -9.16276455e-01 -5.37136078e-01
1.40985260e-02 3.51373643e-01 -5.10119677e-01 -3.36511075... | [12.888812065124512, 1.4021923542022705] |
0ab1e27d-dedd-43e5-86d5-849091a023fe | design-and-analysis-of-optimized-portfolios | 2210.03943 | null | https://arxiv.org/abs/2210.03943v1 | https://arxiv.org/pdf/2210.03943v1.pdf | Design and Analysis of Optimized Portfolios for Selected Sectors of the Indian Stock Market | Portfolio optimization is a challenging problem that has attracted considerable attention and effort from researchers. The optimization of stock portfolios is a particularly hard problem since the stock prices are volatile and estimation of their future volatilities and values, in most cases, is very difficult, if not ... | ['Abhishek Dutta', 'Jaydip Sen'] | 2022-10-08 | null | null | null | null | ['portfolio-optimization'] | ['time-series'] | [-6.06650770e-01 -3.03197414e-01 -1.23647675e-01 6.85141385e-02
-4.30173725e-01 -1.01355255e+00 4.68119085e-01 -7.58536458e-02
-1.75957814e-01 9.15241659e-01 2.06436187e-01 -6.47686541e-01
-8.77077222e-01 -8.22599053e-01 -2.18086123e-01 -8.76052380e-01
-1.62349239e-01 3.02218199e-01 1.54502928e-01 -8.29363018... | [4.632765293121338, 4.09143590927124] |
6b466ee9-b3f3-4dc8-9480-7537973d9232 | high-resolution-boundary-detection-for | 2211.02419 | null | https://arxiv.org/abs/2211.02419v1 | https://arxiv.org/pdf/2211.02419v1.pdf | High-Resolution Boundary Detection for Medical Image Segmentation with Piece-Wise Two-Sample T-Test Augmented Loss | Deep learning methods have contributed substantially to the rapid advancement of medical image segmentation, the quality of which relies on the suitable design of loss functions. Popular loss functions, including the cross-entropy and dice losses, often fall short of boundary detection, thereby limiting high-resolution... | ['Jian Yang', 'Jue Hou', 'Feifei Wang', 'Deqiang Xiao', 'Tianyu Fu', 'Jingfan Fan', 'Hong Song', 'Danni Ai', 'Jiaan Luo', 'Saining Zhang', 'Hanchao Yan', 'Yuhao Wei', 'Yuhang Li', 'Jinhua Su', 'Yucong Lin'] | 2022-11-04 | null | null | null | null | ['boundary-detection'] | ['computer-vision'] | [ 5.68615079e-01 2.09309459e-01 -1.82443738e-01 -6.09992921e-01
-9.24557030e-01 -3.76680166e-01 1.79400414e-01 3.98663193e-01
-4.23519820e-01 7.29595184e-01 -1.48548096e-01 -3.71008515e-01
-1.74362704e-01 -6.35420561e-01 -3.85666281e-01 -1.02763331e+00
-6.61081225e-02 2.66560130e-02 3.33422452e-01 3.64894420... | [14.60712718963623, -2.265944004058838] |
76f54655-2c5f-4f12-9e75-1b81bd5b4ba9 | patch-based-knowledge-distillation-for | null | null | https://dl.acm.org/doi/abs/10.1145/3503161.3548179 | http://www.muyadong.com/paper/acmmm22_sunzc.pdf | Patch-based Knowledge Distillation for Lifelong Person Re-Identification | The task of lifelong person re-identification aims to match a person across multiple cameras given continuous data streams. Similar to other lifelong learning tasks, it severely suffers from the so-called catastrophic forgetting problem, which refers to the notable performance degradation on previously-seen data after ... | ['Yadong Mu', 'Zhicheng Sun'] | 2022-10-10 | null | null | null | acm-multimedia-2022-10 | ['person-re-identification'] | ['computer-vision'] | [ 2.53170311e-01 -1.20550774e-01 -6.69035017e-02 -2.71621615e-01
-6.53422892e-01 -2.59788752e-01 6.42594874e-01 1.35202315e-02
-5.56349933e-01 7.71816015e-01 1.91141814e-01 5.15559912e-01
-2.66020536e-01 -4.83913869e-01 -7.21995294e-01 -7.79059947e-01
1.10024661e-01 3.01187247e-01 2.31499746e-01 -6.23926148... | [14.714506149291992, 1.0506998300552368] |
5dd036a8-78ae-4403-8948-2638d8bee95c | regularization-strategy-for-point-cloud-via | 2102.01929 | null | https://arxiv.org/abs/2102.01929v3 | https://arxiv.org/pdf/2102.01929v3.pdf | Regularization Strategy for Point Cloud via Rigidly Mixed Sample | Data augmentation is an effective regularization strategy to alleviate the overfitting, which is an inherent drawback of the deep neural networks. However, data augmentation is rarely considered for point cloud processing despite many studies proposing various augmentation methods for image data. Actually, regularizati... | ['Sangyoun Lee', 'Sungmin Woo', 'Minhyeok Lee', 'Hyeongmin Lee', 'Junhyeop Lee', 'Jaeha Lee', 'Dogyoon Lee'] | 2021-02-03 | null | http://openaccess.thecvf.com//content/CVPR2021/html/Lee_Regularization_Strategy_for_Point_Cloud_via_Rigidly_Mixed_Sample_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Lee_Regularization_Strategy_for_Point_Cloud_via_Rigidly_Mixed_Sample_CVPR_2021_paper.pdf | cvpr-2021-1 | ['3d-object-classification'] | ['computer-vision'] | [ 8.98732319e-02 4.60056262e-03 1.40015706e-01 -3.03839505e-01
-3.84828538e-01 -4.59498554e-01 5.05475998e-01 3.28209922e-02
-2.65778303e-01 5.08519292e-01 -3.80411632e-02 -2.77166124e-02
-9.20152217e-02 -1.02377999e+00 -1.06373036e+00 -8.95203173e-01
2.24061638e-01 6.35122120e-01 -1.83732167e-01 -3.61379027... | [8.211981773376465, -3.566131114959717] |
c08e4101-60d0-4271-91f9-fa18fc94aa30 | movie101-a-new-movie-understanding-benchmark | 2305.12140 | null | https://arxiv.org/abs/2305.12140v2 | https://arxiv.org/pdf/2305.12140v2.pdf | Movie101: A New Movie Understanding Benchmark | To help the visually impaired enjoy movies, automatic movie narrating systems are expected to narrate accurate, coherent, and role-aware plots when there are no speaking lines of actors. Existing works benchmark this challenge as a normal video captioning task via some simplifications, such as removing role names and e... | ['Qin Jin', 'Ziheng Wang', 'Liang Zhang', 'Anwen Hu', 'Qi Zhang', 'Zihao Yue'] | 2023-05-20 | null | null | null | null | ['video-captioning'] | ['computer-vision'] | [ 3.33875537e-01 -1.52426824e-01 -3.34877908e-01 -5.15430033e-01
-9.60964859e-01 -6.66355133e-01 7.26590216e-01 -3.26806642e-02
-3.03840309e-01 6.47231996e-01 1.19811141e+00 2.46415809e-01
3.39386672e-01 -4.97750431e-01 -5.52458763e-01 -5.03682733e-01
2.92322427e-01 -2.54244924e-01 1.58010185e-01 -3.03294569... | [10.608951568603516, 0.7275850176811218] |
fefa6bce-a0c4-45c2-a117-32a50be92c8d | combating-the-curse-of-multilinguality-in | null | null | https://aclanthology.org/2022.naacl-main.176 | https://aclanthology.org/2022.naacl-main.176.pdf | Combating the Curse of Multilinguality in Cross-Lingual WSD by Aligning Sparse Contextualized Word Representations | In this paper, we advocate for using large pre-trained monolingual language models in cross lingual zero-shot word sense disambiguation (WSD) coupled with a contextualized mapping mechanism. We also report rigorous experiments that illustrate the effectiveness of employing sparse contextualized word representations obt... | ['Gábor Berend'] | null | null | null | null | naacl-2022-7 | ['word-sense-disambiguation'] | ['natural-language-processing'] | [-1.99482098e-01 -1.57550573e-01 -6.29941404e-01 -2.79071093e-01
