paperID stringlengths 36 36 | pwc_id stringlengths 8 47 | arxiv_id stringlengths 6 16 ⌀ | nips_id float64 | url_abs stringlengths 18 329 | url_pdf stringlengths 18 742 | title stringlengths 8 325 | abstract stringlengths 1 7.27k ⌀ | authors stringlengths 2 7.06k | published stringlengths 10 10 ⌀ | conference stringlengths 12 47 ⌀ | conference_url_abs stringlengths 16 198 ⌀ | conference_url_pdf stringlengths 27 199 ⌀ | proceeding stringlengths 6 47 ⌀ | taskID stringlengths 7 1.44k | areaID stringclasses 688
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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
7d464839-1c82-4c17-b186-5939cf4d0834 | convtexttm-an-explainable-convolutional | null | null | https://aclanthology.org/2022.lrec-1.401 | https://aclanthology.org/2022.lrec-1.401.pdf | ConvTextTM: An Explainable Convolutional Tsetlin Machine Framework for Text Classification | Recent advancements in natural language processing (NLP) have reshaped the industry, with powerful language models such as GPT-3 achieving superhuman performance on various tasks. However, the increasing complexity of such models turns them into “black boxes”, creating uncertainty about their internal operation and dec... | ['Lei Jiao', 'Ole-Christoffer Granmo', 'Bimal Bhattarai'] | null | null | null | null | lrec-2022-6 | ['document-classification'] | ['natural-language-processing'] | [ 1.96279019e-01 3.76703382e-01 -3.36888254e-01 -5.44920743e-01
-6.80800438e-01 -5.31262040e-01 5.80383778e-01 1.67422891e-01
-2.29765326e-01 5.74322462e-01 7.12968260e-02 -7.71200418e-01
1.65895477e-01 -9.25298214e-01 -9.82210577e-01 -4.98653382e-01
-1.66344270e-03 6.57391191e-01 2.14108899e-02 -2.58971542... | [9.62418270111084, 7.7499308586120605] |
686daf99-f788-4700-9002-0289ed0653c3 | automatic-face-reenactment | 1602.02651 | null | http://arxiv.org/abs/1602.02651v1 | http://arxiv.org/pdf/1602.02651v1.pdf | Automatic Face Reenactment | We propose an image-based, facial reenactment system that replaces the face
of an actor in an existing target video with the face of a user from a source
video, while preserving the original target performance. Our system is fully
automatic and does not require a database of source expressions. Instead, it is
able to p... | ['Thorsten Thormaehlen', 'Pablo Garrido', 'Levi Valgaerts', 'Christian Theobalt', 'Patrick Perez', 'Ole Rehmsen'] | 2016-02-08 | automatic-face-reenactment-1 | http://openaccess.thecvf.com/content_cvpr_2014/html/Garrido_Automatic_Face_Reenactment_2014_CVPR_paper.html | http://openaccess.thecvf.com/content_cvpr_2014/papers/Garrido_Automatic_Face_Reenactment_2014_CVPR_paper.pdf | cvpr-2014-6 | ['face-reenactment', 'face-transfer'] | ['computer-vision', 'computer-vision'] | [ 3.80287230e-01 -1.66393183e-02 7.58156851e-02 -5.91507673e-01
-7.74189353e-01 -6.16879940e-01 6.53173625e-01 -7.05513656e-01
-4.40095693e-01 2.87764370e-01 1.51659558e-02 2.70381600e-01
1.40006751e-01 -2.59777457e-01 -7.33579993e-01 -6.93918049e-01
1.72771066e-01 2.24016711e-01 2.79720306e-01 -2.55681604... | [12.997709274291992, -0.3532937169075012] |
be172ccc-aec9-4f6d-9acf-616b5221c43e | emotion-cause-pair-extraction-a-new-task-to | 1906.01267 | null | https://arxiv.org/abs/1906.01267v1 | https://arxiv.org/pdf/1906.01267v1.pdf | Emotion-Cause Pair Extraction: A New Task to Emotion Analysis in Texts | Emotion cause extraction (ECE), the task aimed at extracting the potential causes behind certain emotions in text, has gained much attention in recent years due to its wide applications. However, it suffers from two shortcomings: 1) the emotion must be annotated before cause extraction in ECE, which greatly limits its ... | ['Zixiang Ding', 'Rui Xia'] | 2019-06-04 | emotion-cause-pair-extraction-a-new-task-to-1 | https://aclanthology.org/P19-1096 | https://aclanthology.org/P19-1096.pdf | acl-2019-7 | ['emotion-cause-pair-extraction', 'emotion-cause-extraction'] | ['natural-language-processing', 'natural-language-processing'] | [ 3.90627444e-01 -2.25209996e-01 3.93886724e-03 -3.79849613e-01
-8.44379365e-01 -4.51745600e-01 5.59428215e-01 3.09496701e-01
-3.16146910e-01 7.35965967e-01 3.42677534e-01 -2.23693512e-02
-2.94471622e-01 -4.13918346e-01 -2.31001109e-01 -6.30423903e-01
-6.99457079e-02 -5.48836440e-02 -9.55264047e-02 -4.20062877... | [12.628816604614258, 6.207989692687988] |
957efe2e-d97d-4976-8eef-4fdb862d62b3 | a-comprehensive-and-large-scale-dataset-for | null | null | https://openreview.net/forum?id=TnX3iwX_6Iu | https://openreview.net/pdf?id=TnX3iwX_6Iu | A Comprehensive and Large-Scale Dataset for Integrated Argument Mining Tasks | Traditionally, a debate usually requires a manual preparation process, including reading plenty of articles, selecting the claims, identifying the stances of the claims, seeking the evidences for the claims, etc. As the AI debate attracts more attention these years, it is worth exploring the methods to automate the ted... | ['Anonymous'] | 2021-11-16 | null | null | null | acl-arr-november-2021-11 | ['claim-extraction-with-stance-classification', 'claim-evidence-pair-extraction-cepe'] | ['natural-language-processing', 'natural-language-processing'] | [ 5.05443633e-01 3.65969151e-01 -5.91060638e-01 -3.66934776e-01
-1.36442947e+00 -7.14356720e-01 9.38210428e-01 6.86215162e-01
-5.13874650e-01 8.77410114e-01 5.42267561e-01 -7.90878832e-01
-6.67010024e-02 -5.95794320e-01 -6.98320985e-01 -3.37498218e-01
4.75943387e-01 7.11325049e-01 4.17576343e-01 -1.77778482... | [9.451274871826172, 9.544551849365234] |
a335c7d1-00b9-49e9-a373-66599c762484 | genesis-v2-inferring-unordered-object | 2104.09958 | null | https://arxiv.org/abs/2104.09958v3 | https://arxiv.org/pdf/2104.09958v3.pdf | GENESIS-V2: Inferring Unordered Object Representations without Iterative Refinement | Advances in unsupervised learning of object-representations have culminated in the development of a broad range of methods for unsupervised object segmentation and interpretable object-centric scene generation. These methods, however, are limited to simulated and real-world datasets with limited visual complexity. More... | ['Ingmar Posner', 'Oiwi Parker Jones', 'Martin Engelcke'] | 2021-04-20 | null | http://proceedings.neurips.cc/paper/2021/hash/43ec517d68b6edd3015b3edc9a11367b-Abstract.html | http://proceedings.neurips.cc/paper/2021/file/43ec517d68b6edd3015b3edc9a11367b-Paper.pdf | neurips-2021-12 | ['scene-generation', 'unsupervised-object-segmentation'] | ['computer-vision', 'computer-vision'] | [ 6.29321158e-01 4.57573593e-01 2.65978545e-01 -4.20058459e-01
-3.49185616e-01 -6.11346722e-01 1.07790971e+00 3.65599155e-01
-5.18367946e-01 3.74051988e-01 -2.01794673e-02 -3.96465957e-02
-2.18150333e-01 -9.56422806e-01 -7.73744404e-01 -6.49246275e-01
9.68160033e-02 9.22148049e-01 4.37307209e-01 8.76498297... | [9.652274131774902, 0.6852902770042419] |
c270aa83-3b90-4743-b564-7449376ad340 | image-question-answering-using-convolutional | 1511.05756 | null | http://arxiv.org/abs/1511.05756v1 | http://arxiv.org/pdf/1511.05756v1.pdf | Image Question Answering using Convolutional Neural Network with Dynamic Parameter Prediction | We tackle image question answering (ImageQA) problem by learning a
convolutional neural network (CNN) with a dynamic parameter layer whose weights
are determined adaptively based on questions. For the adaptive parameter
prediction, we employ a separate parameter prediction network, which consists
of gated recurrent uni... | ['Hyeonwoo Noh', 'Bohyung Han', 'Paul Hongsuck Seo'] | 2015-11-18 | image-question-answering-using-convolutional-1 | http://openaccess.thecvf.com/content_cvpr_2016/html/Noh_Image_Question_Answering_CVPR_2016_paper.html | http://openaccess.thecvf.com/content_cvpr_2016/papers/Noh_Image_Question_Answering_CVPR_2016_paper.pdf | cvpr-2016-6 | ['multi-modal', 'parameter-prediction'] | ['miscellaneous', 'miscellaneous'] | [ 3.12868923e-01 9.87155586e-02 1.76135883e-01 -6.85266376e-01
-9.95936513e-01 -3.22765350e-01 -1.05209872e-02 3.45120952e-02
-8.20232749e-01 1.04565904e-01 -3.40897925e-02 -3.67859900e-01
1.58008888e-01 -1.09528041e+00 -9.62098002e-01 -8.30892622e-01
9.85145345e-02 6.58512414e-01 6.97362602e-01 -2.13193357... | [10.385440826416016, 1.9846525192260742] |
6c933f00-3d3b-4257-a078-125693e2927d | temporal-pattern-mining-for-analysis-of | 2209.04793 | null | https://arxiv.org/abs/2209.04793v1 | https://arxiv.org/pdf/2209.04793v1.pdf | Temporal Pattern Mining for Analysis of Longitudinal Clinical Data: Identifying Risk Factors for Alzheimer's Disease | A novel framework is proposed for handling the complex task of modelling and analysis of longitudinal, multivariate, heterogeneous clinical data. This method uses temporal abstraction to convert the data into a more appropriate form for modelling, temporal pattern mining, to discover patterns in the complex, longitudin... | ['Arcot Sowmya', 'Henry Brodaty', 'Perminder S. Sachdev', 'Gelareh Mohammadi', 'Annette Spooner'] | 2022-09-11 | null | null | null | null | ['survival-analysis'] | ['miscellaneous'] | [ 6.76537603e-02 -7.30621442e-02 -2.49514878e-01 -4.94027793e-01
-4.17463407e-02 -2.09031254e-01 6.42430365e-01 5.37556887e-01
-3.63934636e-01 1.01976025e+00 3.65211874e-01 -7.15005994e-01
-8.05318356e-01 -5.34627497e-01 2.10281953e-01 -6.05455279e-01
-1.25609648e+00 7.97079444e-01 4.31169271e-01 1.53438831... | [7.979310989379883, 5.497529983520508] |
11f3bdfe-bea2-4ae1-853b-65f25cdab2bf | learning-compatibility-across-categories-for | 1603.09473 | null | http://arxiv.org/abs/1603.09473v3 | http://arxiv.org/pdf/1603.09473v3.pdf | Learning Compatibility Across Categories for Heterogeneous Item Recommendation | Identifying relationships between items is a key task of an online
recommender system, in order to help users discover items that are functionally
complementary or visually compatible. In domains like clothing recommendation,
this task is particularly challenging since a successful system should be
capable of handling ... | ['Ruining He', 'Julian McAuley', 'Charles Packer'] | 2016-03-31 | null | null | null | null | ['product-recommendation'] | ['miscellaneous'] | [ 5.56925870e-02 -4.13977802e-01 -2.49560416e-01 -4.93484855e-01
-2.64156044e-01 -1.02094483e+00 9.19851512e-02 3.83403391e-01
-6.26674201e-03 1.10467307e-01 4.19202894e-01 -2.19782934e-01
-6.36913717e-01 -5.56039095e-01 -6.05041325e-01 -3.42545748e-01
-3.17342371e-01 4.47708845e-01 9.50428694e-02 -6.17348492... | [10.151455879211426, 5.530247211456299] |
e8f81668-b53a-4139-a6de-cc828aa6c0f8 | local-frequency-domain-transformer-networks | 2105.04637 | null | https://arxiv.org/abs/2105.04637v1 | https://arxiv.org/pdf/2105.04637v1.pdf | Local Frequency Domain Transformer Networks for Video Prediction | Video prediction is commonly referred to as forecasting future frames of a video sequence provided several past frames thereof. It remains a challenging domain as visual scenes evolve according to complex underlying dynamics, such as the camera's egocentric motion or the distinct motility per individual object viewed. ... | ['Sven Behnke', 'Jan Nogga', 'Hafez Farazi'] | 2021-05-10 | null | null | null | null | ['motion-segmentation'] | ['computer-vision'] | [ 4.17283654e-01 1.57707259e-01 -1.46104246e-01 -2.68312156e-01
-1.55801073e-01 -6.30914032e-01 8.26926887e-01 -4.47519839e-01
-1.43015862e-01 4.63180482e-01 3.36038619e-01 -1.55304030e-01
2.75485009e-01 -4.86575246e-01 -1.08237994e+00 -7.68008590e-01
-1.37788430e-01 1.69204146e-01 2.28573948e-01 6.56689554... | [8.600727081298828, 0.28991633653640747] |
b7c93619-8996-49ee-a51e-6b49ac095a8f | person-search-challenges-and-solutions-a | 2105.01605 | null | https://arxiv.org/abs/2105.01605v1 | https://arxiv.org/pdf/2105.01605v1.pdf | Person Search Challenges and Solutions: A Survey | Person search has drawn increasing attention due to its real-world applications and research significance. Person search aims to find a probe person in a gallery of scene images with a wide range of applications, such as criminals search, multicamera tracking, missing person search, etc. Early person search works focus... | ['Alex Hauptmann', 'Xiaojun Chang', 'Yun Xiao', 'Pengzhen Ren', 'Xiangtan Lin'] | 2021-05-01 | null | null | null | null | ['person-search'] | ['computer-vision'] | [ 4.46449667e-02 -8.04924130e-01 -2.38936961e-01 -2.83753812e-01
-7.16647208e-01 -6.97391748e-01 8.03593874e-01 -2.03842465e-02
-8.23790193e-01 5.14656186e-01 2.21591845e-01 1.95547119e-01
-1.97352022e-01 -5.57924032e-01 1.43541709e-01 -6.11333609e-01
3.94892216e-01 7.04391181e-01 1.69367954e-01 1.05281798... | [14.762056350708008, 0.8365119695663452] |
751ae346-93b9-42db-8350-7c4c453888cd | twitter-spam-detection-a-systematic-review | 2011.14754 | null | https://arxiv.org/abs/2011.14754v2 | https://arxiv.org/pdf/2011.14754v2.pdf | Twitter Spam Detection: A Systematic Review | Nowadays, with the rise of Internet access and mobile devices around the globe, more people are using social networks for collaboration and receiving real-time information. Twitter, the microblogging that is becoming a critical source of communication and news propagation, has grabbed the attention of spammers to distr... | ['Ebrahim Mahdipour', 'Mohammad Akbari', 'Mostafa Haghi Kashani', 'Sepideh Bazzaz Abkenar'] | 2020-11-30 | null | null | null | null | ['spam-detection'] | ['natural-language-processing'] | [-1.48079082e-01 -5.59889734e-01 -5.27167499e-01 1.74505889e-01
5.66668138e-02 -3.36478710e-01 6.86414301e-01 5.16784847e-01
-3.32372516e-01 6.03018641e-01 1.75936341e-01 -3.29896122e-01
-7.72095844e-02 -1.10229850e+00 3.49110216e-01 -6.03217363e-01
8.29844847e-02 1.65052891e-01 6.71779037e-01 -5.48328042... | [7.88826322555542, 10.057147979736328] |
0993a9ac-b4bf-4046-9185-4f494d15c5ca | jentab-meets-semtab-2021-s-new-challenges | null | null | https://www.semanticscholar.org/paper/JenTab-Meets-SemTab-2021's-New-Challenges-Abdelmageed-Schindler/4f492fee6a7ae51d3f2527d9036a1beaf6f1e44b | http://ceur-ws.org/Vol-3103/paper4.pdf | JenTab Meets SemTab 2021's New Challenges | While tables are a rich source of structured information, their automated use is oftentimes prevented by the inherent ambiguity contained within. Issues ranging from mere typos over inconsistent naming conventions to homonymy among values pose substantial barriers to exploiting this source of knowledge. Although the Se... | ['Sirko Schindler', 'Nora Abdelmageed'] | 2021-10-01 | null | null | null | semtab-iswc-2021-10 | ['graph-matching', 'table-annotation', 'table-annotation', 'column-type-annotation', 'cell-entity-annotation'] | ['graphs', 'knowledge-base', 'natural-language-processing', 'natural-language-processing', 'natural-language-processing'] | [-1.19772665e-01 2.88502932e-01 -2.95007795e-01 -3.51227820e-01
-7.12199330e-01 -1.10264277e+00 6.60023749e-01 5.65219998e-01
-1.02479421e-01 7.10691512e-01 3.76426518e-01 -3.81603181e-01
-4.84015316e-01 -9.32946086e-01 -4.40034389e-01 2.75422186e-01
6.95962384e-02 7.77260482e-01 5.11916518e-01 -5.56194842... | [9.310271263122559, 7.959108352661133] |
dc895f3e-6001-4a6e-968c-a0ee421534d5 | aligntransformer-hierarchical-alignment-of | 2203.10095 | null | https://arxiv.org/abs/2203.10095v1 | https://arxiv.org/pdf/2203.10095v1.pdf | AlignTransformer: Hierarchical Alignment of Visual Regions and Disease Tags for Medical Report Generation | Recently, medical report generation, which aims to automatically generate a long and coherent descriptive paragraph of a given medical image, has received growing research interests. Different from the general image captioning tasks, medical report generation is more challenging for data-driven neural models. This is m... | ['Xian Wu', 'Jing Zhang', 'Xiaoxia Xie', 'Shen Ge', 'Fenglin Liu', 'Di You'] | 2022-03-18 | null | null | null | null | ['medical-report-generation'] | ['medical'] | [ 4.02859569e-01 3.12392086e-01 -1.41482100e-01 -3.35356414e-01
