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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e6120ad7-3811-41f6-8609-eec758086aca | a-closer-look-at-invalid-action-masking-in | 2006.14171 | null | https://arxiv.org/abs/2006.14171v3 | https://arxiv.org/pdf/2006.14171v3.pdf | A Closer Look at Invalid Action Masking in Policy Gradient Algorithms | In recent years, Deep Reinforcement Learning (DRL) algorithms have achieved state-of-the-art performance in many challenging strategy games. Because these games have complicated rules, an action sampled from the full discrete action distribution predicted by the learned policy is likely to be invalid according to the g... | ['Santiago Ontañón', 'Shengyi Huang'] | 2020-06-25 | null | null | null | null | ['real-time-strategy-games'] | ['playing-games'] | [ 1.52196243e-01 9.65157300e-02 -4.73944843e-01 7.08482563e-02
-7.09089696e-01 -7.04693139e-01 6.55064285e-01 -1.27574176e-01
-8.97973418e-01 1.29087758e+00 2.25313172e-01 -5.83098531e-01
-1.48086667e-01 -7.22742736e-01 -7.27004051e-01 -8.21330845e-01
-1.70203626e-01 4.07640249e-01 2.80789733e-01 -1.90982580... | [3.884488582611084, 1.9050101041793823] |
65e676a6-8d20-447e-a541-8bbcdaa4c4f4 | deep-pipeline-embeddings-for-automl | 2305.14009 | null | https://arxiv.org/abs/2305.14009v2 | https://arxiv.org/pdf/2305.14009v2.pdf | Deep Pipeline Embeddings for AutoML | Automated Machine Learning (AutoML) is a promising direction for democratizing AI by automatically deploying Machine Learning systems with minimal human expertise. The core technical challenge behind AutoML is optimizing the pipelines of Machine Learning systems (e.g. the choice of preprocessing, augmentations, models,... | ['Josif Grabocka', 'Sebastian Pineda Arango'] | 2023-05-23 | null | null | null | null | ['automatic-machine-learning-model-selection', 'automl', 'hyperparameter-optimization', 'bayesian-optimization'] | ['methodology', 'methodology', 'methodology', 'methodology'] | [-2.34097868e-01 1.99266687e-01 -2.20939741e-01 -5.52574217e-01
-8.54514599e-01 -8.67511570e-01 6.47040546e-01 1.72270805e-01
-5.06117046e-01 -2.33880833e-01 3.90514225e-01 -1.79894924e-01
-1.64552420e-01 -3.85195166e-01 -9.04256940e-01 -3.68651330e-01
-6.00220114e-02 9.04804826e-01 -6.25653863e-02 8.08223560... | [8.548941612243652, 4.002139568328857] |
cee8799c-ef4d-4202-be34-9009cb99a871 | ratt-leveraging-unlabeled-data-to-guarantee | 2105.00303 | null | https://arxiv.org/abs/2105.00303v2 | https://arxiv.org/pdf/2105.00303v2.pdf | RATT: Leveraging Unlabeled Data to Guarantee Generalization | To assess generalization, machine learning scientists typically either (i) bound the generalization gap and then (after training) plug in the empirical risk to obtain a bound on the true risk; or (ii) validate empirically on holdout data. However, (i) typically yields vacuous guarantees for overparameterized models. Fu... | ['Zachary C. Lipton', 'J. Zico Kolter', 'Sivaraman Balakrishnan', 'Saurabh Garg'] | 2021-05-01 | null | null | null | null | ['holdout-set'] | ['computer-vision'] | [ 1.90514892e-01 3.59688729e-01 -2.82383263e-01 -4.61536169e-01
-1.23129761e+00 -9.68749106e-01 2.83547014e-01 2.34226346e-01
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-1.88165590e-01 -6.71587110e-01 -1.02969742e+00 -1.01287484e+00
-2.32609078e-01 4.15173292e-01 -1.62434742e-01 3.24768215... | [8.125164031982422, 4.067102909088135] |
786aae86-9cd8-4f87-8684-4a2153f8c484 | synthesizing-programs-with-continuous | 2211.00828 | null | https://arxiv.org/abs/2211.00828v2 | https://arxiv.org/pdf/2211.00828v2.pdf | Synthesizing Programs with Continuous Optimization | Automatic software generation based on some specification is known as program synthesis. Most existing approaches formulate program synthesis as a search problem with discrete parameters. In this paper, we present a novel formulation of program synthesis as a continuous optimization problem and use a state-of-the-art e... | ['Abdullah Muzahid', 'Justin Gottschlich', 'Javier Turek', 'Todd A. Anderson', 'Shantanu Mandal'] | 2022-11-02 | null | null | null | null | ['program-synthesis'] | ['computer-code'] | [ 3.71792167e-01 -9.22720358e-02 -2.73556739e-01 -3.56306404e-01
-5.58012128e-01 -6.02924824e-01 6.42482117e-02 -1.03782043e-02
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3.44302416e-01 3.42000216e-01 4.46676731e-01 -4.44509983... | [8.048089981079102, 7.302132606506348] |
f5a188b1-92ae-41a2-b239-0d15de717733 | is-chatgpt-a-highly-fluent-grammatical-error | 2304.01746 | null | https://arxiv.org/abs/2304.01746v1 | https://arxiv.org/pdf/2304.01746v1.pdf | Is ChatGPT a Highly Fluent Grammatical Error Correction System? A Comprehensive Evaluation | ChatGPT, a large-scale language model based on the advanced GPT-3.5 architecture, has shown remarkable potential in various Natural Language Processing (NLP) tasks. However, there is currently a dearth of comprehensive study exploring its potential in the area of Grammatical Error Correction (GEC). To showcase its capa... | ['Yue Zhang', 'Lidia S. Chao', 'Jinpeng Hu', 'Derek F. Wong', 'Kaixin Lan', 'Shu Yang', 'Tao Fang'] | 2023-04-04 | null | null | null | null | ['grammatical-error-correction'] | ['natural-language-processing'] | [-5.17379977e-02 2.30058014e-01 4.09719676e-01 -3.97229493e-01
-1.16232955e+00 -3.43588084e-01 3.71934682e-01 6.77986801e-01
-8.30326557e-01 8.43948483e-01 4.14515108e-01 -6.21131599e-01
9.77077335e-02 -3.25281292e-01 -6.19487941e-01 -5.03265932e-02
-1.27850860e-01 7.74813354e-01 1.52719989e-01 -7.37480581... | [11.085907936096191, 10.716623306274414] |
05e4aa11-fab3-4175-9bbb-cef76cfcad0d | variational-auto-encoding-of-protein | 1712.03346 | null | http://arxiv.org/abs/1712.03346v3 | http://arxiv.org/pdf/1712.03346v3.pdf | Variational auto-encoding of protein sequences | Proteins are responsible for the most diverse set of functions in biology.