-1.23135829e+00 -6.31362796e-01 8.22263598e-01 3.80853355e-01
-7.11670578e-01 9.55323458e-01 5.94273627e-01 -5.10467291e-01
8.19614902e-02 -4.76906091e-01 -3.09257358e-01 -3.23910594e-01
-1.52337337e-02 3.62544149e-01 -2.82626003e-01 -8.19445968... | [10.749703407287598, 9.730742454528809] |
a2482f28-3c35-47f5-99da-1677e01332d5 | raising-the-limit-of-image-rescaling-using | 2303.06747 | null | https://arxiv.org/abs/2303.06747v1 | https://arxiv.org/pdf/2303.06747v1.pdf | Raising The Limit Of Image Rescaling Using Auxiliary Encoding | Normalizing flow models using invertible neural networks (INN) have been widely investigated for successful generative image super-resolution (SR) by learning the transformation between the normal distribution of latent variable $z$ and the conditional distribution of high-resolution (HR) images gave a low-resolution (... | ['Paul Bogdan', 'Le Kang', 'Xin Zhou', 'Zhihong Pan', 'Chenzhong Yin'] | 2023-03-12 | null | null | null | null | ['image-super-resolution'] | ['computer-vision'] | [ 6.65294349e-01 2.11681902e-01 -2.63698041e-01 -3.59217077e-01
-5.86697698e-01 -2.57832617e-01 5.51088274e-01 -6.51921868e-01
-3.21053058e-01 1.03078973e+00 5.14706016e-01 -2.31117755e-01
-5.64739034e-02 -1.09196734e+00 -7.54325390e-01 -9.36095297e-01
1.83754399e-01 -1.94505960e-01 6.80723488e-02 -2.70474076... | [11.139507293701172, -1.9931883811950684] |
40284940-4461-41ed-b837-ff896f60bba0 | zero-shot-clarifying-question-generation-for | 2301.12660 | null | https://arxiv.org/abs/2301.12660v2 | https://arxiv.org/pdf/2301.12660v2.pdf | Zero-shot Clarifying Question Generation for Conversational Search | A long-standing challenge for search and conversational assistants is query intention detection in ambiguous queries. Asking clarifying questions in conversational search has been widely studied and considered an effective solution to resolve query ambiguity. Existing work have explored various approaches for clarifyin... | ['Qingyao Ai', 'Ming Wu', 'Nick Craswell', 'Corby Rosset', 'Yuancheng Tu', 'Zhenduo Wang'] | 2023-01-30 | null | null | null | null | ['natural-questions', 'conversational-search', 'question-generation'] | ['miscellaneous', 'natural-language-processing', 'natural-language-processing'] | [ 2.76810050e-01 4.46744055e-01 -9.13882330e-02 -2.86599517e-01
-1.19446874e+00 -7.83010781e-01 8.29196692e-01 -2.48177871e-01
-5.85980892e-01 8.68215680e-01 5.67023337e-01 -5.76515794e-01
-1.88728750e-01 -4.68008041e-01 -2.55509377e-01 -1.19585022e-01
6.65474892e-01 9.30492580e-01 2.38795668e-01 -8.41711521... | [12.1127290725708, 7.81272554397583] |
3cd677ff-8fff-4dc6-939c-46e2429fd013 | deep-simbad-active-landmark-based-self | 2109.02786 | null | https://arxiv.org/abs/2109.02786v1 | https://arxiv.org/pdf/2109.02786v1.pdf | Deep SIMBAD: Active Landmark-based Self-localization Using Ranking -based Scene Descriptor | Landmark-based robot self-localization has recently garnered interest as a highly-compressive domain-invariant approach for performing visual place recognition (VPR) across domains (e.g., time of day, weather, and season). However, landmark-based self-localization can be an ill-posed problem for a passive observer (e.g... | ['Tanaka Kanji'] | 2021-09-06 | null | null | null | null | ['visual-place-recognition'] | ['computer-vision'] | [-1.73249960e-01 -6.71945810e-02 -6.02998555e-01 -4.44287270e-01
-1.11935210e+00 -7.16512799e-01 4.81360912e-01 1.83796391e-01
-5.21618366e-01 6.81675553e-01 5.04934415e-02 -1.40050367e-01
-3.75912011e-01 -7.09649086e-01 -1.14582372e+00 -6.63666904e-01
-1.98028564e-01 4.36514378e-01 3.00306618e-01 -3.34148675... | [7.417845726013184, -1.98819100856781] |
c62d6c28-8b8f-4ab2-b532-fcb013416b24 | planning-for-complex-non-prehensile | 2303.13352 | null | https://arxiv.org/abs/2303.13352v1 | https://arxiv.org/pdf/2303.13352v1.pdf | Planning for Complex Non-prehensile Manipulation Among Movable Objects by Interleaving Multi-Agent Pathfinding and Physics-Based Simulation | Real-world manipulation problems in heavy clutter require robots to reason about potential contacts with objects in the environment. We focus on pick-and-place style tasks to retrieve a target object from a shelf where some `movable' objects must be rearranged in order to solve the task. In particular, our motivation i... | ['Maxim Likhachev', 'Dhruv Mauria Saxena'] | 2023-03-23 | null | null | null | null | ['motion-planning'] | ['robots'] | [ 1.52439505e-01 2.78050601e-01 -3.23640071e-02 3.59377787e-02
-5.70914328e-01 -1.13975799e+00 4.81122583e-01 2.19550043e-01
-4.26437318e-01 7.58908510e-01 -2.75414675e-01 -3.72512192e-01
-5.37635744e-01 -8.10104609e-01 -1.02238882e+00 -3.75074744e-01
-5.16588569e-01 1.41953838e+00 6.71341538e-01 -6.60577714... | [4.721461772918701, 0.9402682781219482] |
a860398a-bc47-485a-bdd7-e3af58437bf1 | span-based-discontinuous-constituency-parsing-1 | null | null | https://aclanthology.org/2020.emnlp-main.219 | https://aclanthology.org/2020.emnlp-main.219.pdf | Span-based discontinuous constituency parsing: a family of exact chart-based algorithms with time complexities from O(n\^6) down to O(n\^3) | We introduce a novel chart-based algorithm for span-based parsing of discontinuous constituency trees of block degree two, including ill-nested structures. In particular, we show that we can build variants of our parser with smaller search spaces and time complexities ranging from O(n{\^{}}6) down to O(n{\^{}}3). The c... | ['Caio Corro'] | null | null | null | null | emnlp-2020-11 | ['constituency-parsing'] | ['natural-language-processing'] | [ 1.15024403e-01 5.46911657e-01 -3.06673758e-02 -3.91847670e-01
-1.36686599e+00 -1.09970522e+00 -7.65354633e-02 5.92024744e-01
-7.32061207e-01 6.58633649e-01 3.48361820e-01 -1.20660949e+00
1.73227221e-01 -9.42985058e-01 -5.41120470e-01 -3.14285994e-01
-5.24945498e-01 5.07815599e-01 5.86775541e-01 -4.90408540... | [10.319816589355469, 9.662688255310059] |
ac57006e-b4fc-4983-a700-ebe65023b7af | learning-with-noisy-labels-by-adaptive | 2306.04502 | null | https://arxiv.org/abs/2306.04502v2 | https://arxiv.org/pdf/2306.04502v2.pdf | Learning with Noisy Labels by Adaptive Gradient-Based Outlier Removal | An accurate and substantial dataset is essential for training a reliable and well-performing model. However, even manually annotated datasets contain label errors, not to mention automatically labeled ones. Previous methods for label denoising have primarily focused on detecting outliers and their permanent removal - a... | ['Benjamin Roth', 'Lena Zellinger', 'Anastasiia Sedova'] | 2023-06-07 | null | null | null | null | ['learning-with-noisy-labels', 'learning-with-noisy-labels'] | ['computer-vision', 'natural-language-processing'] | [ 2.59168595e-01 8.64034221e-02 7.21003637e-02 -6.55134559e-01