-1.13044035e+00 -8.49511772e-02 6.14224195e-01 -1.31689347e-02
-1.01635568e-01 6.91154122e-01 8.34367752e-01 -1.17509671e-01
-7.31867105e-02 -6.15087748e-01 -4.86360759e-01 -9.74972725e-01
2.39882201e-01 4.00453389e-01 7.65518844e-03 1.95284843... | [15.034757614135742, -1.412042498588562] |
54123187-8a80-492c-85a2-2a1992ce789a | do-multi-hop-question-answering-systems-know | 2002.09919 | null | https://arxiv.org/abs/2002.09919v2 | https://arxiv.org/pdf/2002.09919v2.pdf | Do Multi-Hop Question Answering Systems Know How to Answer the Single-Hop Sub-Questions? | Multi-hop question answering (QA) requires a model to retrieve and integrate information from different parts of a long text to answer a question. Humans answer this kind of complex questions via a divide-and-conquer approach. In this paper, we investigate whether top-performing models for multi-hop questions understan... | ['Hwee Tou Ng', 'Yixuan Tang', 'Anthony K. H. Tung'] | 2020-02-23 | null | https://aclanthology.org/2021.eacl-main.283 | https://aclanthology.org/2021.eacl-main.283.pdf | eacl-2021-2 | ['multi-hop-question-answering'] | ['knowledge-base'] | [-3.11267911e-04 8.96825671e-01 2.16082647e-01 -5.43411016e-01
-1.71129823e+00 -8.55966568e-01 3.30223233e-01 2.01252103e-01
-2.35096868e-02 9.42545652e-01 4.52566892e-01 -8.76403987e-01
-3.65354478e-01 -1.16954005e+00 -7.61239409e-01 8.48668963e-02
3.94460469e-01 1.14637756e+00 7.40037084e-01 -7.99730122... | [11.105262756347656, 7.910545349121094] |
82018524-de2d-4c44-be17-9fce7b49e546 | chestx-ray8-hospital-scale-chest-x-ray | 1705.02315 | null | http://arxiv.org/abs/1705.02315v5 | http://arxiv.org/pdf/1705.02315v5.pdf | ChestX-ray8: Hospital-scale Chest X-ray Database and Benchmarks on Weakly-Supervised Classification and Localization of Common Thorax Diseases | The chest X-ray is one of the most commonly accessible radiological
examinations for screening and diagnosis of many lung diseases. A tremendous
number of X-ray imaging studies accompanied by radiological reports are
accumulated and stored in many modern hospitals' Picture Archiving and
Communication Systems (PACS). On... | ['Mohammadhadi Bagheri', 'Xiaosong Wang', 'Le Lu', 'Yifan Peng', 'Ronald M. Summers', 'Zhiyong Lu'] | 2017-05-05 | chestx-ray8-hospital-scale-chest-x-ray-1 | http://openaccess.thecvf.com/content_cvpr_2017/html/Wang_ChestX-ray8_Hospital-Scale_Chest_CVPR_2017_paper.html | http://openaccess.thecvf.com/content_cvpr_2017/papers/Wang_ChestX-ray8_Hospital-Scale_Chest_CVPR_2017_paper.pdf | cvpr-2017-7 | ['multi-label-image-classification', 'lung-disease-classification'] | ['computer-vision', 'medical'] | [ 1.93995774e-01 -4.47348803e-02 -4.44454670e-01 -5.26028335e-01
-1.63342977e+00 -4.56212580e-01 1.75710097e-01 2.91102529e-01
-4.51150447e-01 6.42343640e-01 1.32318020e-01 -7.44620502e-01
-5.88287711e-01 -7.69509494e-01 -5.68336487e-01 -7.92664707e-01
8.35687518e-02 1.03661025e+00 1.52799040e-01 3.76938283... | [15.255321502685547, -2.0474720001220703] |
546a01ca-2fbd-4c76-bc2a-bbde5e73cec3 | reinforcement-learning | 2005.14419 | null | https://arxiv.org/abs/2005.14419v2 | https://arxiv.org/pdf/2005.14419v2.pdf | Reinforcement Learning | Reinforcement learning (RL) is a general framework for adaptive control, which has proven to be efficient in many domains, e.g., board games, video games or autonomous vehicles. In such problems, an agent faces a sequential decision-making problem where, at every time step, it observes its state, performs an action, re... | ['Olivier Buffet', 'Paul Weng', 'Olivier Pietquin'] | 2020-05-29 | null | null | null | null | ['board-games'] | ['playing-games'] | [ 1.31315172e-01 3.59738052e-01 -8.11640680e-01 1.23405538e-01
-5.32727778e-01 -5.54308116e-01 7.20537543e-01 1.83717966e-01
-7.61623800e-01 1.37344193e+00 -1.68151975e-01 -3.15807879e-01
-3.08365941e-01 -7.80697882e-01 -5.26603401e-01 -1.11814332e+00
-1.37354836e-01 4.73056704e-01 1.62627429e-01 -4.15053248... | [4.168280124664307, 2.1000585556030273] |
28a2dae4-c332-47ed-a6d2-dc8ad3207b7a | clickbait-detection-using-word-embeddings | 1710.02861 | null | http://arxiv.org/abs/1710.02861v1 | http://arxiv.org/pdf/1710.02861v1.pdf | Clickbait detection using word embeddings | Clickbait is a pejorative term describing web content that is aimed at
generating online advertising revenue, especially at the expense of quality or
accuracy, relying on sensationalist headlines or eye-catching thumbnail
pictures to attract click-throughs and to encourage forwarding of the material
over online social ... | ['Vijayasaradhi Indurthi', 'Subba Reddy Oota'] | 2017-10-08 | null | null | null | null | ['clickbait-detection'] | ['natural-language-processing'] | [-2.99427301e-01 -5.35656102e-02 -8.50564361e-01 -4.09833014e-01
-1.08619404e+00 -6.03771985e-01 8.86527836e-01 4.34323221e-01
-4.90259171e-01 5.06138265e-01 2.89306343e-01 -7.45767474e-01
-1.15073398e-01 -7.31603444e-01 -6.73495173e-01 -7.76400790e-02
-2.23362163e-01 1.96028844e-01 3.62440050e-01 -2.28538007... | [7.797877311706543, 9.79059886932373] |
bcdacc24-0cbf-4617-9b51-183831a7e6ea | native-language-identification-using-stacked | 1703.06541 | null | http://arxiv.org/abs/1703.06541v1 | http://arxiv.org/pdf/1703.06541v1.pdf | Native Language Identification using Stacked Generalization | Ensemble methods using multiple classifiers have proven to be the most
successful approach for the task of Native Language Identification (NLI),
achieving the current state of the art. However, a systematic examination of
ensemble methods for NLI has yet to be conducted. Additionally, deeper ensemble
architectures such... | ['Mark Dras', 'Shervin Malmasi'] | 2017-03-19 | null | null | null | null | ['native-language-identification'] | ['natural-language-processing'] | [ 3.27798575e-01 -4.99664038e-01 -2.70263076e-01 -6.10661268e-01
-9.35975730e-01 -8.57671976e-01 1.10685790e+00 3.29877809e-02
-4.80020106e-01 9.52240527e-01 1.88360572e-01 -7.17965782e-01
-2.24478796e-01 -2.14416265e-01 -3.73650402e-01 -4.59384978e-01
-2.16778100e-01 7.84811974e-01 -3.46259803e-01 -2.48366535... | [10.394888877868652, 10.555804252624512] |
c4b845b9-0d0b-48be-a4ec-33f4b18817bc | self-supervised-occupancy-grid-learning-from | 1904.00415 | null | https://arxiv.org/abs/1904.00415v2 | https://arxiv.org/pdf/1904.00415v2.pdf | Road Scene Understanding by Occupancy Grid Learning from Sparse Radar Clusters using Semantic Segmentation | Occupancy grid mapping is an important component in road scene understanding for autonomous driving. It encapsulates information of the drivable area, road obstacles and enables safe autonomous driving. Radars are an emerging sensor in autonomous vehicle vision, becoming more widely used due to their long range sensing... | ['Gilad Cohen', 'Shaul Oron', 'Liat Sless', 'Bat El Shlomo'] | 2019-03-31 | null | null | null | null | ['road-scene-understanding'] | ['computer-vision'] | [ 5.42667210e-01 2.15063188e-02 -1.59997240e-01 -6.96778119e-01
-8.84549379e-01 -5.07411182e-01 8.26879621e-01 -2.83747986e-02
-6.61855280e-01 9.18980896e-01 4.39137258e-02 -3.58854860e-01
-2.24028006e-01 -1.04655659e+00 -8.24588001e-01 -6.80072427e-01
-1.07525907e-01 9.27702844e-01 3.42946619e-01 -3.01688850... | [8.106411933898926, -2.05146861076355] |
b20bd91a-aea6-46dc-b1bb-63a4b2afb7d0 | graph-fairing-convolutional-networks-for | 2010.10274 | null | https://arxiv.org/abs/2010.10274v1 | https://arxiv.org/pdf/2010.10274v1.pdf | Graph Fairing Convolutional Networks for Anomaly Detection | Graph convolution is a fundamental building block for many deep neural networks on graph-structured data. In this paper, we introduce a simple, yet very effective graph convolutional network with skip connections for semi-supervised anomaly detection. The proposed multi-layer network architecture is theoretically motiv... | ['A. Ben Hamza', 'Mahsa Mesgaran'] | 2020-10-20 | null | null | null | null | ['supervised-anomaly-detection', 'semi-supervised-anomaly-detection'] | ['computer-vision', 'computer-vision'] | [-2.76908487e-01 4.79686797e-01 2.73858034e-03 -5.36183178e-01
1.99521050e-01 -5.40993027e-02 4.63760525e-01 8.20557475e-01
-2.94609994e-01 1.41824812e-01 9.46382955e-02 -4.07322407e-01
1.10304520e-01 -1.13423598e+00 -6.77307785e-01 -4.42408115e-01
-7.54893720e-01 2.68930703e-01 3.41096044e-01 -2.79922694... | [7.030810356140137, 6.2321271896362305] |
13ebc249-5f1b-4dd6-804d-1e41d6648518 | progressive-hint-prompting-improves-reasoning | 2304.09797 | null | https://arxiv.org/abs/2304.09797v4 | https://arxiv.org/pdf/2304.09797v4.pdf | Progressive-Hint Prompting Improves Reasoning in Large Language Models | The performance of Large Language Models (LLMs) in reasoning tasks depends heavily on prompt design, with Chain-of-Thought (CoT) and self-consistency being critical methods that enhance this ability. However, these methods do not fully exploit the answers generated by the LLM to guide subsequent responses. This paper p... | ['Yu Li', 'Zhenguo Li', 'Enze Xie', 'Zhengying Liu', 'Chuanyang Zheng'] | 2023-04-19 | https-arxiv-org-abs-2304-09797 | https://arxiv.org/abs/2304.09797 | https://arxiv.org/pdf/2304.09797 | null | ['math-word-problem-solving', 'gsm8k', 'arithmetic-reasoning', 'math-word-problem-solving', 'math-word-problem-solving'] | ['knowledge-base', 'natural-language-processing', 'reasoning', 'reasoning', 'time-series'] | [-3.07857454e-01 7.47094080e-02 -8.69417787e-02 -4.86152828e-01
-1.04237127e+00 -6.79254174e-01 7.20232785e-01 4.21216011e-01
-4.89347875e-01 5.43736875e-01 3.83014679e-01 -7.67667949e-01
-1.12638481e-01 -6.24232888e-01 -5.00366867e-01 -1.66282982e-01
-3.81977856e-02 4.84899104e-01 4.87550735e-01 -4.91854668... | [9.828478813171387, 7.487346649169922] |
8bee9891-7550-4feb-a647-95deac13edd9 | a-study-on-angular-based-embedding-learning | 1908.0399 | null | https://arxiv.org/abs/1908.03990v1 | https://arxiv.org/pdf/1908.03990v1.pdf | A Study on Angular Based Embedding Learning for Text-independent Speaker Verification | Learning a good speaker embedding is important for many automatic speaker recognition tasks, including verification, identification and diarization. The embeddings learned by softmax are not discriminative enough for open-set verification tasks. Angular based embedding learning target can achieve such discriminativenes... | ['Shugong Xu', 'Zongze Ren', 'Zhiyong Chen'] | 2019-08-12 | null | null | null | null | ['text-independent-speaker-verification'] | ['speech'] | [-8.16960260e-02 3.46720368e-01 -3.81481051e-01 -8.32934082e-01
-1.15693402e+00 -6.29490316e-01 5.46968877e-01 3.11467471e-03
-5.33827364e-01 3.87039870e-01 2.56518215e-01 -3.75201166e-01
-2.24530138e-02 -1.14064902e-01 -5.00250757e-01 -7.69618690e-01
-1.64607704e-01 1.34257033e-01 -1.60378039e-01 6.96771895... | [14.303635597229004, 6.077650547027588] |
e57c94a7-05bd-4fab-8767-4690a6694165 | margin-aware-unsupervised-domain-adaptation | null | null | https://aclanthology.org/2020.findings-emnlp.315 | https://aclanthology.org/2020.findings-emnlp.315.pdf | Margin-aware Unsupervised Domain Adaptation for Cross-lingual Text Labeling | Unsupervised domain adaptation addresses the problem of leveraging labeled data in a source domain to learn a well-performing model in a target domain where labels are unavailable. In this paper, we improve upon a recent theoretical work (Zhang et al., 2019b) and adopt the Margin Disparity Discrepancy (MDD) unsupervise... | ['Bing Xiang', 'Kathleen McKeown', 'Cicero Nogueira dos santos', 'Feng Nan', 'Henghui Zhu', 'Ramesh Nallapati', 'Dejiao Zhang'] | 2020-11-01 | null | null | null | findings-of-the-association-for-computational | ['cross-lingual-document-classification'] | ['natural-language-processing'] | [ 1.75053552e-01 2.96432190e-02 -6.19624078e-01 -5.55230141e-01
-1.28496885e+00 -9.51510131e-01 5.35405517e-01 -1.16168726e-02
-5.42629182e-01 1.07954741e+00 -2.07248721e-02 -4.02574807e-01
8.62600803e-02 -5.93359470e-01 -7.60813773e-01 -8.65188122e-01
3.73229027e-01 4.88419026e-01 -3.58978398e-02 -2.53470361... | [10.349250793457031, 3.191943883895874] |
3c84c357-ba99-4fa4-be60-2c67b7f95723 | meta-review-generation-with-checklist-guided | 2305.14647 | null | https://arxiv.org/abs/2305.14647v1 | https://arxiv.org/pdf/2305.14647v1.pdf | Meta-review Generation with Checklist-guided Iterative Introspection | Opinions in the scientific domain can be divergent, leading to controversy or consensus among reviewers. However, current opinion summarization datasets mostly focus on product review domains, which do not account for this variability under the assumption that the input opinions are non-controversial. To address this g... | ['Heng Ji', 'Lu Wang', 'Hou Pong Chan', 'Mankeerat Sidhu', 'Qi Zeng'] | 2023-05-24 | null | null | null | null | ['review-generation'] | ['natural-language-processing'] | [ 4.02337313e-01 6.17888808e-01 -5.73779345e-01 -1.38197139e-01
-8.46131146e-01 -9.19060528e-01 6.00743771e-01 6.42570734e-01
-8.49663690e-02 1.12275600e+00 5.93518257e-01 -7.08634138e-01
-2.08387002e-01 -4.34357345e-01 -5.29660106e-01 -1.92316741e-01
7.98120439e-01 1.04055725e-01 -1.92107603e-01 -7.64273256... | [12.345035552978516, 9.56851577758789] |
b49bc6f3-cf63-4d35-aae6-fc799d0f8bec | deep-markov-spatio-temporal-factorization | 2003.09779 | null | https://arxiv.org/abs/2003.09779v2 | https://arxiv.org/pdf/2003.09779v2.pdf | Deep Markov Spatio-Temporal Factorization | We introduce deep Markov spatio-temporal factorization (DMSTF), a generative model for dynamical analysis of spatio-temporal data. Like other factor analysis methods, DMSTF approximates high dimensional data by a product between time dependent weights and spatially dependent factors. These weights and factors are in tu... | ['J. Benjamin Hutchinson', 'Jan-Willem van de Meent', 'Eli Zachary Sennesh', 'Amirreza Farnoosh', 'Jennifer Dy', 'Behnaz Rezaei', 'Ajay Satpute', 'Zulqarnain Khan', 'Sarah Ostadabbas'] | 2020-03-22 | null | null | null | null | ['time-series-clustering'] | ['time-series'] | [-3.07550937e-01 -2.12260067e-01 -8.85684192e-02 -1.02433950e-01
-4.15317386e-01 -7.60872841e-01 1.10371089e+00 -5.78939080e-01
7.39564300e-02 3.63681197e-01 8.09331954e-01 -2.34311000e-01
-5.36495805e-01 -6.74037695e-01 -5.77634096e-01 -1.15719759e+00
-5.12285709e-01 8.35964739e-01 1.45688325e-01 -5.32572297... | [7.020599842071533, 3.444119930267334] |
65f20a09-8fbf-41bc-81ab-94bcf9dc19ca | going-beyond-research-datasets-novel-intent | 2305.05474 | null | https://arxiv.org/abs/2305.05474v1 | https://arxiv.org/pdf/2305.05474v1.pdf | Going beyond research datasets: Novel intent discovery in the industry setting | Novel intent discovery automates the process of grouping similar messages (questions) to identify previously unknown intents. However, current research focuses on publicly available datasets which have only the question field and significantly differ from real-life datasets. This paper proposes methods to improve the i... | ['Piotr Rybak', 'Robert Mroczkowski', 'Dariusz Kajtoch', 'Tsimur Hadeliya', 'Aleksandra Chrabrowa'] | 2023-05-09 | null | null | null | null | ['intent-discovery'] | ['natural-language-processing'] | [-7.80233666e-02 2.17483714e-02 4.82829101e-02 -9.23935890e-01
-1.02636731e+00 -7.21992075e-01 8.07571590e-01 4.63635437e-02
-4.10241365e-01 1.51607603e-01 7.29662180e-01 -2.41997913e-01
-3.36414203e-02 -3.18854958e-01 -5.18192708e-01 -2.68614888e-01
-1.73635527e-01 9.39409256e-01 2.30310373e-02 -8.96220654... | [12.393815994262695, 7.491898059844971] |
5eba7a83-f3c5-4494-8e82-018c15dd075b | person-search-via-a-mask-guided-two-stream | 1807.08107 | null | http://arxiv.org/abs/1807.08107v1 | http://arxiv.org/pdf/1807.08107v1.pdf | Person Search via A Mask-Guided Two-Stream CNN Model | In this work, we tackle the problem of person search, which is a challenging
task consisted of pedestrian detection and person re-identification~(re-ID).