The ability to extract information from protein sequences and to predict the
effects of mutations is extremely valuable in many domains of biology and
medicine. However the mapping between protein sequence and function is complex
and poorly unde... | ['Eric Kelsic', 'George M. Church', 'Sam Sinai', 'Martin A. Nowak'] | 2017-12-09 | null | null | null | null | ['protein-design'] | ['medical'] | [ 7.08187878e-01 7.54772276e-02 -8.50956589e-02 -4.44753885e-01
-3.26830029e-01 -8.89407992e-01 4.24693495e-01 4.02625471e-01
-4.15241420e-01 1.28961849e+00 2.81787604e-01 -5.36507308e-01
-4.74952208e-03 -4.59671676e-01 -1.02195716e+00 -1.19298065e+00
3.31443138e-02 8.31952274e-01 2.34482720e-01 -3.04729432... | [4.740192890167236, 5.600770950317383] |
647c09e4-1b74-4ae0-98c4-72e8eb3b54b2 | bbc-oxford-british-sign-language-dataset | 2111.03635 | null | https://arxiv.org/abs/2111.03635v1 | https://arxiv.org/pdf/2111.03635v1.pdf | BBC-Oxford British Sign Language Dataset | In this work, we introduce the BBC-Oxford British Sign Language (BOBSL) dataset, a large-scale video collection of British Sign Language (BSL). BOBSL is an extended and publicly released dataset based on the BSL-1K dataset introduced in previous work. We describe the motivation for the dataset, together with statistics... | ['Andrew Zisserman', 'Andrew McParland', 'Rob Cooper', 'Bencie Woll', 'Neil Fox', 'Himel Chowdhury', 'Triantafyllos Afouras', 'Hannah Bull', 'Liliane Momeni', 'Gül Varol', 'Samuel Albanie'] | 2021-11-05 | null | null | null | null | ['sign-language-translation'] | ['computer-vision'] | [ 3.71287167e-02 -1.16675124e-01 -4.29485559e-01 -4.53283578e-01
-9.92802560e-01 -5.88198483e-01 5.50523579e-01 -7.80500174e-01
-8.94886911e-01 5.89593887e-01 1.14289510e+00 -2.14493960e-01
1.51185066e-01 1.89338550e-01 -5.64952970e-01 -5.06655693e-01
1.72265559e-01 2.75459498e-01 3.69717360e-01 -3.07669520... | [9.151691436767578, -6.473094940185547] |
3a25fc97-e529-4226-bc1b-be0db5f3c764 | feded-federated-learning-via-ensemble | null | null | https://aclanthology.org/2020.emnlp-main.165 | https://aclanthology.org/2020.emnlp-main.165.pdf | FedED: Federated Learning via Ensemble Distillation for Medical Relation Extraction | Unlike other domains, medical texts are inevitably accompanied by private information, so sharing or copying these texts is strictly restricted. However, training a medical relation extraction model requires collecting these privacy-sensitive texts and storing them on one machine, which comes in conflict with privacy p... | ['Weijian Sun', 'Yuantao Xie', 'Yantao Jia', 'Jun Zhao', 'Yubo Chen', 'Dianbo Sui'] | null | null | null | null | emnlp-2020-11 | ['medical-relation-extraction'] | ['medical'] | [ 3.51682276e-01 4.98636484e-01 -4.53718692e-01 -4.00217474e-01
-8.95163000e-01 -6.62222385e-01 2.56851703e-01 4.35375243e-01
-6.21296287e-01 9.99485433e-01 2.32309356e-01 -5.86193681e-01
-2.29341567e-01 -9.12334442e-01 -5.66253841e-01 -9.52383876e-01
3.87540236e-02 2.03668207e-01 -1.35417566e-01 2.64714986... | [6.098748207092285, 6.518949508666992] |
0bb710fa-1993-40b5-9013-d985455a172a | ih-vit-vision-transformer-based-integrated | 2302.04521 | null | https://arxiv.org/abs/2302.04521v1 | https://arxiv.org/pdf/2302.04521v1.pdf | IH-ViT: Vision Transformer-based Integrated Circuit Appear-ance Defect Detection | For the problems of low recognition rate and slow recognition speed of traditional detection methods in IC appearance defect detection, we propose an IC appearance defect detection algo-rithm IH-ViT. Our proposed model takes advantage of the respective strengths of CNN and ViT to acquire image features from both local ... | ['Chu Wang', 'Jianlan Guo', 'Yuntao Zou', 'Shuang Gao', 'Xiaoibin Wang'] | 2023-02-09 | null | null | null | null | ['defect-detection'] | ['computer-vision'] | [ 1.90411836e-01 -3.72200102e-01 6.49554729e-02 -2.11790845e-01
-3.42892170e-01 -9.78656933e-02 -1.22676946e-01 -5.11553884e-02
-7.98974559e-02 3.79186660e-01 -4.52728540e-01 -2.44271889e-01
2.01665416e-01 -1.00808251e+00 -1.63581520e-01 -5.78181148e-01
4.62447345e-01 3.33290070e-01 3.54110330e-01 1.16579115... | [7.423717021942139, 1.748457908630371] |
e5a4034f-ecba-4b38-b022-834248a42a64 | native-language-identification-with | null | null | https://aclanthology.org/J18-3003 | https://aclanthology.org/J18-3003.pdf | Native Language Identification With Classifier Stacking and Ensembles | Ensemble methods using multiple classifiers have proven to be among the most successful approaches 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 architectu... | ['Shervin Malmasi', 'Mark Dras'] | 2018-09-01 | null | null | null | cl-2018-9 | ['cross-corpus', 'native-language-identification'] | ['computer-vision', 'natural-language-processing'] | [ 2.88405508e-01 -4.84471321e-01 -1.74255982e-01 -5.51383376e-01
-9.64832723e-01 -8.43846679e-01 1.11499441e+00 3.21325064e-02
-5.68998158e-01 7.49218702e-01 1.79981932e-01 -6.29271150e-01
-2.05322474e-01 -5.32424785e-02 -1.75490826e-01 -4.16944772e-01
-1.35462821e-01 8.24779809e-01 -4.13672060e-01 -3.49023670... | [10.38045883178711, 10.560400009155273] |