-9.69560683e-01 -3.68528783e-01 2.02837631e-01 7.94892132e-01
-5.52744329e-01 5.92606664e-01 9.92172360e-02 -7.33087510e-02
-8.93384293e-02 -4.54334736e-01 -5.80622733e-01 -8.11124027e-01
1.52315587e-01 5.28847337e-01 1.22372828e-01 2.53385454... | [9.403743743896484, 4.046433448791504] |
71e42050-6b2c-4a4e-8c09-4e4c580d9ae3 | diffuseir-diffusion-models-for-isotropic | 2306.12109 | null | https://arxiv.org/abs/2306.12109v1 | https://arxiv.org/pdf/2306.12109v1.pdf | DiffuseIR:Diffusion Models For Isotropic Reconstruction of 3D Microscopic Images | Three-dimensional microscopy is often limited by anisotropic spatial resolution, resulting in lower axial resolution than lateral resolution. Current State-of-The-Art (SoTA) isotropic reconstruction methods utilizing deep neural networks can achieve impressive super-resolution performance in fixed imaging settings. How... | ['Dawei Li', 'Shanghang Zhang', 'Aimin Wang', 'Jiaming Liu', 'Fangxu Zhou', 'Yulu Gan', 'Mingjie Pan'] | 2023-06-21 | null | null | null | null | ['super-resolution'] | ['computer-vision'] | [ 3.99364650e-01 -7.35265063e-03 3.74890655e-01 -2.77462870e-01
-7.30792522e-01 -4.00771558e-01 4.69188005e-01 -5.26241720e-01
-7.33818412e-01 9.49922800e-01 2.51868457e-01 6.28354326e-02
-3.29254627e-01 -6.65407896e-01 -6.03768766e-01 -1.40700984e+00
3.02775919e-01 6.84408426e-01 4.64111388e-01 3.10061932... | [12.870826721191406, -2.7210185527801514] |
66463241-f60f-4d38-a0e3-f0362b074f59 | speaker-recognition-in-realistic-scenario | 2302.13033 | null | https://arxiv.org/abs/2302.13033v1 | https://arxiv.org/pdf/2302.13033v1.pdf | Speaker Recognition in Realistic Scenario Using Multimodal Data | In recent years, an association is established between faces and voices of celebrities leveraging large scale audio-visual information from YouTube. The availability of large scale audio-visual datasets is instrumental in developing speaker recognition methods based on standard Convolutional Neural Networks. Thus, the ... | ['Muhammad Haroon Yousaf', 'Shah Nawaz', 'Muhammad Saad Saeed', 'Saqlain Hussain Shah'] | 2023-02-25 | null | null | null | null | ['speaker-recognition'] | ['speech'] | [-2.19474569e-01 -2.29809731e-01 6.94793314e-02 -5.54648340e-01
-8.55373144e-01 -4.84344125e-01 5.48557162e-01 -2.51238137e-01
-2.96477884e-01 4.37846661e-01 3.25981617e-01 2.24668160e-01
2.04101905e-01 -5.33169866e-01 -6.83109462e-01 -5.83451569e-01
-2.49693487e-02 -1.52836055e-01 -1.33169264e-01 -8.45073955... | [14.527854919433594, 4.929131507873535] |
06cf9073-fdc5-4079-a9db-fe91785b5a6c | graph-based-knowledge-distillation-a-survey | 2302.14643 | null | https://arxiv.org/abs/2302.14643v1 | https://arxiv.org/pdf/2302.14643v1.pdf | Graph-based Knowledge Distillation: A survey and experimental evaluation | Graph, such as citation networks, social networks, and transportation networks, are prevalent in the real world. Graph Neural Networks (GNNs) have gained widespread attention for their robust expressiveness and exceptional performance in various graph applications. However, the efficacy of GNNs is heavily reliant on su... | ['Qinfen Hao', 'Guanzheng Zhang', 'Tongya Zheng', 'Jing Liu'] | 2023-02-27 | null | null | null | null | ['self-knowledge-distillation'] | ['computer-vision'] | [-2.35950917e-01 6.98908925e-01 -6.32775664e-01 -2.05697790e-01
2.34973133e-01 -5.64192235e-01 3.48916203e-01 1.40736222e-01
-1.77899629e-01 1.02227998e+00 -2.67323673e-01 -6.99098766e-01
-5.97617686e-01 -1.23789096e+00 -5.15483618e-01 -6.43607378e-01
-9.55623239e-02 6.36364579e-01 1.90260515e-01 -1.67269096... | [7.310359477996826, 6.320670127868652] |
53378ef4-0a0f-4a57-ac80-7a57b655ca95 | ulip-learning-unified-representation-of | 2212.05171 | null | https://arxiv.org/abs/2212.05171v4 | https://arxiv.org/pdf/2212.05171v4.pdf | ULIP: Learning a Unified Representation of Language, Images, and Point Clouds for 3D Understanding | The recognition capabilities of current state-of-the-art 3D models are limited by datasets with a small number of annotated data and a pre-defined set of categories. In its 2D counterpart, recent advances have shown that similar problems can be significantly alleviated by employing knowledge from other modalities, such... | ['Silvio Savarese', 'Juan Carlos Niebles', 'ran Xu', 'Caiming Xiong', 'Jiajun Wu', 'Roberto Martín-Martín', 'Chen Xing', 'Mingfei Gao', 'Le Xue'] | 2022-12-10 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Gao_ULIP_Learning_a_Unified_Representation_of_Language_Images_and_Point_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Gao_ULIP_Learning_a_Unified_Representation_of_Language_Images_and_Point_CVPR_2023_paper.pdf | cvpr-2023-1 | ['training-free-3d-point-cloud-classification', '3d-point-cloud-classification', '3d-classification', 'zero-shot-transfer-3d-point-cloud'] | ['computer-vision', 'computer-vision', 'computer-vision', 'computer-vision'] | [-1.53782800e-01 -7.06996322e-02 -4.60327029e-01 -3.37736934e-01
-8.77781212e-01 -8.45612407e-01 9.90499616e-01 -2.34694764e-01
-7.84844086e-02 -1.52067155e-01 7.48270229e-02 -4.31943417e-01
2.41910607e-01 -5.78519940e-01 -9.29411769e-01 -2.70040840e-01
3.29064339e-01 8.17902386e-01 1.77228585e-01 -1.10932931... | [8.160085678100586, -3.319859027862549] |
b982c856-7fa3-4c88-ae7e-78cfa7a844a7 | improved-soccer-action-spotting-using-both | 2011.04258 | null | https://arxiv.org/abs/2011.04258v1 | https://arxiv.org/pdf/2011.04258v1.pdf | Improved Soccer Action Spotting using both Audio and Video Streams | In this paper, we propose a study on multi-modal (audio and video) action spotting and classification in soccer videos. Action spotting and classification are the tasks that consist in finding the temporal anchors of events in a video and determine which event they are. This is an important application of general activ... | ['Stéphane Dupont', 'Bastien Vanderplaetse'] | 2020-11-09 | null | null | null | null | ['action-spotting'] | ['computer-vision'] | [ 2.12907955e-01 -4.33623433e-01 -2.06608519e-01 -2.53617078e-01
-1.05243099e+00 -3.62092763e-01 3.15235883e-01 1.75787598e-01
-8.47223461e-01 6.32586598e-01 2.73579180e-01 3.71230572e-01
-1.73282757e-01 -5.45528352e-01 -9.66162264e-01 -5.58851361e-01
-4.30496961e-01 3.57530192e-02 6.41712904e-01 -2.21135467... | [7.994211673736572, 0.18361078202724457] |
