paperID stringlengths 36 36 | pwc_id stringlengths 8 47 | arxiv_id stringlengths 6 16 ⌀ | nips_id float64 | url_abs stringlengths 18 329 | url_pdf stringlengths 18 742 | title stringlengths 8 325 | abstract stringlengths 1 7.27k ⌀ | authors stringlengths 2 7.06k | published stringlengths 10 10 ⌀ | conference stringlengths 12 47 ⌀ | conference_url_abs stringlengths 16 198 ⌀ | conference_url_pdf stringlengths 27 199 ⌀ | proceeding stringlengths 6 47 ⌀ | taskID stringlengths 7 1.44k | areaID stringclasses 688
values | embedding stringlengths 9.26k 12.5k | umap_embedding stringlengths 29 44 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
abc9bd39-e9c1-40e4-a464-e1774635f6f0 | weakly-supervised-multi-object-tracking-and | 2101.00667 | null | https://arxiv.org/abs/2101.00667v1 | https://arxiv.org/pdf/2101.00667v1.pdf | Weakly Supervised Multi-Object Tracking and Segmentation | We introduce the problem of weakly supervised Multi-Object Tracking and Segmentation, i.e. joint weakly supervised instance segmentation and multi-object tracking, in which we do not provide any kind of mask annotation. To address it, we design a novel synergistic training strategy by taking advantage of multi-task lea... | ['Joan Serrat', 'Peter Kontschieder', 'Samuel Rota Bulò', 'Lorenzo Porzi', 'Idoia Ruiz'] | 2021-01-03 | null | null | null | null | ['weakly-supervised-instance-segmentation', 'multi-object-tracking-and-segmentation'] | ['computer-vision', 'computer-vision'] | [ 4.56035495e-01 3.05095881e-01 -2.11583257e-01 -2.91525930e-01
-1.00309598e+00 -7.02680588e-01 5.04006386e-01 9.28030536e-02
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3.85383278e-01 7.42759645e-01 9.48747754e-01 2.08791614... | [9.40257453918457, 0.46682867407798767] |
cec2e810-a2c2-4038-80d5-ffa0c5c1e3eb | adaptive-policy-learning-for-offline-to | 2303.07693 | null | https://arxiv.org/abs/2303.07693v1 | https://arxiv.org/pdf/2303.07693v1.pdf | Adaptive Policy Learning for Offline-to-Online Reinforcement Learning | Conventional reinforcement learning (RL) needs an environment to collect fresh data, which is impractical when online interactions are costly. Offline RL provides an alternative solution by directly learning from the previously collected dataset. However, it will yield unsatisfactory performance if the quality of the o... | ['Jing Jiang', 'Dongsheng Li', 'Xuan Song', 'Pengfei Wei', 'Xufang Luo', 'Han Zheng'] | 2023-03-14 | null | null | null | null | ['offline-rl', 'continuous-control'] | ['playing-games', 'playing-games'] | [-2.64360666e-01 1.41869038e-01 -6.34113491e-01 -2.18648732e-01
-8.26539099e-01 -6.41366899e-01 4.43822771e-01 1.98526904e-01
-7.92204678e-01 1.21462488e+00 -7.15528801e-02 -2.63031930e-01
-2.22773820e-01 -8.37432742e-01 -8.00239325e-01 -9.59268928e-01
-1.91279456e-01 4.30891842e-01 1.02211155e-01 -1.78653002... | [4.1078338623046875, 2.231337785720825] |
494f3a91-ed24-4315-9b56-b4b00a3ec71f | trollmeta-dravidianlangtech-eacl2021-meme | null | null | https://aclanthology.org/2021.dravidianlangtech-1.39 | https://aclanthology.org/2021.dravidianlangtech-1.39.pdf | TrollMeta@DravidianLangTech-EACL2021: Meme classification using deep learning | Memes act as a medium to carry one’s feelings, cultural ideas, or practices by means of symbols, imitations, or simply images. Whenever social media is involved, hurting the feelings of others and abusing others are always a problem. Here we are proposing a system, that classifies the memes into abusive/offensive memes... | ['Chinmaya Hs', 'Manoj Balaji J'] | null | null | null | null | eacl-dravidianlangtech-2021-4 | ['meme-classification'] | ['natural-language-processing'] | [-1.49813220e-01 -1.34291679e-01 2.35325396e-01 1.90537751e-01
6.23309076e-01 -6.44452751e-01 1.15383184e+00 -8.32000971e-02
-5.39042532e-01 9.65086937e-01 5.99254906e-01 1.48571143e-02
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4.74118501e-01 5.71790291e-03 -1.12933233e-01 -8.09550881... | [8.496842384338379, 10.702956199645996] |
c8d33594-b3bf-4fd0-94b2-3324c5eb17a4 | conversational-knowledge-teaching-agent-that | null | null | https://aclanthology.org/W15-4618 | https://aclanthology.org/W15-4618.pdf | Conversational Knowledge Teaching Agent that uses a Knowledge Base | null | ['Kyusong Lee', 'Gary Geunbae Lee', 'Junhwi Choi', 'Sangjun Koo', 'Paul Hongsuck Seo'] | 2015-09-01 | null | null | null | ws-2015-9 | ['knowledge-base-question-answering'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
-2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00
-3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01
-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.167551517486572, 3.5591843128204346] |
c33639b5-ef7d-4ea2-ab1a-cbf52e3062c1 | the-devil-is-in-the-details-on-the-pitfalls-1 | 2306.06918 | null | https://arxiv.org/abs/2306.06918v2 | https://arxiv.org/pdf/2306.06918v2.pdf | The Devil is in the Details: On the Pitfalls of Event Extraction Evaluation | Event extraction (EE) is a crucial task aiming at extracting events from texts, which includes two subtasks: event detection (ED) and event argument extraction (EAE). In this paper, we check the reliability of EE evaluations and identify three major pitfalls: (1) The data preprocessing discrepancy makes the evaluation ... | ['Weixing Shen', 'Zhiyuan Liu', 'Juanzi Li', 'Lei Hou', 'Kaisheng Zeng', 'Feng Yao', 'Xiaozhi Wang', 'Hao Peng'] | 2023-06-12 | null | null | null | null | ['event-extraction'] | ['natural-language-processing'] | [ 1.09013073e-01 7.54720941e-02 -1.47659719e-01 -4.02836680e-01
-1.03182292e+00 -1.01591671e+00 9.26580787e-01 5.79600990e-01
-5.11688709e-01 5.84487736e-01 6.39605939e-01 -6.70559347e-01
-2.10973620e-01 -5.36189258e-01 -7.84368873e-01 -1.35269221e-02
1.62901536e-01 2.31944889e-01 4.83619213e-01 1.69152781... | [9.12414264678955, 9.16459846496582] |
f199e6e6-ceb6-4cb3-b5cb-e2ee57f415c6 | clustering-word-embeddings-with-self | 2101.04197 | null | https://arxiv.org/abs/2101.04197v1 | https://arxiv.org/pdf/2101.04197v1.pdf | Clustering Word Embeddings with Self-Organizing Maps. Application on LaRoSeDa -- A Large Romanian Sentiment Data Set | Romanian is one of the understudied languages in computational linguistics, with few resources available for the development of natural language processing tools. In this paper, we introduce LaRoSeDa, a Large Romanian Sentiment Data Set, which is composed of 15,000 positive and negative reviews collected from one of th... | ['Radu Tudor Ionescu', 'Mihaela Gaman', 'Anca Maria Tache'] | 2021-01-11 | null | null | null | null | ['text-categorization'] | ['natural-language-processing'] | [-3.35305780e-01 5.06790802e-02 -3.10571045e-01 -4.93797362e-01
-1.18854575e-01 -5.79673052e-01 9.23903108e-01 9.33912992e-01
-8.56298804e-01 1.95990250e-01 5.99535525e-01 -2.25462183e-01
-1.14147574e-01 -1.14691317e+00 -9.72570479e-02 -7.63628185e-01
-9.19367373e-02 5.98549545e-01 -7.39602745e-02 -4.95822579... | [10.457915306091309, 8.500446319580078] |
aa3f71ff-9032-4cc6-a5ea-83bed9b87654 | on-the-soft-subnetwork-for-few-shot-class | 2209.07529 | null | https://arxiv.org/abs/2209.07529v2 | https://arxiv.org/pdf/2209.07529v2.pdf | On the Soft-Subnetwork for Few-shot Class Incremental Learning | Inspired by Regularized Lottery Ticket Hypothesis (RLTH), which hypothesizes that there exist smooth (non-binary) subnetworks within a dense network that achieve the competitive performance of the dense network, we propose a few-shot class incremental learning (FSCIL) method referred to as \emph{Soft-SubNetworks (SoftN... | ['Chang D. Yoo', 'Sung Ju Hwang', 'Sultan Rizky Hikmawan Madjid', 'Jaehong Yoon', 'Haeyong Kang'] | 2022-09-15 | null | null | null | null | ['few-shot-class-incremental-learning'] | ['methodology'] | [ 4.37965691e-01 5.43354690e-01 -3.32098126e-01 -3.01792383e-01
-2.36178428e-01 7.70632699e-02 5.62359095e-01 -7.00892881e-02
-5.52026927e-01 9.74261284e-01 1.07054785e-01 2.13514417e-01
-3.58199805e-01 -9.46651459e-01 -9.49726760e-01 -6.96277082e-01
-3.01003635e-01 5.28523743e-01 8.60537589e-01 -8.79253671... | [9.850529670715332, 3.3244740962982178] |
84accb14-6d92-46e1-abbd-572cfeec502d | unity-in-diversity-learning-distributed | 1912.11688 | null | https://arxiv.org/abs/1912.11688v1 | https://arxiv.org/pdf/1912.11688v1.pdf | Unity in Diversity: Learning Distributed Heterogeneous Sentence Representation for Extractive Summarization | Automated multi-document extractive text summarization is a widely studied research problem in the field of natural language understanding. Such extractive mechanisms compute in some form the worthiness of a sentence to be included into the summary. While the conventional approaches rely on human crafted document-indep... | ['Abhishek Kumar Singh', 'Vasudeva Varma', 'Manish Gupta'] | 2019-12-25 | null | null | null | null | ['extractive-document-summarization'] | ['natural-language-processing'] | [ 5.07419586e-01 1.08735219e-01 -5.82966387e-01 -6.23388290e-01
-1.21558881e+00 -5.99071920e-01 8.48882973e-01 7.23140717e-01
-4.05923992e-01 8.14779460e-01 1.37631333e+00 1.79972142e-01
-8.64668936e-03 -4.52713013e-01 -3.40595514e-01 -3.93371969e-01
2.42260441e-01 2.45557711e-01 4.67228368e-02 -5.20955324... | [12.500624656677246, 9.473189353942871] |
25a1b327-787c-4848-b28f-9c23be40212b | a-formal-perspective-on-byte-pair-encoding | 2306.16837 | null | https://arxiv.org/abs/2306.16837v1 | https://arxiv.org/pdf/2306.16837v1.pdf | A Formal Perspective on Byte-Pair Encoding | Byte-Pair Encoding (BPE) is a popular algorithm used for tokenizing data in NLP, despite being devised initially as a compression method. BPE appears to be a greedy algorithm at face value, but the underlying optimization problem that BPE seeks to solve has not yet been laid down. We formalize BPE as a combinatorial op... | ['Ryan Cotterell', 'Mrinmaya Sachan', 'Tim Vieira', 'Li Du', 'Juan Luis Gastaldi', 'Clara Meister', 'Vilém Zouhar'] | 2023-06-29 | null | null | null | null | ['combinatorial-optimization'] | ['methodology'] | [ 4.59751993e-01 2.27251098e-01 -3.55193168e-01 -2.09997773e-01
-1.09883940e+00 -8.75161648e-01 -1.27187401e-01 5.68640411e-01
-9.18112218e-01 7.59693503e-01 -2.94089258e-01 -7.97739387e-01
-4.87151116e-01 -9.02426362e-01 -9.23056364e-01 -7.41233051e-01
-5.89754760e-01 5.87520421e-01 -4.78178933e-02 -2.64139771... | [6.439102649688721, 4.659062385559082] |
47e3f4a4-abad-46c9-ab2d-7996d52c1e66 | a-symmetric-encoder-decoder-with-residual | 1905.11447 | null | https://arxiv.org/abs/1905.11447v1 | https://arxiv.org/pdf/1905.11447v1.pdf | A Symmetric Encoder-Decoder with Residual Block for Infrared and Visible Image Fusion | In computer vision and image processing tasks, image fusion has evolved into an attractive research field. However, recent existing image fusion methods are mostly built on pixel-level operations, which may produce unacceptable artifacts and are time-consuming. In this paper, a symmetric encoder-decoder with a residual... | ['Gwanggil Jeon', 'Zheng Liu', 'Xiaomin Yang', 'Lihua Jian', 'Mingliang Gao', 'David Chisholm'] | 2019-05-27 | null | null | null | null | ['infrared-and-visible-image-fusion'] | ['computer-vision'] | [ 5.78275144e-01 -4.67875779e-01 2.87482381e-01 -4.34558868e-01
-8.11512589e-01 1.37566224e-01 3.68233711e-01 -1.07872859e-02
-4.61171240e-01 6.40777469e-01 8.24242607e-02 1.16265155e-01
1.16855726e-01 -5.32878816e-01 -5.11090219e-01 -1.12763917e+00
5.46998858e-01 -5.08423150e-01 1.95927054e-01 -2.04559237... | [10.538003921508789, -1.8495690822601318] |
5d51ce07-ba0c-4244-b479-cf9e12da7b7f | deeplens-interactive-out-of-distribution-data | 2303.01577 | null | https://arxiv.org/abs/2303.01577v1 | https://arxiv.org/pdf/2303.01577v1.pdf | DeepLens: Interactive Out-of-distribution Data Detection in NLP Models | Machine Learning (ML) has been widely used in Natural Language Processing (NLP) applications. A fundamental assumption in ML is that training data and real-world data should follow a similar distribution. However, a deployed ML model may suffer from out-of-distribution (OOD) issues due to distribution shifts in the rea... | ['Tianyi Zhang', 'Lei Ma', 'Yuheng Huang', 'Zhijie Wang', 'Da Song'] | 2023-03-02 | null | null | null | null | ['text-clustering'] | ['natural-language-processing'] | [-4.08365458e-01 1.65505800e-02 2.24431634e-01 -3.92991841e-01
-3.98229003e-01 -6.80572510e-01 2.65343279e-01 9.81529713e-01
-4.71221358e-01 1.43176913e-01 1.34927016e-02 -3.87394369e-01
-6.13260977e-02 -5.33680975e-01 -5.47720157e-02 -3.07406485e-01
1.06754288e-01 6.13890767e-01 2.57843912e-01 8.33530053... | [10.002263069152832, 7.7473907470703125] |
24356d17-bf27-43b2-b760-7ed834d5d53e | multi-domain-norm-referenced-encoding-enables | 2304.02309 | null | https://arxiv.org/abs/2304.02309v1 | https://arxiv.org/pdf/2304.02309v1.pdf | Multi-Domain Norm-referenced Encoding Enables Data Efficient Transfer Learning of Facial Expression Recognition | People can innately recognize human facial expressions in unnatural forms, such as when depicted on the unusual faces drawn in cartoons or when applied to an animal's features. However, current machine learning algorithms struggle with out-of-domain transfer in facial expression recognition (FER). We propose a biologic... | ['Martin Giese', 'Nick Taubert', 'Alexander Lappe', 'Michael Stettler'] | 2023-04-05 | null | null | null | null | ['facial-expression-recognition'] | ['computer-vision'] | [ 3.24226141e-01 4.11798470e-02 6.56577274e-02 -8.00471902e-01
-9.09536630e-02 -4.37415481e-01 5.71189642e-01 -6.96661770e-01
-5.51447749e-01 9.08628941e-01 -7.36590549e-02 2.58362770e-01
4.78718758e-01 -7.26591587e-01 -8.67704391e-01 -8.30440640e-01
-1.70837894e-01 1.43431976e-01 -3.02064598e-01 -3.78471941... | [13.53787899017334, 1.6827292442321777] |
da15e19c-567b-4bd8-9305-e69f63bb447f | copy-move-image-forgery-detection-based-on | 2109.04381 | null | https://arxiv.org/abs/2109.04381v3 | https://arxiv.org/pdf/2109.04381v3.pdf | Copy-Move Image Forgery Detection Based on Evolving Circular Domains Coverage | The aim of this paper is to improve the accuracy of copy-move forgery detection (CMFD) in image forensics by proposing a novel scheme and the main contribution is evolving circular domains coverage (ECDC) algorithm. The proposed scheme integrates both block-based and keypoint-based forgery detection methods. Firstly, t... | ['Yuejia Han', 'Shulu Han', 'Lu Chen', 'Chengyou Wang', 'Xinghong Hu', 'Shilin Lu'] | 2021-09-09 | null | null | null | null | ['image-forensics'] | ['computer-vision'] | [ 2.59672493e-01 -8.15090239e-01 6.78810431e-03 2.54210651e-01