Instead of sharing representations in a single joint model, we find that
separating detector and re-ID feature extraction yields better performance. In
order to extra... | ['Wanli Ouyang', 'Shanshan Zhang', 'Jian Yang', 'Di Chen', 'Ying Tai'] | 2018-07-21 | person-search-via-a-mask-guided-two-stream-1 | http://openaccess.thecvf.com/content_ECCV_2018/html/Di_Chen_Person_Search_via_ECCV_2018_paper.html | http://openaccess.thecvf.com/content_ECCV_2018/papers/Di_Chen_Person_Search_via_ECCV_2018_paper.pdf | eccv-2018-9 | ['person-search'] | ['computer-vision'] | [ 4.93664257e-02 -3.94844800e-01 1.75726175e-01 -3.37800056e-01
-8.39368701e-01 -3.75578821e-01 4.73836899e-01 -1.50434151e-01
-1.07010877e+00 7.77993619e-01 -5.07158563e-02 1.21558525e-01
3.45147550e-01 -7.12584078e-01 -7.50047445e-01 -6.66581154e-01
1.60777867e-01 4.01262611e-01 4.65590864e-01 2.07003251... | [14.79239273071289, 0.8354737162590027] |
85f5e8d1-93b3-4dba-912b-3a925a184625 | information-based-disentangled-representation | 2103.13283 | null | https://arxiv.org/abs/2103.13283v1 | https://arxiv.org/pdf/2103.13283v1.pdf | Information-based Disentangled Representation Learning for Unsupervised MR Harmonization | Accuracy and consistency are two key factors in computer-assisted magnetic resonance (MR) image analysis. However, contrast variation from site to site caused by lack of standardization in MR acquisition impedes consistent measurements. In recent years, image harmonization approaches have been proposed to compensate fo... | ['Jerry L. Prince', 'Peter A. Calabresi', 'Yufan He', 'Yihao Liu', 'Aaron Carass', 'Blake E. Dewey', 'Lianrui Zuo'] | 2021-03-24 | null | null | null | null | ['image-harmonization'] | ['computer-vision'] | [ 2.27010950e-01 4.03362699e-02 -2.32720375e-01 -4.64083880e-01
-1.21436024e+00 -1.82064980e-01 2.24934191e-01 2.19003752e-01
-6.58237755e-01 7.53165364e-01 3.13039839e-01 1.24369517e-01
-7.08041370e-01 -4.03329819e-01 -4.04725760e-01 -8.54343116e-01
-3.94796804e-02 6.65316701e-01 1.46602824e-01 -1.45013496... | [13.782447814941406, -2.344944715499878] |
8f6af934-d66d-489c-96aa-d8d6686888c1 | one-shot-learning-from-a-demonstration-with-1 | 2203.04806 | null | https://arxiv.org/abs/2203.04806v1 | https://arxiv.org/pdf/2203.04806v1.pdf | One-Shot Learning from a Demonstration with Hierarchical Latent Language | Humans have the capability, aided by the expressive compositionality of their language, to learn quickly by demonstration. They are able to describe unseen task-performing procedures and generalize their execution to other contexts. In this work, we introduce DescribeWorld, an environment designed to test this sort of ... | ['Benjamin Van Durme', 'Harm van Seijen', 'Ida Momennejad', 'Romain Laroche', 'Matthew Hausknecht', 'Marc-Alexandre Côté', 'Xingdi Yuan', 'Nathaniel Weir'] | 2022-03-09 | null | null | null | null | ['one-shot-learning'] | ['methodology'] | [ 2.70478636e-01 3.89717400e-01 2.78468311e-01 -4.08629358e-01
-5.19646049e-01 -1.03762150e+00 1.26000607e+00 7.31236339e-02
-2.88684994e-01 7.09962487e-01 3.95463526e-01 -3.70943397e-01
8.25802013e-02 -7.07371891e-01 -6.80970311e-01 -3.81115377e-01
-3.12965214e-01 9.99978602e-01 6.97411075e-02 -3.84873182... | [4.298157215118408, 0.9836122989654541] |
8ae4381a-354b-42c5-9e2f-ed5460df74b7 | improving-speaker-verification-with-self | 2305.10517 | null | https://arxiv.org/abs/2305.10517v1 | https://arxiv.org/pdf/2305.10517v1.pdf | Improving Speaker Verification with Self-Pretrained Transformer Models | Recently, fine-tuning large pre-trained Transformer models using downstream datasets has received a rising interest. Despite their success, it is still challenging to disentangle the benefits of large-scale datasets and Transformer structures from the limitations of the pre-training. In this paper, we introduce a hiera... | ['Jan Černocký', 'Lukáš Burget', 'Ladislav Mošner', 'Themos Stafylakis', 'Oldřich Plchot', 'Junyi Peng'] | 2023-05-17 | null | null | null | null | ['speaker-verification'] | ['speech'] | [-6.21139295e-02 7.52702877e-02 1.46081829e-02 -6.79248273e-01
-1.30599308e+00 -7.14729905e-01 5.93051851e-01 -2.69625545e-01
-4.13034528e-01 6.35680974e-01 4.16020662e-01 -4.53398347e-01
7.41490200e-02 -3.45064849e-01 -6.86136603e-01 -5.22006989e-01
2.24493146e-01 7.42774189e-01 1.93558991e-01 -2.64902651... | [14.226432800292969, 6.408614635467529] |
6d31b5d3-b456-4c65-afb6-44d48f6e17b0 | learning-deep-representations-for-scene | 1706.02493 | null | http://arxiv.org/abs/1706.02493v2 | http://arxiv.org/pdf/1706.02493v2.pdf | Learning Deep Representations for Scene Labeling with Semantic Context Guided Supervision | Scene labeling is a challenging classification problem where each input image
requires a pixel-level prediction map. Recently, deep-learning-based methods
have shown their effectiveness on solving this problem. However, we argue that
the large intra-class variation provides ambiguous training information and
hinders th... | ['Zhe Wang', 'Hongsheng Li', 'Wanli Ouyang', 'Xiaogang Wang'] | 2017-06-08 | null | null | null | null | ['scene-labeling'] | ['computer-vision'] | [ 2.28937984e-01 -2.37094373e-01 -4.86495435e-01 -9.94242668e-01
-2.28713900e-01 -3.86768937e-01 4.42646742e-01 1.25413343e-01
-5.33067167e-01 5.49627364e-01 1.53057510e-02 -4.66006286e-02
-6.91233426e-02 -8.55628252e-01 -7.24993289e-01 -7.53563583e-01
-9.30304676e-02 6.06248565e-02 7.27648377e-01 1.01173170... | [9.594000816345215, 1.9131540060043335] |
c2673258-dbb8-4c48-a9b1-422ca3fd9e4b | natgen-generative-pre-training-by | 2206.07585 | null | https://arxiv.org/abs/2206.07585v2 | https://arxiv.org/pdf/2206.07585v2.pdf | NatGen: Generative pre-training by "Naturalizing" source code | Pre-trained Generative Language models (e.g. PLBART, CodeT5, SPT-Code) for source code yielded strong results on several tasks in the past few years, including code generation and translation. These models have adopted varying pre-training objectives to learn statistics of code construction from very large-scale corpor... | ['Baishakhi Ray', 'Premkumar Devanbu', 'Yangruibo Ding', 'Toufique Ahmed', 'Saikat Chakraborty'] | 2022-06-15 | null | null | null | null | ['code-translation'] | ['computer-code'] | [ 2.55862564e-01 3.78438354e-01 -1.48392215e-01 -2.13901550e-01
-1.11058998e+00 -7.30050862e-01 5.88269770e-01 -3.26005854e-02
1.38854429e-01 3.57810885e-01 3.27538460e-01 -5.96017599e-01
3.19812775e-01 -8.55733812e-01 -1.04028976e+00 -7.64080361e-02
-6.28078356e-02 2.50394344e-01 1.19473971e-01 -4.96823817... | [7.767595291137695, 7.859583854675293] |
53418f85-4e7b-42c1-afdd-a2e62064683a | robust-target-localization-in-2d-a-value-at | 2307.00548 | null | https://arxiv.org/abs/2307.00548v2 | https://arxiv.org/pdf/2307.00548v2.pdf | Robust Target Localization in 2D: A Value-at-Risk Approach | This paper consider considers the problem of locating a two dimensional target from range-measurements containing outliers. Assuming that the number of outlier is known, we formulate the problem of minimizing inlier losses while ignoring outliers. This leads to a combinatorial, non-convex, non-smooth problem involving ... | ['João Xavier', 'João Domingos'] | 2023-07-02 | null | null | null | null | ['portfolio-optimization'] | ['time-series'] | [-1.87292382e-01 2.01344609e-01 2.56132901e-01 4.59827147e-02
-1.32454967e+00 -7.66386509e-01 3.22708756e-01 3.88544470e-01
-3.52077991e-01 6.73507750e-01 -1.20302297e-01 -3.38261485e-01
-5.12156427e-01 -6.59507513e-01 -9.78706837e-01 -1.00674725e+00
-4.43588823e-01 5.32351077e-01 -3.49990204e-02 6.57080561... | [6.747450351715088, 3.9457013607025146] |
ed333c6d-8fd9-4013-8194-d788e1b081d5 | scam-transferring-humans-between-images-with | 2210.04883 | null | https://arxiv.org/abs/2210.04883v1 | https://arxiv.org/pdf/2210.04883v1.pdf | SCAM! Transferring humans between images with Semantic Cross Attention Modulation | A large body of recent work targets semantically conditioned image generation. Most such methods focus on the narrower task of pose transfer and ignore the more challenging task of subject transfer that consists in not only transferring the pose but also the appearance and background. In this work, we introduce SCAM (S... | ['Vicky Kalogeiton', 'David Picard', 'Nicolas Dufour'] | 2022-10-10 | null | null | null | null | ['reconstruction', 'pose-transfer'] | ['computer-vision', 'computer-vision'] | [ 6.19308054e-01 3.58312011e-01 2.06330106e-01 -4.86023098e-01
-1.02110410e+00 -3.89295101e-01 9.49302316e-01 -8.11174214e-01
-3.76043469e-02 8.18126500e-01 2.19232589e-01 1.31504536e-01
3.29056889e-01 -7.22549558e-01 -1.08597445e+00 -1.01628542e+00
3.56160909e-01 6.61248326e-01 2.65890539e-01 -4.00617659... | [11.61235237121582, -0.5833448171615601] |
50b5d7ec-1d4b-4ad0-ba83-a4380a2eec7f | learning-thermodynamically-constrained | 2306.17004 | null | https://arxiv.org/abs/2306.17004v1 | https://arxiv.org/pdf/2306.17004v1.pdf | Learning thermodynamically constrained equations of state with uncertainty | Numerical simulations of high energy-density experiments require equation of state (EOS) models that relate a material's thermodynamic state variables -- specifically pressure, volume/density, energy, and temperature. EOS models are typically constructed using a semi-empirical parametric methodology, which assumes a ph... | ['Michael D. Shields', 'Dimitrios Tsapetis', 'Jim A. Gaffney', 'Himanshu Sharma'] | 2023-06-29 | null | null | null | null | ['gpr', 'gpr'] | ['computer-vision', 'miscellaneous'] | [-1.80523172e-01 -1.15758076e-01 -6.63438961e-02 -3.61685395e-01
-7.79089928e-01 -6.20875321e-02 6.48826420e-01 5.26567400e-01
-2.95535475e-01 9.60267544e-01 -2.79744625e-01 -3.78518969e-01
-3.65740478e-01 -9.39375699e-01 -7.91543841e-01 -9.83087182e-01
1.86594054e-01 1.07459438e+00 2.93700218e-01 1.04324080... | [6.368881702423096, 3.473710536956787] |
79ee40f5-64a8-4ce2-8e1a-9e6fc8e32cc2 | world-to-words-grounded-open-vocabulary | 2306.08685 | null | https://arxiv.org/abs/2306.08685v1 | https://arxiv.org/pdf/2306.08685v1.pdf | World-to-Words: Grounded Open Vocabulary Acquisition through Fast Mapping in Vision-Language Models | The ability to connect language units to their referents in the physical world, referred to as grounding, is crucial to learning and understanding grounded meanings of words. While humans demonstrate fast mapping in new word learning, it remains unclear whether modern vision-language models can truly represent language... | ['Joyce Chai', 'Jiayi Pan', 'Ziqiao Ma'] | 2023-06-14 | null | null | null | null | ['grounded-open-vocabulary-acquisition'] | ['natural-language-processing'] | [ 9.91790444e-02 2.46037871e-01 -2.06236113e-02 -2.02660784e-01
-4.39029843e-01 -7.23296404e-01 7.48387098e-01 2.59154975e-01
-4.55500871e-01 5.41804492e-01 1.98008522e-01 -4.75112259e-01
1.28132448e-01 -8.54749382e-01 -9.97490287e-01 -2.05896899e-01
-3.04255873e-01 4.54496771e-01 1.44156098e-01 -5.74689746... | [10.642595291137695, 1.8430994749069214] |
12d7feff-d131-425a-9645-51d5780a10f2 | data-uncertainty-guided-multi-phase-learning | 2103.16368 | null | https://arxiv.org/abs/2103.16368v1 | https://arxiv.org/pdf/2103.16368v1.pdf | Data-Uncertainty Guided Multi-Phase Learning for Semi-Supervised Object Detection | In this paper, we delve into semi-supervised object detection where unlabeled images are leveraged to break through the upper bound of fully-supervised object detection models. Previous semi-supervised methods based on pseudo labels are severely degenerated by noise and prone to overfit to noisy labels, thus are defici... | ['Shengjin Wang', 'Lu Fang', 'Ye Guo', 'YaLi Li', 'Zhenyu Wang'] | 2021-03-29 | null | http://openaccess.thecvf.com//content/CVPR2021/html/Wang_Data-Uncertainty_Guided_Multi-Phase_Learning_for_Semi-Supervised_Object_Detection_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Wang_Data-Uncertainty_Guided_Multi-Phase_Learning_for_Semi-Supervised_Object_Detection_CVPR_2021_paper.pdf | cvpr-2021-1 | ['semi-supervised-object-detection'] | ['computer-vision'] | [ 1.51092723e-01 2.01484114e-01 -4.43283528e-01 -5.68955958e-01
-1.23345208e+00 -8.05543363e-01 5.00987411e-01 -3.57303098e-02
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3.92429113e-01 7.59442925e-01 5.66687524e-01 3.96310180... | [9.202065467834473, 1.2710559368133545] |
de415047-aa5c-46ce-b1f1-32a0c6f856a9 | leveraging-deep-learning-techniques-on | 2304.09282 | null | https://arxiv.org/abs/2304.09282v1 | https://arxiv.org/pdf/2304.09282v1.pdf | Leveraging Deep Learning Techniques on Collaborative Filtering Recommender Systems | With the exponentially increasing volume of online data, searching and finding required information have become an extensive and time-consuming task. Recommender Systems as a subclass of information retrieval and decision support systems by providing personalized suggestions helping users access what they need more eff... | ['Javad Mohammadzadeh', 'Ali Fallahi RahmatAbadi'] | 2023-04-18 | null | null | null | null | ['collaborative-filtering'] | ['miscellaneous'] | [-4.21741158e-01 -4.97451544e-01 -3.83848727e-01 -6.19793415e-01
-2.82130599e-01 -5.01284182e-01 4.25976038e-01 7.95174111e-03
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-8.66196156e-01 -1.00343323e+00 -1.63736045e-01 -3.94843727e-01
-4.53089289e-02 6.39748275e-01 3.36142406e-02 -7.87519395... | [10.097545623779297, 5.738048553466797] |