70cbbf19-a9d9-491d-9236-adbee81f628f | memory-augmented-neural-networks-for | 1802.00938 | null | https://arxiv.org/abs/1802.00938v2 | https://arxiv.org/pdf/1802.00938v2.pdf | DeepProcess: Supporting business process execution using a MANN-based recommender system | Process-aware Recommender systems can provide critical decision support functionality to aid business process execution by recommending what actions to take next. Based on recent advances in the field of deep learning, we present a novel memory-augmented neural network (MANN) based approach for constructing a process-a... | ['Hoa Dam', 'Aditya Ghose', 'Truyen Tran', 'Asjad Khan', 'Renuka Sindhgatta', 'Kien Do', 'Hung Le'] | 2018-02-03 | null | null | null | null | ['activity-prediction', 'activity-prediction'] | ['computer-vision', 'time-series'] | [ 5.06236315e-01 -7.42267370e-02 -5.45266628e-01 -4.06760126e-01
-2.64860749e-01 -4.52777475e-01 9.08032060e-01 -2.19855160e-02
-8.54819715e-02 1.12207204e-01 6.69231296e-01 -1.10742438e+00
-4.52659965e-01 -8.74279916e-01 -5.99491119e-01 -1.71386585e-01
1.18032888e-01 7.48778701e-01 1.84551433e-01 -3.35489601... | [8.613099098205566, 5.933106422424316] |
2dc41d06-04e5-4fba-be94-09c6db651bb5 | openldn-learning-to-discover-novel-classes | 2207.02261 | null | https://arxiv.org/abs/2207.02261v2 | https://arxiv.org/pdf/2207.02261v2.pdf | OpenLDN: Learning to Discover Novel Classes for Open-World Semi-Supervised Learning | Semi-supervised learning (SSL) is one of the dominant approaches to address the annotation bottleneck of supervised learning. Recent SSL methods can effectively leverage a large repository of unlabeled data to improve performance while relying on a small set of labeled data. One common assumption in most SSL methods is... | ['Mubarak Shah', 'Fahad Shahbaz Khan', 'Salman Khan', 'Navid Kardan', 'Mamshad Nayeem Rizve'] | 2022-07-05 | null | null | null | null | ['open-world-semi-supervised-learning'] | ['computer-vision'] | [ 2.94987738e-01 1.62275031e-01 -7.33104706e-01 -5.58924854e-01
-1.07626700e+00 -7.84972668e-01 3.24136555e-01 3.71626168e-01
-3.99274647e-01 8.39156985e-01 -2.44019002e-01 -6.38238620e-04
-1.52842104e-02 -4.70455468e-01 -6.30508900e-01 -8.27355683e-01
2.76062727e-01 6.63407385e-01 1.83299929e-01 3.51785541... | [9.52745246887207, 3.4814610481262207] |
9228c627-2499-4553-b016-65005dcaf844 | recall-and-learn-a-memory-augmented-solver | 2109.13112 | null | https://arxiv.org/abs/2109.13112v1 | https://arxiv.org/pdf/2109.13112v1.pdf | Recall and Learn: A Memory-augmented Solver for Math Word Problems | In this article, we tackle the math word problem, namely, automatically answering a mathematical problem according to its textual description. Although recent methods have demonstrated their promising results, most of these methods are based on template-based generation scheme which results in limited generalization ca... | ['Ming Yang', 'Da Cao', 'Jiao Xu', 'Jiawei Wang', 'Shifeng Huang'] | 2021-09-27 | null | https://aclanthology.org/2021.findings-emnlp.68 | https://aclanthology.org/2021.findings-emnlp.68.pdf | findings-emnlp-2021-11 | ['math-word-problem-solving', 'math-word-problem-solving', 'math-word-problem-solving'] | ['knowledge-base', 'reasoning', 'time-series'] | [ 2.54059523e-01 -3.23174242e-03 8.55070353e-02 -2.19158292e-01
-5.88721156e-01 -3.69259268e-01 7.50390172e-01 5.11727393e-01
-2.86092311e-01 6.13862813e-01 9.38549638e-02 -2.41173789e-01
-5.15904009e-01 -1.35527742e+00 -6.14170849e-01 -2.16074154e-01
5.54649711e-01 4.28675264e-01 3.73310000e-01 -3.88301015... | [10.755148887634277, 8.050178527832031] |
d3851193-e3a6-455e-b9ef-e8366926520b | implicit-neural-deformation-for-multi-view | 2112.02494 | null | https://arxiv.org/abs/2112.02494v2 | https://arxiv.org/pdf/2112.02494v2.pdf | Implicit Neural Deformation for Sparse-View Face Reconstruction | In this work, we present a new method for 3D face reconstruction from sparse-view RGB images. Unlike previous methods which are built upon 3D morphable models (3DMMs) with limited details, we leverage an implicit representation to encode rich geometric features. Our overall pipeline consists of two major components, in... | ['Chongyang Ma', 'Nong Sang', 'Mengtian Li', 'Yi Zheng', 'Haibin Huang', 'Moran Li'] | 2021-12-05 | null | null | null | null | ['3d-face-reconstruction', 'face-reconstruction'] | ['computer-vision', 'computer-vision'] | [ 1.49630249e-01 2.33790115e-01 -5.16049610e-03 -7.90060580e-01
-7.06037104e-01 -4.36422259e-01 4.34214413e-01 -8.15898776e-01
3.16792130e-01 2.02042297e-01 1.94145203e-01 1.29261002e-01
3.46067518e-01 -7.50063896e-01 -9.69018519e-01 -4.00771052e-01
1.38195664e-01 6.69761896e-01 -2.40203664e-01 6.71886094... | [13.075584411621094, -0.0381205715239048] |
24f19759-753f-483d-90e7-b89da22b51f7 | explaining-deep-neural-networks-using | 1908.02374 | null | https://arxiv.org/abs/1908.02374v2 | https://arxiv.org/pdf/1908.02374v2.pdf | Explaining Image Classifiers using Statistical Fault Localization | The black-box nature of deep neural networks (DNNs) makes it impossible to understand why a particular output is produced, creating demand for "Explainable AI". In this paper, we show that statistical fault localization (SFL) techniques from software engineering deliver high quality explanations of the outputs of DNNs,... | ['Xiaowei Huang', 'Youcheng Sun', 'Daniel Kroening', 'Hana Chockler'] | 2019-08-06 | null | null | null | null | ['fault-localization'] | ['computer-code'] | [ 2.01632068e-01 7.34738171e-01 -8.82918164e-02 -6.57413304e-01
-3.16379368e-01 -4.58576173e-01 5.12685955e-01 -1.56670317e-01
4.06207323e-01 7.66443372e-01 1.36257425e-01 -5.68735242e-01
-4.92066890e-01 -6.14530504e-01 -1.01911390e+00 -3.83108348e-01
1.05537362e-02 3.83103162e-01 2.51162708e-01 4.11634780... | [8.931354522705078, 5.75799560546875] |
fe77dd06-0831-40ab-a679-6458bf97298c | robustness-of-on-device-models-adversarial | 2101.04401 | null | https://arxiv.org/abs/2101.04401v2 | https://arxiv.org/pdf/2101.04401v2.pdf | Robustness of on-device Models: Adversarial Attack to Deep Learning Models on Android Apps | Deep learning has shown its power in many applications, including object detection in images, natural-language understanding, and speech recognition. To make it more accessible to end users, many deep learning models are now embedded in mobile apps. Compared to offloading deep learning from smartphones to the cloud, pe... | ['Chunyang Chen', 'Han Hu', 'Yujin Huang'] | 2021-01-12 | null | null | null | null | ['mobile-security'] | ['miscellaneous'] | [-3.01263072e-02 -5.02108708e-02 -4.27692115e-01 -6.81108050e-03
-5.08467019e-01 -9.71510947e-01 3.04253697e-01 -6.92516983e-01
-9.54543203e-02 4.34160471e-01 -4.77457106e-01 -9.51099813e-01
2.37716600e-01 -5.22707224e-01 -1.23589540e+00 -1.21438861e-01
-1.41958028e-01 4.26046811e-02 3.94628733e-01 -2.23134439... | [14.419920921325684, 9.679676055908203] |