-9.05324399e-01 -5.43031454e-01 4.49511796e-01 1.46171466e-01
-3.55426401e-01 3.30631495e-01 -1.17621096e-02 -1.05132833e-01
-1.47853255e-01 -8.02524030e-01 -1.88768715e-01 -9.94129360e-01
-2.03725502e-01 -2.60802090e-01 6.82712257e-01 -1.16877630... | [12.342192649841309, 0.9292060136795044] |
31384a4e-2b3c-4775-a4fc-6603c8151474 | tell-me-what-happened-unifying-text-guided | 2211.12824 | null | https://arxiv.org/abs/2211.12824v2 | https://arxiv.org/pdf/2211.12824v2.pdf | Tell Me What Happened: Unifying Text-guided Video Completion via Multimodal Masked Video Generation | Generating a video given the first several static frames is challenging as it anticipates reasonable future frames with temporal coherence. Besides video prediction, the ability to rewind from the last frame or infilling between the head and tail is also crucial, but they have rarely been explored for video completion.... | ['Sean Bell', 'William Yang Wang', 'Jong-Chyi Su', 'Cheng-Yang Fu', 'Ning Zhang', 'Licheng Yu', 'Tsu-Jui Fu'] | 2022-11-23 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Fu_Tell_Me_What_Happened_Unifying_Text-Guided_Video_Completion_via_Multimodal_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Fu_Tell_Me_What_Happened_Unifying_Text-Guided_Video_Completion_via_Multimodal_CVPR_2023_paper.pdf | cvpr-2023-1 | ['video-generation', 'video-prediction', 'text-to-video-generation'] | ['computer-vision', 'computer-vision', 'natural-language-processing'] | [ 3.02633166e-01 1.82508811e-01 -2.08412409e-01 -2.09436953e-01
-5.43827772e-01 -4.82798636e-01 6.57487094e-01 -3.69232476e-01
1.46735283e-02 5.54750979e-01 2.20104039e-01 -4.67172116e-01
3.02869916e-01 -4.51222450e-01 -9.71482694e-01 -5.40972173e-01
-1.26351967e-01 -1.49467692e-01 4.77778435e-01 -7.24447668... | [10.813764572143555, -0.5674301981925964] |
1b093d8b-1681-4f71-b529-9aaaf7a4b666 | discriminative-extended-canonical-correlation | 1306.2100 | null | http://arxiv.org/abs/1306.2100v1 | http://arxiv.org/pdf/1306.2100v1.pdf | Discriminative extended canonical correlation analysis for pattern set matching | In this paper we address the problem of matching sets of vectors embedded in
the same input space. We propose an approach which is motivated by canonical
correlation analysis (CCA), a statistical technique which has proven successful
in a wide variety of pattern recognition problems. Like CCA when applied to the
matchi... | ['Ognjen Arandjelovic'] | 2013-06-10 | null | null | null | null | ['set-matching'] | ['computer-vision'] | [ 4.19534087e-01 -5.35145044e-01 4.74659741e-01 -3.38218212e-01
-8.76944661e-01 -7.68358827e-01 8.60820472e-01 -3.34950328e-01
-2.61889964e-01 3.88595164e-01 -1.72053009e-01 -2.70250648e-01
-7.03463674e-01 -3.95456553e-01 -2.28334412e-01 -1.13727665e+00
-3.87317091e-01 4.62667614e-01 -9.38960388e-02 -1.86940923... | [7.837284088134766, 4.179357528686523] |
36ae33a4-d4ef-4ae3-ba5a-832c5dc7b165 | word-movers-embedding-from-word2vec-to | 1811.01713 | null | http://arxiv.org/abs/1811.01713v1 | http://arxiv.org/pdf/1811.01713v1.pdf | Word Mover's Embedding: From Word2Vec to Document Embedding | While the celebrated Word2Vec technique yields semantically rich
representations for individual words, there has been relatively less success in
extending to generate unsupervised sentences or documents embeddings. Recent
work has demonstrated that a distance measure between documents called
\emph{Word Mover's Distance... | ['Michael J. Witbrock', 'Pin-Yu Chen', 'Pradeep Ravikumar', 'Lingfei Wu', 'Kun Xu', 'Ian E. H. Yen', 'Fangli Xu', 'Avinash Balakrishnan'] | 2018-10-30 | word-movers-embedding-from-word2vec-to-1 | https://aclanthology.org/D18-1482 | https://aclanthology.org/D18-1482.pdf | emnlp-2018-10 | ['document-embedding'] | ['methodology'] | [ 2.86466569e-01 -2.02994704e-01 -3.33680302e-01 -4.01598632e-01
-6.40367448e-01 -5.96115172e-01 9.72489774e-01 8.60797167e-01
-7.95609295e-01 4.17720705e-01 7.17680156e-01 -5.63491225e-01
-2.31343433e-01 -7.32407212e-01 -7.38160014e-02 -5.72793901e-01
2.09763814e-02 3.92187059e-01 8.18568096e-02 -3.84578228... | [10.586630821228027, 8.692458152770996] |
97faa926-5387-430c-bb66-2c989a27ac1c | 3d-room-layout-estimation-from-a-cubemap-of | 2207.09291 | null | https://arxiv.org/abs/2207.09291v1 | https://arxiv.org/pdf/2207.09291v1.pdf | 3D Room Layout Estimation from a Cubemap of Panorama Image via Deep Manhattan Hough Transform | Significant geometric structures can be compactly described by global wireframes in the estimation of 3D room layout from a single panoramic image. Based on this observation, we present an alternative approach to estimate the walls in 3D space by modeling long-range geometric patterns in a learnable Hough Transform blo... | ['Yue Gao', 'Zhou Xue', 'Chao Wen', 'Yining Zhao'] | 2022-07-19 | null | null | null | null | ['3d-room-layouts-from-a-single-rgb-panorama'] | ['computer-vision'] | [-2.38218699e-02 1.84691802e-01 3.11352134e-01 -6.69701278e-01
-5.82933247e-01 -4.73511755e-01 4.28518474e-01 -1.89308017e-01
-5.84409237e-02 2.41610274e-01 3.39107543e-01 -4.27763730e-01
-9.29465294e-02 -1.02741611e+00 -1.17117679e+00 -2.51563251e-01
-2.36783475e-01 5.10856390e-01 8.65967721e-02 1.77564472... | [8.716876029968262, -2.876457452774048] |
ab3859ba-9b84-407e-8eb5-355aedb451e7 | data-efficient-large-scale-place-recognition | 2303.11739 | null | https://arxiv.org/abs/2303.11739v2 | https://arxiv.org/pdf/2303.11739v2.pdf | Data-efficient Large Scale Place Recognition with Graded Similarity Supervision | Visual place recognition (VPR) is a fundamental task of computer vision for visual localization. Existing methods are trained using image pairs that either depict the same place or not. Such a binary indication does not consider continuous relations of similarity between images of the same place taken from different po... | ['Nicolai Petkov', 'Nicola Strisciuglio', 'Maria Leyva-Vallina'] | 2023-03-21 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Leyva-Vallina_Data-Efficient_Large_Scale_Place_Recognition_With_Graded_Similarity_Supervision_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Leyva-Vallina_Data-Efficient_Large_Scale_Place_Recognition_With_Graded_Similarity_Supervision_CVPR_2023_paper.pdf | cvpr-2023-1 | ['visual-localization', 'visual-place-recognition'] | ['computer-vision', 'computer-vision'] | [ 4.09235731e-02 -3.40960711e-01 -3.36769938e-01 -5.06184280e-01
-7.69524038e-01 -6.88086092e-01 8.02978694e-01 5.22972584e-01
-6.76423371e-01 4.92035925e-01 -2.10525021e-01 -1.11415647e-01
-1.82555348e-01 -5.88392258e-01 -9.16249573e-01 -4.79979932e-01
-5.55920079e-02 3.03040504e-01 4.38253343e-01 -4.78603393... | [7.778864860534668, -1.8825548887252808] |
c9fbf8aa-523d-4ccc-adf7-9b215dd37472 | data-augmentation-for-biomedical-factoid-1 | 2204.04711 | null | https://arxiv.org/abs/2204.04711v1 | https://arxiv.org/pdf/2204.04711v1.pdf | Data Augmentation for Biomedical Factoid Question Answering | We study the effect of seven data augmentation (da) methods in factoid question answering, focusing on the biomedical domain, where obtaining training instances is particularly difficult. We experiment with data from the BioASQ challenge, which we augment with training instances obtained from an artificial biomedical m... | ['Ion Androutsopoulos', 'Prodromos Malakasiotis', 'Dimitris Pappas'] | 2022-04-10 | null | https://aclanthology.org/2022.bionlp-1.6 | https://aclanthology.org/2022.bionlp-1.6.pdf | bionlp-acl-2022-5 | ['machine-reading-comprehension'] | ['natural-language-processing'] | [ 8.58686328e-01 6.01382494e-01 -2.17410363e-02 -1.55172214e-01
-1.07330120e+00 -4.48588997e-01 6.80247605e-01 8.53582144e-01
-1.02908003e+00 9.04332340e-01 7.84664810e-01 -8.49682689e-01
-1.36993334e-01 -5.98046184e-01 -7.62077749e-01 -2.54944116e-01
1.47505566e-01 7.03711808e-01 -4.86681126e-02 -5.85283875... | [8.709301948547363, 8.59571647644043] |
e42275fc-a7f7-4ecc-a9f9-a7518c8dd749 | read-bad-a-new-dataset-and-evaluation-scheme | 1705.03311 | null | http://arxiv.org/abs/1705.03311v2 | http://arxiv.org/pdf/1705.03311v2.pdf | READ-BAD: A New Dataset and Evaluation Scheme for Baseline Detection in Archival Documents | Text line detection is crucial for any application associated with Automatic
Text Recognition or Keyword Spotting. Modern algorithms perform good on
well-established datasets since they either comprise clean data or
simple/homogeneous page layouts. We have collected and annotated 2036 archival
document images from diff... | ['Markus Diem', 'Tobias Grüning', 'Florian Kleber', 'Roger Labahn', 'Stefan Fiel'] | 2017-05-09 | null | null | null | null | ['line-detection'] | ['computer-vision'] | [ 4.00308669e-01 -4.08303469e-01 7.94136897e-03 -2.96475232e-01
-1.09483397e+00 -7.20252693e-01 7.36038625e-01 4.80227470e-01
-4.85828608e-01 7.44031847e-01 -1.11061662e-01 -3.33360225e-01
7.38109276e-02 -4.08013284e-01 -6.37243390e-01 -5.24407923e-01
3.29918891e-01 6.57920122e-01 5.50352812e-01 1.14394978... | [11.802828788757324, 2.612698554992676] |
47cb0850-486f-48e9-b583-fab955335248 | advances-in-apparent-conceptual-physics | 2303.17012 | null | https://arxiv.org/abs/2303.17012v3 | https://arxiv.org/pdf/2303.17012v3.pdf | Advances in apparent conceptual physics reasoning in GPT-4 | ChatGPT is built on a large language model trained on an enormous corpus of human text to emulate human conversation. Despite lacking any explicit programming regarding the laws of physics, recent work has demonstrated that GPT-3.5 could pass an introductory physics course at some nominal level and register something c... | ['Colin G. West'] | 2023-03-29 | null | null | null | null | ['conceptual-physics'] | ['miscellaneous'] | [-2.14970440e-01 6.64147079e-01 -5.23892371e-03 -3.10584128e-01
-6.06221735e-01 -8.98560107e-01 6.79481685e-01 4.81698334e-01
-3.30564946e-01 6.80861831e-01 3.30250382e-01 -1.01341200e+00
-4.16963845e-01 -9.69289124e-01 -8.16171646e-01 -2.14381784e-01
2.38121942e-01 5.99176645e-01 2.48390302e-01 -7.69880235... | [9.937244415283203, 7.388355731964111] |
87fbf2a2-c416-4bbd-8a78-81db8f1c5e10 | jampatoisnli-a-jamaican-patois-natural | 2212.03419 | null | https://arxiv.org/abs/2212.03419v1 | https://arxiv.org/pdf/2212.03419v1.pdf | JamPatoisNLI: A Jamaican Patois Natural Language Inference Dataset | JamPatoisNLI provides the first dataset for natural language inference in a creole language, Jamaican Patois. Many of the most-spoken low-resource languages are creoles. These languages commonly have a lexicon derived from a major world language and a distinctive grammar reflecting the languages of the original speaker... | ['Christopher Manning', 'John Hewitt', 'Ruth-Ann Armstrong'] | 2022-12-07 | null | null | null | null | ['cross-lingual-transfer'] | ['natural-language-processing'] | [-3.01099747e-01 7.51464516e-02 -4.99244452e-01 -4.95838523e-01
-8.19021046e-01 -7.65884638e-01 9.69016492e-01 -6.32239804e-02
-7.40838885e-01 8.55073035e-01 7.54917026e-01 -6.32521808e-01
1.82323515e-01 -8.68954420e-01 -8.75286698e-01 -2.32917979e-01
1.15919866e-01 1.11862922e+00 3.20589840e-02 -6.52126551... | [10.861273765563965, 9.947779655456543] |
26ef6c2f-be11-4a7a-af01-f901e2cf917e | morpho-syntactic-lexicon-generation-using | 1512.05030 | null | http://arxiv.org/abs/1512.05030v3 | http://arxiv.org/pdf/1512.05030v3.pdf | Morpho-syntactic Lexicon Generation Using Graph-based Semi-supervised Learning | Morpho-syntactic lexicons provide information about the morphological and
syntactic roles of words in a language. Such lexicons are not available for all
languages and even when available, their coverage can be limited. We present a
graph-based semi-supervised learning method that uses the morphological,
syntactic and ... | ['Ryan Mcdonald', 'Manaal Faruqui', 'Radu Soricut'] | 2015-12-16 | morpho-syntactic-lexicon-generation-using-1 | https://aclanthology.org/Q16-1001 | https://aclanthology.org/Q16-1001.pdf | tacl-2016-1 | ['morphological-tagging'] | ['natural-language-processing'] | [-1.09853916e-01 3.55390340e-01 -4.30031180e-01 -5.16094804e-01
-9.89423692e-01 -1.20261836e+00 3.15718979e-01 7.37979770e-01
-6.25267565e-01 9.73761737e-01 5.59143066e-01 -6.15246654e-01
1.66258261e-01 -9.01817560e-01 -2.90943503e-01 -1.39237270e-01
-4.52601649e-02 6.80299342e-01 5.58531463e-01 -4.04730409... | [10.35770320892334, 10.000677108764648] |
269ff6bd-e684-4035-9178-827bb08d0cd1 | high-order-correlation-preserved-incomplete | null | null | https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=9718038 | https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=9718038 | High-order Correlation Preserved Incomplete Multi-view Subspace Clustering | Incomplete multi-view clustering aims to exploit theinformation of multiple incomplete views to partition data into their clusters. Existing methods only utilize the pair-wise sample correlation and pair-wise view correlation to improve the clustering performance but neglect the high-order correlation of samples and th... | ['and En Zhu', 'Wei zhang', 'Senior Member', 'Xinwang Liu', 'Xiao Zheng', 'IEEE', 'Member', 'Chang Tang', 'Zhenglai Li'] | 2022-02-21 | null | null | null | ieee-transactions-on-image-processing-2022-2 | ['incomplete-multi-view-clustering', 'multi-view-subspace-clustering'] | ['computer-vision', 'computer-vision'] | [-4.72796738e-01 -3.89658600e-01 -2.27624699e-01 -2.36123487e-01
-4.03301060e-01 -5.63172996e-01 2.11258814e-01 -4.66644764e-01
7.06298500e-02 1.71393797e-01 5.38054228e-01 2.82227784e-01
-5.25285482e-01 -4.04206783e-01 -3.82212609e-01 -1.03755391e+00
1.93460807e-01 4.26867098e-01 1.28416938e-03 1.45442933... | [8.263309478759766, 4.641043186187744] |