ad6f480c-bc23-4957-ac6c-0fe33adb4a5f | group-extract-and-aggregate-summarizing-a | 1910.05032 | null | https://arxiv.org/abs/1910.05032v1 | https://arxiv.org/pdf/1910.05032v1.pdf | Group, Extract and Aggregate: Summarizing a Large Amount of Finance News for Forex Movement Prediction | Incorporating related text information has proven successful in stock market prediction. However, it is a huge challenge to utilize texts in the enormous forex (foreign currency exchange) market because the associated texts are too redundant. In this work, we propose a BERT-based Hierarchical Aggregation Model to summa... | ['Xu sun', 'Shuming Ma', 'Qi Su', 'Deli Chen', 'Ruihan Bao', 'Keiko Harimoto'] | 2019-10-11 | group-extract-and-aggregate-summarizing-a-1 | https://aclanthology.org/D19-5106 | https://aclanthology.org/D19-5106.pdf | ws-2019-11 | ['stock-market-prediction'] | ['time-series'] | [-6.20544016e-01 -2.54359514e-01 -6.61824584e-01 -1.22260310e-01
-9.32042301e-01 -6.79272711e-01 1.07304132e+00 3.98330092e-01
-3.64056438e-01 9.41920757e-01 1.29658258e+00 -2.91318536e-01
-4.20607440e-02 -8.27256739e-01 -4.97994572e-01 -3.44024837e-01
-8.02486464e-02 5.42354226e-01 3.09585243e-01 -3.97791654... | [4.40833044052124, 4.286250114440918] |
fb9c9529-71f5-4f99-9e22-8a4a66a72498 | robust-reinforcement-learning-with | 2206.06841 | null | https://arxiv.org/abs/2206.06841v1 | https://arxiv.org/pdf/2206.06841v1.pdf | Robust Reinforcement Learning with Distributional Risk-averse formulation | Robust Reinforcement Learning tries to make predictions more robust to changes in the dynamics or rewards of the system. This problem is particularly important when the dynamics and rewards of the environment are estimated from the data. In this paper, we approximate the Robust Reinforcement Learning constrained with a... | ['Erwan Le Pennec', 'Stéphanie Allassonière', 'Pierre Clavier'] | 2022-06-14 | null | null | null | null | ['distributional-reinforcement-learning'] | ['methodology'] | [ 1.41520062e-02 4.30973291e-01 4.93680350e-02 -2.65986711e-01
-7.78254092e-01 -2.60005176e-01 3.49068105e-01 3.91679257e-01
-1.01874816e+00 1.45041215e+00 -1.02922745e-01 1.95786208e-01
-8.05852175e-01 -6.50906980e-01 -9.30449426e-01 -8.23585033e-01
-5.49497545e-01 3.42461675e-01 1.47218734e-01 -4.59589630... | [4.292448997497559, 2.3087759017944336] |
e7cabd66-d233-4aaf-af28-8e0805ebf33e | multilingual-synthetic-question-and-answer | 2010.12008 | null | https://arxiv.org/abs/2010.12008v3 | https://arxiv.org/pdf/2010.12008v3.pdf | Towards Zero-Shot Multilingual Synthetic Question and Answer Generation for Cross-Lingual Reading Comprehension | We propose a simple method to generate multilingual question and answer pairs on a large scale through the use of a single generative model. These synthetic samples can be used to improve the zero-shot performance of multilingual QA models on target languages. Our proposed multi-task training of the generative model on... | ['Linting Xue', 'Mihir Sanjay Kale', 'Noah Constant', 'Siamak Shakeri'] | 2020-10-22 | null | https://aclanthology.org/2021.inlg-1.4 | https://aclanthology.org/2021.inlg-1.4.pdf | inlg-acl-2021-8 | ['cross-lingual-question-answering'] | ['natural-language-processing'] | [-2.23078460e-01 5.77121139e-01 5.84864467e-02 -6.05083346e-01
-1.98953199e+00 -7.82891273e-01 6.72385037e-01 -2.25710273e-01
-4.39111412e-01 1.20171344e+00 1.93537652e-01 -5.24647534e-01
4.75002587e-01 -8.90556931e-01 -9.06877100e-01 -4.46309000e-01
6.30047023e-01 1.20562398e+00 1.23393171e-01 -7.53184736... | [11.411441802978516, 8.423086166381836] |
97e8adfc-ef1f-4e52-9a32-fef3df13bc5d | mask2former-for-video-instance-segmentation | 2112.10764 | null | https://arxiv.org/abs/2112.10764v1 | https://arxiv.org/pdf/2112.10764v1.pdf | Mask2Former for Video Instance Segmentation | We find Mask2Former also achieves state-of-the-art performance on video instance segmentation without modifying the architecture, the loss or even the training pipeline. In this report, we show universal image segmentation architectures trivially generalize to video segmentation by directly predicting 3D segmentation v... | ['Alexander G. Schwing', 'Rohit Girdhar', 'Alexander Kirillov', 'Ishan Misra', 'Anwesa Choudhuri', 'Bowen Cheng'] | 2021-12-20 | null | null | null | null | ['video-instance-segmentation'] | ['computer-vision'] | [ 4.39240560e-02 1.86741754e-01 -4.40881222e-01 -3.91758323e-01
-7.95634151e-01 -7.69043982e-01 1.46246389e-01 -4.90970224e-01
-3.23914766e-01 3.63359809e-01 -3.14368516e-01 -6.51566267e-01
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-1.91513062e-01 3.64600569e-01 7.45225012e-01 1.59580290... | [9.262736320495605, 0.022270046174526215] |
5f1e30be-315f-4aef-994d-0d7275205344 | trigonometric-comparison-measure-a-feature | null | null | https://doi.org/10.1016/j.datak.2018.10.003 | https://doi.org/10.1016/j.datak.2018.10.003 | Trigonometric comparison measure: A feature selection method for text categorization | Text data represented using vector space model is high dimensional data since the number of words can easily grow to tens of thousands for a moderate sized dataset. It may contain lots of redundant or irrelevant features that degrade the performance of a classifier for text categorization. To address this problem, feat... | ['See Young Zzang', 'Kyoungok Kim'] | 2019-01-02 | null | null | null | null | ['text-categorization'] | ['natural-language-processing'] | [ 6.39049162e-04 -5.60764551e-01 -1.84945539e-01 -5.25288463e-01