bf6c11f3-5bc4-45b1-b01f-f88f9f3194d3 | trade-off-between-communication-and-1 | 2305.04423 | null | https://arxiv.org/abs/2305.04423v1 | https://arxiv.org/pdf/2305.04423v1.pdf | Trade-off Between Communication and Positioning in Millimeter Wave Systems with Bounded and Unbounded Positioning Errors | Millimeter wave has proven to be effective in the integrated positioning and communication (IPAC) system. In this work, we establish a millimeter wave IPAC system by leveraging the inner coupling relationship between estimated data rate and positioning error. Moreover, we formulate robust power allocation problems by m... | ['Shiyin Li', 'Ruixin Yang', 'Shuai Ma', 'Junchang Sun'] | 2023-05-08 | null | null | null | null | ['robust-design'] | ['miscellaneous'] | [ 1.23750746e-01 4.50149447e-01 2.71451026e-01 1.26733780e-01
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-9.70891640e-02 -1.29639357e-01 -5.70740461e-01 1.82561949... | [6.170117378234863, 1.3630609512329102] |
146717c5-dbe0-4a96-933b-a610093e93d5 | dccrn-kws-an-audio-bias-based-model-for-noise | 2305.12331 | null | https://arxiv.org/abs/2305.12331v3 | https://arxiv.org/pdf/2305.12331v3.pdf | DCCRN-KWS: an audio bias based model for noise robust small-footprint keyword spotting | Real-world complex acoustic environments especially the ones with a low signal-to-noise ratio (SNR) will bring tremendous challenges to a keyword spotting (KWS) system. Inspired by the recent advances of neural speech enhancement and context bias in speech recognition, we propose a robust audio context bias based DCCRN... | ['Lei Xie', 'Long Ma', 'Sining Sun', 'Xiong Wang', 'Shubo Lv'] | 2023-05-21 | null | null | null | null | ['small-footprint-keyword-spotting', 'keyword-spotting', 'speech-enhancement'] | ['speech', 'speech', 'speech'] | [ 0.46059433 -0.3345368 0.09412286 -0.47058883 -1.4259113 -0.2109551
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0.01289 -0.32145017 0.0316256 0.08560326 -1.5020516 0.51968557
0.68344975 1.299815 0.8... | [14.70246696472168, 6.122887134552002] |
261366e6-1c43-4be0-809f-83c1ffc03e2e | score-based-generative-models-for | 2306.13843 | null | https://arxiv.org/abs/2306.13843v1 | https://arxiv.org/pdf/2306.13843v1.pdf | Score-based Generative Models for Photoacoustic Image Reconstruction with Rotation Consistency Constraints | Photoacoustic tomography (PAT) is a newly emerged imaging modality which enables both high optical contrast and acoustic depth of penetration. Reconstructing images of photoacoustic tomography from limited amount of senser data is among one of the major challenges in photoacoustic imaging. Previous works based on deep ... | ['Fei Gao', 'Jianwen Luo', 'Liming Nie', 'Hengrong Lan', 'Shangqing Tong'] | 2023-06-24 | null | null | null | null | ['image-reconstruction'] | ['computer-vision'] | [ 6.81303203e-01 -4.44554053e-02 5.58959901e-01 -3.18277955e-01
-1.21179950e+00 -3.26871425e-01 4.60252762e-01 -7.51811028e-01
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-4.12730306e-01 -7.20533609e-01 -6.92084134e-01 -1.39490759e+00
3.45616579e-01 4.84328419e-01 2.56867975e-01 4.68208313... | [11.73662281036377, -2.3468804359436035] |
d4b3876c-9ed8-4c85-823c-c0e33304e019 | visual-semantic-slam-with-landmarks-for-large | 2001.01028 | null | https://arxiv.org/abs/2001.01028v1 | https://arxiv.org/pdf/2001.01028v1.pdf | Visual Semantic SLAM with Landmarks for Large-Scale Outdoor Environment | Semantic SLAM is an important field in autonomous driving and intelligent agents, which can enable robots to achieve high-level navigation tasks, obtain simple cognition or reasoning ability and achieve language-based human-robot-interaction. In this paper, we built a system to creat a semantic 3D map by combining 3D p... | ['Zirui Zhao', 'Yijun Mao', 'Pengju Ren', 'Yan Ding', 'Nanning Zheng'] | 2020-01-04 | null | null | null | null | ['semantic-slam'] | ['computer-vision'] | [-3.62316221e-01 -1.50990576e-01 5.84874339e-02 -8.38017225e-01
-1.14637055e-01 -4.05313283e-01 5.47457337e-01 -1.08599380e-01
-5.78063667e-01 5.56526124e-01 -4.77492988e-01 -3.65089625e-01
-1.89625069e-01 -1.25415993e+00 -7.87967086e-01 -1.13262028e-01
-1.69760510e-01 1.08481073e+00 7.68346250e-01 -7.32752085... | [7.50096321105957, -2.1524956226348877] |
9561c25e-9038-4179-acb6-e26d5ea5a54d | character-n-gram-embeddings-to-improve-rnn | 1906.05506 | null | https://arxiv.org/abs/1906.05506v1 | https://arxiv.org/pdf/1906.05506v1.pdf | Character n-gram Embeddings to Improve RNN Language Models | This paper proposes a novel Recurrent Neural Network (RNN) language model that takes advantage of character information. We focus on character n-grams based on research in the field of word embedding construction (Wieting et al. 2016). Our proposed method constructs word embeddings from character n-gram embeddings and ... | ['Masaaki Nagata', 'Jun Suzuki', 'Sho Takase'] | 2019-06-13 | null | null | null | null | ['headline-generation'] | ['natural-language-processing'] | [-5.56324646e-02 1.10278524e-01 -7.26443172e-01 2.83728391e-02
-4.79021043e-01 -1.10283382e-01 7.12585330e-01 2.11213976e-02
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3.97941411e-01 -8.51376414e-01 -2.75379062e-01 -3.33556205e-01
4.92299274e-02 2.14183316e-01 -2.60791391e-01 -3.74615967... | [10.920461654663086, 8.86082935333252] |
0989d8fb-3fbf-4b90-baeb-2dfce6d79bb3 | ju_cse-a-conditional-random-field-crf-based | null | null | https://aclanthology.org/S14-2063 | https://aclanthology.org/S14-2063.pdf | JU\_CSE: A Conditional Random Field (CRF) Based Approach to Aspect Based Sentiment Analysis | null | ['Sivaji yopadhyay', 'B', 'Soumik al', 'M', 'Dipankar Das', 'Braja Gopal Patra'] | 2014-08-01 | null | null | null | semeval-2014-8 | ['subjectivity-analysis'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
-2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00
-3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01
-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.2457170486450195, 3.8112142086029053] |
c41a549f-645c-455a-9f24-8b5f66731c30 | evaluation-of-latent-space-disentanglement-in | 2110.05587 | null | https://arxiv.org/abs/2110.05587v1 | https://arxiv.org/pdf/2110.05587v1.pdf | Evaluation of Latent Space Disentanglement in the Presence of Interdependent Attributes | Controllable music generation with deep generative models has become increasingly reliant on disentanglement learning techniques. However, current disentanglement metrics, such as mutual information gap (MIG), are often inadequate and misleading when used for evaluating latent representations in the presence of interde... | ['Alexander Lerch', 'Karn N. Watcharasupat'] | 2021-10-11 | null | null | null | null | ['music-generation', 'music-generation'] | ['audio', 'music'] | [ 2.55976081e-01 -1.51843861e-01 -8.62689018e-02 -3.42667818e-01
-6.97142959e-01 -7.09621489e-01 8.02028596e-01 2.08285540e-01
-2.07625493e-01 8.68719518e-01 5.74620485e-01 7.17782825e-02
-6.66871369e-01 -7.12383032e-01 -1.77509665e-01 -5.77955842e-01
1.39200866e-01 5.54499865e-01 -4.60164517e-01 -1.26331717... | [9.313465118408203, 4.8592963218688965] |
664974ee-7393-4c99-bc8b-32d24be6506d | band-biomedical-alert-news-dataset | 2305.1448 | null | https://arxiv.org/abs/2305.14480v1 | https://arxiv.org/pdf/2305.14480v1.pdf | BAND: Biomedical Alert News Dataset | Infectious disease outbreaks continue to pose a significant threat to human health and well-being. To improve disease surveillance and understanding of disease spread, several surveillance systems have been developed to monitor daily news alerts and social media. However, existing systems lack thorough epidemiological ... | ['Nigel Collier', 'David Buckeridge', 'Anya Okhmatovskaia', 'Yannan Shen', 'Zaiqiao Meng', 'Meiru Zhang', 'Zihao Fu'] | 2023-05-23 | null | null | null | null | ['epidemiology', 'event-extraction', 'named-entity-recognition-ner'] | ['medical', 'natural-language-processing', 'natural-language-processing'] | [ 1.84879616e-01 1.65180787e-01 -2.46814683e-01 -2.03737170e-01
-7.70247161e-01 -5.22354007e-01 6.20401204e-01 1.05004573e+00
-5.92127800e-01 9.71494079e-01 7.15250194e-01 -3.63394588e-01
-2.40366757e-01 -8.58004928e-01 -5.29883206e-01 -3.81150037e-01
-3.57644707e-01 4.86230940e-01 1.51872307e-01 -1.69187531... | [8.51162338256836, 9.226598739624023] |
e15cf729-cc61-4d88-b6e9-0c8cb5d44a85 | dc-mbr-distributional-cooling-for-minimum | 2212.04205 | null | https://arxiv.org/abs/2212.04205v2 | https://arxiv.org/pdf/2212.04205v2.pdf | DC-MBR: Distributional Cooling for Minimum Bayesian Risk Decoding | Minimum Bayesian Risk Decoding (MBR) emerges as a promising decoding algorithm in Neural Machine Translation. However, MBR performs poorly with label smoothing, which is surprising as label smoothing provides decent improvement with beam search and improves generality in various tasks. In this work, we show that the is... | ['Yue Zhang', 'Jie zhou', 'Fandong Meng', 'Jin Xu', 'Jianhao Yan'] | 2022-12-08 | null | null | null | null | ['nmt'] | ['computer-code'] | [ 5.95108926e-01 1.87954307e-01 -4.31288034e-01 -6.32345438e-01
-1.27002203e+00 -5.65339983e-01 5.00642061e-01 -1.30870447e-01
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2.93441325e-01 -2.50726551e-01 -8.59602988e-01 -9.10455704e-01
2.29249731e-01 3.79817605e-01 -9.62094963e-02 2.31514759... | [11.536667823791504, 9.800569534301758] |
81cb83fa-847f-4616-b2ef-71058cbdf10a | accurate-learning-of-graph-representations-1 | 2102.11533 | null | https://arxiv.org/abs/2102.11533v4 | https://arxiv.org/pdf/2102.11533v4.pdf | Accurate Learning of Graph Representations with Graph Multiset Pooling | Graph neural networks have been widely used on modeling graph data, achieving impressive results on node classification and link prediction tasks. Yet, obtaining an accurate representation for a graph further requires a pooling function that maps a set of node representations into a compact form. A simple sum or averag... | ['Sung Ju Hwang', 'Minki Kang', 'Jinheon Baek'] | 2021-02-23 | accurate-learning-of-graph-representations | https://openreview.net/forum?id=JHcqXGaqiGn | https://openreview.net/pdf?id=JHcqXGaqiGn | iclr-2021-1 | ['graph-reconstruction'] | ['graphs'] | [ 1.61746770e-01 4.61204916e-01 -3.76901358e-01 -1.89791739e-01
-3.80858600e-01 -6.11623704e-01 4.93761182e-01 5.24625123e-01
-9.39195454e-02 4.89564866e-01 1.03031568e-01 -3.15254539e-01
-2.16026515e-01 -1.38990688e+00 -7.13165581e-01 -7.92780757e-01
-4.17086840e-01 3.64709258e-01 3.92510593e-01 -1.10143736... | [7.092013835906982, 6.343496799468994] |
398bce5c-c699-467b-86ff-a0eb54d2b70a | a-latent-space-model-for-hla-compatibility | 2211.02234 | null | https://arxiv.org/abs/2211.02234v1 | https://arxiv.org/pdf/2211.02234v1.pdf | A Latent Space Model for HLA Compatibility Networks in Kidney Transplantation | Kidney transplantation is the preferred treatment for people suffering from end-stage renal disease. Successful kidney transplants still fail over time, known as graft failure; however, the time to graft failure, or graft survival time, can vary significantly between different recipients. A significant biological facto... | ['Kevin S. Xu', 'Zhipeng Huang'] | 2022-11-04 | null | null | null | null | ['survival-analysis'] | ['miscellaneous'] | [ 2.93384250e-02 -2.18960002e-01 -6.07280910e-01 -6.42103553e-01
-1.22546539e-01 -6.97667956e-01 2.95864493e-01 5.63676357e-01
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-2.53089309e-01 -1.31671762e+00 -1.73201188e-01 -6.10386431e-01
-5.74511051e-01 7.56893992e-01 -3.66745621e-01 2.20506623... | [7.1548237800598145, 5.251214504241943] |
1ec4983b-3e43-4696-a3da-3baabbd301f2 | user-localization-using-rf-sensing-a | 2205.10321 | null | https://arxiv.org/abs/2205.10321v1 | https://arxiv.org/pdf/2205.10321v1.pdf | User Localization using RF Sensing: A Performance comparison between LIS and mmWave Radars | Since electromagnetic signals are omnipresent, Radio Frequency (RF)-sensing has the potential to become a universal sensing mechanism with applications in localization, smart-home, retail, gesture recognition, intrusion detection, etc. Two emerging technologies in RF-sensing, namely sensing through Large Intelligent Su... | ['Stephan Sigg', 'Zheng-Hua Tan', 'Elisabeth de Carvalho', 'Petar Popovski', 'Dariush Salami', 'Cristian J. Vaca-Rubio'] | 2022-05-17 | null | null | null | null | ['gesture-recognition'] | ['computer-vision'] | [ 5.29904306e-01 -1.88714772e-01 1.76605597e-01 -1.91099346e-01
-5.58987975e-01 -6.13801241e-01 5.52339435e-01 -2.40507647e-01
-4.06187683e-01 9.86789346e-01 -1.45567000e-01 -4.06626523e-01
-5.32869756e-01 -1.18121946e+00 -2.90814042e-01 -8.64513874e-01
-3.47719401e-01 2.78578430e-01 1.82430029e-01 -1.24334404... | [6.557775497436523, 0.8021537661552429] |
f7634584-e041-401d-98a7-99a3dae2fdf9 | explanation-generation-for-multi-modal-multi | 2008.03573 | null | https://arxiv.org/abs/2008.03573v1 | https://arxiv.org/pdf/2008.03573v1.pdf | Explanation Generation for Multi-Modal Multi-Agent Path Finding with Optimal Resource Utilization using Answer Set Programming | The multi-agent path finding (MAPF) problem is a combinatorial search problem that aims at finding paths for multiple agents (e.g., robots) in an environment (e.g., an autonomous warehouse) such that no two agents collide with each other, and subject to some constraints on the lengths of paths. We consider a general ve... | ['Esra Erdem', 'Aysu Bogatarkan'] | 2020-08-08 | null | null | null | null | ['multi-agent-path-finding'] | ['playing-games'] | [-2.06446320e-01 4.63732362e-01 -1.58002123e-01 -2.15704471e-01
-2.35998318e-01 -1.08419073e+00 1.26536489e-01 5.30100524e-01
-4.23673615e-02 9.83544827e-01 -4.60817516e-01 -6.13259077e-01
-8.95841956e-01 -1.15174723e+00 -8.57894540e-01 -6.03154063e-01
-4.39559072e-01 1.08899009e+00 2.45932728e-01 -4.62738276... | [4.932080268859863, 1.7714234590530396] |