20cd8ea1-191a-4b2d-9f4f-9f26f0b54463 | language-acquisition-through-intention | null | null | https://aclanthology.org/2022.coling-1.2 | https://aclanthology.org/2022.coling-1.2.pdf | Language Acquisition through Intention Reading and Pattern Finding | One of AI’s grand challenges consists in the development of autonomous agents with communication systems offering the robustness, flexibility and adaptivity found in human languages. While the processes through which children acquire language are by now relatively well understood, a faithful computational operationalis... | ['Katrien Beuls', 'Paul Van Eecke', 'Jonas Doumen', 'Jens Nevens'] | null | null | null | null | coling-2022-10 | ['language-acquisition'] | ['natural-language-processing'] | [ 4.12108541e-01 6.31292045e-01 4.20259595e-01 -5.24061382e-01
-1.38296604e-01 -7.92198300e-01 8.65151286e-01 5.76563060e-01
-2.26165175e-01 2.09040999e-01 1.86034694e-01 -3.01488698e-01
-3.53051782e-01 -1.13454151e+00 -6.16416991e-01 -4.77827638e-01
9.51374397e-02 7.21176326e-01 4.61129487e-01 -5.40860832... | [4.357571125030518, 1.2104380130767822] |
dc2932c7-d5ba-498b-9610-e23fa16fec75 | mdace-mimic-documents-annotated-with-code-1 | 2307.03859 | null | https://arxiv.org/abs/2307.03859v1 | https://arxiv.org/pdf/2307.03859v1.pdf | MDACE: MIMIC Documents Annotated with Code Evidence | We introduce a dataset for evidence/rationale extraction on an extreme multi-label classification task over long medical documents. One such task is Computer-Assisted Coding (CAC) which has improved significantly in recent years, thanks to advances in machine learning technologies. Yet simply predicting a set of final ... | ['Matthew R. Gormley', 'Benjamin Striner', 'Edmond Lu', 'Russell Klopfer', 'April Russell', 'Rana Jafari', 'Hua Cheng'] | 2023-07-07 | mdace-mimic-documents-annotated-with-code | https://aclanthology.org/2023.acl-long.416/ | https://aclanthology.org/2023.acl-long.416.pdf | acl-2023-7 | ['multi-label-classification', 'extreme-multi-label-classification', 'multi-label-classification', 'document-classification'] | ['computer-vision', 'methodology', 'methodology', 'natural-language-processing'] | [ 1.81137726e-01 3.36272418e-01 -6.73949301e-01 -5.80367088e-01
-1.27023387e+00 -4.22387421e-01 -2.07288675e-02 1.10506964e+00
-3.40169877e-01 8.39000046e-01 4.25399482e-01 -8.33752453e-01
-4.43883687e-01 -4.99207973e-01 -5.77056170e-01 -2.22016960e-01
-7.93552846e-02 7.25340724e-01 -4.08762455e-01 3.28291148... | [8.05708122253418, 6.7769575119018555] |
4c17dd92-3da7-4998-947b-be0ca31a7599 | underwater-image-color-correction-by | 2010.10748 | null | https://arxiv.org/abs/2010.10748v1 | https://arxiv.org/pdf/2010.10748v1.pdf | Underwater Image Color Correction by Complementary Adaptation | In this paper, we propose a novel approach for underwater image color correction based on a Tikhonov type optimization model in the CIELAB color space. It presents a new variational interpretation of the complementary adaptation theory in psychophysics, which establishes the connection between colorimetric notions and ... | ['Yuchen He'] | 2020-10-21 | null | null | null | null | ['color-constancy'] | ['computer-vision'] | [ 2.20093176e-01 -3.73486847e-01 8.87734830e-01 -3.33844423e-01
-8.32952634e-02 -6.66275203e-01 2.09137186e-01 1.06666811e-01
-1.03831971e+00 8.24226856e-01 -1.48637876e-01 -7.76089132e-02
-1.56050146e-01 -7.44292557e-01 -6.65794969e-01 -1.18171680e+00
-5.39951921e-02 -5.51390946e-01 1.67154387e-01 -5.42453349... | [10.686236381530762, -3.3101577758789062] |
22a7002d-2ee2-4fe0-a272-b7b5f5192d81 | robustness-evaluation-of-deep-unsupervised | 2207.03576 | null | https://arxiv.org/abs/2207.03576v1 | https://arxiv.org/pdf/2207.03576v1.pdf | Robustness Evaluation of Deep Unsupervised Learning Algorithms for Intrusion Detection Systems | Recently, advances in deep learning have been observed in various fields, including computer vision, natural language processing, and cybersecurity. Machine learning (ML) has demonstrated its ability as a potential tool for anomaly detection-based intrusion detection systems to build secure computer networks. Increasin... | ['Froduald Kabanza', 'Pierre-Marting Tardif', 'Marc Frappier', 'Jean-Charles Verdier', 'Arian Soltani', "D'Jeff Kanda Nkashama"] | 2022-06-25 | null | null | null | null | ['data-poisoning'] | ['adversarial'] | [ 6.44145906e-02 -2.79793531e-01 -2.13310152e-01 2.33674459e-02
1.78064518e-02 -6.48021042e-01 8.19579542e-01 7.39973962e-01
-5.66527128e-01 5.32053173e-01 -2.95486420e-01 -7.47361600e-01
-1.51358634e-01 -9.83464599e-01 -6.49768710e-01 -7.89138019e-01
-5.70439100e-01 3.21973681e-01 1.26723677e-01 -3.99848849... | [5.393251419067383, 7.429030895233154] |
4aa2d588-6e0d-49ba-8d04-847ab7aacbea | tracking-progress-in-multi-agent-path-finding | 2305.08446 | null | https://arxiv.org/abs/2305.08446v1 | https://arxiv.org/pdf/2305.08446v1.pdf | Tracking Progress in Multi-Agent Path Finding | Multi-Agent Path Finding (MAPF) is an important core problem for many new and emerging industrial applications. Many works appear on this topic each year, and a large number of substantial advancements and performance improvements have been reported. Yet measuring overall progress in MAPF is difficult: there are many p... | ['Peter J. Stuckey', 'Daniel D. Harabor', 'Muhammad Aamir Cheema', 'Zhe Chen', 'Bojie Shen'] | 2023-05-15 | null | null | null | null | ['multi-agent-path-finding'] | ['playing-games'] | [-4.44241650e-02 -1.74397066e-01 -5.27255297e-01 5.45161963e-02
-8.00163984e-01 -7.46190608e-01 3.88135225e-01 2.64451563e-01
2.54542660e-02 1.30872965e+00 -2.39756078e-01 -3.50235224e-01
-5.73157132e-01 -8.53639126e-01 -3.81319851e-01 -6.09119833e-01
-6.65046573e-01 7.83623636e-01 4.70339328e-01 -3.71281534... | [4.965987682342529, 1.9059443473815918] |
b43d0539-da47-4005-9cad-cc08b678f9bd | explorekit-automatic-feature-generation-and | null | null | https://ieeexplore.ieee.org/document/7837936 | http://people.eecs.berkeley.edu/~dawnsong/papers/icdm-2016.pdf | ExploreKit: Automatic Feature Generation and Selection | Feature generation is one of the challenging aspects of machine learning. We present ExploreKit, a framework for automated feature generation. ExploreKit generates a large set of candidate features by combining information in the original features, with the aim of maximizing predictive performance according to user-sel... | ['Gilad Katz', 'Eui Chul Richard Shin', 'Dawn Song'] | 2016-01-01 | null | null | null | icdm-2016-2016-1 | ['automated-feature-engineering'] | ['methodology'] | [ 2.24515021e-01 -2.20295474e-01 -1.76939368e-01 -4.59150970e-01
-9.95513618e-01 -5.92777431e-01 4.88452047e-01 2.41489545e-01
-2.60832042e-01 7.86024034e-01 2.62302663e-02 1.06235221e-02
-4.50483471e-01 -7.55367279e-01 -8.84306282e-02 -4.70663577e-01
-1.96055770e-01 3.92288119e-01 6.73031881e-02 5.48855402... | [8.237040519714355, 4.5144572257995605] |
b14d14b6-f9ec-4459-9864-29a0ec364f35 | constrained-nonnegative-matrix-factorization | 2003.01041 | null | https://arxiv.org/abs/2003.01041v5 | https://arxiv.org/pdf/2003.01041v5.pdf | Constrained Nonnegative Matrix Factorization for Blind Hyperspectral Unmixing incorporating Endmember Independence | Hyperspectral unmixing (HU) has become an important technique in exploiting hyperspectral data since it decomposes a mixed pixel into a collection of endmembers weighted by fractional abundances. The endmembers of a hyperspectral image (HSI) are more likely to be generated by independent sources and be mixed in a macro... | ['H. M. V. R. Herath', 'G. M. R. I. Godaliyadda', 'B. Rathnayake', 'H. M. H. K. Weerasooriya', 'M. P. B. Ekanayake', 'S. Herath', 'D. Y. L. Ranasinghe', 'E. M. M. B. Ekanayake'] | 2020-03-02 | null | null | null | null | ['hyperspectral-unmixing'] | ['computer-vision'] | [ 7.59438694e-01 -6.58703864e-01 3.23486403e-02 -8.25980399e-03
-4.52729911e-01 -5.98177850e-01 3.29924285e-01 -9.52377692e-02
-2.38710880e-01 8.40829134e-01 1.87952727e-01 -2.46792570e-01
-2.87819475e-01 -8.77197444e-01 -5.54879546e-01 -1.34922504e+00
1.03772536e-01 5.65599017e-02 -5.21769881e-01 -6.75712004... | [10.082701683044434, -2.0442872047424316] |
8e3669af-6d7c-4c4f-905f-de01400dd01a | using-image-extracted-features-to-determine | 1911.01333 | null | https://arxiv.org/abs/1911.01333v2 | https://arxiv.org/pdf/1911.01333v2.pdf | Using image-extracted features to determine heart rate and blink duration for driver sleepiness detection | Heart rate and blink duration are two vital physiological signals which give information about cardiac activity and consciousness. Monitoring these two signals is crucial for various applications such as driver drowsiness detection. As there are several problems posed by the conventional systems to be used for continuo... | ['Hamid Soltanian-Zadeh', 'Armin Mohammadie-Zand', 'Erfan Darzi'] | 2019-11-04 | null | null | null | null | ['heart-rate-variability'] | ['medical'] | [-1.06832221e-01 -3.62707525e-01 -6.11411147e-02 -3.23122591e-01
1.38596267e-01 -3.37026715e-01 1.12642571e-02 1.09766923e-01
-6.27950907e-01 9.96882260e-01 -3.00002009e-01 -4.85507280e-01
-2.52402127e-02 -2.67658383e-01 1.68924406e-01 -1.13629580e+00
3.96537900e-01 -1.68410718e-01 -4.35639918e-02 1.69076100... | [13.600665092468262, 2.9452240467071533] |
f64e1507-5a34-473a-ad8c-c5a668e90c8c | robust-spatiotemporal-traffic-forecasting | 2306.14126 | null | https://arxiv.org/abs/2306.14126v1 | https://arxiv.org/pdf/2306.14126v1.pdf | Robust Spatiotemporal Traffic Forecasting with Reinforced Dynamic Adversarial Training | Machine learning-based forecasting models are commonly used in Intelligent Transportation Systems (ITS) to predict traffic patterns and provide city-wide services. However, most of the existing models are susceptible to adversarial attacks, which can lead to inaccurate predictions and negative consequences such as cong... | ['Hao liu', 'Weijia Zhang', 'Fan Liu'] | 2023-06-25 | null | null | null | null | ['adversarial-robustness', 'self-knowledge-distillation'] | ['adversarial', 'computer-vision'] | [-3.16611230e-02 -2.28782505e-01 -2.82988966e-01 -2.31333226e-01
-6.45011008e-01 -6.08530760e-01 5.80852747e-01 -3.50680590e-01
-7.83550963e-02 8.31694603e-01 3.30758952e-02 -7.83164322e-01
6.56944662e-02 -1.12652731e+00 -8.95373404e-01 -7.12385058e-01
1.01263054e-01 1.64482608e-01 6.67808533e-01 -6.40832186... | [5.455996513366699, 7.815692901611328] |
11950f48-8e6c-4c64-bbb2-8aada2087250 | a-dual-contrastive-framework-for-low-resource | 2204.00796 | null | https://arxiv.org/abs/2204.00796v1 | https://arxiv.org/pdf/2204.00796v1.pdf | A Dual-Contrastive Framework for Low-Resource Cross-Lingual Named Entity Recognition | Cross-lingual Named Entity Recognition (NER) has recently become a research hotspot because it can alleviate the data-hungry problem for low-resource languages. However, few researches have focused on the scenario where the source-language labeled data is also limited in some specific domains. A common approach for thi... | ['Shengyi Jiang', 'Ziyu Yang', 'Nankai Lin', 'Yingwen Fu'] | 2022-04-02 | null | null | null | null | ['cross-lingual-ner'] | ['natural-language-processing'] | [-1.89263467e-02 -2.09507033e-01 -3.43837172e-01 -5.40753543e-01
-1.24576461e+00 -7.44438410e-01 4.54120547e-01 -1.29995257e-01
-5.49594343e-01 9.27406549e-01 3.23005468e-01 -4.96880084e-01
4.68934834e-01 -6.66074395e-01 -6.76079214e-01 -3.88347924e-01
5.44202149e-01 4.21328247e-01 -2.80240059e-01 -3.68335605... | [10.056985855102539, 9.679342269897461] |
43000d4e-cecf-4ddb-8ab3-7cb2c1df0837 | hatsuki-an-anime-character-like-robot-figure | 2003.14121 | null | https://arxiv.org/abs/2003.14121v1 | https://arxiv.org/pdf/2003.14121v1.pdf | HATSUKI : An anime character like robot figure platform with anime-style expressions and imitation learning based action generation | Japanese character figurines are popular and have pivot position in Otaku culture. Although numerous robots have been developed, less have focused on otaku-culture or on embodying the anime character figurine. Therefore, we take the first steps to bridge this gap by developing Hatsuki, which is a humanoid robot platfor... | ['Tetsuya OGATA', 'Kuo-Hao Shu', 'Chang-Chieh Chiu', 'Mohammed Al-Sada', 'Pin-Chu Yang', 'Tito Pradhono Tomo', 'Kanata Suzuki', 'Nelson Yalta', 'Kevin Kuo'] | 2020-03-31 | null | null | null | null | ['action-generation'] | ['computer-vision'] | [-4.74935919e-01 3.91331166e-01 3.36799681e-01 8.09035525e-02
2.32460916e-01 -5.58648527e-01 3.66426706e-01 -8.02984953e-01
-1.26764372e-01 8.35608363e-01 4.47429299e-01 3.49774569e-01
1.94889709e-01 -5.04493773e-01 -5.07508636e-01 -5.96996844e-01
-1.98759973e-01 4.27316487e-01 -3.16560492e-02 -7.60835826... | [5.216435432434082, 0.4351019263267517] |
74646dcc-af55-44fb-90f3-07e127032e95 | songs-across-borders-singable-and | 2305.16816 | null | https://arxiv.org/abs/2305.16816v1 | https://arxiv.org/pdf/2305.16816v1.pdf | Songs Across Borders: Singable and Controllable Neural Lyric Translation | The development of general-domain neural machine translation (NMT) methods has advanced significantly in recent years, but the lack of naturalness and musical constraints in the outputs makes them unable to produce singable lyric translations. This paper bridges the singability quality gap by formalizing lyric translat... | ['Ye Wang', 'Min-Yen Kan', 'Xichu Ma', 'Longshen Ou'] | 2023-05-26 | null | null | null | null | ['nmt'] | ['computer-code'] | [ 3.63823444e-01 2.53281165e-02 -4.68469441e-01 -1.63997084e-01
-1.42299056e+00 -9.05301452e-01 4.19254094e-01 -6.98568881e-01
-1.10874720e-01 8.31886053e-01 6.22955263e-01 -3.35500121e-01
3.22126001e-01 -2.84439266e-01 -6.25712574e-01 -2.88334519e-01
6.43751979e-01 5.88919222e-01 -6.99891508e-01 -4.60489213... | [11.624926567077637, 10.242122650146484] |
bc63f955-606f-407f-97fe-d56bbe800516 | openel-an-annotated-corpus-for-entity-linking | null | null | https://aclanthology.org/2022.lrec-1.241 | https://aclanthology.org/2022.lrec-1.241.pdf | OpenEL: An Annotated Corpus for Entity Linking and Discourse in Open Domain Dialogue | Entity linking in dialogue is the task of mapping entity mentions in utterances to a target knowledge base. Prior work on entity linking has mainly focused on well-written articles such as Wikipedia, annotated newswire, or domain-specific datasets. We extend the study of entity linking to open domain dialogue by presen... | ['Beth Ann Hockey', 'Marilyn Walker', 'Leanne Rolston', 'Wen Cui'] | null | null | null | null | lrec-2022-6 | ['coreference-resolution'] | ['natural-language-processing'] | [-2.32843623e-01 1.00651681e+00 -3.99525523e-01 -3.56053114e-01