147f3dac-3512-466d-bc50-df4ecb0ac21b | a-short-survey-of-systematic-generalization | 2211.11956 | null | https://arxiv.org/abs/2211.11956v1 | https://arxiv.org/pdf/2211.11956v1.pdf | A Short Survey of Systematic Generalization | This survey includes systematic generalization and a history of how machine learning addresses it. We aim to summarize and organize the related information of both conventional and recent improvements. We first look at the definition of systematic generalization, then introduce Classicist and Connectionist. We then dis... | ['Yuanpeng Li'] | 2022-11-22 | null | null | null | null | ['systematic-generalization'] | ['reasoning'] | [ 1.63289979e-01 4.17661704e-02 -8.02839935e-01 -3.73854518e-01
-1.37565523e-01 -6.18901491e-01 5.86434722e-01 -3.91302519e-02
-2.77905554e-01 9.96285081e-01 7.91574121e-02 -4.83177006e-01
-5.68515897e-01 -8.04526806e-01 -3.62733990e-01 -6.71590149e-01
-3.95266235e-01 1.87127918e-01 1.47748343e-03 -6.92472219... | [9.358468055725098, 6.471621990203857] |
aa06379c-0e90-4ceb-8018-72100e92508d | protein-structure-prediction-until-casp15 | 2212.07702 | null | https://arxiv.org/abs/2212.07702v1 | https://arxiv.org/pdf/2212.07702v1.pdf | Protein Structure Prediction until CASP15 | In Dec 2020, the results of AlphaFold2 were presented at CASP14, sparking a revolution in the field of protein structure predictions. For the first time, a purely computational method could challenge experimental accuracy for structure prediction of single protein domains. The code of AlphaFold2 was released in the sum... | ['Arne Elofsson'] | 2022-12-15 | null | null | null | null | ['protein-design'] | ['medical'] | [ 2.25370646e-01 4.33970720e-01 -1.30708799e-01 -2.94010013e-01
-3.82377267e-01 -6.65133059e-01 3.17046076e-01 5.42127490e-01
-1.20890290e-01 1.25396609e+00 2.14345098e-01 -5.77253997e-01
1.05150163e-01 -3.40104073e-01 -7.19233811e-01 -7.51014352e-01
-8.14448074e-02 7.49189377e-01 3.84309828e-01 -3.59478533... | [4.720815181732178, 5.364953994750977] |
252505ce-b4c0-47a9-b523-c37f6b27fc58 | flipnerf-flipped-reflection-rays-for-few-shot | 2306.17723 | null | https://arxiv.org/abs/2306.17723v1 | https://arxiv.org/pdf/2306.17723v1.pdf | FlipNeRF: Flipped Reflection Rays for Few-shot Novel View Synthesis | Neural Radiance Field (NeRF) has been a mainstream in novel view synthesis with its remarkable quality of rendered images and simple architecture. Although NeRF has been developed in various directions improving continuously its performance, the necessity of a dense set of multi-view images still exists as a stumbling ... | ['Nojun Kwak', 'Yeonjin Chang', 'Seunghyeon Seo'] | 2023-06-30 | null | null | null | null | ['depth-estimation', 'novel-view-synthesis'] | ['computer-vision', 'computer-vision'] | [ 6.58392832e-02 -2.77886748e-01 1.23797692e-01 -5.04642963e-01
-8.66104484e-01 -2.12024823e-01 5.02383113e-01 -4.18730319e-01
-1.65574417e-01 7.01814413e-01 2.66761929e-01 2.12279588e-01
-1.90751731e-01 -1.04890668e+00 -1.01694536e+00 -9.45259154e-01
4.47675228e-01 3.50423336e-01 2.59615034e-01 -2.15623140... | [9.28121566772461, -2.749241352081299] |
3f972feb-a227-4909-943a-8085e159ff10 | towards-accurate-and-reliable-change | 2305.19513 | null | https://arxiv.org/abs/2305.19513v1 | https://arxiv.org/pdf/2305.19513v1.pdf | Towards Accurate and Reliable Change Detection of Remote Sensing Images via Knowledge Review and Online Uncertainty Estimation | Change detection (CD) is an essential task for various real-world applications, such as urban management and disaster assessment. However, previous methods primarily focus on improving the accuracy of CD, while neglecting the reliability of detection results. In this paper, we propose a novel change detection network, ... | ['Xinzhong Zhu', 'Kun Sun', 'Weiying Xie', 'Xianju Li', 'Chang Tang', 'Zhenglai Li'] | 2023-05-31 | null | null | null | null | ['change-detection'] | ['computer-vision'] | [-1.05938047e-01 -2.29512319e-01 2.58819722e-02 -4.86504734e-01
-9.75786924e-01 -3.38140279e-01 7.35942364e-01 1.91599563e-01
-2.40685388e-01 9.72133815e-01 8.59494880e-02 -2.14297637e-01
-1.33037835e-01 -1.26049638e+00 -6.19388878e-01 -7.60004103e-01
-1.20659374e-01 -2.17793174e-02 4.74732727e-01 -1.24938890... | [9.672961235046387, -1.2651631832122803] |
3dd15130-801c-416c-9d3d-b15a542a5ea8 | intra-inter-interaction-network-with-latent | null | null | https://aclanthology.org/2020.coling-main.437 | https://aclanthology.org/2020.coling-main.437.pdf | Intra-/Inter-Interaction Network with Latent Interaction Modeling for Multi-turn Response Selection | Multi-turn response selection has been extensively studied and applied to many real-world applications in recent years. However, current methods typically model the interactions between multi-turn utterances and candidate responses with iterative approaches, which is not practical as the turns of conversations vary. Be... | ['Wai Lam', 'Wenxuan Zhang', 'Yang Deng'] | 2020-12-01 | null | null | null | coling-2020-8 | ['multi-view-subspace-clustering'] | ['computer-vision'] | [ 2.48317257e-01 -3.26257437e-01 -1.85089424e-01 -9.55638945e-01
-1.09535015e+00 -4.93179440e-01 5.18219471e-01 -2.55951226e-01
4.99425232e-02 2.39120632e-01 8.39574099e-01 5.32712787e-02
-2.06745695e-02 -1.92257524e-01 4.97890525e-02 -7.20347524e-01
4.68986213e-01 5.76135933e-01 -8.56538117e-02 -3.57908845... | [12.612022399902344, 7.583490371704102] |
eb19b8d9-3906-42c5-8ecc-24b4cdee82d5 | stem-seg-spatio-temporal-embeddings-for | 2003.08429 | null | https://arxiv.org/abs/2003.08429v3 | https://arxiv.org/pdf/2003.08429v3.pdf | STEm-Seg: Spatio-temporal Embeddings for Instance Segmentation in Videos | Existing methods for instance segmentation in videos typi-cally involve multi-stage pipelines that follow the tracking-by-detectionparadigm and model a video clip as a sequence of images. Multiple net-works are used to detect objects in individual frames, and then associatethese detections over time. Hence, these metho... | ['Laura Leal-Taixé', 'Aljoša Ošep', 'Sabarinath Mahadevan', 'Bastian Leibe', 'Ali Athar'] | 2020-03-18 | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/1299_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123560154.pdf | eccv-2020-8 | ['video-instance-segmentation', 'unsupervised-video-object-segmentation'] | ['computer-vision', 'computer-vision'] | [-1.72219917e-01 -2.55964369e-01 -4.05273825e-01 -2.62712985e-01
-9.23099697e-01 -7.15201616e-01 8.24099004e-01 1.49151489e-01
-6.85927570e-01 2.37517208e-01 -7.66035020e-02 -4.28880826e-02
6.68233782e-02 -5.46140373e-01 -1.06216168e+00 -6.29825234e-01
-2.38039777e-01 3.80869299e-01 6.82414830e-01 3.30618501... | [9.10243034362793, -0.1208840012550354] |