-2.22775295e-01 -5.84155738e-01 6.10013366e-01 9.15681422e-01
-6.39640093e-01 7.68081427e-01 2.41643950e-01 -3.35571647e-01
-7.41713166e-01 -1.05260229e+00 1.78440511e-01 -6.02978289e-01
-1.17085531e-01 6.43405676e-01 3.11192334e-01 -2.04309911... | [10.544482231140137, 7.21713924407959] |
15d7ca43-aace-4e4c-8735-f7386563cc59 | achieving-strong-regularization-for-deep | null | null | https://openreview.net/forum?id=Bys_NzbC- | https://openreview.net/pdf?id=Bys_NzbC- | Achieving Strong Regularization for Deep Neural Networks | L1 and L2 regularizers are critical tools in machine learning due to their ability to simplify solutions. However, imposing strong L1 or L2 regularization with gradient descent method easily fails, and this limits the generalization ability of the underlying neural networks. To understand this phenomenon, we investigat... | ['Chiu Man Ho', 'Dae Hoon Park', 'Yi Chang'] | 2018-01-01 | null | null | null | iclr-2018-1 | ['l2-regularization'] | ['methodology'] | [-1.23342581e-01 -4.81405482e-02 -5.60707629e-01 -3.38793576e-01
-3.37859303e-01 -4.91283506e-01 1.26007676e-01 -6.62777126e-02
-3.78133833e-01 8.76626611e-01 2.02876478e-01 -4.11291271e-01
-8.44167322e-02 -5.21672130e-01 -7.60498226e-01 -7.75493681e-01
6.17703125e-02 -3.73081326e-01 1.01475202e-01 -1.79018766... | [8.434615135192871, 3.607640266418457] |
5fb4009e-7b1d-4e4e-a010-3eafb6fa3d81 | a-markovian-formalism-for-active-querying | 2306.08001 | null | https://arxiv.org/abs/2306.08001v1 | https://arxiv.org/pdf/2306.08001v1.pdf | A Markovian Formalism for Active Querying | Active learning algorithms have been an integral part of recent advances in artificial intelligence. However, the research in the field is widely varying and lacks an overall organizing leans. We outline a Markovian formalism for the field of active learning and survey the literature to demonstrate the organizing capab... | ['Sid Ijju'] | 2023-06-13 | null | null | null | null | ['active-learning', 'active-learning'] | ['methodology', 'natural-language-processing'] | [ 4.90438491e-01 7.24023700e-01 -5.04065990e-01 -6.11519694e-01
-6.73905015e-01 -6.26917064e-01 1.00591207e+00 4.88224655e-01
-7.47467995e-01 8.73480797e-01 -2.76766773e-02 -9.18573588e-02
-5.89936256e-01 -9.07601357e-01 -5.58497071e-01 -9.28780437e-01
-1.15151703e-01 7.20774591e-01 5.57379723e-01 1.69725910... | [9.508034706115723, 4.260340213775635] |
489a0ed4-1f59-4e59-9bf7-0bcb8be4aaa7 | privacy-preserving-collaborative-chinese-text | 2305.05602 | null | https://arxiv.org/abs/2305.05602v1 | https://arxiv.org/pdf/2305.05602v1.pdf | Privacy-Preserving Collaborative Chinese Text Recognition with Federated Learning | In Chinese text recognition, to compensate for the insufficient local data and improve the performance of local few-shot character recognition, it is often necessary for one organization to collect a large amount of data from similar organizations. However, due to the natural presence of private information in text dat... | ['xiangyang xue', 'Bin Li', 'Haiyang Yu', 'Shangchao Su'] | 2023-05-09 | null | null | null | null | ['personalized-federated-learning'] | ['methodology'] | [ 9.99824107e-02 -4.86243308e-01 -2.95101911e-01 -7.07601011e-01
-8.90933156e-01 -3.74580741e-01 1.53078958e-01 -9.85770822e-02
-4.03748572e-01 6.65629387e-01 8.55170712e-02 -3.20264071e-01
3.53428870e-02 -7.66356468e-01 -6.14682555e-01 -9.34840977e-01
6.44521952e-01 3.60942781e-01 7.87559245e-03 1.86291456... | [5.845946788787842, 6.353536128997803] |
0b59cda7-e6b7-4dd5-a8a1-47af579d0634 | sindiffusion-learning-a-diffusion-model-from | 2211.12445 | null | https://arxiv.org/abs/2211.12445v1 | https://arxiv.org/pdf/2211.12445v1.pdf | SinDiffusion: Learning a Diffusion Model from a Single Natural Image | We present SinDiffusion, leveraging denoising diffusion models to capture internal distribution of patches from a single natural image. SinDiffusion significantly improves the quality and diversity of generated samples compared with existing GAN-based approaches. It is based on two core designs. First, SinDiffusion is ... | ['Houqiang Li', 'Lu Yuan', 'Dong Chen', 'Dongdong Chen', 'Wengang Zhou', 'Jianmin Bao', 'Weilun Wang'] | 2022-11-22 | null | null | null | null | ['image-outpainting'] | ['computer-vision'] | [ 3.91724676e-01 -5.14997467e-02 9.52034891e-02 2.59270146e-02
-3.75237823e-01 -4.67008144e-01 4.24796999e-01 -5.29821932e-01
1.65690944e-01 8.31035137e-01 3.54914784e-01 1.86144173e-01
1.76453635e-01 -8.99504840e-01 -7.24652350e-01 -1.09134579e+00
4.79288489e-01 2.26663351e-02 2.87133485e-01 -3.20959181... | [11.455056190490723, -0.8792266249656677] |
2d362ee8-68e2-4a40-84dc-18a28957c12d | scalable-object-detection-for-stylized | 1711.09822 | null | http://arxiv.org/abs/1711.09822v2 | http://arxiv.org/pdf/1711.09822v2.pdf | Scalable Object Detection for Stylized Objects | Following recent breakthroughs in convolutional neural networks and
monolithic model architectures, state-of-the-art object detection models can
reliably and accurately scale into the realm of up to thousands of classes.