-9.74276423e-01 -1.02311325e+00 8.58921349e-01 3.94184053e-01
-5.87313235e-01 1.18704748e+00 8.67789567e-01 -2.62024868e-02
-7.37392008e-02 -5.94703615e-01 -3.52497399e-01 4.39522639e-02
-5.09900451e-02 1.34938705e+00 3.85729492e-01 -8.10038030... | [9.69675350189209, 9.388348579406738] |
9924bfc7-d540-499f-ada9-5d435c0f937b | you-only-hear-once-a-yolo-like-algorithm-for | 2109.00962 | null | https://arxiv.org/abs/2109.00962v3 | https://arxiv.org/pdf/2109.00962v3.pdf | You Only Hear Once: A YOLO-like Algorithm for Audio Segmentation and Sound Event Detection | Audio segmentation and sound event detection are crucial topics in machine listening that aim to detect acoustic classes and their respective boundaries. It is useful for audio-content analysis, speech recognition, audio-indexing, and music information retrieval. In recent years, most research articles adopt segmentati... | ['Eduardo Reck Miranda', 'David Moffat', 'Satvik Venkatesh'] | 2021-09-01 | null | null | null | null | ['music-information-retrieval'] | ['music'] | [ 3.17954570e-01 -2.04475984e-01 1.08853929e-01 -1.40185073e-01
-1.16384375e+00 -3.06533813e-01 3.48563418e-02 2.93937474e-01
-4.57979023e-01 2.16178924e-01 1.37512460e-01 -1.70053765e-01
2.73177326e-01 -5.82266390e-01 -4.48306680e-01 -6.79100156e-01
-8.71962011e-02 8.16498548e-02 4.73126054e-01 1.71158284... | [15.263826370239258, 5.233578205108643] |
c8370b90-57a4-409e-b6a3-1933ab655f0b | thermal-infrared-image-inpainting-via-edge | 2210.16000 | null | https://arxiv.org/abs/2210.16000v1 | https://arxiv.org/pdf/2210.16000v1.pdf | Thermal Infrared Image Inpainting via Edge-Aware Guidance | Image inpainting has achieved fundamental advances with deep learning. However, almost all existing inpainting methods aim to process natural images, while few target Thermal Infrared (TIR) images, which have widespread applications. When applied to TIR images, conventional inpainting methods usually generate distorted... | ['Kejie Huang', 'Quan Sun', 'Changyou Men', 'Haibin Shen', 'Zeyu Wang'] | 2022-10-28 | null | null | null | null | ['image-inpainting'] | ['computer-vision'] | [ 7.96866357e-01 -2.51415908e-01 9.36205089e-02 -2.42397636e-01
-6.26553178e-01 -9.92248580e-02 1.99438483e-01 -9.71786499e-01
-3.05790603e-01 6.69994831e-01 6.40397444e-02 -1.22878842e-01
2.15955228e-01 -6.18549287e-01 -8.15238893e-01 -8.94349396e-01
7.93268085e-01 -6.24775328e-02 -1.59686685e-01 -3.14345807... | [11.169771194458008, -1.7742215394973755] |
8282dab6-70a4-43f3-81da-61e59ff9765a | uncertainty-guided-source-free-domain | 2208.07591 | null | https://arxiv.org/abs/2208.07591v1 | https://arxiv.org/pdf/2208.07591v1.pdf | Uncertainty-guided Source-free Domain Adaptation | Source-free domain adaptation (SFDA) aims to adapt a classifier to an unlabelled target data set by only using a pre-trained source model. However, the absence of the source data and the domain shift makes the predictions on the target data unreliable. We propose quantifying the uncertainty in the source model predicti... | ['Arno Solin', 'Elisa Ricci', 'Nicu Sebe', 'Juho Kannala', 'Andrea Pilzer', 'Martin Trapp', 'Subhankar Roy'] | 2022-08-16 | null | null | null | null | ['source-free-domain-adaptation'] | ['computer-vision'] | [ 4.34021503e-01 5.30534267e-01 -3.96249354e-01 -6.02054119e-01
-1.12269914e+00 -7.83733189e-01 8.06130707e-01 7.31017441e-02
-3.40789735e-01 9.03282821e-01 2.98191756e-02 -3.72226350e-03
-2.85986483e-01 -4.74244267e-01 -8.41398239e-01 -8.87268126e-01
3.20397943e-01 9.01013494e-01 3.72384280e-01 9.02807117... | [10.255929946899414, 3.239940881729126] |
5d8f516f-3e1d-4b2e-876f-f93a0e28df74 | on-the-tour-towards-dpllmapf-and-beyond | 1907.07631 | null | https://arxiv.org/abs/1907.07631v1 | https://arxiv.org/pdf/1907.07631v1.pdf | On the Tour Towards DPLL(MAPF) and Beyond | We discuss milestones on the tour towards DPLL(MAPF), a multi-agent path finding (MAPF) solver fully integrated with the Davis-Putnam-Logemann-Loveland (DPLL) propositional satisfiability testing algorithm through satisfiability modulo theories (SMT). The task in MAPF is to navigate agents in an undirected graph in a n... | ['Pavel Surynek'] | 2019-07-11 | null | null | null | null | ['multi-agent-path-finding'] | ['playing-games'] | [ 1.90795749e-01 7.52866805e-01 7.82334432e-02 -1.05753914e-01
-5.57422280e-01 -9.44788456e-01 3.32979560e-01 1.74022943e-01
9.51490924e-03 1.12545204e+00 -4.40930575e-01 -7.32234180e-01
-7.62787580e-01 -1.43020713e+00 -8.01227629e-01 -5.15327573e-01
-5.84994495e-01 1.46501863e+00 4.70771223e-01 -5.33533692... | [4.98733377456665, 1.8910847902297974] |
f290727d-3642-44d0-b5f1-62af48c62da4 | deep-reinforcement-learning-based-mapless | 2304.03593 | null | https://arxiv.org/abs/2304.03593v1 | https://arxiv.org/pdf/2304.03593v1.pdf | Deep Reinforcement Learning-Based Mapless Crowd Navigation with Perceived Risk of the Moving Crowd for Mobile Robots | Classical map-based navigation methods are commonly used for robot navigation, but they often struggle in crowded environments due to the Frozen Robot Problem (FRP). Deep reinforcement learning-based methods address the FRP problem, however, suffer from the issues of generalization and scalability. To overcome these ch... | ['Owais Ahmed Malik', 'Ong Wee Hong', 'Hafiq Anas'] | 2023-04-07 | null | null | null | null | ['robot-navigation'] | ['robots'] | [-5.95881045e-01 -4.17386880e-03 5.72116613e-01 1.40174210e-01
-3.88885766e-01 -3.26029927e-01 4.47111249e-01 4.35284004e-02
-1.07117796e+00 1.00600731e+00 -1.02281980e-01 -2.94683397e-01
1.01601630e-01 -8.13257515e-01 -6.99273944e-01 -9.30083930e-01
-5.20045519e-01 5.99404156e-01 9.15367365e-01 -9.03435946... | [4.804263591766357, 1.0210928916931152] |
b0e6634a-f8a9-4de5-b922-afd46db3ce3d | learning-not-to-reconstruct-anomalies | 2110.09742 | null | https://arxiv.org/abs/2110.09742v2 | https://arxiv.org/pdf/2110.09742v2.pdf | Learning Not to Reconstruct Anomalies | Video anomaly detection is often seen as one-class classification (OCC) problem due to the limited availability of anomaly examples. Typically, to tackle this problem, an autoencoder (AE) is trained to reconstruct the input with training set consisting only of normal data. At test time, the AE is then expected to well ... | ['Seung-Ik Lee', 'Jae-Yeong Lee', 'Muhammad Zaigham Zaheer', 'Marcella Astrid'] | 2021-10-19 | null | null | null | null | ['one-class-classification'] | ['miscellaneous'] | [ 4.23841715e-01 -1.70287266e-01 2.62878358e-01 -8.81857574e-02
-3.45353246e-01 -4.16034281e-01 3.77695113e-01 1.33189306e-01
-2.26430103e-01 4.66964543e-01 -2.44567081e-01 -3.85156125e-01
2.89360434e-01 -9.51417863e-01 -1.15726268e+00 -8.74710321e-01
-2.96063304e-01 5.76248839e-02 1.92961961e-01 -2.13259563... | [7.693503379821777, 2.053621530532837] |
fe25ff58-f2fc-4bc8-a71e-1a1b8316c73c | a-multi-task-learning-network-using-shared | null | null | https://proceedings-of-deim.github.io/DEIM2021/papers/D13-2.pdf | https://proceedings-of-deim.github.io/DEIM2021/papers/D13-2.pdf | A multi-task learning network using shared BERT models for aspect-based sentiment analysis | Abstract Aspect-based sentiment analysis (ABSA) aims to predict the sentiment polarity of specific aspect words occurring
in a text. ABSA includes aspect-category sentiment analysis (ACSA) and aspect-target sentiment analysis (ATSA). There have
been many previous studies addressing both tasks through RNNs and other n... | ['Mizuho Iwaihara', 'Quanzhen Liu'] | 2020-12-27 | null | null | null | deim-forum-2020-12 | ['aspect-based-sentiment-analysis'] | ['natural-language-processing'] | [-8.35995972e-02 -1.45628005e-01 -3.14425319e-01 -7.26397812e-01
-1.18942404e+00 -4.33686465e-01 6.57262385e-01 1.28156260e-01
-4.33362991e-01 3.57401103e-01 5.03411233e-01 -4.70031053e-01
2.53639668e-01 -8.58611763e-01 -6.68778360e-01 -5.52857697e-01
3.03833425e-01 4.76381540e-01 2.91156042e-02 -5.86060762... | [11.459037780761719, 6.679445266723633] |
0d76e18a-a636-46e7-a3b6-e76e75497e08 | transfer-from-multiple-mdps | null | null | http://papers.nips.cc/paper/4435-transfer-from-multiple-mdps | http://papers.nips.cc/paper/4435-transfer-from-multiple-mdps.pdf | Transfer from Multiple MDPs | Transfer reinforcement learning (RL) methods leverage on the experience collected on a set of source tasks to speed-up RL algorithms. A simple and effective approach is to transfer samples from source tasks and include them in the training set used to solve a target task. In this paper, we investigate the theoretical p... | ['Marcello Restelli', 'Alessandro Lazaric'] | 2011-12-01 | null | null | null | neurips-2011-12 | ['transfer-reinforcement-learning'] | ['methodology'] | [ 4.08029705e-01 -1.67798880e-03 -2.67745584e-01 -1.60458520e-01
-9.35477376e-01 -6.81779802e-01 5.98731041e-01 -9.17906966e-03
-6.68213725e-01 1.39529514e+00 9.63337719e-02 1.04103638e-02
-6.55901730e-02 -5.63771725e-01 -9.56823111e-01 -6.29963279e-01
-1.22806072e-01 7.11440206e-01 3.10008712e-02 -4.03259814... | [4.107717037200928, 1.6583055257797241] |
e73d16ed-21a2-4754-bf22-329bfc316436 | subject-based-non-contrastive-self-supervised | 2305.10347 | null | https://arxiv.org/abs/2305.10347v1 | https://arxiv.org/pdf/2305.10347v1.pdf | Subject-based Non-contrastive Self-Supervised Learning for ECG Signal Processing | Extracting information from the electrocardiography (ECG) signal is an essential step in the design of digital health technologies in cardiology. In recent years, several machine learning (ML) algorithms for automatic extraction of information in ECG have been proposed. Supervised learning methods have successfully bee... | ['Sadasivan Puthusserypady', 'Jakob Bardram', 'Adrian Atienza'] | 2023-05-12 | null | null | null | null | ['electrocardiography-ecg'] | ['methodology'] | [ 2.79741883e-01 1.76493838e-01 5.24480548e-03 -5.13540924e-01
-3.05896044e-01 -3.18155259e-01 1.69947430e-01 6.94346607e-01
-3.15205604e-01 4.80612099e-01 -1.15605302e-01 -1.12142392e-01
-3.16371232e-01 -7.02002168e-01 -2.80715644e-01 -7.14276433e-01
-2.75549680e-01 2.97682703e-01 -3.52086164e-02 -3.38319659... | [14.208958625793457, 3.2772817611694336] |
97e22201-f252-46a8-8e60-fa918254c184 | wearing-the-same-outfit-in-different-ways-a | 2211.16989 | null | https://arxiv.org/abs/2211.16989v1 | https://arxiv.org/pdf/2211.16989v1.pdf | Wearing the Same Outfit in Different Ways -- A Controllable Virtual Try-on Method | An outfit visualization method generates an image of a person wearing real garments from images of those garments. Current methods can produce images that look realistic and preserve garment identity, captured in details such as collar, cuffs, texture, hem, and sleeve length. However, no current method can both control... | ['David Forsyth', 'Shao-Yu Chang', 'Jeffrey Zhang', 'Kedan Li'] | 2022-11-29 | null | null | null | null | ['virtual-try-on'] | ['computer-vision'] | [ 3.29671741e-01 -3.42568867e-02 1.65134236e-01 -1.93821304e-02
-5.49060442e-02 -1.01825690e+00 3.14319640e-01 -2.01725766e-01
2.00573429e-01 5.49298823e-01 2.80557275e-01 -2.45028213e-01
-5.16673587e-02 -8.27874482e-01 -6.65550530e-01 -3.68319511e-01
7.04914285e-03 5.20953357e-01 2.64083356e-01 -4.12423521... | [9.325028419494629, -3.2030563354492188] |
cf7fa349-9122-4223-8698-5ee83de66819 | spacing-loss-for-discovering-novel-categories | 2204.10595 | null | https://arxiv.org/abs/2204.10595v1 | https://arxiv.org/pdf/2204.10595v1.pdf | Spacing Loss for Discovering Novel Categories | Novel Class Discovery (NCD) is a learning paradigm, where a machine learning model is tasked to semantically group instances from unlabeled data, by utilizing labeled instances from a disjoint set of classes. In this work, we first characterize existing NCD approaches into single-stage and two-stage methods based on wh... | ['Vineeth N Balasubramanian', 'Kai Han', 'Piyush Rai', 'Soma Biswas', 'Gaurav Aggarwal', 'Sujoy Paul', 'K J Joseph'] | 2022-04-22 | null | null | null | null | ['novel-class-discovery', 'novel-class-discovery'] | ['computer-vision', 'methodology'] | [ 2.75986046e-01 2.13614781e-03 -5.65568447e-01 -8.06181371e-01
-9.50201571e-01 -8.39120328e-01 7.09884763e-01 1.86603621e-01
-3.98602009e-01 6.70505941e-01 -3.26367803e-02 -3.97677660e-01
-2.33822614e-01 -4.61171329e-01 -4.21033978e-01 -5.74323595e-01
4.60194796e-02 3.38534057e-01 -7.50083327e-02 4.75308746... | [9.621468544006348, 3.2050628662109375] |
6279ab56-c8a8-4039-b16d-e62ba4af3977 | controlling-synthetic-characters-in | 2101.02231 | null | https://arxiv.org/abs/2101.02231v1 | https://arxiv.org/pdf/2101.02231v1.pdf | Controlling Synthetic Characters in Simulations: A Case for Cognitive Architectures and Sigma | Simulations, along with other similar applications like virtual worlds and video games, require computational models of intelligence that generate realistic and credible behavior for the participating synthetic characters. Cognitive architectures, which are models of the fixed structure underlying intelligent behavior ... | ['Jeremy Nuttal', 'Seyed Sajjadi', 'Paul S. Rosenbloom', 'Volkan Ustun'] | 2021-01-06 | null | null | null | null | ['distributional-reinforcement-learning'] | ['methodology'] | [-1.17893383e-01 6.33394301e-01 5.34894109e-01 -1.68770514e-02
1.35441646e-01 -7.35141218e-01 1.15850699e+00 1.36543149e-02
-1.46258637e-01 5.27812719e-01 2.94047862e-01 -3.63419741e-01
-7.67986715e-01 -1.10960996e+00 -4.48923111e-01 -3.24077427e-01
-2.94867665e-01 8.89141083e-01 2.00752214e-01 -8.54757845... | [4.1568756103515625, 1.2320975065231323] |