21b91d90-715d-4ee8-b40c-78acea8d877c | dense-captioning-with-joint-inference-and | 1611.06949 | null | http://arxiv.org/abs/1611.06949v2 | http://arxiv.org/pdf/1611.06949v2.pdf | Dense Captioning with Joint Inference and Visual Context | Dense captioning is a newly emerging computer vision topic for understanding
images with dense language descriptions. The goal is to densely detect visual
concepts (e.g., objects, object parts, and interactions between them) from
images, labeling each with a short descriptive phrase. We identify two key
challenges of d... | ['Li-Jia Li', 'Kevin Tang', 'Linjie Yang', 'Jianchao Yang'] | 2016-11-21 | dense-captioning-with-joint-inference-and-1 | http://openaccess.thecvf.com/content_cvpr_2017/html/Yang_Dense_Captioning_With_CVPR_2017_paper.html | http://openaccess.thecvf.com/content_cvpr_2017/papers/Yang_Dense_Captioning_With_CVPR_2017_paper.pdf | cvpr-2017-7 | ['dense-captioning'] | ['computer-vision'] | [ 1.79888129e-01 4.45485413e-01 -9.10429657e-02 -3.12480122e-01
-8.19177568e-01 -6.90821052e-01 6.26842439e-01 2.01130629e-01
-1.36478737e-01 5.23812354e-01 4.70461458e-01 2.52672639e-02
5.19145668e-01 -1.68135509e-01 -1.03298402e+00 -5.49599648e-01
2.41713032e-01 7.84247756e-01 3.01501364e-01 3.89821455... | [10.466954231262207, 1.4386290311813354] |
644ac3f6-1813-4787-a401-8689269bc014 | persformer-3d-lane-detection-via-perspective-1 | 2203.11089 | null | https://arxiv.org/abs/2203.11089v3 | https://arxiv.org/pdf/2203.11089v3.pdf | PersFormer: 3D Lane Detection via Perspective Transformer and the OpenLane Benchmark | Methods for 3D lane detection have been recently proposed to address the issue of inaccurate lane layouts in many autonomous driving scenarios (uphill/downhill, bump, etc.). Previous work struggled in complex cases due to their simple designs of the spatial transformation between front view and bird's eye view (BEV) an... | ['Junchi Yan', 'Yu Qiao', 'Jianping Shi', 'Conghui He', 'Hongyang Li', 'Xiangwei Geng', 'Jiajie Xu', 'Zehan Zheng', 'Yang Li', 'Chonghao Sima', 'Li Chen'] | 2022-03-21 | persformer-3d-lane-detection-via-perspective | https://arxiv.org/abs/2203.11089 | https://arxiv.org/pdf/2203.11089.pdf | null | ['3d-lane-detection', 'lane-detection'] | ['computer-vision', 'computer-vision'] | [-3.99272770e-01 -2.20657140e-01 -1.84956536e-01 -6.16019607e-01
-1.07589257e+00 -8.40832651e-01 6.96997762e-01 -3.80639195e-01
-3.13464254e-01 3.71031821e-01 2.03620613e-01 -6.76563501e-01
2.48233989e-01 -4.63426650e-01 -7.60590255e-01 -4.82998520e-01
-6.81660101e-02 4.31113839e-01 8.12458813e-01 -5.38335681... | [7.89096736907959, -1.7657333612442017] |
c1b93a93-df8d-40d5-9e37-7733687ce483 | a-semi-supervised-approach-to-message-stance | 1902.03097 | null | http://arxiv.org/abs/1902.03097v1 | http://arxiv.org/pdf/1902.03097v1.pdf | A semi-supervised approach to message stance classification | Social media communications are becoming increasingly prevalent; some useful,
some false, whether unwittingly or maliciously. An increasing number of rumours
daily flood the social networks. Determining their veracity in an autonomous
way is a very active and challenging field of research, with a variety of
methods pro... | ['Nikolaos Kaplis', 'Ioannis Agrafiotis', 'Jason R. C. Nurse', 'Georgios Giasemidis'] | 2019-01-29 | null | null | null | null | ['rumour-detection'] | ['natural-language-processing'] | [-2.07911972e-02 2.53491044e-01 -4.81631309e-01 -3.20818543e-01
-1.93722039e-01 -5.06947756e-01 1.08709693e+00 6.80133104e-01
-3.17135155e-01 1.04690170e+00 3.42351258e-01 -2.87049085e-01
1.06814438e-02 -1.02212119e+00 -1.57383144e-01 -5.33508718e-01
-4.21491474e-01 7.51511693e-01 7.12731898e-01 -6.51912928... | [8.258944511413574, 10.15349292755127] |
37d71184-b012-47f6-8621-6bc19997083d | 190503415 | 1905.03415 | null | https://arxiv.org/abs/1905.03415v2 | https://arxiv.org/pdf/1905.03415v2.pdf | PPGNet: Learning Point-Pair Graph for Line Segment Detection | In this paper, we present a novel framework to detect line segments in man-made environments. Specifically, we propose to describe junctions, line segments and relationships between them with a simple graph, which is more structured and informative than end-point representation used in existing line segment detection m... | ['Zhengxin Li', 'Ziheng Zhang', 'Ning Bi', 'Kun Huang', 'Shenghua Gao', 'Jinlei Wang', 'Jia Zheng', 'Yanyu Xu', 'Weixin Luo'] | 2019-05-09 | ppgnet-learning-point-pair-graph-for-line | http://openaccess.thecvf.com/content_CVPR_2019/html/Zhang_PPGNet_Learning_Point-Pair_Graph_for_Line_Segment_Detection_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Zhang_PPGNet_Learning_Point-Pair_Graph_for_Line_Segment_Detection_CVPR_2019_paper.pdf | cvpr-2019-6 | ['line-segment-detection'] | ['computer-vision'] | [-9.39604566e-02 3.63997743e-02 -2.37014845e-01 -3.69145781e-01
-4.38893676e-01 -6.83903813e-01 4.12102997e-01 8.40308368e-02
-1.15379365e-02 4.50285137e-01 -1.87928334e-01 -5.81248522e-01
2.16627583e-01 -9.75999475e-01 -8.99400532e-01 6.92153582e-03
-2.49120548e-01 5.44641092e-02 7.42931306e-01 -3.80314767... | [8.26313591003418, -1.7037243843078613] |
7fff1a37-2230-4b50-b4f1-85a891065645 | modeling-complex-systems-a-case-study-of | 2110.02947 | null | https://arxiv.org/abs/2110.02947v2 | https://arxiv.org/pdf/2110.02947v2.pdf | Modeling complex systems: A case study of compartmental models in epidemiology | Compartmental epidemic models have been widely used for predicting the course of epidemics, from estimating the basic reproduction number to guiding intervention policies. Studies commonly acknowledge these models' assumptions but less often justify their validity in the specific context in which they are being used. O... | ['Alexander F. Siegenfeld', 'Yaneer Bar-Yam', 'Pratyush K. Kollepara'] | 2021-10-06 | null | null | null | null | ['epidemiology'] | ['medical'] | [-1.88206714e-02 -3.86933744e-01 -3.19760054e-01 2.01059073e-01
1.42620057e-01 -4.47710752e-01 7.32026458e-01 2.00296968e-01
-6.60026610e-01 8.52520645e-01 4.31551524e-02 -1.03212726e+00
-5.48895717e-01 -7.16234565e-01 -4.56350148e-02 -6.70459986e-01
-5.20669460e-01 6.65535629e-01 2.93275088e-01 -5.21979392... | [5.968226432800293, 4.426397800445557] |
3d3499c6-1e0d-4fed-81c3-b2b4de99c158 | wbcatt-a-white-blood-cell-dataset-annotated | 2306.13531 | null | https://arxiv.org/abs/2306.13531v1 | https://arxiv.org/pdf/2306.13531v1.pdf | WBCAtt: A White Blood Cell Dataset Annotated with Detailed Morphological Attributes | The examination of blood samples at a microscopic level plays a fundamental role in clinical diagnostics, influencing a wide range of medical conditions. For instance, an in-depth study of White Blood Cells (WBCs), a crucial component of our blood, is essential for diagnosing blood-related diseases such as leukemia and... | ['Bihan Wen', 'Winnie Pang', 'Satoshi Tsutsui'] | 2023-06-23 | null | null | null | null | ['explainable-artificial-intelligence'] | ['computer-vision'] | [ 1.71491206e-01 7.81639591e-02 -1.94144070e-01 -4.49583858e-01