Things quickly break down, however, when scaling into the tens of thousands,
or, eventually, to mil... | ['Willi Richert', 'Thilo Will', 'William Darling', 'Clemens Marschner', 'Aayush Garg'] | 2017-11-27 | null | null | null | null | ['logo-recognition'] | ['computer-vision'] | [ 8.85687023e-02 -2.18371585e-01 -1.80698544e-01 -1.25497073e-01
-8.09280872e-01 -7.89326608e-01 6.57171190e-01 2.41279781e-01
-1.55080736e-01 1.32168740e-01 -4.01588261e-01 5.53335063e-03
2.41823703e-01 -9.11920190e-01 -1.08516049e+00 -3.14239740e-01
-2.78966334e-02 8.52936745e-01 8.87148976e-01 -1.42238200... | [9.33842945098877, 1.2319231033325195] |
db4c0c1c-741c-4e59-99c8-70c50be2e9fa | weakly-supervised-action-segmentation-and | null | null | http://openaccess.thecvf.com//content/ICCV2021/html/Lu_Weakly-Supervised_Action_Segmentation_and_Alignment_via_Transcript-Aware_Union-of-Subspaces_Learning_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Lu_Weakly-Supervised_Action_Segmentation_and_Alignment_via_Transcript-Aware_Union-of-Subspaces_Learning_ICCV_2021_paper.pdf | Weakly-Supervised Action Segmentation and Alignment via Transcript-Aware Union-of-Subspaces Learning | We address the problem of learning to segment actions from weakly-annotated videos, i.e., videos accompanied by transcripts (ordered list of actions). We propose a framework in which we model actions with a union of low-dimensional subspaces, learn the subspaces using transcripts and refine video features that lend... | ['Ehsan Elhamifar', 'Zijia Lu'] | 2021-01-01 | null | null | null | iccv-2021-1 | ['weakly-supervised-action-segmentation'] | ['computer-vision'] | [ 4.38882679e-01 8.59849826e-02 -4.74493712e-01 -5.16442537e-01
-8.07127953e-01 -7.56477118e-01 3.00305516e-01 -4.99503344e-01
-2.79679000e-01 3.02842140e-01 7.66599000e-01 1.26724958e-01
1.18966587e-01 -2.32771575e-01 -1.12547421e+00 -8.26144874e-01
-1.21734060e-01 6.21948361e-01 2.07012698e-01 3.69154483... | [8.558161735534668, 0.6555895805358887] |
40e6b1ac-01a7-47b2-97b4-79a09dca1642 | lexicon-learning-for-few-shot-neural-sequence | 2106.03993 | null | https://arxiv.org/abs/2106.03993v1 | https://arxiv.org/pdf/2106.03993v1.pdf | Lexicon Learning for Few-Shot Neural Sequence Modeling | Sequence-to-sequence transduction is the core problem in language processing applications as diverse as semantic parsing, machine translation, and instruction following. The neural network models that provide the dominant solution to these problems are brittle, especially in low-resource settings: they fail to generali... | ['Jacob Andreas', 'Ekin Akyürek'] | 2021-06-07 | null | null | null | null | ['systematic-generalization'] | ['reasoning'] | [ 5.40877938e-01 2.04344094e-01 -5.13722181e-01 -4.78148848e-01
-7.47905433e-01 -7.51531422e-01 6.03532195e-01 1.54402554e-01
-5.19987702e-01 9.57078397e-01 4.67755139e-01 -1.13473248e+00
2.78362095e-01 -7.67716348e-01 -1.06259215e+00 -4.46583293e-02
3.47707361e-01 6.56543791e-01 1.86326846e-01 -5.41574121... | [10.715076446533203, 9.037577629089355] |
e4e88cd7-7a41-4886-be93-f039a14e87dc | first-order-motion-model-for-image-animation-1 | 2003.00196 | null | https://arxiv.org/abs/2003.00196v3 | https://arxiv.org/pdf/2003.00196v3.pdf | First Order Motion Model for Image Animation | Image animation consists of generating a video sequence so that an object in a source image is animated according to the motion of a driving video. Our framework addresses this problem without using any annotation or prior information about the specific object to animate. Once trained on a set of videos depicting objec... | ['Stéphane Lathuilière', 'Elisa Ricci', 'Aliaksandr Siarohin', 'Sergey Tulyakov', 'Nicu Sebe'] | 2020-02-29 | first-order-motion-model-for-image-animation | http://papers.nips.cc/paper/8935-first-order-motion-model-for-image-animation | http://papers.nips.cc/paper/8935-first-order-motion-model-for-image-animation.pdf | neurips-2019-12 | ['video-reconstruction', 'image-animation'] | ['computer-vision', 'computer-vision'] | [ 3.45186710e-01 1.54929250e-01 -2.29381353e-01 -2.90360481e-01
-4.63412076e-01 -7.34330654e-01 7.64283240e-01 -5.13630450e-01
-7.67244324e-02 4.46297169e-01 1.08606070e-01 2.36655727e-01
5.28639495e-01 -6.13898993e-01 -9.79058146e-01 -7.74983525e-01
8.23567063e-02 4.12851095e-01 3.78511906e-01 -3.03603876... | [10.820234298706055, -0.7983798384666443] |
9cf5f293-22f1-4ebe-92c2-c28d76f7d767 | remask-a-robust-information-masking-approach | 2305.02858 | null | https://arxiv.org/abs/2305.02858v1 | https://arxiv.org/pdf/2305.02858v1.pdf | ReMask: A Robust Information-Masking Approach for Domain Counterfactual Generation | Domain shift is a big challenge in NLP, thus, many approaches resort to learning domain-invariant features to mitigate the inference phase domain shift. Such methods, however, fail to leverage the domain-specific nuances relevant to the task at hand. To avoid such drawbacks, domain counterfactual generation aims to tra... | ['Soujanya Poria', 'Somak Aditya', 'Navonil Majumdar', 'Rishabh Bhardwaj', 'Pengfei Hong'] | 2023-05-04 | null | null | null | null | ['intent-classification'] | ['natural-language-processing'] | [ 4.90207225e-01 1.50146130e-02 -5.19700527e-01 -3.86140466e-01
-1.38615096e+00 -8.68396103e-01 8.46569061e-01 -3.16275544e-02
-3.59087318e-01 1.24103880e+00 3.51605296e-01 -4.20167506e-01
2.40227252e-01 -5.86905777e-01 -8.44696999e-01 -5.96989393e-01
3.62796813e-01 3.08726877e-01 -7.11131990e-02 -2.96627492... | [10.32102108001709, 3.1466715335845947] |
b74fdb67-4fd5-45ef-9354-d8a71e870e79 | sciannotate-a-tool-for-integrating-weak | 2208.10241 | null | https://arxiv.org/abs/2208.10241v1 | https://arxiv.org/pdf/2208.10241v1.pdf | SciAnnotate: A Tool for Integrating Weak Labeling Sources for Sequence Labeling | Weak labeling is a popular weak supervision strategy for Named Entity Recognition (NER) tasks, with the goal of reducing the necessity for hand-crafted annotations. Although there are numerous remarkable annotation tools for NER labeling, the subject of integrating weak labeling sources is still unexplored. We introduc... | ['Le Song', 'Chao Zhang', 'Yinghao Li', 'Leonard Thong', 'Haozheng Luo', 'Mengyang Liu'] | 2022-08-07 | null | null | null | null | ['text-annotation'] | ['natural-language-processing'] | [ 6.83092475e-02 3.72726113e-01 -3.24388631e-02 -2.68675864e-01
-1.06356573e+00 -8.59803915e-01 4.89780486e-01 3.60815734e-01
-7.12729514e-01 8.68328154e-01 2.25170195e-01 -3.94819379e-01
2.50083148e-01 -5.49764216e-01 -5.03078759e-01 -6.30388796e-01
8.12981725e-01 4.80016768e-01 1.98967189e-01 7.68278092... | [9.648259162902832, 9.286924362182617] |
e7d750b9-ec48-4528-b387-3335ed5df792 | foreground-background-segmentation-based-on | 1410.6472 | null | http://arxiv.org/abs/1410.6472v1 | http://arxiv.org/pdf/1410.6472v1.pdf | Foreground-Background Segmentation Based on Codebook and Edge Detector | Background modeling techniques are used for moving object detection in video.