1b5bb8af-5579-4be6-8dbe-ccd8dd2db950 | blue-at-memotion-2-0-2022-you-have-my-image | 2202.07543 | null | https://arxiv.org/abs/2202.07543v3 | https://arxiv.org/pdf/2202.07543v3.pdf | BLUE at Memotion 2.0 2022: You have my Image, my Text and my Transformer | Memes are prevalent on the internet and continue to grow and evolve alongside our culture. An automatic understanding of memes propagating on the internet can shed light on the general sentiment and cultural attitudes of people. In this work, we present team BLUE's solution for the second edition of the MEMOTION shared... | ['Ioan-Bogdan Iordache', 'Adrian Cosma', 'Ana-Maria Bucur'] | 2022-02-15 | null | null | null | null | ['meme-classification'] | ['natural-language-processing'] | [-1.62655205e-01 -3.61831844e-01 3.45836997e-01 -1.81856185e-01
-6.52310133e-01 -5.81194520e-01 9.86377180e-01 4.36882943e-01
-6.80836320e-01 4.94906723e-01 6.14633918e-01 1.29039481e-01
6.21690989e-01 -6.50342643e-01 -3.81451786e-01 -1.02882415e-01
5.28181791e-01 3.29411566e-01 7.50428140e-02 -7.84927428... | [8.525493621826172, 10.725275993347168] |
55859447-726b-4225-ac62-1e35a751873a | structure-aware-dropedge-towards-deep-graph | 2306.12091 | null | https://arxiv.org/abs/2306.12091v1 | https://arxiv.org/pdf/2306.12091v1.pdf | Structure-Aware DropEdge Towards Deep Graph Convolutional Networks | It has been discovered that Graph Convolutional Networks (GCNs) encounter a remarkable drop in performance when multiple layers are piled up. The main factor that accounts for why deep GCNs fail lies in over-smoothing, which isolates the network output from the input with the increase of network depth, weakening expres... | ['Junzhou Huang', 'Fuchun Sun', 'Tingyang Xu', 'Yu Rong', 'Wenbing Huang', 'Jiaqi Han'] | 2023-06-21 | null | null | null | null | ['node-classification'] | ['graphs'] | [-3.1570829e-02 4.4081894e-01 -8.9243777e-02 -2.3301847e-01
-9.9037908e-02 -6.2252462e-01 6.1180198e-01 1.6949734e-01
-3.9973611e-01 4.9051017e-01 1.2813629e-01 -2.9419506e-01
-3.5186791e-01 -8.9158416e-01 -8.4195995e-01 -7.8836560e-01
-5.4271913e-01 1.5636626e-01 5.3099465e-01 -2.2896917e-01
-5.4473970e-02... | [6.830257892608643, 6.037235260009766] |
c1a57908-5c6c-4c64-859f-faf588463968 | dual-path-convolutional-image-text-embedding | 1711.05535 | null | https://arxiv.org/abs/1711.05535v4 | https://arxiv.org/pdf/1711.05535v4.pdf | Dual-Path Convolutional Image-Text Embeddings with Instance Loss | Matching images and sentences demands a fine understanding of both modalities. In this paper, we propose a new system to discriminatively embed the image and text to a shared visual-textual space. In this field, most existing works apply the ranking loss to pull the positive image / text pairs close and push the negati... | ['Yi-Dong Shen', 'Mingliang Xu', 'Michael Garrett', 'Yi Yang', 'Zhedong Zheng', 'Liang Zheng'] | 2017-11-15 | null | null | null | null | ['person-retrieval', 'nlp-based-person-retrival'] | ['computer-vision', 'computer-vision'] | [ 1.87723543e-02 -2.86362022e-01 -3.85544837e-01 -6.12333536e-01
-7.54972935e-01 -3.57458621e-01 6.70341790e-01 -3.80145595e-03
-6.56789541e-01 3.46822381e-01 1.59658432e-01 1.32317320e-01
-6.59204572e-02 -6.31595790e-01 -6.86175585e-01 -6.49394214e-01
3.74525398e-01 3.68046016e-01 -9.74329095e-03 -5.96718863... | [10.892440795898438, 1.256798505783081] |
e1184058-106f-4cc9-84b6-edff758e8d48 | clarifying-system-1-2-through-the-common | 2305.10654 | null | https://arxiv.org/abs/2305.10654v1 | https://arxiv.org/pdf/2305.10654v1.pdf | Clarifying System 1 & 2 through the Common Model of Cognition | There have been increasing challenges to dual-system descriptions of System-1 and System-2, critiquing them as imprecise and fostering misconceptions. We address these issues here by way of Dennett's appeal to use computational thinking as an analytical tool, specifically we employ the Common Model of Cognition. Result... | ['Robert L. West', 'Brendan Conway-Smith'] | 2023-05-18 | null | null | null | null | ['misconceptions'] | ['miscellaneous'] | [ 7.47176632e-03 2.51640171e-01 3.53990614e-01 -3.59853730e-03
4.16867554e-01 -8.02464306e-01 9.33939457e-01 6.03432119e-01
-1.13134488e-01 -5.32127023e-02 2.78753817e-01 -1.37583566e+00
-7.24200428e-01 -6.17892385e-01 -1.28482699e-01 -5.68560883e-02
1.60324171e-01 -2.05665343e-02 1.34728923e-01 -6.15430474... | [9.402811050415039, 7.082505226135254] |
41c62aa4-44f0-40d2-aad4-c8585d2dea96 | learning-from-synthetic-animals | 1912.08265 | null | https://arxiv.org/abs/1912.08265v2 | https://arxiv.org/pdf/1912.08265v2.pdf | Learning from Synthetic Animals | Despite great success in human parsing, progress for parsing other deformable articulated objects, like animals, is still limited by the lack of labeled data. In this paper, we use synthetic images and ground truth generated from CAD animal models to address this challenge. To bridge the domain gap between real and syn... | ['Weichao Qiu', 'Gregory Hager', 'Jiteng Mu', 'Alan Yuille'] | 2019-12-17 | learning-from-synthetic-animals-1 | http://openaccess.thecvf.com/content_CVPR_2020/html/Mu_Learning_From_Synthetic_Animals_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Mu_Learning_From_Synthetic_Animals_CVPR_2020_paper.pdf | cvpr-2020-6 | ['human-parsing'] | ['computer-vision'] | [ 3.75153184e-01 2.80012578e-01 -2.15480879e-01 -5.60272157e-01
-9.86764133e-01 -9.46789265e-01 4.92666185e-01 -3.17031056e-01
-5.36976933e-01 8.05195332e-01 -4.57220487e-02 6.50922060e-02
4.50632721e-01 -3.98147583e-01 -1.34592175e+00 -1.91691056e-01
1.74951911e-01 7.02341616e-01 6.90708280e-01 -1.63562804... | [7.4850335121154785, -1.062756896018982] |
51d4d1fe-a02e-4e7b-b2fb-67f432ebba26 | unsupervised-skin-tissue-segmentation-for | null | null | https://www.sciencedirect.com/science/article/abs/pii/S0167865517303860 | https://www.sciencedirect.com/science/article/abs/pii/S0167865517303860 | Unsupervised skin tissue segmentation for remote photoplethysmography | Segmentation is a critical step for many algorithms, especially for remote photoplethysmography (rPPG) applications as only the skin surface provides information. Moreover, it has been shown that the rPPG signal is not distributed homogeneously across the skin. Most of the time, algorithms get input information from fa... | ['Julien Dubois', 'Alamin Mansouri', 'Yannick Benezeth', 'Richard Macwan', 'Serge Bobbia'] | 2019-06-01 | null | null | null | pattern-recognition-letters-2019-6 | ['face-detection'] | ['computer-vision'] | [ 3.97222221e-01 2.28869710e-02 -2.02008516e-01 -2.08971605e-01
-5.41242540e-01 -4.18458968e-01 2.51633793e-01 -9.37996283e-02
-2.94408441e-01 7.15899944e-01 -1.88217372e-01 4.88117278e-01
2.64775246e-01 -5.25625587e-01 -8.11034590e-02 -1.25334132e+00
-2.98581142e-02 2.37096593e-01 4.04114753e-01 1.92716554... | [13.8807954788208, 2.735976219177246] |
562e678a-4cad-4923-b571-81a055772a46 | decoupled-and-memory-reinforced-networks | 2102.10795 | null | https://arxiv.org/abs/2102.10795v1 | https://arxiv.org/pdf/2102.10795v1.pdf | Decoupled and Memory-Reinforced Networks: Towards Effective Feature Learning for One-Step Person Search | The goal of person search is to localize and match query persons from scene images. For high efficiency, one-step methods have been developed to jointly handle the pedestrian detection and identification sub-tasks using a single network. There are two major challenges in the current one-step approaches. One is the mutu... | ['Yi Yang', 'Nong Sang', 'Changxin Gao', 'Zhedong Zheng', 'Chuchu Han'] | 2021-02-22 | null | null | null | null | ['person-search'] | ['computer-vision'] | [-1.69870928e-01 -5.66457570e-01 1.25568941e-01 -5.58721006e-01
-6.67613149e-01 -2.89120287e-01 3.70581120e-01 5.71546936e-03
-8.73523355e-01 6.12356901e-01 9.28643644e-02 2.76657969e-01
-2.13065609e-01 -7.21839666e-01 -5.77605069e-01 -7.69571722e-01
1.22366033e-01 3.83622646e-01 4.07016128e-01 2.95556299... | [14.782841682434082, 0.826081395149231] |
36057931-3bb2-4fb7-a048-d9daf9eb942d | abess-a-fast-best-subset-selection-library-in | 2110.09697 | null | https://arxiv.org/abs/2110.09697v2 | https://arxiv.org/pdf/2110.09697v2.pdf | abess: A Fast Best Subset Selection Library in Python and R | We introduce a new library named abess that implements a unified framework of best-subset selection for solving diverse machine learning problems, e.g., linear regression, classification, and principal component analysis. Particularly, the abess certifiably gets the optimal solution within polynomial times with high pr... | ['Xueqin Wang', 'Junxian Zhu', 'Shiyun Lin', 'Yanhang Zhang', 'Kangkang Jiang', 'Junhao Huang', 'Liyuan Hu', 'Jin Zhu'] | 2021-10-19 | null | null | null | null | ['sparse-learning'] | ['methodology'] | [-1.51973844e-01 -4.69400227e-01 -4.62839395e-01 -4.32906449e-01
-1.28325522e+00 -5.77790797e-01 -1.81190774e-01 2.31021550e-03
-9.57674384e-02 6.60502374e-01 -3.43279511e-01 -4.69372153e-01
-1.63234085e-01 -6.25737607e-01 -6.67259097e-01 -8.64181161e-01
-7.62207434e-02 5.39857030e-01 -1.55375183e-01 1.08255416... | [7.305428981781006, 4.387258052825928] |
6eb21137-e74e-4c5f-971f-5641b3811b1e | simple-unsupervised-summarization-by-1 | 1907.13337 | null | https://arxiv.org/abs/1907.13337v1 | https://arxiv.org/pdf/1907.13337v1.pdf | Simple Unsupervised Summarization by Contextual Matching | We propose an unsupervised method for sentence summarization using only language modeling. The approach employs two language models, one that is generic (i.e. pretrained), and the other that is specific to the target domain. We show that by using a product-of-experts criteria these are enough for maintaining continuous... | ['Alexander M. Rush', 'Jiawei Zhou'] | 2019-07-31 | simple-unsupervised-summarization-by | https://aclanthology.org/P19-1503 | https://aclanthology.org/P19-1503.pdf | acl-2019-7 | ['abstractive-sentence-summarization', 'unsupervised-sentence-summarization'] | ['natural-language-processing', 'natural-language-processing'] | [ 4.98675883e-01 7.01626658e-01 -1.18122727e-01 -4.72381055e-01
-1.08811128e+00 -4.17107821e-01 5.68113565e-01 6.42553210e-01
-5.87069690e-01 7.20572054e-01 7.89549828e-01 -1.20123342e-01
1.97999701e-01 -6.12249494e-01 -5.84387720e-01 -3.36896479e-01
3.33529770e-01 5.41226864e-01 2.68394172e-01 -5.40029526... | [12.486316680908203, 9.48287296295166] |
ce625b41-6de1-48af-8cf9-13b676ebf8bb | tell-me-how-to-ask-again-question-data | 2010.01475 | null | https://arxiv.org/abs/2010.01475v1 | https://arxiv.org/pdf/2010.01475v1.pdf | Tell Me How to Ask Again: Question Data Augmentation with Controllable Rewriting in Continuous Space | In this paper, we propose a novel data augmentation method, referred to as Controllable Rewriting based Question Data Augmentation (CRQDA), for machine reading comprehension (MRC), question generation, and question-answering natural language inference tasks. We treat the question data augmentation task as a constrained... | ['Ming Zhou', 'Nan Duan', 'Jiancheng Lv', 'Jiusheng Chen', 'Yu Yan', 'Jie Fu', 'Yeyun Gong', 'Dayiheng Liu'] | 2020-10-04 | null | https://aclanthology.org/2020.emnlp-main.467 | https://aclanthology.org/2020.emnlp-main.467.pdf | emnlp-2020-11 | ['question-rewriting'] | ['natural-language-processing'] | [ 5.69194734e-01 4.51230049e-01 2.80785978e-01 -6.32399797e-01
-9.82369840e-01 -5.59064448e-01 4.81064200e-01 2.54960895e-01
-3.48228157e-01 7.17485249e-01 5.05653679e-01 -7.55416155e-01
-8.33128113e-03 -1.04714119e+00 -7.21030235e-01 -2.84113400e-02
7.65799105e-01 4.28661495e-01 6.86565787e-02 -6.75811231... | [11.441704750061035, 8.111491203308105] |
0cd83a58-fd37-4ac2-898a-231f14e2cda7 | shadow-background-noise-3d-spatial | 2207.03064 | null | https://arxiv.org/abs/2207.03064v2 | https://arxiv.org/pdf/2207.03064v2.pdf | Shadow-Background-Noise 3D Spatial Decomposition Using Sparse Low-Rank Gaussian Properties for Video-SAR Moving Target Shadow Enhancement | Moving target shadows among video synthetic aperture radar (Video-SAR) images are always interfered by low scattering backgrounds and cluttered noises, causing poor detec-tion-tracking accuracy. Thus, a shadow-background-noise 3D spatial decomposition (SBN-3D-SD) model is proposed to enhance shadows for higher detectio... | ['Xu Zhan', 'Jun Shi', 'Zhenyu Yang', 'Tianwen Zhang', 'Xiaowo Xu', 'Xiaoling Zhang'] | 2022-07-07 | null | null | null | null | ['shadow-detection'] | ['computer-vision'] | [ 9.27597508e-02 -6.12292945e-01 1.59163490e-01 -2.75942180e-02
-5.81831276e-01 -4.94297802e-01 6.15766525e-01 -7.13257730e-01
-2.26823360e-01 5.43292046e-01 4.29784954e-01 -4.82048005e-01
-2.30303891e-02 -3.81823421e-01 -3.90910029e-01 -1.10800743e+00
-1.98979452e-01 1.04433978e-02 6.57671690e-01 -1.68440565... | [8.21466064453125, -1.095902919769287] |
3421c301-759d-4cfe-85d8-c673381a2869 | extractive-summarization-of-legal-decisions | 2210.12437 | null | https://arxiv.org/abs/2210.12437v1 | https://arxiv.org/pdf/2210.12437v1.pdf | Extractive Summarization of Legal Decisions using Multi-task Learning and Maximal Marginal Relevance | Summarizing legal decisions requires the expertise of law practitioners, which is both time- and cost-intensive. This paper presents techniques for extractive summarization of legal decisions in a low-resource setting using limited expert annotated data. We test a set of models that locate relevant content using a sequ... | ['Matthias Grabmair', 'Shanshan Xu', 'Abhishek Agarwal'] | 2022-10-22 | null | null | null | null | ['extractive-summarization'] | ['natural-language-processing'] | [ 4.40813303e-01 5.29216945e-01 -7.69320130e-01 -4.17784870e-01
-1.97830844e+00 -1.00136435e+00 7.25700438e-01 7.63355851e-01
-5.91093481e-01 1.25790823e+00 1.16934919e+00 -5.59913099e-01
-2.79824376e-01 -2.47570232e-01 -2.73068875e-01 -1.99104443e-01
3.84301007e-01 6.61617756e-01 9.84243602e-02 -2.13954359... | [12.09402847290039, 9.55251407623291] |