ac69d44f-c9a3-46d8-b3a2-22ff8f8f6155 | history-repeats-overcoming-catastrophic | 2305.18675 | null | https://arxiv.org/abs/2305.18675v1 | https://arxiv.org/pdf/2305.18675v1.pdf | History Repeats: Overcoming Catastrophic Forgetting For Event-Centric Temporal Knowledge Graph Completion | Temporal knowledge graph (TKG) completion models typically rely on having access to the entire graph during training. However, in real-world scenarios, TKG data is often received incrementally as events unfold, leading to a dynamic non-stationary data distribution over time. While one could incorporate fine-tuning to e... | ['Aram Galstyan', 'Mohammad Rostami', 'Mehrnoosh Mirtaheri'] | 2023-05-30 | null | null | null | null | ['knowledge-graph-completion', 'temporal-knowledge-graph-completion'] | ['knowledge-base', 'knowledge-base'] | [-1.16518920e-03 6.15998171e-02 -2.16058969e-01 -3.17203030e-02
-2.61760056e-01 -4.49410647e-01 3.99243623e-01 3.56705546e-01
-3.69648904e-01 7.97949731e-01 3.75847518e-01 -1.88887373e-01
-3.27266932e-01 -1.00661981e+00 -9.42200243e-01 -6.10604525e-01
-4.09856349e-01 2.36653313e-01 3.06827128e-01 2.24130526... | [9.715310096740723, 3.7691140174865723] |
6683be9e-1f2a-4841-8c8b-f7e3900b6425 | imgcl-revisiting-graph-contrastive-learning | 2205.11332 | null | https://arxiv.org/abs/2205.11332v2 | https://arxiv.org/pdf/2205.11332v2.pdf | ImGCL: Revisiting Graph Contrastive Learning on Imbalanced Node Classification | Graph contrastive learning (GCL) has attracted a surge of attention due to its superior performance for learning node/graph representations without labels. However, in practice, the underlying class distribution of unlabeled nodes for the given graph is usually imbalanced. This highly imbalanced class distribution inev... | ['Jian Li', 'Peilin Zhao', 'Ziqi Gao', 'Lanqing Li', 'Liang Zeng'] | 2022-05-23 | null | null | null | null | ['online-clustering'] | ['computer-vision'] | [ 1.63488865e-01 3.80176932e-01 -7.24758625e-01 -2.40351394e-01
-3.94983441e-01 -4.42350715e-01 3.58750671e-01 6.80321813e-01
2.70113707e-01 5.49480140e-01 1.67943463e-02 -3.14663112e-01
-3.37227881e-01 -1.05881977e+00 -5.51236570e-01 -9.17925596e-01
-1.60796255e-01 5.36255896e-01 -8.09422433e-02 -5.05155958... | [7.260021686553955, 6.066234111785889] |
95fc80d9-6356-47a0-b8df-b27e3ae4fa64 | a-novel-online-action-detection-framework | 2003.07734 | null | https://arxiv.org/abs/2003.07734v1 | https://arxiv.org/pdf/2003.07734v1.pdf | A Novel Online Action Detection Framework from Untrimmed Video Streams | Online temporal action localization from an untrimmed video stream is a challenging problem in computer vision. It is challenging because of i) in an untrimmed video stream, more than one action instance may appear, including background scenes, and ii) in online settings, only past and current information is available.... | ['Seong-Whan Lee', 'Nam-Gyu Cho', 'Da-Hye Yoon'] | 2020-03-17 | null | null | null | null | ['online-action-detection'] | ['computer-vision'] | [ 5.41651845e-01 -3.08719993e-01 -4.23355818e-01 -6.54581636e-02
-4.79892641e-01 -5.17877162e-01 5.72359324e-01 -1.87074468e-02
-6.43066227e-01 6.45422280e-01 3.64086658e-01 2.77200341e-02
-2.43909638e-02 -2.57244319e-01 -6.70525551e-01 -6.69795275e-01
-4.87087160e-01 2.45569833e-02 8.07650745e-01 1.31519869... | [8.355982780456543, 0.601382315158844] |
ab7182bd-5eda-4932-a1d9-1beb4237dc5f | container-few-shot-named-entity-recognition-1 | null | null | https://openreview.net/forum?id=nxEqWd4Ddth | https://openreview.net/pdf?id=nxEqWd4Ddth | CONTaiNER: Few-Shot Named Entity Recognition via Contrastive Learning | Named Entity Recognition (NER) in Few-Shot setting is imperative for entity tagging in low resource domains. Existing approaches only learn class-specific semantic features and intermediate representations from source domains. This affects generalizability to unseen target domains, resulting in suboptimal performances.... | ['Anonymous'] | 2021-10-16 | null | null | null | acl-arr-october-2021-10 | ['few-shot-ner'] | ['natural-language-processing'] | [-3.10104400e-01 -9.93754566e-02 -7.62102827e-02 -4.62268412e-01
-1.25478160e+00 -8.71186018e-01 5.37554026e-01 4.04175043e-01
-9.48710680e-01 8.86204898e-01 3.20020288e-01 1.72027498e-02
7.56166577e-02 -6.05811775e-01 -1.74479023e-01 -3.50045085e-01
-2.06745982e-01 4.62930709e-01 2.53431886e-01 3.94574180... | [9.670868873596191, 9.410017013549805] |
cead63e9-4ca4-4045-998f-1881c1333017 | global-structure-aware-drum-transcription | 2105.05791 | null | https://arxiv.org/abs/2105.05791v1 | https://arxiv.org/pdf/2105.05791v1.pdf | Global Structure-Aware Drum Transcription Based on Self-Attention Mechanisms | This paper describes an automatic drum transcription (ADT) method that directly estimates a tatum-level drum score from a music signal, in contrast to most conventional ADT methods that estimate the frame-level onset probabilities of drums. To estimate a tatum-level score, we propose a deep transcription model that con... | ['Kazuyoshi Yoshii', 'Ryo Nishikimi', 'Ryoto Ishizuka'] | 2021-05-12 | null | null | null | null | ['drum-transcription'] | ['music'] | [ 3.68582398e-01 -1.81272641e-01 2.15758085e-01 1.06324948e-01
-1.41802299e+00 -4.14970666e-01 4.22634371e-02 -3.51229191e-01
-1.90792084e-01 3.18372041e-01 4.84985679e-01 2.32719913e-01
-7.98946992e-02 -4.99463767e-01 -7.55828202e-01 -8.03627968e-01
-4.07065637e-02 9.76350084e-02 1.63405687e-01 -4.95843329... | [15.794845581054688, 5.513692855834961] |
f733a69c-9074-490e-bef9-b03ee446c788 | learning-to-recover-causal-relationship-from | 2305.02640 | null | https://arxiv.org/abs/2305.02640v2 | https://arxiv.org/pdf/2305.02640v2.pdf | Learning to Recover Causal Relationship from Indefinite Data in the Presence of Latent Confounders | In Causal Discovery with latent variables, We define two data paradigms: definite data: a single-skeleton structure with observed nodes single-value, and indefinite data: a set of multi-skeleton structures with observed nodes multi-value. Multi,skeletons induce low sample utilization and multi values induce incapabilit... | ['Qing Yang', 'Xinyu Yang', 'Hang Chen'] | 2023-05-04 | null | null | null | null | ['causal-discovery'] | ['knowledge-base'] | [ 3.07122290e-01 4.78139609e-01 -7.38996267e-01 -3.71123761e-01
-2.70055592e-01 -5.76135933e-01 7.68765807e-01 -1.65284231e-01
2.21534938e-01 1.01220322e+00 5.68174303e-01 -4.96968895e-01
-7.71830261e-01 -1.06108606e+00 -8.46435070e-01 -9.50252235e-01
-4.19627011e-01 3.17907274e-01 -2.39853770e-01 2.48610958... | [7.880381107330322, 5.347923755645752] |
f331ab1b-3d6c-4d05-92f1-23152ccc8520 | dkt-diverse-knowledge-transfer-transformer | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Gao_DKT_Diverse_Knowledge_Transfer_Transformer_for_Class_Incremental_Learning_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Gao_DKT_Diverse_Knowledge_Transfer_Transformer_for_Class_Incremental_Learning_CVPR_2023_paper.pdf | DKT: Diverse Knowledge Transfer Transformer for Class Incremental Learning | Deep neural networks suffer from catastrophic forgetting in class incremental learning, where the classification accuracy of old classes drastically deteriorates when the networks learn the knowledge of new classes. Many works have been proposed to solve the class incremental learning problem. However, most of them... | ['Yihong Gong', 'Xing Wei', 'Jie Cheng', 'Songlin Dong', 'Yuhang He', 'Xinyuan Gao'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['class-incremental-learning', 'incremental-learning', 'general-knowledge'] | ['computer-vision', 'methodology', 'miscellaneous'] | [ 1.92684561e-01 -3.33995610e-01 1.92408010e-01 -3.20954859e-01
-2.13654637e-01 -1.45987034e-01 3.44920695e-01 -1.61867768e-01
-7.08310962e-01 1.03921068e+00 -1.57542318e-01 9.27869827e-02
-3.41119558e-01 -6.43986821e-01 -7.27392852e-01 -9.86399889e-01
3.59986216e-01 1.82884708e-01 8.05290699e-01 -8.38157460... | [9.867061614990234, 3.335108757019043] |
5108b586-f1f8-4f91-b809-d97a9ca68a4c | gaitgci-generative-counterfactual-1 | 2306.03428 | null | https://arxiv.org/abs/2306.03428v1 | https://arxiv.org/pdf/2306.03428v1.pdf | GaitGCI: Generative Counterfactual Intervention for Gait Recognition | Gait is one of the most promising biometrics that aims to identify pedestrians from their walking patterns. However, prevailing methods are susceptible to confounders, resulting in the networks hardly focusing on the regions that reflect effective walking patterns. To address this fundamental problem in gait recognitio... | ['Xi Li', 'Yining Lin', 'Yunlong Yu', 'Wei Su', 'Pengyi Zhang', 'Huanzhang Dou'] | 2023-06-06 | gaitgci-generative-counterfactual | http://openaccess.thecvf.com//content/CVPR2023/html/Dou_GaitGCI_Generative_Counterfactual_Intervention_for_Gait_Recognition_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Dou_GaitGCI_Generative_Counterfactual_Intervention_for_Gait_Recognition_CVPR_2023_paper.pdf | cvpr-2023-1 | ['gait-recognition'] | ['computer-vision'] | [ 4.22015749e-02 -3.06528490e-02 -3.38061750e-01 -2.84010708e-01
-3.89078766e-01 -1.83197688e-02 5.93705356e-01 -5.40421367e-01
-2.22743616e-01 9.51619089e-01 6.10608518e-01 -3.46417040e-01
-1.45622581e-01 -1.04062057e+00 -7.64723241e-01 -7.69275546e-01
-4.29464668e-01 1.40773252e-01 -3.21881361e-02 7.03985840... | [14.43189811706543, 1.3009371757507324] |
67ede698-154b-404f-a5c8-f2c7f74c93ec | a-modular-vision-language-navigation-and | 2101.07891 | null | https://arxiv.org/abs/2101.07891v1 | https://arxiv.org/pdf/2101.07891v1.pdf | A modular vision language navigation and manipulation framework for long horizon compositional tasks in indoor environment | In this paper we propose a new framework - MoViLan (Modular Vision and Language) for execution of visually grounded natural language instructions for day to day indoor household tasks. While several data-driven, end-to-end learning frameworks have been proposed for targeted navigation tasks based on the vision and lang... | ['Soumik Sarkar', 'Qisai Liu', 'Fateme Fotouhif', 'Homagni Saha'] | 2021-01-19 | null | null | null | null | ['vision-language-navigation'] | ['computer-vision'] | [ 1.49366140e-01 -1.48592712e-02 2.59232193e-01 -4.59405094e-01
-6.48993433e-01 -8.88901532e-01 7.64111459e-01 1.25651598e-01
-5.42023242e-01 4.21191990e-01 2.66048759e-01 -8.09178412e-01
-6.85476512e-02 -4.39974099e-01 -1.26604581e+00 -3.83896261e-01
-1.06642112e-01 5.80746472e-01 3.24362338e-01 -6.26527488... | [4.459507465362549, 0.5895899534225464] |
2163a059-d1f0-4c6e-9eee-afc10c30655f | a-comprehensive-survey-of-artificial | 2307.03195 | null | https://arxiv.org/abs/2307.03195v1 | https://arxiv.org/pdf/2307.03195v1.pdf | A Comprehensive Survey of Artificial Intelligence Techniques for Talent Analytics | In today's competitive and fast-evolving business environment, it is a critical time for organizations to rethink how to make talent-related decisions in a quantitative manner. Indeed, the recent development of Big Data and Artificial Intelligence (AI) techniques have revolutionized human resource management. The avail... | ['Hui Xiong', 'HengShu Zhu', 'Chen Zhu', 'Ying Sun', 'Qi Zhang', 'Dazhong Shen', 'Rui Zha', 'Le Zhang', 'Chuan Qin'] | 2023-07-03 | null | null | null | null | ['management', 'decision-making'] | ['miscellaneous', 'reasoning'] | [-1.91292688e-01 -3.43496948e-01 -5.91124237e-01 -2.87165374e-01
1.56493321e-01 -1.45195752e-01 3.44824702e-01 7.24877298e-01
-5.12913644e-01 5.56997478e-01 7.96986297e-02 -7.95451775e-02
-5.68837821e-01 -1.14039087e+00 -6.74429014e-02 -2.68184870e-01
3.02994519e-01 8.44884157e-01 -5.68849802e-01 -6.49909675... | [8.999442100524902, 6.422003269195557] |
34cc80dc-d558-42f3-b9ce-7289a783388e | diffusion-convolutional-recurrent-neural | 1707.01926 | null | http://arxiv.org/abs/1707.01926v3 | http://arxiv.org/pdf/1707.01926v3.pdf | Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting | Spatiotemporal forecasting has various applications in neuroscience, climate
and transportation domain. Traffic forecasting is one canonical example of such
learning task. The task is challenging due to (1) complex spatial dependency on
road networks, (2) non-linear temporal dynamics with changing road conditions
and (... | ['Rose Yu', 'Cyrus Shahabi', 'Yaguang Li', 'Yan Liu'] | 2017-07-06 | diffusion-convolutional-recurrent-neural-1 | https://openreview.net/forum?id=SJiHXGWAZ | https://openreview.net/pdf?id=SJiHXGWAZ | iclr-2018-1 | ['spatio-temporal-forecasting'] | ['time-series'] | [ 2.02358402e-02 -1.61261529e-01 -4.22545314e-01 -5.30832171e-01
-2.03666523e-01 -2.28152588e-01 1.03859270e+00 -5.69974065e-01
-6.75181895e-02 7.44700193e-01 6.53825641e-01 -8.42224956e-01
-1.01605780e-01 -8.85259986e-01 -7.86042094e-01 -4.80207503e-01
-2.94823140e-01 4.31276232e-01 5.78496039e-01 -4.13292825... | [6.481614589691162, 2.084784984588623] |
c2157453-703b-4a4b-b212-442df4f90155 | sequential-knockoffs-for-variable-selection | 2303.14281 | null | https://arxiv.org/abs/2303.14281v1 | https://arxiv.org/pdf/2303.14281v1.pdf | Sequential Knockoffs for Variable Selection in Reinforcement Learning | In real-world applications of reinforcement learning, it is often challenging to obtain a state representation that is parsimonious and satisfies the Markov property without prior knowledge. Consequently, it is common practice to construct a state which is larger than necessary, e.g., by concatenating measurements over... | ['Eric B. Laber', 'Chengchun Shi', 'Zhengling Qi', 'Hengrui Cai', 'Tao Ma'] | 2023-03-24 | null | null | null | null | ['variable-selection'] | ['methodology'] | [ 1.23482846e-01 2.91983318e-02 -6.54588580e-01 2.42607638e-01
-6.55140996e-01 -7.56931067e-01 3.85519534e-01 1.38118789e-01
-5.75545609e-01 1.17464852e+00 -3.40528101e-01 -6.92335188e-01
-3.77252251e-01 -5.62083364e-01 -7.25617230e-01 -1.21759403e+00
-3.58746856e-01 5.64040482e-01 -2.72588357e-02 3.10841590... | [4.497102737426758, 2.4600398540496826] |
b0805d65-791d-468b-b6e6-469f2fc9a0c7 | ebola-optimization-search-algorithm-eosa-a | 2106.01416 | null | https://arxiv.org/abs/2106.01416v2 | https://arxiv.org/pdf/2106.01416v2.pdf | Ebola Optimization Search Algorithm (EOSA): A new metaheuristic algorithm based on the propagation model of Ebola virus disease | The Ebola virus and the disease in effect tend to randomly move individuals in the population around susceptible, infected, quarantined, hospitalized, recovered, and dead sub-population. Motivated by the effectiveness in propagating the disease through the virus, a new bio-inspired and population-based optimization alg... | ['Absalom E. Ezugwu', 'Olaide N. Oyelade'] | 2021-06-02 | null | null | null | null | ['metaheuristic-optimization'] | ['methodology'] | [ 1.87621653e-01 -6.26964688e-01 1.57974977e-02 4.97570217e-01