Many algorithms exist in the field of object detection with different purposes.
In this paper, we propose an improvement of moving object detection based on
codebook segmentation. We associate the original codebook algorithm with an
edge dete... | ['Eugène C. Ezin', 'Mikaël A. Mousse', 'Cina Motamed'] | 2014-10-23 | null | null | null | null | ['moving-object-detection'] | ['computer-vision'] | [ 1.85575828e-01 -5.45605123e-01 7.65593126e-02 3.35914828e-02
-1.48352146e-01 -3.91243219e-01 3.77637208e-01 3.22050929e-01
-6.76078260e-01 3.37313205e-01 -1.73718676e-01 -2.79388309e-01
1.33037627e-01 -6.63207829e-01 -2.27829069e-01 -6.81286275e-01
-2.25046754e-01 7.18599930e-02 1.00220716e+00 5.45531884... | [8.9120512008667, -0.8833074569702148] |
24e63234-2dfb-49d3-9445-753d326123ac | data-free-backbone-fine-tuning-for-pruned | 2306.12881 | null | https://arxiv.org/abs/2306.12881v1 | https://arxiv.org/pdf/2306.12881v1.pdf | Data-Free Backbone Fine-Tuning for Pruned Neural Networks | Model compression techniques reduce the computational load and memory consumption of deep neural networks. After the compression operation, e.g. parameter pruning, the model is normally fine-tuned on the original training dataset to recover from the performance drop caused by compression. However, the training data is ... | ['Vasileios Belagiannis', 'Klaus Dietmayer', 'Achyut Hegde', 'Adrian Holzbock'] | 2023-06-22 | null | null | null | null | ['pose-estimation', '2d-human-pose-estimation', 'model-compression'] | ['computer-vision', 'computer-vision', 'methodology'] | [ 4.83396024e-01 4.22911525e-01 -1.71129480e-01 -3.47073615e-01
-4.38641906e-01 -3.03209782e-01 3.80732045e-02 -3.44097614e-02
-7.81401873e-01 6.64331317e-01 -1.70235738e-01 -1.37469828e-01
2.07618073e-01 -7.48280108e-01 -1.30139339e+00 -5.74707687e-01
6.60593137e-02 5.46819031e-01 2.07028806e-01 3.55209827... | [8.586209297180176, 3.2162117958068848] |
d55e20ec-2668-43aa-808d-f22576666979 | trankit-a-light-weight-transformer-based | 2101.03289 | null | https://arxiv.org/abs/2101.03289v5 | https://arxiv.org/pdf/2101.03289v5.pdf | Trankit: A Light-Weight Transformer-based Toolkit for Multilingual Natural Language Processing | We introduce Trankit, a light-weight Transformer-based Toolkit for multilingual Natural Language Processing (NLP). It provides a trainable pipeline for fundamental NLP tasks over 100 languages, and 90 pretrained pipelines for 56 languages. Built on a state-of-the-art pretrained language model, Trankit significantly out... | ['Viet Dac Lai', 'Minh Van Nguyen', 'Thien Huu Nguyen', 'Amir Pouran Ben Veyseh'] | 2021-01-09 | null | https://aclanthology.org/2021.eacl-demos.10 | https://aclanthology.org/2021.eacl-demos.10.pdf | eacl-2021-2 | ['morphological-tagging', 'multilingual-nlp'] | ['natural-language-processing', 'natural-language-processing'] | [-5.21530926e-01 -8.07748362e-02 -2.50290930e-01 -3.78158927e-01
-1.45580387e+00 -1.04605269e+00 2.78748184e-01 2.93282211e-01
-5.46372592e-01 5.90541780e-01 3.85665774e-01 -8.33546519e-01
5.45528054e-01 -5.26755512e-01 -6.98859870e-01 -3.23499054e-01
1.83389395e-01 6.38878703e-01 2.13716701e-01 -3.17085177... | [10.504111289978027, 9.973572731018066] |
9a46a272-b615-44be-91bf-1c67dcdf9e18 | inference-from-stationary-time-sequences-via | 2006.03258 | null | https://arxiv.org/abs/2006.03258v4 | https://arxiv.org/pdf/2006.03258v4.pdf | Learned Factor Graphs for Inference from Stationary Time Sequences | The design of methods for inference from time sequences has traditionally relied on statistical models that describe the relation between a latent desired sequence and the observed one. A broad family of model-based algorithms have been derived to carry out inference at controllable complexity using recursive computati... | ['Yonina C. Eldar', 'Nariman Farsad', 'Andrea J. Goldsmith', 'Nir Shlezinger'] | 2020-06-05 | null | null | null | null | ['sleep-stage-detection'] | ['medical'] | [ 5.19727588e-01 -3.94213013e-02 -4.74359095e-01 -5.14726460e-01
-5.53445518e-01 -4.40214872e-01 4.40640748e-01 6.65400252e-02
-3.52745384e-01 7.65325785e-01 -3.80560189e-01 -7.13585556e-01
-3.73541504e-01 -5.39187014e-01 -7.45357037e-01 -8.98602068e-01
-6.37740672e-01 5.92384338e-01 -2.56919060e-02 5.07065915... | [6.572680950164795, 1.6391844749450684] |