464159bb-cbc0-463f-b610-2724b2f4434d | the-franz-parisi-criterion-and-computational | 2205.09727 | null | https://arxiv.org/abs/2205.09727v2 | https://arxiv.org/pdf/2205.09727v2.pdf | The Franz-Parisi Criterion and Computational Trade-offs in High Dimensional Statistics | Many high-dimensional statistical inference problems are believed to possess inherent computational hardness. Various frameworks have been proposed to give rigorous evidence for such hardness, including lower bounds against restricted models of computation (such as low-degree functions), as well as methods rooted in st... | ['Ilias Zadik', 'Alexander S. Wein', 'Tselil Schramm', 'Samuel B. Hopkins', 'Ahmed El Alaoui', 'Afonso S. Bandeira'] | 2022-05-19 | null | null | null | null | ['additive-models'] | ['methodology'] | [ 5.61883271e-01 5.58884621e-01 1.76552787e-01 -3.25175852e-01
-9.91995752e-01 -5.70809841e-01 5.40741682e-01 2.95738757e-01
2.77208388e-02 5.72069228e-01 3.51969153e-02 -3.92083853e-01
-7.87295759e-01 -1.01446819e+00 -8.44800949e-01 -1.16422129e+00
-5.72631657e-01 4.76727903e-01 1.62625045e-01 -2.17294469... | [6.871797561645508, 5.0019636154174805] |
4055b9f4-82e8-486c-996d-59a762b788b2 | an-intelligent-decision-support-ensemble | 2210.14906 | null | https://arxiv.org/abs/2210.14906v1 | https://arxiv.org/pdf/2210.14906v1.pdf | An Intelligent Decision Support Ensemble Voting Model for Coronary Artery Disease Prediction in Smart Healthcare Monitoring Environments | Coronary artery disease (CAD) is one of the most common cardiac diseases worldwide and causes disability and economic burden. It is the world's leading and most serious cause of mortality, with approximately 80% of deaths reported in low- and middle-income countries. The preferred and most precise diagnostic tool for C... | ['El Houssine El Mazoudi', 'Noureddine Elalami', 'Jamila Elalami', 'Anas Maach'] | 2022-10-25 | null | null | null | null | ['disease-prediction'] | ['medical'] | [-3.28484744e-01 -3.96321267e-01 -3.10830981e-01 -1.81293726e-01
-1.92789882e-01 -1.78533494e-01 2.42317110e-01 3.02079409e-01
-1.86641634e-01 1.06196702e+00 -3.04432958e-02 -8.92047286e-01
-6.37911916e-01 -7.25001276e-01 2.53803104e-01 -7.40218520e-01
1.33944929e-01 6.76491022e-01 1.47067532e-01 1.84979793... | [8.431498527526855, 4.857220649719238] |
fa475c1e-76eb-42ab-9ea6-4b1cc4d57789 | bi-lstm-price-prediction-based-on-attention | 2212.03443 | null | https://arxiv.org/abs/2212.03443v2 | https://arxiv.org/pdf/2212.03443v2.pdf | Bi-LSTM Price Prediction based on Attention Mechanism | With the increasing enrichment and development of the financial derivatives market, the frequency of transactions is also faster and faster. Due to human limitations, algorithms and automatic trading have recently become the focus of discussion. In this paper, we propose a bidirectional LSTM neural network based on an ... | ['Ye Li', 'Leyi Cui', 'Jiashu Lou'] | 2022-12-07 | null | null | null | null | ['feature-engineering'] | ['methodology'] | [-9.02952552e-01 -4.33310449e-01 -9.06683579e-02 -8.90154913e-02
-4.65051793e-02 -4.60130185e-01 5.37928283e-01 -3.50363106e-01
-4.91508782e-01 7.29074895e-01 2.03297615e-01 -5.14624715e-01
-1.61180213e-01 -8.48432064e-01 -5.51108122e-01 -5.91613114e-01
-1.53564394e-01 1.58766046e-01 2.01456714e-02 -2.74580836... | [4.440049171447754, 4.2337775230407715] |
39389e25-ff4e-43b7-b420-741ba9939684 | sisua-semi-supervised-generative-autoencoder | null | null | https://www.biorxiv.org/content/10.1101/631382v1 | https://www.biorxiv.org/content/biorxiv/early/2019/05/08/631382.full-text.pdf | SISUA: Semi-Supervised Generative Autoencoder for Single Cell Data | Single-cell transcriptomics offers a tool to study the diversity of cell phenotypes through snapshots of the abundance of mRNA in individual cells. Often there is additional information available besides the single cell gene expression counts, such as bulk transcriptome data from the same tissue, or quantification of s... | ['Merja Heinäniemi', 'Ville Hautamäki', 'Gerardo González', 'Juha Mehtonen', 'Roger Kramer', 'Trung Ngo Trong'] | 2019-05-08 | null | null | null | icml-workshop-on-computational-biology-2019 | ['single-cell-modeling'] | ['medical'] | [-6.98349718e-03 -2.92278886e-01 1.58896834e-01 -1.40779331e-01
-6.30246401e-01 -6.32677853e-01 5.73927224e-01 1.88369751e-01
-5.88341951e-01 1.20560658e+00 2.13698059e-01 2.72713184e-01
8.40683281e-02 -8.65833819e-01 -5.44290125e-01 -1.55623198e+00
5.05407095e-01 9.23084676e-01 -3.06335330e-01 2.23567918... | [6.688995361328125, 5.09923791885376] |
b6d770b3-0a12-4a9d-9460-0e23fc6084ef | dstcgcn-learning-dynamic-spatial-temporal | 2307.00518 | null | https://arxiv.org/abs/2307.00518v1 | https://arxiv.org/pdf/2307.00518v1.pdf | DSTCGCN: Learning Dynamic Spatial-Temporal Cross Dependencies for Traffic Forecasting | Traffic forecasting is essential to intelligent transportation systems, which is challenging due to the complicated spatial and temporal dependencies within a road network. Existing works usually learn spatial and temporal dependencies separately, ignoring the dependencies crossing spatial and temporal dimensions. In t... | ['Ling Chen', 'Binqing Wu'] | 2023-07-02 | null | null | null | null | ['graph-construction'] | ['graphs'] | [-1.43842995e-01 -3.61264765e-01 -2.57546842e-01 -7.38382280e-01
-3.24609101e-01 -3.28938305e-01 6.29718542e-01 -5.05796432e-01
-1.76576942e-01 5.71188271e-01 9.86381769e-02 -9.55868006e-01
-5.88720083e-01 -1.12385547e+00 -6.65300012e-01 -7.49877274e-01
-5.34115851e-01 2.98380375e-01 7.36528397e-01 -2.19428450... | [6.459216594696045, 2.068203926086426] |
5f776c56-bac5-47ad-8154-e40dfb442827 | learning-enhancement-from-degradation-a | 2303.04603 | null | https://arxiv.org/abs/2303.04603v1 | https://arxiv.org/pdf/2303.04603v1.pdf | Learning Enhancement From Degradation: A Diffusion Model For Fundus Image Enhancement | The quality of a fundus image can be compromised by numerous factors, many of which are challenging to be appropriately and mathematically modeled. In this paper, we introduce a novel diffusion model based framework, named Learning Enhancement from Degradation (LED), for enhancing fundus images. Specifically, we first ... | ['Xiaoying Tang', 'Wenhan Luo', 'Huaqing He', 'Yijin Huang', 'Li Lin', 'Puijin Cheng'] | 2023-03-08 | null | null | null | null | ['image-enhancement'] | ['computer-vision'] | [ 2.70030469e-01 -1.50698513e-01 3.58088762e-02 -3.75992000e-01
-7.98642635e-01 -4.96512443e-01 4.05493528e-01 -6.37694895e-02
-3.90093476e-01 7.53643751e-01 4.72127587e-01 -1.74316719e-01
-2.55736887e-01 -5.17126024e-01 -4.34717417e-01 -9.12774682e-01
5.56348152e-02 -3.74488682e-01 2.08374169e-02 1.21427394... | [15.70450210571289, -3.8908276557922363] |
affaa7db-12bf-4421-beb4-2be5d8b34069 | a-deep-face-identification-network-enhanced | 1805.00324 | null | http://arxiv.org/abs/1805.00324v1 | http://arxiv.org/pdf/1805.00324v1.pdf | A Deep Face Identification Network Enhanced by Facial Attributes Prediction | In this paper, we propose a new deep framework which predicts facial
attributes and leverage it as a soft modality to improve face identification
performance. Our model is an end to end framework which consists of a
convolutional neural network (CNN) whose output is fanned out into two separate
branches; the first bran... | ['Nasser M. Nasrabadi', 'Jeremy Dawson', 'Fariborz Taherkhani'] | 2018-04-20 | null | null | null | null | ['gender-prediction'] | ['computer-vision'] | [ 1.85302824e-01 1.23272650e-01 -2.87813753e-01 -1.09272552e+00
-3.05534601e-01 -4.27899778e-01 6.99821651e-01 -4.16010112e-01
-2.01664820e-01 4.41295117e-01 8.78949165e-02 1.13386869e-01
2.94067971e-02 -7.12585807e-01 -5.20281434e-01 -7.24295676e-01
3.33589464e-01 5.74093759e-01 -3.51932853e-01 6.45806715... | [13.468602180480957, 0.8603034019470215] |
0aa71286-89de-4a95-b34f-282641e0eeae | towards-enhancing-health-coaching-dialogue-in | null | null | https://aclanthology.org/2022.coling-1.58 | https://aclanthology.org/2022.coling-1.58.pdf | Towards Enhancing Health Coaching Dialogue in Low-Resource Settings | Health coaching helps patients identify and accomplish lifestyle-related goals, effectively improving the control of chronic diseases and mitigating mental health conditions. However, health coaching is cost-prohibitive due to its highly personalized and labor-intensive nature. In this paper, we propose to build a dial... | ['Shweta Yadav', 'Nikolaos Agadakos', 'Ben Gerber', 'Bing Liu', 'Lisa Sharp', 'Brian Ziebart', 'Barbara Di Eugenio', 'Yue Zhou'] | null | null | null | null | coling-2022-10 | ['empathetic-response-generation'] | ['natural-language-processing'] | [ 2.74456572e-02 8.60300124e-01 -4.40217465e-01 -4.92508680e-01
-5.99888384e-01 -2.30034649e-01 2.38395393e-01 5.00493050e-01
-1.15297221e-01 9.54503834e-01 8.32186341e-01 2.43414983e-01
9.45952488e-04 -5.41443348e-01 1.72074184e-01 -2.44569734e-01
3.87960702e-01 8.40630352e-01 -6.53781295e-01 -4.38398778... | [13.108696937561035, 7.64446496963501] |
87fb4865-d8b8-4d56-885b-c4cbf3c87059 | cca-mdd-a-coupled-cross-attention-based | 2111.08191 | null | https://arxiv.org/abs/2111.08191v2 | https://arxiv.org/pdf/2111.08191v2.pdf | CoCA-MDD: A Coupled Cross-Attention based Framework for Streaming Mispronunciation Detection and Diagnosis | Mispronunciation detection and diagnosis (MDD) is a popular research focus in computer-aided pronunciation training (CAPT) systems. End-to-end (e2e) approaches are becoming dominant in MDD. However an e2e MDD model usually requires entire speech utterances as input context, which leads to significant time latency espec... | ['Xiao Chen', 'Qun Liu', 'Xin Jiang', 'Yasheng Wang', 'Yuanyuan Guo', 'Baohua Xu', 'Yu Ting Yeung', 'Wenyong Huang', 'Liqun Deng', 'Nianzu Zheng'] | 2021-11-16 | null | null | null | null | ['phone-level-pronunciation-scoring'] | ['speech'] | [ 1.97598599e-02 -7.48424232e-02 1.70394868e-01 -4.82723743e-01
-1.48964846e+00 -3.31347913e-01 4.31018591e-01 1.58842504e-01
-4.04128551e-01 3.78948122e-01 4.54981744e-01 -4.81808007e-01
2.88802356e-01 -1.76496565e-01 -6.10793948e-01 -4.09135848e-01
2.44320109e-01 -6.85094018e-03 -1.59917444e-01 7.11577609... | [14.619193077087402, 6.228481769561768] |
49ed58cf-e31f-4acf-95fd-d715a60627fe | multilingual-dependency-parsing-for-low-1 | null | null | https://aclanthology.org/2021.iwpt-1.9 | https://aclanthology.org/2021.iwpt-1.9.pdf | Multilingual Dependency Parsing for Low-Resource African Languages: Case Studies on Bambara, Wolof, and Yoruba | This paper describes a methodology for syntactic knowledge transfer between high-resource languages to extremely low-resource languages. The methodology consists in leveraging multilingual BERT self-attention model pretrained on large datasets to develop a multilingual multi-task model that can predict Universal Depend... | ['Cheikh M. Bamba Dione'] | null | null | null | null | acl-iwpt-2021-8 | ['multilingual-word-embeddings'] | ['methodology'] | [-5.45044601e-01 -1.86261218e-02 -2.46166483e-01 -4.02095675e-01
-6.26771331e-01 -4.92954344e-01 4.95010614e-01 3.57172698e-01
-8.68911564e-01 1.26896584e+00 5.55923522e-01 -4.91831809e-01
-4.89239255e-03 -5.95664740e-01 -6.79915011e-01 -4.12589580e-01
-1.58106610e-01 7.24951625e-01 1.83298618e-01 -6.63280964... | [10.530600547790527, 9.953643798828125] |
69348a1a-71be-4049-ac7d-00c2dee6b4ef | heat-hyperedge-attention-networks | 2201.12113 | null | https://arxiv.org/abs/2201.12113v2 | https://arxiv.org/pdf/2201.12113v2.pdf | HEAT: Hyperedge Attention Networks | Learning from structured data is a core machine learning task. Commonly, such data is represented as graphs, which normally only consider (typed) binary relationships between pairs of nodes. This is a substantial limitation for many domains with highly-structured data. One important such domain is source code, where hy... | ['Miltiadis Allamanis', 'Marc Brockschmidt', 'Dobrik Georgiev'] | 2022-01-28 | null | null | null | null | ['knowledge-base-completion', 'knowledge-base-completion'] | ['graphs', 'knowledge-base'] | [ 2.14132648e-02 5.18064260e-01 -7.19837546e-01 -3.36094648e-01
-3.82533491e-01 -5.03481805e-01 3.98043394e-01 6.60963714e-01
5.96233644e-02 4.98682380e-01 5.02577960e-01 -6.30191028e-01
-1.67174846e-01 -1.34477663e+00 -1.02458978e+00 -1.61706552e-01
-4.84355211e-01 3.39224428e-01 3.33086312e-01 -3.27308238... | [7.485530853271484, 7.83091402053833] |
c4ac3d7b-e809-442f-9a85-1131d53d6dce | a-demographic-attribute-guided-approach-to | 2205.10254 | null | https://arxiv.org/abs/2205.10254v1 | https://arxiv.org/pdf/2205.10254v1.pdf | A Demographic Attribute Guided Approach to Age Estimation | Face-based age estimation has attracted enormous attention due to wide applications to public security surveillance, human-computer interaction, etc. With vigorous development of deep learning, age estimation based on deep neural network has become the mainstream practice. However, seeking a more suitable problem parad... | ['Heng Zhao', 'Liaojun Pang', 'Kaituo Zhang', 'Zhicheng Cao'] | 2022-05-20 | null | null | null | null | ['age-estimation', 'age-estimation'] | ['computer-vision', 'miscellaneous'] | [-7.43045360e-02 -5.37850931e-02 -7.30310893e-03 -7.86773503e-01
-1.26444608e-01 2.40601182e-01 5.25767684e-01 3.69965397e-02
-5.63800454e-01 6.73626661e-01 2.91752905e-01 1.31910369e-01
-1.67950556e-01 -1.00797236e+00 -4.41154957e-01 -8.58190656e-01
-1.82786599e-01 1.24518268e-01 -2.38553420e-01 -1.19580038... | [13.56002426147461, 0.8558171987533569] |
0633767a-2c00-49f4-8ee7-01607450300d | twitter-sentiment-analysis-via-bi-sense-emoji | 1807.07961 | null | http://arxiv.org/abs/1807.07961v2 | http://arxiv.org/pdf/1807.07961v2.pdf | Twitter Sentiment Analysis via Bi-sense Emoji Embedding and Attention-based LSTM | Sentiment analysis on large-scale social media data is important to bridge
the gaps between social media contents and real world activities including