2.78230160e-01 -2.35842973e-01 3.76049310e-01 9.69086289e-02
-5.56869030e-01 1.45501196e+00 -5.04269958e-01 -1.86064422e-01
-8.26545119e-01 -8.89656067e-01 -6.14254437e-02 -1.24578452e+00
-4.71910506e-01 6.69768751e-01 -6.71934560e-02 -5.43477654... | [5.67725944519043, 3.475543975830078] |
c780d5b8-191a-4fa6-b049-6a485ab4462e | a-splitting-based-iterative-algorithm-for-gpu | 1905.00934 | null | https://arxiv.org/abs/1905.00934v1 | https://arxiv.org/pdf/1905.00934v1.pdf | A Splitting-Based Iterative Algorithm for GPU-Accelerated Statistical Dual-Energy X-Ray CT Reconstruction | When dealing with material classification in baggage at airports, Dual-Energy Computed Tomography (DECT) allows characterization of any given material with coefficients based on two attenuative effects: Compton scattering and photoelectric absorption. However, straightforward projection-domain decomposition methods for... | ['Avinash Kak', 'Tanmay Prakash', 'Ankit Manerikar', 'Fangda Li'] | 2019-05-02 | null | null | null | null | ['material-classification'] | ['computer-vision'] | [ 2.76034683e-01 -3.29240561e-01 3.68578792e-01 8.10837001e-02
-9.09393013e-01 -9.20125321e-02 4.29555416e-01 3.00759096e-02
-2.76942253e-01 8.91124129e-01 1.92154139e-01 -1.71951145e-01
-1.98062360e-01 -9.23246861e-01 -3.78292412e-01 -1.11417305e+00
-9.27420035e-02 9.54814136e-01 2.21823499e-01 1.86066270... | [12.938994407653809, -2.6838901042938232] |
b69317d7-7d44-429d-a114-da69ef3ed80c | ner-to-mrc-named-entity-recognition | 2305.03970 | null | https://arxiv.org/abs/2305.03970v1 | https://arxiv.org/pdf/2305.03970v1.pdf | NER-to-MRC: Named-Entity Recognition Completely Solving as Machine Reading Comprehension | Named-entity recognition (NER) detects texts with predefined semantic labels and is an essential building block for natural language processing (NLP). Notably, recent NER research focuses on utilizing massive extra data, including pre-training corpora and incorporating search engines. However, these methods suffer from... | ['Hayato Yamana', 'Tetsuya Sakai', 'Xinyu Zhu', 'Junjie Wang', 'Yuxiang Zhang'] | 2023-05-06 | null | null | null | null | ['named-entity-recognition-ner', 'reading-comprehension', 'machine-reading-comprehension'] | ['natural-language-processing', 'natural-language-processing', 'natural-language-processing'] | [ 4.13852930e-01 3.09991628e-01 1.88837443e-02 -2.85478294e-01
-1.17888820e+00 -6.53317511e-01 5.30089617e-01 4.48641479e-01
-1.05744410e+00 6.96739197e-01 5.18730581e-01 -5.25792360e-01
-1.52644202e-01 -7.09745347e-01 -6.16984785e-01 -3.04506756e-02
5.38401842e-01 6.16788328e-01 2.93333262e-01 -3.62109452... | [9.675138473510742, 9.487702369689941] |
62932547-1322-4546-ac2b-c6bbf60b9ab6 | ssn-dibertsity-lt-edi-eacl2021-hope-speech | null | null | https://aclanthology.org/2021.ltedi-1.12 | https://aclanthology.org/2021.ltedi-1.12.pdf | ssn_diBERTsity@LT-EDI-EACL2021:Hope Speech Detection on multilingual YouTube comments via transformer based approach | In recent times, there exists an abundance of research to classify abusive and offensive texts focusing on negative comments but only minimal research using the positive reinforcement approach. The task was aimed at classifying texts into ‘Hope_speech’, ‘Non_hope_speech’, and ‘Not in language’. The datasets were provid... | ['Senthil Kumar B', 'Thenmozhi D.', 'Avantika Balaji', 'Akshay Ramakrishnan', 'Arunima S'] | null | null | null | null | eacl-ltedi-2021-4 | ['hope-speech-detection'] | ['natural-language-processing'] | [-2.45802224e-01 2.92093962e-01 -6.08960032e-01 -2.12625653e-01
-5.45427740e-01 -6.65044069e-01 1.01936471e+00 3.22948247e-01
-6.08347416e-01 8.52127612e-01 6.86865807e-01 -5.10457575e-01
-5.94986156e-02 -4.01216984e-01 -9.83079374e-02 -3.97205085e-01
7.93597847e-03 4.43402499e-01 -1.70716569e-01 -8.16102743... | [8.918639183044434, 10.740311622619629] |
529cceb4-3a30-4f86-b8ef-6bb3f362e32a | towards-a-standard-evaluation-method-for | null | null | https://aclanthology.info/papers/N15-1060/n15-1060 | https://www.aclweb.org/anthology/N15-1060 | Towards a standard evaluation method for grammatical error detection and correction | null | ['Ted Briscoe', 'Mariano Felice'] | 2015-05-01 | null | null | null | hlt-2015-5 | ['grammatical-error-detection'] | ['natural-language-processing'] | [-2.44508207e-01 3.89024585e-01 -2.65282035e-01 -2.15905145e-01
-8.60921741e-02 -7.76765764e-01 4.48510379e-01 -7.23253429e-01
-5.48377395e-01 1.31954515e+00 3.66348401e-02 -9.49533224e-01
-2.40340635e-01 -1.05564880e+00 -8.44053447e-01 -8.75781775e-01
-7.42435038e-01 6.86515033e-01 1.44298598e-01 -6.52004302... | [-1.5391756296157837, 15.869182586669922] |
74c4c829-1271-4a8a-9872-6050be196b9e | enhancing-patent-retrieval-using-text-and | 2211.01976 | null | https://arxiv.org/abs/2211.01976v1 | https://arxiv.org/pdf/2211.01976v1.pdf | Enhancing Patent Retrieval using Text and Knowledge Graph Embeddings: A Technical Note | Patent retrieval influences several applications within engineering design research, education, and practice as well as applications that concern innovation, intellectual property, and knowledge management etc. In this article, we propose a method to retrieve patents relevant to an initial set of patents, by synthesizi... | ['Jianxi Luo', 'Guangtong Li', 'L Siddharth'] | 2022-11-03 | null | null | null | null | ['knowledge-graph-embedding', 'knowledge-graph-embeddings', 'knowledge-graph-embeddings'] | ['graphs', 'graphs', 'methodology'] | [ 2.28460148e-01 1.06963858e-01 -4.36125875e-01 2.99750775e-01
-4.13305819e-01 -9.62044835e-01 9.46926236e-01 6.18485451e-01
-1.85220808e-01 5.30278206e-01 2.96359211e-01 -6.27961218e-01
-9.57534134e-01 -1.02520788e+00 -7.21517026e-01 -2.00888157e-01
1.90759316e-01 1.01323865e-01 -1.93140283e-01 8.37438740... | [9.743995666503906, 8.28078556060791] |
c28952ec-9034-414e-8e3e-a0609bbda3b0 | a-robust-likelihood-model-for-novelty | 2306.03331 | null | https://arxiv.org/abs/2306.03331v1 | https://arxiv.org/pdf/2306.03331v1.pdf | A Robust Likelihood Model for Novelty Detection | Current approaches to novelty or anomaly detection are based on deep neural networks. Despite their effectiveness, neural networks are also vulnerable to imperceptible deformations of the input data. This is a serious issue in critical applications, or when data alterations are generated by an adversarial attack. While... | ['Gianfranco Doretto', 'Donald A. Adjeroh', 'Shivang Patel', 'Ranya Almohsen'] | 2023-06-06 | null | null | null | null | ['adversarial-attack'] | ['adversarial'] | [ 3.86146426e-01 -1.25231752e-02 2.50503570e-01 -6.30194023e-02
-4.45217371e-01 -6.70094728e-01 8.19727182e-01 6.10958397e-01
-6.88230217e-01 6.82102859e-01 -2.43415311e-01 -3.27549934e-01
-1.63671017e-01 -7.60731757e-01 -1.05649805e+00 -8.79199862e-01
-2.32544973e-01 1.73518077e-01 6.63151145e-01 -1.40278712... | [7.681933403015137, 2.3269927501678467] |
22b0a7c4-77f3-4819-9e5d-93cb462cb3af | l1-regularized-reconstruction-error-as-alpha | 1702.02744 | null | http://arxiv.org/abs/1702.02744v1 | http://arxiv.org/pdf/1702.02744v1.pdf | L1-regularized Reconstruction Error as Alpha Matte | Sampling-based alpha matting methods have traditionally followed the
compositing equation to estimate the alpha value at a pixel from a pair of
foreground (F) and background (B) samples. The (F,B) pair that produces the
least reconstruction error is selected, followed by alpha estimation. The
significance of that resid... | ['Jubin Johnson', 'Hisham Cholakkal', 'Deepu Rajan'] | 2017-02-09 | null | null | null | null | ['video-matting'] | ['computer-vision'] | [ 3.02949309e-01 -4.75396514e-01 -9.73303616e-02 -2.95944214e-01
-9.04022396e-01 -1.71610788e-01 4.68006998e-01 2.42065579e-01
-3.49947244e-01 7.71757662e-01 1.75363511e-01 1.75989553e-01
1.95741907e-01 -7.82447398e-01 -8.81560326e-01 -1.22082579e+00
-4.01170813e-02 1.34954780e-01 2.79714704e-01 2.93716133... | [10.830143928527832, -1.7092657089233398] |
dc5943fd-60c5-4d1c-b0c3-20377244103d | chinese-characters-mapping-table-of-japanese | null | null | https://aclanthology.org/L12-1140 | https://aclanthology.org/L12-1140.pdf | Chinese Characters Mapping Table of Japanese, Traditional Chinese and Simplified Chinese | Chinese characters are used both in Japanese and Chinese, which are called Kanji and Hanzi respectively. Chinese characters contain significant semantic information, a mapping table between Kanji and Hanzi can be very useful for many Japanese-Chinese bilingual applications, such as machine translation and cross-lingual... | ['Sadao Kurohashi', 'Toshiaki Nakazawa', 'Chenhui Chu'] | 2012-05-01 | null | null | null | lrec-2012-5 | ['cross-lingual-information-retrieval'] | ['natural-language-processing'] | [-1.62726760e-01 -3.81342620e-01 -1.41031936e-01 -9.63100493e-02
-4.26213562e-01 -7.49242663e-01 4.61586535e-01 -4.94561940e-01
-6.90649569e-01 1.25787389e+00 5.89609504e-01 -2.17634901e-01
8.89022350e-02 -9.59914088e-01 -2.03881681e-01 -4.27146345e-01
6.12040937e-01 8.41069818e-01 5.01943350e-01 -5.73716760... | [9.961840629577637, 9.812102317810059] |
1037d104-daaa-4c71-a41d-e1a14d0a3704 | 6d-object-pose-estimation-from-approximate-3d | 2303.13241 | null | https://arxiv.org/abs/2303.13241v3 | https://arxiv.org/pdf/2303.13241v3.pdf | 6D Object Pose Estimation from Approximate 3D Models for Orbital Robotics | We present a novel technique to estimate the 6D pose of objects from single images where the 3D geometry of the object is only given approximately and not as a precise 3D model. To achieve this, we employ a dense 2D-to-3D correspondence predictor that regresses 3D model coordinates for every pixel. In addition to the 3... | ['Rudolph Triebel', 'Manuel Stoiber', 'Martin Sundermeyer', 'Maximilian Durner', 'Maximilian Ulmer'] | 2023-03-23 | null | null | null | null | ['6d-pose-estimation'] | ['computer-vision'] | [-1.03390977e-01 1.01423189e-01 6.15807204e-03 -4.07030821e-01
-8.94817591e-01 -5.45947313e-01 6.66804433e-01 -1.75110325e-01
-6.53758705e-01 3.23644370e-01 -1.97829902e-01 -6.88576400e-02
7.66220018e-02 -4.30688798e-01 -1.00136209e+00 -6.88758552e-01
-1.13099508e-01 1.17889690e+00 5.98766327e-01 -7.42742792... | [7.362337589263916, -2.522981643676758] |
6c69634f-2a84-481c-8fe0-532614a3daba | inverse-category-frequency-based-supervised | 1012.2609 | null | http://arxiv.org/abs/1012.2609v4 | http://arxiv.org/pdf/1012.2609v4.pdf | Inverse-Category-Frequency based supervised term weighting scheme for text categorization | Term weighting schemes often dominate the performance of many classifiers,
such as kNN, centroid-based classifier and SVMs. The widely used term weighting
scheme in text categorization, i.e., tf.idf, is originated from information
retrieval (IR) field. The intuition behind idf for text categorization seems
less reasona... | ['HUI ZHANG', 'Deqing Wang'] | 2010-12-13 | null | null | null | null | ['cross-corpus'] | ['computer-vision'] | [ 1.52249590e-01 -4.02267933e-01 -6.74005330e-01 -5.63075244e-01
-4.64317441e-01 -6.20457470e-01 1.05124271e+00 7.09572911e-01
-8.78948152e-01 5.62113166e-01 5.11997879e-01 -4.63435829e-01
-8.30302358e-01 -5.29531181e-01 1.82583869e-01 -6.58052325e-01
2.63931364e-01 3.26628655e-01 3.10042858e-01 -2.11769745... | [10.46532154083252, 7.286811351776123] |
0e6700c3-fe2d-4105-8756-6634e0d001e4 | anomaly-detection-with-conditioned-denoising | 2305.15956 | null | https://arxiv.org/abs/2305.15956v1 | https://arxiv.org/pdf/2305.15956v1.pdf | Anomaly Detection with Conditioned Denoising Diffusion Models | Reconstruction-based methods have struggled to achieve competitive performance on anomaly detection. In this paper, we introduce Denoising Diffusion Anomaly Detection (DDAD). We propose a novel denoising process for image reconstruction conditioned on a target image. This results in a coherent restoration that closely ... | ['Jawad Tayyub', 'Thomas Brox', 'Arian Mousakhan'] | 2023-05-25 | null | null | null | null | ['image-reconstruction'] | ['computer-vision'] | [ 7.02876687e-01 -2.79281318e-01 6.15498126e-01 -3.29286128e-01
-1.32981694e+00 -5.89547753e-01 1.18355608e+00 2.99235106e-01
-4.31318551e-01 1.34669989e-01 1.37983501e-01 -1.03450194e-01
-8.49855989e-02 -4.38401043e-01 -8.54724884e-01 -1.10005617e+00
-1.09036162e-01 -1.63068816e-01 8.97358358e-02 -1.50415331... | [7.720911026000977, 2.1462795734405518] |
6fe26bc5-6ed3-4311-b0f4-134cd15be126 | gnn-based-android-malware-detection-with | 2201.07537 | null | https://arxiv.org/abs/2201.07537v9 | https://arxiv.org/pdf/2201.07537v9.pdf | Graph Neural Network-based Android Malware Classification with Jumping Knowledge | This paper presents a new Android malware detection method based on Graph Neural Networks (GNNs) with Jumping-Knowledge (JK). Android function call graphs (FCGs) consist of a set of program functions and their inter-procedural calls. Thus, this paper proposes a GNN-based method for Android malware detection by capturin... | ['Marius Portmann', 'Marcus Gallagher', 'Mohanad Sarhan', 'Siamak Layeghy', 'Wai Weng Lo'] | 2022-01-19 | null | null | null | null | ['android-malware-detection'] | ['miscellaneous'] | [ 2.94791132e-01 -2.34569505e-01 -6.55168772e-01 -4.12552692e-02
5.07550351e-02 -2.56132007e-01 5.80270588e-01 6.23808475e-04
4.24050912e-02 3.43637377e-01 -2.20009431e-01 -7.84876347e-01
-3.72413188e-01 -9.25875127e-01 -5.94489813e-01 -1.45380899e-01
-4.90691543e-01 -1.96431428e-01 3.91212463e-01 -1.92292854... | [14.418560981750488, 9.677380561828613] |
836ddf65-a7bc-4ae3-aab4-8c1f68b69003 | self-supervised-learning-for-video | 1905.00875 | null | https://arxiv.org/abs/1905.00875v5 | https://arxiv.org/pdf/1905.00875v5.pdf | Self-supervised Learning for Video Correspondence Flow | The objective of this paper is self-supervised learning of feature embeddings that are suitable for matching correspondences along the videos, which we term correspondence flow. By leveraging the natural spatial-temporal coherence in videos, we propose to train a ``pointer'' that reconstructs a target frame by copying ... | ['Weidi Xie', 'Zihang Lai'] | 2019-05-02 | null | null | null | null | ['video-correspondence-flow', 'unsupervised-video-object-segmentation'] | ['computer-vision', 'computer-vision'] | [ 1.24480791e-01 -2.59810518e-02 -4.83446062e-01 -1.59719020e-01