ddde0dd0-5a41-49da-8d4c-3c066a320860 | transrac-encoding-multi-scale-temporal | 2204.01018 | null | https://arxiv.org/abs/2204.01018v1 | https://arxiv.org/pdf/2204.01018v1.pdf | TransRAC: Encoding Multi-scale Temporal Correlation with Transformers for Repetitive Action Counting | Counting repetitive actions are widely seen in human activities such as physical exercise. Existing methods focus on performing repetitive action counting in short videos, which is tough for dealing with longer videos in more realistic scenarios. In the data-driven era, the degradation of such generalization capability... | ['Shenghua Gao', 'Zhengxin Li', 'Dongze Lian', 'Yiqun Zhao', 'Sixun Dong', 'Huazhang Hu'] | 2022-04-03 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Hu_TransRAC_Encoding_Multi-Scale_Temporal_Correlation_With_Transformers_for_Repetitive_Action_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Hu_TransRAC_Encoding_Multi-Scale_Temporal_Correlation_With_Transformers_for_Repetitive_Action_CVPR_2022_paper.pdf | cvpr-2022-1 | ['repetitive-action-counting'] | ['computer-vision'] | [ 4.62816805e-01 -5.23474634e-01 -5.94486237e-01 -2.56039143e-01
-6.18439436e-01 -4.03222293e-01 3.86672884e-01 -6.60339072e-02
-4.94062185e-01 6.89806938e-01 5.23056805e-01 1.34695098e-01
-1.32103980e-01 -5.57372808e-01 -5.51385701e-01 -6.22219622e-01
-2.54659921e-01 6.32264093e-03 5.48752129e-01 1.56388864... | [8.31790542602539, 0.5544586181640625] |
fc5c5d75-d75d-4a01-8620-8d79f5933663 | task3-dcase2021-challenge-sound-event | 2107.14561 | null | https://arxiv.org/abs/2107.14561v1 | https://arxiv.org/pdf/2107.14561v1.pdf | TASK3 DCASE2021 Challenge: Sound event localization and detection using squeeze-excitation residual CNNs | Sound event localisation and detection (SELD) is a problem in the field of automatic listening that aims at the temporal detection and localisation (direction of arrival estimation) of sound events within an audio clip, usually of long duration. Due to the amount of data present in the datasets related to this problem,... | ['Maximo Cobos', 'Francesc J. Ferri', 'Pedro Zuccarello', 'Sergi Perez-Castanos', 'Javier Naranjo-Alcazar'] | 2021-07-30 | null | null | null | null | ['direction-of-arrival-estimation', 'sound-event-localization-and-detection'] | ['audio', 'audio'] | [ 1.23921484e-01 -1.53490424e-01 7.78850377e-01 -6.14026040e-02
-7.56994963e-01 -3.49404156e-01 3.79408479e-01 2.67732471e-01
-5.84769368e-01 3.90175223e-01 6.23162866e-01 1.60918925e-02
-3.80315006e-01 -5.63882411e-01 -4.23798919e-01 -6.42617166e-01
-3.98213148e-01 -6.54124236e-03 6.95794821e-01 -2.64434785... | [15.162205696105957, 5.254172325134277] |
62a89d0d-9fdc-4d73-9b1f-2427f0b60d3d | rita-a-study-on-scaling-up-generative-protein | 2205.05789 | null | https://arxiv.org/abs/2205.05789v2 | https://arxiv.org/pdf/2205.05789v2.pdf | RITA: a Study on Scaling Up Generative Protein Sequence Models | In this work we introduce RITA: a suite of autoregressive generative models for protein sequences, with up to 1.2 billion parameters, trained on over 280 million protein sequences belonging to the UniRef-100 database. Such generative models hold the promise of greatly accelerating protein design. We conduct the first s... | ['Debora Marks', 'Iacopo Poli', 'Pascal Notin', 'Niccoló Zanichelli', 'Daniel Hesslow'] | 2022-05-11 | null | null | null | null | ['protein-design'] | ['medical'] | [ 3.31519365e-01 3.63381535e-01 5.27178459e-02 -4.03226435e-01
-6.95155084e-01 -6.53770685e-01 1.37726605e-01 -3.12427014e-01
-1.84701815e-01 1.01856351e+00 3.05515945e-01 -6.13815427e-01
-1.20520927e-01 -4.12358284e-01 -9.95480955e-01 -8.66198897e-01
-1.23011909e-01 1.02310038e+00 5.40582053e-02 -3.45094562... | [4.700778484344482, 5.6249189376831055] |
536d6498-a603-4210-b4ff-60a19758b7bc | context-patch-face-hallucination-based-on | 1809.00665 | null | http://arxiv.org/abs/1809.00665v2 | http://arxiv.org/pdf/1809.00665v2.pdf | Context-Patch Face Hallucination Based on Thresholding Locality-constrained Representation and Reproducing Learning | Face hallucination is a technique that reconstruct high-resolution (HR) faces
from low-resolution (LR) faces, by using the prior knowledge learned from HR/LR
face pairs. Most state-of-the-arts leverage position-patch prior knowledge of
human face to estimate the optimal representation coefficients for each image
patch.... | ['Suhua Tang', 'Yi Yu', 'Junjun Jiang', 'Akiko Aizawa', 'Jiayi Ma', 'Kiyoharu Aizawa'] | 2018-09-03 | null | null | null | null | ['face-hallucination'] | ['computer-vision'] | [ 1.78069666e-01 6.33110991e-04 -6.55450672e-02 -8.29030126e-02
-8.05836141e-01 -4.19481285e-03 2.86565632e-01 -6.31824791e-01
1.99025311e-03 6.74206793e-01 3.55229318e-01 3.18939060e-01
5.13323210e-02 -7.09833264e-01 -6.30943477e-01 -9.32183444e-01
4.13384944e-01 -1.82560325e-01 -2.56679446e-01 -3.12863514... | [12.844440460205078, -0.0016654033679515123] |
b8a112d9-3402-4713-88bf-9da591136132 | response-to-significance-and-stability-of | 2206.04934 | null | https://arxiv.org/abs/2206.04934v1 | https://arxiv.org/pdf/2206.04934v1.pdf | Response to: Significance and stability of deep learning-based identification of subtypes within major psychiatric disorders. Molecular Psychiatry (2022) | Recently, Winter and Hahn [1] commented on our work on identifying subtypes of major psychiatry disorders (MPDs) based on neurobiological features using machine learning [2]. They questioned the generalizability of our methods and the statistical significance, stability, and overfitting of the results, and proposed a p... | ['Weixiong Zhang', 'Fei Wang', 'Xizhe Zhang'] | 2022-06-10 | null | null | null | null | ['misconceptions'] | ['miscellaneous'] | [ 2.17992872e-01 2.93818712e-01 -3.93895388e-01 -6.05855644e-01