political election prediction, individual and public emotional status
monitoring and analysis, and so on. Although textual sentiment analysis has
been well studied based ... | ['Jianbo Yuan', 'Jiebo Luo', 'Yuxiao Chen', 'Quanzeng You'] | 2018-07-20 | null | null | null | null | ['twitter-sentiment-analysis'] | ['natural-language-processing'] | [-3.29884619e-01 -2.25878581e-02 -2.21871197e-01 -6.65569901e-01
-2.00060800e-01 -2.06382826e-01 4.82485175e-01 4.25073653e-01
-6.37154698e-01 4.15764093e-01 6.17042065e-01 -3.36768150e-01
3.94144654e-01 -8.89874995e-01 -7.05651194e-02 -3.87547493e-01
1.75888062e-01 -3.56668979e-01 -1.29798442e-01 -7.87269235... | [11.3532075881958, 6.848485946655273] |
6e10369c-8874-4b8c-8998-61412531bcab | armanemo-a-persian-dataset-for-text-based | 2207.11808 | null | https://arxiv.org/abs/2207.11808v1 | https://arxiv.org/pdf/2207.11808v1.pdf | ArmanEmo: A Persian Dataset for Text-based Emotion Detection | With the recent proliferation of open textual data on social media platforms, Emotion Detection (ED) from Text has received more attention over the past years. It has many applications, especially for businesses and online service providers, where emotion detection techniques can help them make informed commercial deci... | ['Hossein Zeinali', 'Hamid Habibzadeh Moshtaghin', 'Javad Peymanfard', 'Hossein Mirzaee'] | 2022-07-24 | null | null | null | null | ['emotion-classification', 'emotion-classification'] | ['computer-vision', 'natural-language-processing'] | [-5.28145671e-01 -7.09607899e-02 -2.06953615e-01 -6.38195634e-01
-5.09574234e-01 -5.44230342e-01 3.52828443e-01 2.50827163e-01
-4.75490808e-01 6.58327460e-01 2.58566678e-01 -5.31903543e-02
3.01425815e-01 -4.82101738e-01 1.41609028e-01 -4.58674610e-01
5.77377863e-02 3.78426105e-01 -3.56339604e-01 -6.57693326... | [12.668844223022461, 6.175174713134766] |
ab3d8bd6-a725-4b28-bc8a-99f27d29b095 | bottom-up-constituency-parsing-and-nested | 2110.05419 | null | https://arxiv.org/abs/2110.05419v2 | https://arxiv.org/pdf/2110.05419v2.pdf | Bottom-Up Constituency Parsing and Nested Named Entity Recognition with Pointer Networks | Constituency parsing and nested named entity recognition (NER) are similar tasks since they both aim to predict a collection of nested and non-crossing spans. In this work, we cast nested NER to constituency parsing and propose a novel pointing mechanism for bottom-up parsing to tackle both tasks. The key idea is based... | ['Kewei Tu', 'Songlin Yang'] | 2021-10-11 | null | https://aclanthology.org/2022.acl-long.171 | https://aclanthology.org/2022.acl-long.171.pdf | acl-2022-5 | ['constituency-parsing', 'nested-named-entity-recognition'] | ['natural-language-processing', 'natural-language-processing'] | [-7.44216219e-02 4.01713639e-01 -4.11537588e-01 -5.69003761e-01
-1.15388870e+00 -1.01047802e+00 7.92911053e-02 4.94036049e-01
-4.05080825e-01 4.89667028e-01 4.19312268e-01 -7.76459336e-01
3.05340022e-01 -1.04827428e+00 -8.13703358e-01 -1.91016257e-01
-1.15612140e-02 4.03896123e-01 5.52487373e-01 -1.29172847... | [10.048771858215332, 9.581765174865723] |
dc0f5d11-93d7-4be9-b5eb-4b286a45e85a | measuring-and-improving-compositional | null | null | https://openreview.net/forum?id=-B3vVVeVyTr | https://openreview.net/pdf?id=-B3vVVeVyTr | Measuring and Improving Compositional Generalization in Text-to-SQL via Component Alignment | Recently, the challenge of compositional generalization in NLP has attracted more and more attention. Specifically, many prior works show that neural networks struggle with compositional generalization where training and testing distributions differ. However, most of these works are based on word-level synthetic data o... | ['Anonymous'] | 2021-10-16 | null | null | null | acl-arr-october-2021-10 | ['text-to-sql'] | ['computer-code'] | [ 6.14654422e-01 1.61847249e-01 -1.37379453e-01 -7.85206914e-01
-7.06688166e-01 -7.53895402e-01 4.31400508e-01 1.91204716e-02
-2.86977381e-01 8.65606129e-01 1.24787934e-01 -5.94315946e-01
4.20618027e-01 -1.03321779e+00 -1.00755715e+00 -3.28028500e-01
1.54375777e-01 6.05039954e-01 4.03514266e-01 -4.31160629... | [11.252366065979004, 9.009405136108398] |
f03d4101-0439-49fc-88b5-1142d7d3afb7 | iiitt-lt-edi-eacl2021-hope-speech-detection | 2104.09066 | null | https://arxiv.org/abs/2104.09066v1 | https://arxiv.org/pdf/2104.09066v1.pdf | IIITT@LT-EDI-EACL2021-Hope Speech Detection: There is always Hope in Transformers | In a world filled with serious challenges like climate change, religious and political conflicts, global pandemics, terrorism, and racial discrimination, an internet full of hate speech, abusive and offensive content is the last thing we desire for. In this paper, we work to identify and promote positive and supportive... | ['Bharathi Raja Chakravarthi', 'Sajeetha Thavareesan', 'Ruba Priyadharshini', 'Adeep Hande', 'Karthik Puranik'] | 2021-04-19 | iiitt-lt-edi-eacl2021-hope-speech-detection-1 | https://aclanthology.org/2021.ltedi-1.13 | https://aclanthology.org/2021.ltedi-1.13.pdf | null | ['hope-speech-detection'] | ['natural-language-processing'] | [-3.83401334e-01 3.80449623e-01 -6.34102762e-01 9.40471292e-02
-4.86318409e-01 -6.69138551e-01 1.24267375e+00 5.19807696e-01
-2.49443561e-01 9.38283741e-01 1.27200878e+00 -6.24796867e-01
2.15124905e-01 -5.51914513e-01 3.39501172e-01 -9.50839892e-02
1.36733890e-01 1.27658576e-01 -3.17944407e-01 -9.63733256... | [8.876163482666016, 10.619806289672852] |
b8286124-ef59-466d-87d8-6b3a50d7e824 | improving-empathetic-response-generation-by | null | null | https://aclanthology.org/2021.findings-emnlp.70 | https://aclanthology.org/2021.findings-emnlp.70.pdf | Improving Empathetic Response Generation by Recognizing Emotion Cause in Conversations | Current approaches to empathetic response generation focus on learning a model to predict an emotion label and generate a response based on this label and have achieved promising results. However, the emotion cause, an essential factor for empathetic responding, is ignored. The emotion cause is a stimulus for human emo... | ['Ruifeng Xu', 'Jiachen Du', 'Yu Cao', 'Wei Wang', 'Haolin Deng', 'YuHan Liu', 'Jun Gao'] | null | null | null | null | findings-emnlp-2021-11 | ['empathetic-response-generation', 'recognizing-emotion-cause-in-conversations'] | ['natural-language-processing', 'natural-language-processing'] | [ 1.51440397e-01 9.65228453e-02 -2.35288486e-01 -1.01787663e+00
-5.42681575e-01 -3.46723557e-01 4.13351297e-01 -1.50627956e-01
-1.91980585e-01 6.54495776e-01 9.84283626e-01 1.51875004e-01
4.05061424e-01 -7.93171644e-01 -5.57059608e-02 -5.05779326e-01
6.47133410e-01 1.95223421e-01 -7.60642946e-01 -6.06302977... | [13.145059585571289, 7.614245414733887] |
95f5f8e3-12f9-4c02-9e2a-440fe519d25b | spatio-temporal-tubelet-feature-aggregation | 2004.00451 | null | https://arxiv.org/abs/2004.00451v2 | https://arxiv.org/pdf/2004.00451v2.pdf | Spatio-temporal Tubelet Feature Aggregation and Object Linking in Videos | This paper addresses the problem of how to exploit spatio-temporal information available in videos to improve the object detection precision. We propose a two stage object detector called FANet based on short-term spatio-temporal feature aggregation to give a first detection set, and long-term object linking to refine ... | ['Víctor M. Brea', 'Manuel Mucientes', 'Daniel Cores'] | 2020-04-01 | null | null | null | null | ['small-object-detection'] | ['computer-vision'] | [ 1.85630110e-03 -1.77021567e-02 4.73624319e-02 -3.28249723e-01
-8.66863728e-01 -4.15899664e-01 4.97807384e-01 2.53222972e-01
-1.11827207e+00 4.13653404e-01 -2.07345232e-01 2.48282805e-01
1.92574367e-01 -6.41837776e-01 -1.11417687e+00 -6.50477946e-01
-2.87121952e-01 1.83503389e-01 1.13986325e+00 1.01580741... | [8.85002326965332, -0.1820325404405594] |
e04e7397-cbc1-49c5-8916-2bdaec09ff48 | overprompt-enhancing-chatgpt-capabilities | 2305.14973 | null | https://arxiv.org/abs/2305.14973v1 | https://arxiv.org/pdf/2305.14973v1.pdf | OverPrompt: Enhancing ChatGPT Capabilities through an Efficient In-Context Learning Approach | The exceptional performance of pre-trained large language models has revolutionised various applications, but their adoption in production environments is hindered by prohibitive costs and inefficiencies, particularly when utilising long prompts. This paper proposes OverPrompt, an in-context learning method aimed at im... | ['Lin Gui', 'Yulan He', 'Runcong Zhao', 'Jiazheng Li'] | 2023-05-24 | null | null | null | null | ['sentiment-analysis'] | ['natural-language-processing'] | [ 3.79700750e-01 -4.80921566e-02 -4.12411541e-01 -5.05904198e-01
-9.22986746e-01 -6.01963639e-01 9.89595175e-01 6.06471062e-01
-8.10943842e-01 7.02265501e-01 1.87086120e-01 -5.74395418e-01
1.83455557e-01 -4.34117496e-01 -4.32656199e-01 -4.30737853e-01
2.61010174e-02 4.33523059e-01 -8.67406279e-02 -3.04277152... | [10.803539276123047, 8.406320571899414] |
743768d0-9e68-4a7d-8e7f-5f1db0e2dde8 | knowledge-enriched-visual-storytelling | 1912.01496 | null | https://arxiv.org/abs/1912.01496v1 | https://arxiv.org/pdf/1912.01496v1.pdf | Knowledge-Enriched Visual Storytelling | Stories are diverse and highly personalized, resulting in a large possible output space for story generation. Existing end-to-end approaches produce monotonous stories because they are limited to the vocabulary and knowledge in a single training dataset. This paper introduces KG-Story, a three-stage framework that allo... | ['Lun-Wei Ku', "Ting-Hao 'Kenneth' Huang", 'Tzu-Yuan Lin', 'Chih-Chia Li', 'Chi-Yang Hsu', 'Zi-Yuan Chen', 'Chao-Chun Hsu'] | 2019-12-03 | null | null | null | null | ['visual-storytelling'] | ['natural-language-processing'] | [ 2.51745284e-02 4.28340107e-01 -1.88209172e-02 -4.31502573e-02
-8.36345255e-01 -7.93325961e-01 8.39462578e-01 1.47010073e-01
-3.94582041e-02 8.23098063e-01 8.64153981e-01 1.36937663e-01
4.83266339e-02 -1.00550270e+00 -7.36594200e-01 -2.85241246e-01
1.30653203e-01 7.80180752e-01 2.87581980e-01 -3.86568695... | [11.211369514465332, 0.784960150718689] |
2ebadc83-f1fb-4298-a495-3ad93c11394f | deep-multi-metric-learning-for-text | 2007.10479 | null | https://arxiv.org/abs/2007.10479v1 | https://arxiv.org/pdf/2007.10479v1.pdf | Deep multi-metric learning for text-independent speaker verification | Text-independent speaker verification is an important artificial intelligence problem that has a wide spectrum of applications, such as criminal investigation, payment certification, and interest-based customer services. The purpose of text-independent speaker verification is to determine whether two given uncontrolled... | ['Wenyu Liu', 'Xinggang Wang', 'Bin Feng', 'Jiwei Xu'] | 2020-07-17 | null | null | null | null | ['text-independent-speaker-verification'] | ['speech'] | [-7.60591552e-02 -2.22632840e-01 7.17176497e-02 -9.18032825e-01
-1.22018600e+00 -3.82216930e-01 1.87103078e-01 -3.37787002e-01
-4.76814687e-01 4.57491636e-01 4.83397357e-02 -5.43936074e-01
1.80997252e-01 -2.18014836e-01 -5.76779008e-01 -9.24446523e-01
3.00375652e-02 3.83452594e-01 -3.40272695e-01 -2.77382672... | [14.266674995422363, 6.049466133117676] |
fcc99fa2-900c-4fce-8bbb-b77895014c8e | dynamic-vertex-replacement-grammars | 2303.11553 | null | https://arxiv.org/abs/2303.11553v2 | https://arxiv.org/pdf/2303.11553v2.pdf | Dynamic Vertex Replacement Grammars | Context-free graph grammars have shown a remarkable ability to model structures in real-world relational data. However, graph grammars lack the ability to capture time-changing phenomena since the left-to-right transitions of a production rule do not represent temporal change. In the present work, we describe dynamic v... | ['Tim Weninger', 'Grant Boquet', 'Timothy La Fond', 'Justus Isaiah Hibshman', 'Daniel Gonzalez Cedre'] | 2023-03-21 | null | null | null | null | ['graph-similarity'] | ['graphs'] | [ 2.52290457e-01 6.79096341e-01 -1.48190707e-01 -4.26399380e-01
7.63560832e-02 -7.86444604e-01 1.14697564e+00 3.07197928e-01
3.14072251e-01 7.78375030e-01 -1.34000391e-01 -6.43347144e-01
-3.70014668e-01 -1.29306889e+00 -8.29752445e-01 -2.94967264e-01
-6.78658009e-01 8.97728801e-01 7.14516699e-01 -5.32863140... | [7.181717872619629, 6.07648229598999] |
0f761c39-a855-4259-9032-347ef449ba8c | bottom-up-skill-discovery-from-unsegmented | 2109.13841 | null | https://arxiv.org/abs/2109.13841v2 | https://arxiv.org/pdf/2109.13841v2.pdf | Bottom-Up Skill Discovery from Unsegmented Demonstrations for Long-Horizon Robot Manipulation | We tackle real-world long-horizon robot manipulation tasks through skill discovery. We present a bottom-up approach to learning a library of reusable skills from unsegmented demonstrations and use these skills to synthesize prolonged robot behaviors. Our method starts with constructing a hierarchical task structure fro... | ['Yuke Zhu', 'Peter Stone', 'Yifeng Zhu'] | 2021-09-28 | null | null | null | null | ['robot-manipulation'] | ['robots'] | [ 2.67984003e-01 3.47669154e-01 4.18865383e-02 -1.35298461e-01
-8.56552601e-01 -6.62386179e-01 4.70142096e-01 -2.49043837e-01
-5.00902891e-01 1.10992336e+00 -8.00365880e-02 -2.79380493e-02
-3.88083458e-01 -2.35646851e-02 -1.07031536e+00 -4.81798291e-01
-6.50917113e-01 1.02658367e+00 6.09313190e-01 -3.84318531... | [4.476128101348877, 0.9703730344772339] |
8260d72d-dd4e-42aa-ab15-f07239146990 | findings-of-the-tsar-2022-shared-task-on | 2302.02888 | null | https://arxiv.org/abs/2302.02888v1 | https://arxiv.org/pdf/2302.02888v1.pdf | Findings of the TSAR-2022 Shared Task on Multilingual Lexical Simplification | We report findings of the TSAR-2022 shared task on multilingual lexical simplification, organized as part of the Workshop on Text Simplification, Accessibility, and Readability TSAR-2022 held in conjunction with EMNLP 2022. The task called the Natural Language Processing research community to contribute with methods to... | ['Marcos Zampieri', 'Kai North', 'Matthew Shardlow', 'Kim Cheng SHEANG', 'Daniel Ferrés', 'Sanja Štajner', 'Horacio Saggion'] | 2023-02-06 | null | null | null | null | ['lexical-simplification'] | ['natural-language-processing'] | [-3.38413924e-01 3.85419667e-01 1.74098625e-03 -9.04462188e-02