-3.14929307e-01 -4.24467891e-01 3.04011762e-01 1.05429649e+00
-1.73830286e-01 8.49929154e-01 2.95586318e-01 -3.12329143e-01
-7.59185702e-02 -8.58989954e-01 1.52752372e-02 -1.05637801e+00
1.93782151e-01 1.05925548e+00 -2.12439477e-01 2.17372239... | [15.03522777557373, -3.0780580043792725] |
4728f9e2-6a66-45d4-b5fc-529b98a6daf8 | pareidolia-face-reenactment | null | null | http://openaccess.thecvf.com//content/CVPR2021/html/Song_Pareidolia_Face_Reenactment_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Song_Pareidolia_Face_Reenactment_CVPR_2021_paper.pdf | Pareidolia Face Reenactment | We present a new application direction named Pareidolia Face Reenactment, which is defined as animating a static illusory face to move in tandem with a human face in the video. For the large differences between pareidolia face reenactment and traditional human face reenactment, two main challenges are introduced, i... | ['Ran He', 'Chen Change Loy', 'Chen Qian', 'Chaoyou Fu', 'Wayne Wu', 'Linsen Song'] | 2021-06-19 | null | null | null | cvpr-2021-1 | ['texture-synthesis', 'face-reenactment'] | ['computer-vision', 'computer-vision'] | [ 2.05108017e-01 1.31747931e-01 1.86470687e-01 -4.08236049e-02
-3.14863712e-01 -4.81826156e-01 6.28062606e-01 -1.09579360e+00
2.32788578e-01 4.05873358e-01 3.70001167e-01 1.80298463e-01
2.32426301e-02 -5.88486075e-01 -5.64751267e-01 -8.57085407e-01
4.48143125e-01 4.97970343e-01 -2.76178587e-02 -1.36139140... | [12.724838256835938, -0.3030398488044739] |
c6dc0d99-ce0c-4d93-a2f1-6b9028550c20 | medmcqa-a-large-scale-multi-subject-multi | 2203.14371 | null | https://arxiv.org/abs/2203.14371v1 | https://arxiv.org/pdf/2203.14371v1.pdf | MedMCQA : A Large-scale Multi-Subject Multi-Choice Dataset for Medical domain Question Answering | This paper introduces MedMCQA, a new large-scale, Multiple-Choice Question Answering (MCQA) dataset designed to address real-world medical entrance exam questions. More than 194k high-quality AIIMS \& NEET PG entrance exam MCQs covering 2.4k healthcare topics and 21 medical subjects are collected with an average token ... | ['Malaikannan Sankarasubbu', 'Logesh Kumar Umapathi', 'Ankit Pal'] | 2022-03-27 | null | null | null | null | ['multiple-choice-qa'] | ['natural-language-processing'] | [-1.43039629e-01 3.83939475e-01 -4.00145471e-01 -4.47699606e-01
-1.44931519e+00 -7.70499051e-01 -1.82810381e-01 6.23792112e-01
-5.79736888e-01 9.80984032e-01 5.14383614e-01 -8.94556582e-01
-9.95214283e-01 -7.02140629e-01 -3.52616072e-01 -1.16033636e-01
2.16786250e-01 8.39253664e-01 2.63240397e-01 -4.63157862... | [8.875441551208496, 8.513313293457031] |
11db914b-3e7c-4fc7-af53-dd4ea61e279e | self-supervised-auxiliary-learning-for-graph | 2103.00771 | null | https://arxiv.org/abs/2103.00771v2 | https://arxiv.org/pdf/2103.00771v2.pdf | Self-supervised Auxiliary Learning for Graph Neural Networks via Meta-Learning | In recent years, graph neural networks (GNNs) have been widely adopted in the representation learning of graph-structured data and provided state-of-the-art performance in various applications such as link prediction, node classification, and recommendation. Motivated by recent advances of self-supervision for represen... | ['Hyunwoo J. Kim', 'Jung-Woo Ha', 'Kyung-Min Kim', 'Sunyoung Kwon', 'Jinyoung Park', 'Dasol Hwang'] | 2021-03-01 | null | null | null | null | ['auxiliary-learning'] | ['methodology'] | [ 4.67448682e-01 5.43663502e-01 -7.68382192e-01 -3.41421902e-01
-1.61843628e-01 -1.88637882e-01 5.44600487e-01 5.83659053e-01
-1.51390687e-01 7.27971554e-01 -1.26319289e-01 -5.52955091e-01
-2.34192640e-01 -1.08520961e+00 -5.88430643e-01 -6.40028715e-01
-1.65907145e-01 5.35427988e-01 3.97724360e-01 -3.28696400... | [7.35932731628418, 6.279865264892578] |
76ac5c08-943b-424c-b486-ebd3a3f62d54 | uncertainty-aware-cross-lingual-transfer-with | null | null | https://aclanthology.org/2022.findings-naacl.153 | https://aclanthology.org/2022.findings-naacl.153.pdf | Uncertainty-Aware Cross-Lingual Transfer with Pseudo Partial Labels | Large-scale multilingual pre-trained language models have achieved remarkable performance in zero-shot cross-lingual tasks. A recent study has demonstrated the effectiveness of self-learning-based approach on cross-lingual transfer, where only unlabeled data of target languages are required, without any efforts to anno... | ['Chang-Tien Lu', 'Fanglan Chen', 'Jianfeng He', 'Xuchao Zhang', 'Shuo Lei'] | null | null | null | null | findings-naacl-2022-7 | ['self-learning'] | ['natural-language-processing'] | [-2.98171878e-01 -1.04290366e-01 -5.07672429e-01 -7.79974937e-01
-1.61909902e+00 -5.00623882e-01 6.14776909e-01 -2.31064186e-01
-9.37874138e-01 1.25914729e+00 1.65072605e-01 -2.17095166e-01
5.24647534e-01 -3.66531760e-01 -8.23585093e-01 -4.02154952e-01
2.10742384e-01 7.11358130e-01 6.94584772e-02 4.62146476... | [10.033404350280762, 9.713398933410645] |
c36f9985-8bac-43f7-9bcb-07067781d68c | dfraud3-multi-component-fraud-detection | 2006.05718 | null | https://arxiv.org/abs/2006.05718v2 | https://arxiv.org/pdf/2006.05718v2.pdf | DFraud3- Multi-Component Fraud Detection freeof Cold-start | Fraud review detection is a hot research topic inrecent years. The Cold-start is a particularly new but significant problem referring to the failure of a detection system to recognize the authenticity of a new user. State-of-the-art solutions employ a translational knowledge graph embedding approach (TransE) to model t... | ['Mohammed Bennamoun', 'Wei Liu', 'Roberto Togneri', 'Saeedreza Shehnepoor'] | 2020-06-10 | null | null | null | null | ['component-classification'] | ['natural-language-processing'] | [-2.93988198e-01 2.59150356e-01 -3.59976143e-01 1.55802563e-01
-2.36631081e-01 -3.28116864e-01 6.41116619e-01 5.09428382e-01
-1.68956071e-01 5.49661279e-01 -1.48331314e-01 -3.34113181e-01
-1.85196966e-01 -1.00421190e+00 -5.20476818e-01 -2.96085984e-01
-1.73853606e-01 5.05608261e-01 3.00779771e-02 -4.18709427... | [6.888189315795898, 5.875773906707764] |