-7.20698178e-01 -6.91226542e-01 5.95530570e-01 -9.60702971e-02
-4.92377132e-01 4.35323238e-01 1.64831176e-01 5.10408692e-02
6.41822964e-02 -3.93062741e-01 -1.13042986e+00 -5.37164450e-01
-4.10921127e-01 6.61024749e-02 6.50861382e-01 1.38046965... | [8.975142478942871, -0.22064420580863953] |
5345cb4a-57b9-4a17-bb72-cc0b7c607968 | motiontrack-learning-motion-predictor-for | 2306.02585 | null | https://arxiv.org/abs/2306.02585v1 | https://arxiv.org/pdf/2306.02585v1.pdf | MotionTrack: Learning Motion Predictor for Multiple Object Tracking | Significant advancements have been made in multi-object tracking (MOT) with the development of detection and re-identification (ReID) techniques. Despite these developments, the task of accurately tracking objects in scenarios with homogeneous appearance and heterogeneous motion remains challenging due to the insuffici... | ['DaCheng Tao', 'Zhigang Luo', 'Huayue Cai', 'Xiang Zhang', 'Long Lan', 'Yujie Zhong', 'Qiong Cao', 'Changcheng Xiao'] | 2023-06-05 | null | null | null | null | ['motion-prediction', 'object-tracking', 'multiple-object-tracking', 'multi-object-tracking'] | ['computer-vision', 'computer-vision', 'computer-vision', 'computer-vision'] | [-1.03271842e-01 -7.11854935e-01 -5.68859518e-01 3.97181185e-03
-6.39347255e-01 -5.13065219e-01 5.94024897e-01 -1.16075777e-01
-6.20548844e-01 5.66891491e-01 -2.90845148e-02 3.42984274e-02
-1.53890342e-01 -2.33429134e-01 -8.33731592e-01 -8.39035273e-01
-2.50351518e-01 3.52655709e-01 7.14219928e-01 1.68107390... | [6.31555700302124, -2.06581974029541] |
e608730a-a700-45a4-b03c-40b9b69a85c4 | empowering-graph-representation-learning-with | null | null | https://openreview.net/forum?id=BJeRykBKDH | https://openreview.net/pdf?id=BJeRykBKDH | Empowering Graph Representation Learning with Paired Training and Graph Co-Attention | Through many recent advances in graph representation learning, performance achieved on tasks involving graph-structured data has substantially increased in recent years---mostly on tasks involving node-level predictions. The setup of prediction tasks over entire graphs (such as property prediction for a molecule, or si... | ['Jian Tang', 'Pietro Lio', 'Petar Velickovic', 'Yu-Hsiang Huang', 'Andreea Deac'] | 2019-09-25 | null | null | null | null | ['graph-regression'] | ['graphs'] | [ 7.61071384e-01 5.42336285e-01 -4.86087352e-01 -1.43393859e-01
-6.02774560e-01 -6.53166592e-01 4.65171874e-01 9.94656980e-01
-1.25203328e-02 8.16196322e-01 2.29459733e-01 -7.47092128e-01
-3.81311387e-01 -7.82592356e-01 -9.86989856e-01 -7.59533703e-01
-6.63870156e-01 6.64069831e-01 1.92807913e-01 -2.17047915... | [6.656718730926514, 6.23756217956543] |
77f67343-65e2-445f-af0c-8e1faf047294 | temporally-consistent-video-transformer-for | 2210.02396 | null | https://arxiv.org/abs/2210.02396v2 | https://arxiv.org/pdf/2210.02396v2.pdf | Temporally Consistent Transformers for Video Generation | To generate accurate videos, algorithms have to understand the spatial and temporal dependencies in the world. Current algorithms enable accurate predictions over short horizons but tend to suffer from temporal inconsistencies. When generated content goes out of view and is later revisited, the model invents different ... | ['Pieter Abbeel', 'Stephen James', 'Danijar Hafner', 'Wilson Yan'] | 2022-10-05 | null | null | null | null | ['video-generation', 'video-prediction'] | ['computer-vision', 'computer-vision'] | [ 6.31984770e-02 -1.88114345e-01 -2.94188827e-01 -2.67379224e-01
-4.24440593e-01 -8.74842703e-01 1.00145209e+00 -1.44517615e-01
4.45558643e-03 8.18654120e-01 7.07740247e-01 -1.88269213e-01
-2.11982697e-01 -7.62637675e-01 -1.02021790e+00 -3.67525876e-01
-5.17601252e-01 3.86952430e-01 3.69554967e-01 -1.82024986... | [10.59855842590332, -0.4130774438381195] |
0e2f2836-6425-4819-a25f-a2d8dc90565b | revisiting-the-centroid-based-method-a-strong | 1708.07690 | null | http://arxiv.org/abs/1708.07690v1 | http://arxiv.org/pdf/1708.07690v1.pdf | Revisiting the Centroid-based Method: A Strong Baseline for Multi-Document Summarization | The centroid-based model for extractive document summarization is a simple
and fast baseline that ranks sentences based on their similarity to a centroid
vector. In this paper, we apply this ranking to possible summaries instead of
sentences and use a simple greedy algorithm to find the best summary.
Furthermore, we sh... | ['Demian Gholipour Ghalandari'] | 2017-08-25 | null | null | null | ws-2017-9 | ['extractive-document-summarization'] | ['natural-language-processing'] | [ 2.56750107e-01 1.74517259e-01 -2.01917171e-01 -4.17329341e-01
-1.41611099e+00 -9.27879512e-01 8.63142788e-01 9.23712969e-01
-4.88137454e-01 9.77491438e-01 1.02668333e+00 -1.30682632e-01
-1.47557080e-01 -4.46111679e-01 -4.45482284e-01 -3.36327821e-01
-1.30067900e-01 6.34213209e-01 3.90331507e-01 -2.73088217... | [12.525203704833984, 9.51862907409668] |
d2286d4e-202c-4207-938f-4913d719c7d6 | precision-isnt-everything-a-hybrid-approach | null | null | https://aclanthology.org/W12-2027 | https://aclanthology.org/W12-2027.pdf | Precision Isn't Everything: A Hybrid Approach to Grammatical Error Detection | null | ['Joel Tetreault', 'Aoife Cahill', 'Michael Heilman'] | 2012-06-01 | null | null | null | ws-2012-6 | ['grammatical-error-detection'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
-2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00
-3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01
-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.303390026092529, 3.6828300952911377] |
f3f92f4e-16d7-4824-81d9-31abc58afdae | the-mi-motion-dataset-and-benchmark-for-3d | 2306.13566 | null | https://arxiv.org/abs/2306.13566v2 | https://arxiv.org/pdf/2306.13566v2.pdf | The MI-Motion Dataset and Benchmark for 3D Multi-Person Motion Prediction | 3D multi-person motion prediction is a challenging task that involves modeling individual behaviors and interactions between people. Despite the emergence of approaches for this task, comparing them is difficult due to the lack of standardized training settings and benchmark datasets. In this paper, we introduce the Mu... | ['Zizhao Wu', 'Yu Ding', 'Hao Wen', 'Yikai Luo', 'Xiao Zhou', 'Xiaogang Peng'] | 2023-06-23 | null | null | null | null | ['motion-prediction'] | ['computer-vision'] | [ 3.22550312e-02 -4.66145247e-01 -2.91317612e-01 -2.05603153e-01
-4.93630350e-01 -1.76009849e-01 6.53787553e-01 -4.25579816e-01
-3.70963514e-01 5.43097615e-01 8.57102275e-01 3.44029039e-01
1.64704546e-01 -4.59419847e-01 -3.48594338e-01 -2.86560416e-01
-3.46100688e-01 5.01183212e-01 3.37910086e-01 -5.29516265... | [7.244718551635742, -0.34252363443374634] |
a857edfb-76c5-4e29-bc64-5c6499bb0f9f | extrapolation-to-complete-basis-set-limit-in | 2303.14760 | null | https://arxiv.org/abs/2303.14760v3 | https://arxiv.org/pdf/2303.14760v3.pdf | Extrapolation to complete basis-set limit in density-functional theory by quantile random-forest models | The numerical precision of density-functional-theory (DFT) calculations depends on a variety of computational parameters, one of the most critical being the basis-set size. The ultimate precision is reached with an infinitely large basis set, i.e., in the limit of a complete basis set (CBS). Our aim in this work is to ... | ['Claudia Draxl', 'Matthias Scheffler', 'Sven Lubeck', 'Luca Ghiringhelli', 'Christian Carbogno', 'Daniel T. Speckhard'] | 2023-03-26 | null | null | null | null | ['prediction-intervals', 'total-energy'] | ['miscellaneous', 'miscellaneous'] | [ 1.54099194e-02 -1.96872488e-01 -2.23115399e-01 -3.00446302e-01
-1.06510043e+00 1.57674644e-02 5.85436165e-01 4.29846704e-01
-3.39654654e-01 1.49350715e+00 -1.43433616e-01 -4.93174613e-01
-1.54485792e-01 -8.66332948e-01 -4.52063233e-01 -1.22063994e+00
1.27495557e-01 6.47549510e-01 3.71700197e-01 -2.58596271... | [5.3129801750183105, 5.2033891677856445] |
02a6bc88-f8bf-4d03-88dc-53f626184b4d | circuitnet-an-open-source-dataset-for-machine | 2208.01040 | null | https://arxiv.org/abs/2208.01040v4 | https://arxiv.org/pdf/2208.01040v4.pdf | CircuitNet: An Open-Source Dataset for Machine Learning Applications in Electronic Design Automation (EDA) | The electronic design automation (EDA) community has been actively exploring machine learning (ML) for very large-scale integrated computer-aided design (VLSI CAD). Many studies explored learning-based techniques for cross-stage prediction tasks in the design flow to achieve faster design convergence. Although building... | ['Ru Huang', 'Runsheng Wang', 'Wei Liu', 'Yibo Lin', 'Yuxiang Zhao', 'Zhuomin Chai'] | 2022-08-01 | null | null | null | null | ['machine-learning', 'machine-learning'] | ['methodology', 'miscellaneous'] | [-3.13301623e-01 -3.12959291e-02 -1.01596546e+00 -6.97396934e-01
-9.12775874e-01 -2.81364210e-02 -7.26776645e-02 -1.27020502e-03
2.59814501e-01 6.76640987e-01 -3.53022933e-01 -7.97217011e-01
-7.66281262e-02 -7.07923710e-01 -4.35331374e-01 7.97843933e-02
2.07024649e-01 7.22697616e-01 -1.70959368e-01 2.79779658... | [6.003670692443848, 3.3473901748657227] |
73854762-43d4-4b3a-a060-8e527490fffe | designing-stable-neural-networks-using-convex | 2306.17332 | null | https://arxiv.org/abs/2306.17332v1 | https://arxiv.org/pdf/2306.17332v1.pdf | Designing Stable Neural Networks using Convex Analysis and ODEs | Motivated by classical work on the numerical integration of ordinary differential equations we present a ResNet-styled neural network architecture that encodes non-expansive (1-Lipschitz) operators, as long as the spectral norms of the weights are appropriately constrained. This is to be contrasted with the ordinary Re... | ['Carola-Bibiane Schönlieb', 'Brynjulf Owren', 'Davide Murari', 'Matthias J. Ehrhardt', 'Elena Celledoni', 'Ferdia Sherry'] | 2023-06-29 | null | null | null | null | ['deblurring', 'image-denoising', 'numerical-integration'] | ['computer-vision', 'computer-vision', 'miscellaneous'] | [ 3.58995587e-01 4.11618203e-01 4.77918953e-01 -5.16321957e-02
-2.55665302e-01 -4.43635643e-01 4.10169274e-01 -2.68313617e-01
-6.81970954e-01 7.65924811e-01 1.35863706e-01 -1.39028564e-01
-2.51884937e-01 -6.81316733e-01 -8.81215572e-01 -1.08123505e+00
-1.96975783e-01 1.12704523e-01 5.00020459e-02 -5.72319925... | [11.840034484863281, -2.421694755554199] |
bb19b657-35b9-4301-b678-5a2deff35041 | leveraging-cross-utterance-context-for-asr | 2306.16903 | null | https://arxiv.org/abs/2306.16903v1 | https://arxiv.org/pdf/2306.16903v1.pdf | Leveraging Cross-Utterance Context For ASR Decoding | While external language models (LMs) are often incorporated into the decoding stage of automated speech recognition systems, these models usually operate with limited context. Cross utterance information has been shown to be beneficial during second pass re-scoring, however this limits the hypothesis space based on the... | ['Anton Ragni', 'Robert Flynn'] | 2023-06-29 | null | null | null | null | ['speech-recognition'] | ['speech'] | [ 5.20409286e-01 2.20754728e-01 7.50756562e-02 -6.39542758e-01
-1.46596313e+00 -4.52547282e-01 5.76362193e-01 1.57799482e-01
-8.86111021e-01 6.67951643e-01 4.30043072e-01 -4.58582759e-01
7.84194991e-02 -4.22915146e-02 -5.65664947e-01 -4.36857611e-01
1.62819281e-01 5.40146589e-01 2.88817495e-01 -8.20784420... | [14.321025848388672, 6.885508060455322] |
37ee7c6f-3259-4eaa-a939-d01280222052 | neural-academic-paper-generation | 1912.01982 | null | https://arxiv.org/abs/1912.01982v1 | https://arxiv.org/pdf/1912.01982v1.pdf | Neural Academic Paper Generation | In this work, we tackle the problem of structured text generation, specifically academic paper generation in $\LaTeX{}$, inspired by the surprisingly good results of basic character-level language models. Our motivation is using more recent and advanced methods of language modeling on a more complex dataset of $\LaTeX{... | ['Özgur Özdemir', 'Uras Mutlu', 'Samet Demir'] | 2019-12-02 | null | null | null | null | ['paper-generation'] | ['natural-language-processing'] | [ 3.09887409e-01 2.66676784e-01 2.63380527e-01 -1.64274529e-01
-1.06310928e+00 -7.22122431e-01 6.92986310e-01 3.20281029e-01
-6.51715025e-02 1.07699478e+00 -7.38850310e-02 -6.54143155e-01
5.32766730e-02 -1.10129321e+00 -7.20547616e-01 -3.33451420e-01
7.92976394e-02 4.11697119e-01 -1.56546056e-01 -1.25218928... | [11.979154586791992, 9.007416725158691] |
1a7707a4-a7d4-4064-b63b-89170a009dd4 | a-model-data-driven-network-embedding | 2211.15002 | null | https://arxiv.org/abs/2211.15002v1 | https://arxiv.org/pdf/2211.15002v1.pdf | A Model-data-driven Network Embedding Multidimensional Features for Tomographic SAR Imaging | Deep learning (DL)-based tomographic SAR imaging algorithms are gradually being studied. Typically, they use an unfolding network to mimic the iterative calculation of the classical compressive sensing (CS)-based methods and process each range-azimuth unit individually. However, only one-dimensional features are effect... | ['Tianjiao Zeng', 'Shunjun Wei', 'Jun Shi', 'Xu Zhan', 'Xiaoling Zhang', 'Yu Ren'] | 2022-11-28 | null | null | null | null | ['compressive-sensing', 'network-embedding'] | ['computer-vision', 'methodology'] | [ 3.33376288e-01 -2.28419155e-01 4.45985228e-01 -5.84337294e-01
-7.59385765e-01 -4.71128188e-02 3.55147451e-01 -5.98910272e-01
-2.87327349e-01 3.89889896e-01 1.94777369e-01 -1.82261884e-01
-5.14943302e-01 -1.16541898e+00 -6.40710354e-01 -9.01165485e-01
5.13592437e-02 3.60539824e-01 4.41135354e-02 -2.30116993... | [10.628059387207031, -2.187991142272949] |
2775cccb-841b-4909-a268-2abcf9a4f7ad | amrize-then-parse-enhancing-amr-parsing-with | null | null | https://openreview.net/forum?id=5Q-ihWzhSi | https://openreview.net/pdf?id=5Q-ihWzhSi | AMRize, then Parse! Enhancing AMR Parsing with PseudoAMR Data | As Abstract Meaning Representation (AMR) implicitly involves compound semantic annotations, we hypothesize auxiliary tasks which are semantically or formally related can better enhance AMR parsing. With carefully designed control experiments, we find that 1) Semantic role labeling (SRL) and dependency parsing (DP), wou... | ['Anonymous'] | 2021-11-16 | null | null | null | acl-arr-november-2021-11 | ['semantic-role-labeling'] | ['natural-language-processing'] | [ 7.08783865e-01 6.59512639e-01 -4.90673155e-01 -6.68426454e-01