-1.15823805e+00 -5.49300373e-01 6.18648231e-01 7.94238806e-01
-1.07775450e+00 9.99393463e-01 8.37576032e-01 -2.13042244e-01
1.81052878e-01 -3.64957154e-01 -3.79387975e-01 2.34182313e-01
5.21832824e-01 9.66589212e-01 -1.01454213e-01 -9.23870087... | [10.941949844360352, 10.409415245056152] |
a2e60cb4-0d4f-4f87-9bb5-b2f85add512c | improving-the-robustness-of-federated | 2204.13414 | null | https://arxiv.org/abs/2204.13414v1 | https://arxiv.org/pdf/2204.13414v1.pdf | Improving the Robustness of Federated Learning for Severely Imbalanced Datasets | With the ever increasing data deluge and the success of deep neural networks, the research of distributed deep learning has become pronounced. Two common approaches to achieve this distributed learning is synchronous and asynchronous weight update. In this manuscript, we have explored very simplistic synchronous weight... | ['Ashish Ghosh', 'Debasrita Chakraborty'] | 2022-04-28 | null | null | null | null | ['imbalanced-classification'] | ['miscellaneous'] | [-3.30753893e-01 2.83508766e-02 3.47614795e-01 -2.07746252e-01
-1.74381167e-01 -4.93253656e-02 2.30075568e-01 4.68539655e-01
-8.85158002e-01 9.47214842e-01 -3.45663309e-01 -7.43663907e-02
-1.63978890e-01 -8.43254507e-01 -6.74558342e-01 -1.26237428e+00
-1.06952399e-01 8.97803903e-01 5.09050786e-01 4.25331369... | [8.173552513122559, 3.3303709030151367] |
d7932d0e-504e-4666-8860-f44148003c8b | emotion-distribution-learning-from-texts | null | null | https://aclanthology.org/D16-1061 | https://aclanthology.org/D16-1061.pdf | Emotion Distribution Learning from Texts | null | ['Xin Geng', 'Yin Zhou', 'Xuan Zhang', 'Quan Zhao', 'Deyu Zhou'] | 2016-11-01 | null | null | null | emnlp-2016-11 | ['product-recommendation'] | ['miscellaneous'] | [-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... | [-7.254841327667236, 3.8090314865112305] |
b7f1c56a-db2f-4dea-b9f1-39ad3c2cbd7d | situation-recognition-visual-semantic-role | null | null | http://openaccess.thecvf.com/content_cvpr_2016/html/Yatskar_Situation_Recognition_Visual_CVPR_2016_paper.html | http://openaccess.thecvf.com/content_cvpr_2016/papers/Yatskar_Situation_Recognition_Visual_CVPR_2016_paper.pdf | Situation Recognition: Visual Semantic Role Labeling for Image Understanding | This paper introduces situation recognition, the problem of producing a concise summary of the situation an image depicts including: (1) the main activity (e.g., clipping), (2) the participating actors, objects, substances, and locations (e.g., man, shears, sheep, wool, and field) and most importantly (3) the roles the... | ['Luke Zettlemoyer', 'Ali Farhadi', 'Mark Yatskar'] | 2016-06-01 | null | null | null | cvpr-2016-6 | ['grounded-situation-recognition', 'situation-recognition'] | ['computer-vision', 'computer-vision'] | [ 6.02801263e-01 -9.83706303e-03 -2.22268566e-01 -2.23587275e-01
-2.96771795e-01 -7.82499731e-01 1.16434228e+00 2.28742674e-01
-2.97648996e-01 5.10882616e-01 1.02519739e+00 2.28762716e-01
-7.01304302e-02 -2.56467670e-01 -7.26272941e-01 -6.31146371e-01
-8.37909356e-02 4.54978764e-01 4.35140908e-01 3.52512486... | [8.249226570129395, 0.6669843196868896] |
b5e03f68-fa12-47f6-b129-bb0d837d9e2b | dudornext-a-hybrid-model-for-dual-domain | 2303.10611 | null | https://arxiv.org/abs/2303.10611v1 | https://arxiv.org/pdf/2303.10611v1.pdf | DuDoRNeXt: A hybrid model for dual-domain undersampled MRI reconstruction | Undersampled MRI reconstruction is crucial for accelerating clinical scanning procedures. Recent deep learning methods for MRI reconstruction adopt CNN or ViT as backbone, which lack in utilizing the complementary properties of CNN and ViT. In this paper, we propose DuDoRNeXt, whose backbone hybridizes CNN and ViT in a... | ['S. Kevin Zhou', 'Ziqi Gao'] | 2023-03-19 | null | null | null | null | ['layout-design', 'mri-reconstruction'] | ['computer-vision', 'computer-vision'] | [ 5.69995120e-02 8.86670500e-02 -6.62828833e-02 -4.62088317e-01
-1.13113058e+00 -2.41610199e-01 3.76543313e-01 3.73556130e-02
-5.34423053e-01 6.43839180e-01 5.46986938e-01 -4.37489420e-01
-1.79643512e-01 -5.40364623e-01 -5.08440733e-01 -6.97760582e-01
-2.69882590e-01 3.93690497e-01 4.39417899e-01 -2.02946663... | [13.99292278289795, -2.5054984092712402] |
fd8e6417-71d3-4e7c-b5e7-b64f9b790d66 | one-shot-scene-graph-generation | 2202.10824 | null | https://arxiv.org/abs/2202.10824v2 | https://arxiv.org/pdf/2202.10824v2.pdf | One-shot Scene Graph Generation | As a structured representation of the image content, the visual scene graph (visual relationship) acts as a bridge between computer vision and natural language processing. Existing models on the scene graph generation task notoriously require tens or hundreds of labeled samples. By contrast, human beings can learn visu... | ['Heng Tao Shen', 'Lianli Gao', 'Jingkuan Song', 'Yuyu Guo'] | 2022-02-22 | null | null | null | null | ['scene-graph-generation'] | ['computer-vision'] | [ 1.54842883e-01 3.66942376e-01 -1.33435667e-01 -5.51668406e-01
-1.72189802e-01 -3.39441121e-01 6.25498116e-01 2.14394823e-01
-8.13430026e-02 5.46276033e-01 2.78886139e-01 -1.44239590e-01
7.17236400e-02 -1.18056071e+00 -1.04200304e+00 -4.65677530e-01
1.89190269e-01 1.11281544e-01 1.12820901e-01 -3.16254199... | [10.426462173461914, 1.7129364013671875] |
9a6ce385-2923-4a05-8ccd-e4ac9ce9ecf4 | learnability-with-pac-semantics-for-multi | 2306.0549 | null | https://arxiv.org/abs/2306.05490v1 | https://arxiv.org/pdf/2306.05490v1.pdf | Learnability with PAC Semantics for Multi-agent Beliefs | The tension between deduction and induction is perhaps the most fundamental issue in areas such as philosophy, cognition and artificial intelligence. In an influential paper, Valiant recognised that the challenge of learning should be integrated with deduction. In particular, he proposed a semantics to capture the qual... | ['Brendan Juba', 'Vaishak Belle', 'Ionela G. Mocanu'] | 2023-06-08 | null | null | null | null | ['philosophy'] | ['miscellaneous'] | [ 2.46229053e-01 9.70591068e-01 -1.33045629e-01 -2.79103726e-01
-1.12591672e+00 -7.26016283e-01 5.82064092e-01 3.63630503e-01
-4.87421334e-01 1.06364775e+00 1.06627457e-01 -6.37925506e-01
-7.50399649e-01 -1.12175405e+00 -1.13963759e+00 -8.08570504e-01
-1.83676302e-01 7.52882898e-01 4.02168185e-01 -1.91814750... | [8.593092918395996, 6.605257034301758] |
6412cb91-b5b2-43ba-88ac-416292e7e9e0 | pre-trained-language-models-with-domain | null | null | https://www.sciencedirect.com/science/article/pii/S0950705122007328 | https://reader.elsevier.com/reader/sd/pii/S0950705122007328?token=6B0D860FD9A7EA3A7BFE32EF631BCD4F592ADB7606EF0F28451448E7470CE1EF7C7DFE161EB4A1F743EC314FCDD5608C&originRegion=eu-west-1&originCreation=20220724191534 | Pre-trained language models with domain knowledge for biomedical extractive summarization | Biomedical text summarization is a critical task for comprehension of an ever-growing amount of biomedical literature. Pre-trained language models (PLMs) with transformer-based architectures have been shown to greatly improve performance in biomedical text mining tasks. However, existing methods for text summarization ... | ['QianqianXie;Jennifer Amy Bishop;PrayagTiwari;Sophia Ananiadoua'] | 2022-07-19 | null | null | null | knowledge-based-systems-2022-7 | ['pico', 'extractive-summarization'] | ['natural-language-processing', 'natural-language-processing'] | [ 5.21376073e-01 4.95454967e-01 -4.83701050e-01 -2.01669946e-01
-1.19739354e+00 -3.15245748e-01 3.72050494e-01 7.47588396e-01
-3.46740365e-01 1.06043828e+00 1.09912467e+00 -2.02319950e-01
-1.47676080e-01 -5.19764781e-01 -8.55431736e-01 -4.43835229e-01
2.65461624e-01 7.67251492e-01 -3.01418975e-02 -2.33299717... | [12.140429496765137, 9.342761993408203] |
272b393a-1ad1-4448-9396-0a89ce2214a9 | bipartite-graph-reasoning-gans-for-person | 2008.04381 | null | https://arxiv.org/abs/2008.04381v2 | https://arxiv.org/pdf/2008.04381v2.pdf | Bipartite Graph Reasoning GANs for Person Image Generation | We present a novel Bipartite Graph Reasoning GAN (BiGraphGAN) for the challenging person image generation task. The proposed graph generator mainly consists of two novel blocks that aim to model the pose-to-pose and pose-to-image relations, respectively. Specifically, the proposed Bipartite Graph Reasoning (BGR) block ... | ['Nicu Sebe', 'Hao Tang', 'Philip H. S. Torr', 'Song Bai'] | 2020-08-10 | null | null | null | null | ['pose-transfer'] | ['computer-vision'] | [-2.03063980e-01 3.26580018e-01 4.97549683e-01 -3.57413620e-01
-4.81259674e-01 -4.02517885e-01 5.78747869e-01 -4.37538803e-01
1.55462578e-01 6.78371549e-01 2.46432111e-01 4.40229386e-01
1.60394665e-02 -1.00740123e+00 -7.79328108e-01 -4.97766227e-01
2.83203304e-01 4.42390293e-01 1.08684123e-01 -4.82215941... | [11.981534004211426, -0.8177694082260132] |
7002be68-9ba8-45c1-9908-0d6c4222c9b1 | auxiliary-tasks-in-multi-task-learning | 1805.06334 | null | http://arxiv.org/abs/1805.06334v2 | http://arxiv.org/pdf/1805.06334v2.pdf | Auxiliary Tasks in Multi-task Learning | Multi-task convolutional neural networks (CNNs) have shown impressive results
for certain combinations of tasks, such as single-image depth estimation (SIDE)
and semantic segmentation. This is achieved by pushing the network towards
learning a robust representation that generalizes well to different atomic
tasks. We ex... | ['Marco Körner', 'Lukas Liebel'] | 2018-05-16 | null | null | null | null | ['road-scene-understanding'] | ['computer-vision'] | [ 4.37388271e-01 2.01076537e-01 7.48419017e-02 -5.12593091e-01
-8.30039144e-01 -2.89357394e-01 7.37455964e-01 -2.50896007e-01
-5.69495320e-01 9.27761376e-01 -2.05668107e-01 -9.98300407e-03
-1.51686445e-01 -8.84297550e-01 -1.16156816e+00 -4.99029726e-01
1.13714367e-01 4.68949080e-01 5.75317204e-01 -4.63234067... | [9.518941879272461, 1.2689512968063354] |
49d6fda3-2333-41cc-ba84-297e7a57b07b | prediction-of-bottleneck-points-for | 1911.04676 | null | https://arxiv.org/abs/1911.04676v1 | https://arxiv.org/pdf/1911.04676v1.pdf | Prediction of Bottleneck Points for Manipulation Planning in Cluttered Environment using a 3D Convolutional Neural Network | Latest research in industrial robotics is aimed at making human robot collaboration possible seamlessly. For this purpose, industrial robots are expected to work on the fly in unstructured and cluttered environments and hence the subject of perception driven motion planning plays a vital role. Sampling based motion pla... | ['B. K. Rout', 'Indraneel Patil', 'V. Kalaichelvi'] | 2019-11-12 | null | null | null | null | ['industrial-robots'] | ['robots'] | [ 5.59382737e-01 3.76938045e-01 2.10390121e-01 -9.90416482e-02
-5.23161352e-01 -3.75611871e-01 6.54641449e-01 5.32136038e-02
-2.75736749e-01 9.04687166e-01 -1.49067253e-01 -4.15405542e-01
-6.25411570e-01 -6.62597954e-01 -4.14081573e-01 -7.11257935e-01
-3.77262741e-01 1.20356452e+00 4.64486390e-01 -5.10381401... | [4.852146625518799, 1.3439222574234009] |
6ce84307-21bd-4afd-921d-0a3f12201ed5 | detection-and-rectification-of-arbitrary | 2103.00785 | null | https://arxiv.org/abs/2103.00785v1 | https://arxiv.org/pdf/2103.00785v1.pdf | Detection and Rectification of Arbitrary Shaped Scene Texts by using Text Keypoints and Links | Detection and recognition of scene texts of arbitrary shapes remain a grand challenge due to the super-rich text shape variation in text line orientations, lengths, curvatures, etc. This paper presents a mask-guided multi-task network that detects and rectifies scene texts of arbitrary shapes reliably. Three types of k... | ['Steven Hoi', 'Shijian Lu', 'Chuhui Xue'] | 2021-03-01 | null | null | null | null | ['scene-text-detection'] | ['computer-vision'] | [ 1.49776354e-01 -5.72030842e-01 5.73327765e-02 -2.25518540e-01
-6.87612772e-01 -8.56966436e-01 7.15209126e-01 3.18295658e-01
-6.54650033e-02 1.11701049e-01 1.58093646e-01 9.67165083e-02
-1.10829987e-01 -2.87902743e-01 -6.40674353e-01 -6.04117155e-01
3.56884837e-01 8.56196642e-01 5.20545900e-01 -1.40404940... | [12.079937934875488, 2.27795672416687] |
a9780e88-9f15-4811-9054-3d99dcfca9c3 | advancing-the-state-of-the-art-in-open-domain | 1812.10757 | null | http://arxiv.org/abs/1812.10757v1 | http://arxiv.org/pdf/1812.10757v1.pdf | Advancing the State of the Art in Open Domain Dialog Systems through the Alexa Prize | Building open domain conversational systems that allow users to have engaging
conversations on topics of their choice is a challenging task. Alexa Prize was
launched in 2016 to tackle the problem of achieving natural, sustained,
coherent and engaging open-domain dialogs. In the second iteration of the
competition in 20... | ['Dilek Hakkani-Tur', 'Kate Bland', 'Ming Cheng', 'Han Song', 'Rohit Prasad', 'Raefer Gabriel', 'Sanju Pancholi', 'Jeff Nunn', 'Gene Hwang', 'Arindam Mandal', 'Anu Venkatesh', 'Yi Pan', 'Sanjeev Kwatra', 'Qing Liu', 'Nate Michel', 'Lauren Stubel', 'Karthik Gopalakrishnan', 'Behnam Hedayatnia', 'Anna Gottardi', 'Qinglan... | 2018-12-27 | null | null | null | null | ['open-domain-dialog'] | ['natural-language-processing'] | [-1.97518587e-01 6.06741428e-01 7.71718696e-02 -5.67533195e-01
-9.96744931e-01 -8.92421722e-01 8.50139856e-01 3.94238308e-02
-2.23314181e-01 8.98066223e-01 9.41828251e-01 -2.15507329e-01
2.06124231e-01 -4.03902769e-01 8.77275392e-02 6.20517693e-02
1.54699013e-01 1.01447070e+00 2.53889579e-02 -8.40257406... | [12.669913291931152, 7.965521812438965] |
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