c7226876-cd0e-4dab-a1a0-6aecc142f172 | seeing-the-pose-in-the-pixels-learning-pose | 2306.09331 | null | https://arxiv.org/abs/2306.09331v1 | https://arxiv.org/pdf/2306.09331v1.pdf | Seeing the Pose in the Pixels: Learning Pose-Aware Representations in Vision Transformers | Human perception of surroundings is often guided by the various poses present within the environment. Many computer vision tasks, such as human action recognition and robot imitation learning, rely on pose-based entities like human skeletons or robotic arms. However, conventional Vision Transformer (ViT) models uniform... | ['Srijan Das', 'Aman Chadha', 'Dominick Reilly'] | 2023-06-15 | null | null | null | null | ['pose-prediction', 'action-recognition-in-videos', 'video-alignment', 'action-recognition', 'imitation-learning'] | ['computer-vision', 'computer-vision', 'computer-vision', 'computer-vision', 'methodology'] | [ 2.50462055e-01 9.02287290e-02 -1.68446422e-01 -2.68446237e-01
-7.69437492e-01 -6.05570734e-01 8.36806774e-01 -3.65990996e-01
-3.67072582e-01 4.27222878e-01 3.44244242e-01 4.75945659e-02
-5.17407581e-02 -3.53649706e-01 -1.10746324e+00 -7.54688501e-01
2.59390414e-01 4.25675184e-01 3.63916427e-01 -2.54970223... | [7.905465126037598, 0.16718408465385437] |
1ae76daa-8278-49cb-853f-c7ce4115ad20 | from-chatgpt-to-threatgpt-impact-of | 2307.00691 | null | https://arxiv.org/abs/2307.00691v1 | https://arxiv.org/pdf/2307.00691v1.pdf | From ChatGPT to ThreatGPT: Impact of Generative AI in Cybersecurity and Privacy | Undoubtedly, the evolution of Generative AI (GenAI) models has been the highlight of digital transformation in the year 2022. As the different GenAI models like ChatGPT and Google Bard continue to foster their complexity and capability, it's critical to understand its consequences from a cybersecurity perspective. Seve... | ['Lopamudra Praharaj', 'Eli Parker', 'Kshitiz Aryal', 'CharanKumar Akiri', 'Maanak Gupta'] | 2023-07-03 | null | null | null | null | ['code-generation', 'malware-detection'] | ['computer-code', 'miscellaneous'] | [ 3.89909409e-02 2.68197298e-01 1.65862422e-02 2.08821163e-01
-8.83343294e-02 -1.41166270e+00 7.23586440e-01 -2.55291294e-02
-1.84165016e-02 5.55433393e-01 1.04537919e-01 -1.05028236e+00
-2.74201095e-01 -8.97646546e-01 -3.40411603e-01 -3.85702670e-01
-1.28262147e-01 1.12310208e-01 -3.90884668e-01 -3.68585169... | [6.20848274230957, 7.782863616943359] |
fdb86f85-93c6-46d2-8abd-fd2310eb3d71 | cross-linked-variational-autoencoders-for | null | null | https://openreview.net/forum?id=BkghJoRNO4 | https://openreview.net/pdf?id=BkghJoRNO4 | Cross-Linked Variational Autoencoders for Generalized Zero-Shot Learning | Most approaches in generalized zero-shot learning rely on cross-modal mapping between an image feature space and a class embedding space or on generating artificial image features. However, learning a shared cross-modal embedding by aligning the latent spaces of modality-specific autoencoders is shown to be promising i... | ['Zeynep Akata', 'Trevor Darrell', 'Samarth Sinha', 'Sayna Ebrahimi', 'Edgar Schönfeld'] | 2019-03-24 | null | null | null | iclr-workshop-lld-2019 | ['generalized-zero-shot-learning', 'generalized-zero-shot-learning'] | ['computer-vision', 'methodology'] | [ 2.01065570e-01 2.50340879e-01 -5.63949823e-01 -4.42252100e-01
-9.43336308e-01 -1.36877477e-01 1.11455452e+00 -2.64024287e-01
-2.91623205e-01 4.62665975e-01 6.15846336e-01 3.92443001e-01
-1.00111291e-01 -8.80332112e-01 -8.84436011e-01 -8.67261350e-01
1.39618859e-01 2.56217539e-01 1.04075456e-02 -1.65597573... | [10.122066497802734, 2.3482532501220703] |
6ad43eae-cca4-4490-b5c3-576152233eb7 | classification-of-luminal-subtypes-in-full | 2301.09282 | null | https://arxiv.org/abs/2301.09282v1 | https://arxiv.org/pdf/2301.09282v1.pdf | Classification of Luminal Subtypes in Full Mammogram Images Using Transfer Learning | Automatic identification of patients with luminal and non-luminal subtypes during a routine mammography screening can support clinicians in streamlining breast cancer therapy planning. Recent machine learning techniques have shown promising results in molecular subtype classification in mammography; however, they are h... | ['Andreas Maier', 'Prathmesh Madhu', 'Adarsh Bhandary Panambur'] | 2023-01-23 | null | null | null | null | ['breast-cancer-detection', 'breast-cancer-detection'] | ['knowledge-base', 'medical'] | [ 7.83175886e-01 6.23222113e-01 -1.01514423e+00 -8.30269575e-01
-1.42698038e+00 -2.95856804e-01 5.62963963e-01 6.47158265e-01
-4.24434304e-01 7.30877340e-01 2.72077739e-01 -9.14875627e-01
-2.14194655e-01 -7.54298389e-01 -6.90123737e-01 -8.79118800e-01
-9.36924666e-02 5.99700034e-01 1.73074976e-01 1.32558629... | [15.208754539489746, -2.606182336807251] |
e6835e5b-b2c1-4f8b-b1fe-d0a6febf3fa9 | sensitive-data-detection-with-high-throughput-1 | 2305.03169 | null | https://arxiv.org/abs/2305.03169v2 | https://arxiv.org/pdf/2305.03169v2.pdf | Sensitive Data Detection with High-Throughput Machine Learning Models in Electrical Health Records | In the era of big data, there is an increasing need for healthcare providers, communities, and researchers to share data and collaborate to improve health outcomes, generate valuable insights, and advance research. The Health Insurance Portability and Accountability Act of 1996 (HIPAA) is a federal law designed to prot... | ['Xiaoqian Jiang', 'Kai Zhang'] | 2023-04-30 | null | null | null | null | ['de-identification'] | ['natural-language-processing'] | [ 3.32157016e-01 -3.07480874e-03 -5.16762376e-01 -4.91072685e-01
-9.50310230e-01 -6.47928059e-01 -1.74090609e-01 9.43600535e-01
-2.03092530e-01 8.28054726e-01 6.50606036e-01 -4.60863352e-01
-1.88861340e-01 -1.00857997e+00 -5.25117636e-01 -4.97815669e-01
1.80073172e-01 2.90083021e-01 -5.88991530e-02 5.74254282... | [6.883928298950195, 6.843059539794922] |
ce76ea54-3355-446f-92a6-0595cdf47510 | unsupervised-view-invariant-human-posture | 2109.08730 | null | https://arxiv.org/abs/2109.08730v1 | https://arxiv.org/pdf/2109.08730v1.pdf | Unsupervised View-Invariant Human Posture Representation | Most recent view-invariant action recognition and performance assessment approaches rely on a large amount of annotated 3D skeleton data to extract view-invariant features. However, acquiring 3D skeleton data can be cumbersome, if not impractical, in in-the-wild scenarios. To overcome this problem, we present a novel u... | ['Majid Mirmehdi', 'Björn Ommer', 'Faegheh Sardari'] | 2021-09-17 | null | null | null | null | ['3d-pose-estimation', 'action-analysis', 'action-assessment', '3d-human-action-recognition'] | ['computer-vision', 'computer-vision', 'computer-vision', 'computer-vision'] | [ 3.49526465e-01 -1.48473859e-01 -2.45683968e-01 -4.62400764e-01
-1.03065193e+00 -5.37429154e-01 4.78928745e-01 -3.99404049e-01
-5.93759060e-01 4.10618484e-01 5.76389790e-01 4.90004450e-01
-2.04142630e-01 -2.18725577e-01 -6.17285669e-01 -5.56873143e-01
-1.81866959e-01 5.77706993e-01 3.19681108e-01 -3.52992296... | [7.1785149574279785, -0.6285148859024048] |
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