-1.46367681e+00 -5.98277569e-01 4.62545872e-01 2.44531661e-01
-4.60041314e-01 6.62313819e-01 8.37570727e-01 -5.34010231e-01
2.59277552e-01 -5.91853976e-01 -7.83167362e-01 -3.95633012e-01
4.03334081e-01 6.16330743e-01 2.86990963e-02 -3.72514039... | [10.495331764221191, 9.342334747314453] |
edc8674f-b149-4edc-915d-f045ca93c3f6 | deep-deterministic-independent-component | 2202.02951 | null | https://arxiv.org/abs/2202.02951v2 | https://arxiv.org/pdf/2202.02951v2.pdf | Deep Deterministic Independent Component Analysis for Hyperspectral Unmixing | We develop a new neural network based independent component analysis (ICA) method by directly minimizing the dependence amongst all extracted components. Using the matrix-based R{\'e}nyi's $\alpha$-order entropy functional, our network can be directly optimized by stochastic gradient descent (SGD), without any variatio... | ['Jose C. Principe', 'Shujian Yu', 'Hongming Li'] | 2022-02-07 | null | null | null | null | ['hyperspectral-unmixing'] | ['computer-vision'] | [ 3.41137886e-01 -1.08674034e-01 3.93538794e-04 -2.21443132e-01
-7.07033396e-01 -7.45399654e-01 2.77096629e-01 -5.14426291e-01
-3.04915339e-01 8.94033968e-01 -1.89704686e-01 -5.82861960e-01
-5.54576397e-01 -5.60896635e-01 -5.86308241e-01 -1.13466239e+00
-3.29972245e-02 1.61506772e-01 -7.90268958e-01 -1.43612996... | [10.06545352935791, -2.0241808891296387] |
4ce1e892-25d2-4d4a-91cd-b53712a60488 | isotropic-gaussian-processes-on-finite-spaces | 2211.01689 | null | https://arxiv.org/abs/2211.01689v3 | https://arxiv.org/pdf/2211.01689v3.pdf | Isotropic Gaussian Processes on Finite Spaces of Graphs | We propose a principled way to define Gaussian process priors on various sets of unweighted graphs: directed or undirected, with or without loops. We endow each of these sets with a geometric structure, inducing the notions of closeness and symmetries, by turning them into a vertex set of an appropriate metagraph. Buil... | ['Andreas Krause', 'Vignesh Ram Somnath', 'Mohammad Reza Karimi', 'Viacheslav Borovitskiy'] | 2022-11-03 | null | null | null | null | ['molecular-property-prediction'] | ['miscellaneous'] | [ 5.99014878e-01 5.10199070e-01 1.77391663e-01 -1.89230919e-01
-3.47387284e-01 -7.78993726e-01 7.95771420e-01 4.01309848e-01
-5.45539737e-01 6.25663698e-01 1.78990856e-01 -5.08506894e-01
-5.89237094e-01 -1.05049002e+00 -7.90263712e-01 -1.23269486e+00
-3.76050979e-01 7.67436922e-01 2.39609629e-01 1.35929003... | [6.992221355438232, 4.95497989654541] |
a98fde42-e27e-4737-a3f9-c94a0bcc3265 | an-asynchronous-kalman-filter-for-hybrid | 2012.05590 | null | https://arxiv.org/abs/2012.05590v4 | https://arxiv.org/pdf/2012.05590v4.pdf | An Asynchronous Kalman Filter for Hybrid Event Cameras | Event cameras are ideally suited to capture HDR visual information without blur but perform poorly on static or slowly changing scenes. Conversely, conventional image sensors measure absolute intensity of slowly changing scenes effectively but do poorly on high dynamic range or quickly changing scenes. In this paper, w... | ['Robert Mahony', 'Cedric Scheerlinck', 'Yonhon Ng', 'Ziwei Wang'] | 2020-12-10 | null | http://openaccess.thecvf.com//content/ICCV2021/html/Wang_An_Asynchronous_Kalman_Filter_for_Hybrid_Event_Cameras_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Wang_An_Asynchronous_Kalman_Filter_for_Hybrid_Event_Cameras_ICCV_2021_paper.pdf | iccv-2021-1 | ['video-reconstruction'] | ['computer-vision'] | [ 4.87342864e-01 -6.50795639e-01 2.40249634e-01 -3.00678641e-01
-6.53037012e-01 -5.11265516e-01 6.99160159e-01 -1.14277914e-01
-5.99163294e-01 7.13673532e-01 2.19384328e-01 2.45438039e-01
1.53935567e-01 -4.96436834e-01 -6.94359601e-01 -6.59781635e-01
-8.90616924e-02 8.28502029e-02 6.36751473e-01 4.04227972... | [10.508879661560059, -2.112046003341675] |
69a39251-ded7-4e31-a651-b90e05a66a03 | predicting-patient-outcomes-with-graph | 2101.03940 | null | https://arxiv.org/abs/2101.03940v1 | https://arxiv.org/pdf/2101.03940v1.pdf | Predicting Patient Outcomes with Graph Representation Learning | Recent work on predicting patient outcomes in the Intensive Care Unit (ICU) has focused heavily on the physiological time series data, largely ignoring sparse data such as diagnoses and medications. When they are included, they are usually concatenated in the late stages of a model, which may struggle to learn from rar... | ['Pietro Liò', 'Nicholas Lane', 'Petar Veličković', 'Catherine Tong', 'Emma Rocheteau'] | 2021-01-11 | null | null | null | null | ['length-of-stay-prediction', 'predicting-patient-outcomes'] | ['medical', 'medical'] | [ 2.80582577e-01 4.41855431e-01 -4.43143189e-01 -2.36101851e-01
-3.06731254e-01 2.55569536e-03 -2.40577757e-02 7.55618155e-01
-2.14968041e-01 7.40574956e-01 5.17899811e-01 -6.48449063e-01
-6.70049071e-01 -8.40280116e-01 -4.50665623e-01 -6.14447474e-01
-8.27534914e-01 6.78309560e-01 -3.51291716e-01 6.66571036... | [7.916439056396484, 6.4517364501953125] |
5a2a3d5a-81c6-47bd-9632-e2a3faff208a | type-aware-decomposed-framework-for-few-shot | 2302.06397 | null | https://arxiv.org/abs/2302.06397v1 | https://arxiv.org/pdf/2302.06397v1.pdf | Type-Aware Decomposed Framework for Few-Shot Named Entity Recognition | Despite the recent success achieved by several two-stage prototypical networks in few-shot named entity recognition (NER) task, the over-detected false spans at span detection stage and the inaccurate and unstable prototypes at type classification stage remain to be challenging problems. In this paper, we propose a nov... | ['Tieyun Qian', 'Yongqi Li'] | 2023-02-13 | null | null | null | null | ['few-shot-ner', 'type'] | ['natural-language-processing', 'speech'] | [-1.12989478e-01 -2.05091730e-01 -3.56233120e-01 -3.65429789e-01
-7.45085418e-01 -5.11444628e-01 3.14357698e-01 1.22924685e-01
-6.69664145e-01 8.84412229e-01 -1.12211490e-02 -2.09977180e-02
-1.12291776e-01 -8.04037571e-01 -4.53287780e-01 -1.89992219e-01
-1.66538402e-01 2.09177241e-01 6.11863911e-01 -1.10465540... | [9.531679153442383, 9.414990425109863] |
ed1e75e6-f711-44e3-b662-231d49351bdb | a-graph-transduction-game-for-multi-target | 1806.07227 | null | http://arxiv.org/abs/1806.07227v2 | http://arxiv.org/pdf/1806.07227v2.pdf | A Graph Transduction Game for Multi-target Tracking | Semi-supervised learning is a popular class of techniques to learn from
labeled and unlabeled data. The paper proposes an application of a recently
proposed approach of graph transduction that exploits game theoretic notions to
the problem of multiple people tracking. Within the proposed framework, targets
are consider... | ['Rita Cucchiara', 'Tewodros Mulugeta Dagnew', 'Marcello Pelillo', 'Dalia Coppi'] | 2018-06-12 | null | null | null | null | ['multiple-people-tracking'] | ['computer-vision'] | [ 3.79821025e-02 3.20468843e-01 -1.12971887e-01 -1.78400561e-01
-4.57712740e-01 -5.72789371e-01 5.47392488e-01 7.32419714e-02
-6.63750410e-01 6.82117939e-01 -1.05248846e-01 3.32124144e-01
-1.46415859e-01 -5.95894873e-01 -5.97481787e-01 -9.20771241e-01
-2.70682424e-01 4.97813225e-01 4.81069922e-01 -4.25944701... | [6.749677658081055, -1.8653616905212402] |
b4d4938b-b88f-4cf3-ad5c-417e1df6e81f | optimal-inference-in-contextual-stochastic | 2306.07948 | null | https://arxiv.org/abs/2306.07948v1 | https://arxiv.org/pdf/2306.07948v1.pdf | Optimal Inference in Contextual Stochastic Block Models | The contextual stochastic block model (cSBM) was proposed for unsupervised community detection on attributed graphs where both the graph and the high-dimensional node information correlate with node labels. In the context of machine learning on graphs, the cSBM has been widely used as a synthetic dataset for evaluating... | ['L. Zdeborová', 'O. Duranthon'] | 2023-06-06 | null | null | null | null | ['stochastic-block-model', 'community-detection'] | ['graphs', 'graphs'] | [ 4.11691546e-01 4.98100966e-01 -1.76565021e-01 -1.70839787e-01
-1.92130551e-01 -3.28097403e-01 9.12265778e-01 7.33146369e-01
-3.19466978e-01 5.30575812e-01 -1.50806949e-01 -6.16786957e-01
-6.74885273e-01 -1.02110457e+00 -5.58428586e-01 -8.02724838e-01
-5.48281133e-01 9.14005518e-01 4.09764946e-01 -9.99568775... | [7.011303424835205, 5.482759475708008] |
301f0131-46fd-46ce-9a7d-01aaf583cb7a | rssi-based-outdoor-localization-with-single | 2004.10083 | null | https://arxiv.org/abs/2004.10083v1 | https://arxiv.org/pdf/2004.10083v1.pdf | RSSI-based Outdoor Localization with Single Unmanned Aerial Vehicle | Localization of a target object has been performed conventionally using multiple terrestrial reference nodes. This paradigm is recently shifted towards utilization of unmanned aerial vehicles (UAVs) for locating target objects. Since locating of a target using simultaneous multiple UAVs is costly and impractical, achie... | ['Yakup Genc', 'Hasari Celebi', 'Seyma Yucer', 'Mesih Veysi Kilinc', 'Furkan Tektas', 'Yusuf Sinan Akgul', 'Ilyas Kandemir'] | 2020-04-20 | null | null | null | null | ['outdoor-localization'] | ['robots'] | [ 1.31533686e-02 -4.33929652e-01 1.38271123e-01 3.84099483e-02
-5.43665171e-01 -9.91833627e-01 4.19645131e-01 3.49726856e-01
-5.00550508e-01 8.53314340e-01 -4.10345644e-01 -6.06645823e-01
-5.57920218e-01 -6.42654657e-01 -3.94196481e-01 -9.55341160e-01
-2.28527650e-01 -2.26628333e-01 3.24301273e-01 1.09388083... | [6.224552154541016, 1.1503371000289917] |
3edcf6b1-59e2-4a25-b414-c9df6ea3cbe7 | exponential-utility-maximization-in-small | 2208.06549 | null | https://arxiv.org/abs/2208.06549v1 | https://arxiv.org/pdf/2208.06549v1.pdf | Exponential utility maximization in small/large financial markets | Obtaining utility maximizing optimal portfolios in closed form is a challenging issue when the return vector follows a more general distribution than the normal one. In this note, we give closed form expressions, in markets based on finitely many assets, for optimal portfolios that maximize the expected exponential uti... | ['Hasanjan Sayit', 'Miklós Rásonyi'] | 2022-08-13 | null | null | null | null | ['portfolio-optimization'] | ['time-series'] | [-4.58633989e-01 1.93406031e-01 -9.59674492e-02 -1.39214080e-02
-8.27604890e-01 -1.06294954e+00 1.69663593e-01 -3.93006891e-01
-4.10217822e-01 1.00308323e+00 -2.89883405e-01 -5.69199383e-01
-7.68460572e-01 -1.31077456e+00 -3.55065405e-01 -6.90910876e-01
-8.80101919e-02 8.36453080e-01 -1.41807169e-01 7.55215809... | [4.932520866394043, 3.9309303760528564] |
fed7cdeb-f7fc-4e2a-b1f3-59f550ab4222 | tedb-system-description-to-a-shared-task-on | 2301.06602 | null | https://arxiv.org/abs/2301.06602v1 | https://arxiv.org/pdf/2301.06602v1.pdf | TEDB System Description to a Shared Task on Euphemism Detection 2022 | In this report, we describe our Transformers for euphemism detection baseline (TEDB) submissions to a shared task on euphemism detection 2022. We cast the task of predicting euphemism as text classification. We considered Transformer-based models which are the current state-of-the-art methods for text classification. W... | ['Peratham Wiriyathammabhum'] | 2023-01-16 | null | null | null | null | ['sarcasm-detection'] | ['natural-language-processing'] | [-1.26373813e-01 2.13755623e-01 -2.46292144e-01 -4.05993551e-01
-4.67432678e-01 -6.92065895e-01 8.87736559e-01 6.33011639e-01
-6.03228390e-01 1.17239468e-01 5.71162164e-01 -1.87894404e-01
4.79443878e-01 -7.19348848e-01 -4.61176336e-02 -4.89139467e-01
3.39383304e-01 9.80309993e-02 -5.71414590e-01 -8.00041676... | [8.86322021484375, 10.99011516571045] |
ba2a1ac3-18ef-4543-808f-87ddc12a1efe | identity-aware-textual-visual-matching-with | 1708.01988 | null | http://arxiv.org/abs/1708.01988v1 | http://arxiv.org/pdf/1708.01988v1.pdf | Identity-Aware Textual-Visual Matching with Latent Co-attention | Textual-visual matching aims at measuring similarities between sentence
descriptions and images. Most existing methods tackle this problem without
effectively utilizing identity-level annotations. In this paper, we propose an
identity-aware two-stage framework for the textual-visual matching problem. Our
stage-1 CNN-LS... | ['Shuang Li', 'Wei Yang', 'Tong Xiao', 'Hongsheng Li', 'Xiaogang Wang'] | 2017-08-07 | identity-aware-textual-visual-matching-with-1 | http://openaccess.thecvf.com/content_iccv_2017/html/Li_Identity-Aware_Textual-Visual_Matching_ICCV_2017_paper.html | http://openaccess.thecvf.com/content_ICCV_2017/papers/Li_Identity-Aware_Textual-Visual_Matching_ICCV_2017_paper.pdf | iccv-2017-10 | ['nlp-based-person-retrival'] | ['computer-vision'] | [ 4.01983440e-01 -1.42329633e-01 -2.79606879e-01 -5.84951580e-01
-1.18457186e+00 -3.17425221e-01 6.33310676e-01 1.03656828e-01
-5.80600798e-01 1.48008019e-01 3.34717065e-01 5.64467832e-02
3.24240148e-01 -4.16499704e-01 -8.68331432e-01 -3.65992695e-01
3.37138474e-01 1.39624357e-01 -3.61215957e-02 7.98733011... | [10.875665664672852, 1.3888276815414429] |
9e551e60-c2b4-46d8-8e15-b10fec907e2d | segmentation-free-vehicle-license-plate | 1701.06439 | null | http://arxiv.org/abs/1701.06439v1 | http://arxiv.org/pdf/1701.06439v1.pdf | Segmentation-free Vehicle License Plate Recognition using ConvNet-RNN | While vehicle license plate recognition (VLPR) is usually done with a sliding
window approach, it can have limited performance on datasets with characters
that are of variable width. This can be solved by hand-crafting algorithms to
prescale the characters. While this approach can work fairly well, the
recognizer is on... | ['Teik Koon Cheang', 'Yong Haur Tay', 'Yong Shean Chong'] | 2017-01-23 | null | null | null | null | ['license-plate-recognition'] | ['computer-vision'] | [ 5.61022103e-01 -3.54275525e-01 2.95716920e-03 -3.23127449e-01
-5.23684442e-01 -5.31291187e-01 4.53153253e-01 -4.24480438e-01
-7.16558814e-01 4.60189402e-01 -2.92670637e-01 -6.21320724e-01
2.50896245e-01 -8.00517738e-01 -6.28647804e-01 -6.24493241e-01
3.29039305e-01 2.28902623e-01 7.56102204e-01 -9.39126909... | [9.845322608947754, -4.925079822540283] |
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