-5.06592095e-01 -4.11794305e-01 3.08653563e-01 5.18840611e-01
-5.40146172e-01 8.66365969e-01 1.22450195e-01 -4.87588584e-01
-5.58673799e-01 -1.57929555e-01 -8.85233805e-02 -3.19370657e-01
-5.17606378e-01 6.72742426e-01 -1.46494687e-01 1.76876299... | [8.118496894836426, 5.647578716278076] |
e5f53afa-f10a-4f28-9b56-2fa63b19e102 | llm-assisted-generation-of-hardware | 2306.14027 | null | https://arxiv.org/abs/2306.14027v1 | https://arxiv.org/pdf/2306.14027v1.pdf | LLM-assisted Generation of Hardware Assertions | The security of computer systems typically relies on a hardware root of trust. As vulnerabilities in hardware can have severe implications on a system, there is a need for techniques to support security verification activities. Assertion-based verification is a popular verification technique that involves capturing des... | ['Jeyavijayan Rajendran', 'Ramesh Karri', 'Shailja Thakur', 'Brendan Dolan-Gavitt', 'Benjamin Tan', 'Hammond Pearce', 'Rahul Kande'] | 2023-06-24 | null | null | null | null | ['code-generation'] | ['computer-code'] | [ 2.13666290e-01 7.73983970e-02 -5.82000971e-01 -4.04302299e-01
-7.17867136e-01 -8.61454308e-01 6.30169690e-01 7.06987321e-01
2.91227311e-01 5.34356654e-01 -8.04700553e-02 -1.55165613e+00
5.69647431e-01 -7.63129711e-01 -8.28619301e-01 5.54271460e-01
-1.17928170e-01 -4.02066857e-01 7.85041928e-01 -5.27551949... | [7.786944389343262, 7.624784469604492] |
f5684463-858b-4080-9503-d0742e1de83c | learning-sampling-dictionaries-for-efficient | 2306.00851 | null | https://arxiv.org/abs/2306.00851v1 | https://arxiv.org/pdf/2306.00851v1.pdf | Learning Sampling Dictionaries for Efficient and Generalizable Robot Motion Planning with Transformers | Motion planning is integral to robotics applications such as autonomous driving, surgical robots, and industrial manipulators. Existing planning methods lack scalability to higher-dimensional spaces, while recent learning based planners have shown promise in accelerating sampling-based motion planners (SMP) but lack ge... | ['Michael Yip', 'Ahmed H Qureshi', 'Jacob J Johnson'] | 2023-06-01 | null | null | null | null | ['motion-planning'] | ['robots'] | [ 4.61966880e-02 5.90281069e-01 -4.77732122e-01 -1.78536270e-02
-9.21415150e-01 -2.51040727e-01 4.95219231e-01 -1.21537104e-01
-4.00849432e-01 7.25176752e-01 2.06620514e-01 -4.88838434e-01
-4.83164579e-01 -8.70064795e-01 -8.80519092e-01 -6.85798645e-01
-2.75930434e-01 1.27872348e+00 3.80961806e-01 -4.11431909... | [4.67059850692749, 1.0042400360107422] |
04506bd7-96b2-4e9b-8fad-5708c5dbd69e | improving-analytical-tomographic | 1609.06604 | null | http://arxiv.org/abs/1609.06604v1 | http://arxiv.org/pdf/1609.06604v1.pdf | Improving analytical tomographic reconstructions through consistency conditions | This work introduces and characterizes a fast parameterless filter based on
the Helgason-Ludwig consistency conditions, used to improve the accuracy of
analytical reconstructions of tomographic undersampled datasets. The filter,
acting in the Radon domain, extrapolates intermediate projections between those
existing. T... | ['Marco Stampanoni', 'Filippo Arcadu', 'Jakob Vogel', 'Federica Marone'] | 2016-09-21 | null | null | null | null | ['tomographic-reconstructions'] | ['medical'] | [ 1.23778023e-01 1.75927907e-01 4.17219013e-01 -1.98433772e-01
-3.56805384e-01 6.49601594e-02 6.05707943e-01 -3.22219878e-01
-6.71358824e-01 1.06968939e+00 3.82091939e-01 -2.54234612e-01
-1.21310458e-01 -9.77826893e-01 -5.00396490e-01 -6.12992108e-01
-8.59404448e-03 7.11254776e-01 6.12507880e-01 5.38630784... | [12.810315132141113, -2.756901979446411] |
af0a4b4a-dab9-40ba-bb49-0e02ba28fdf3 | neural-interpretation-of-generic-source-code | 2304.00989 | null | https://arxiv.org/abs/2304.00989v1 | https://arxiv.org/pdf/2304.00989v1.pdf | Neural Interpretation of Generic Source Code | Can a generic (Python) program be executed statement-by-statement by neural networks composed according to the source code? We formulate the Abstract Neural Execution Problem and introduce Neural Interpretation, the first neural model that abstractly executes generic source code, where every variable has a vector encod... | ['Jin Tian', 'Yaojie Hu'] | 2023-03-23 | null | null | null | null | ['variable-misuse'] | ['computer-code'] | [ 2.94099122e-01 5.33806026e-01 -5.89855075e-01 -6.51962340e-01
-1.69444352e-01 -4.58741158e-01 2.14321077e-01 -8.88331160e-02
-7.02837482e-02 6.63602293e-01 1.93364948e-01 -1.12923610e+00
3.79595131e-01 -1.19472599e+00 -1.37112927e+00 -2.96741396e-01
-3.55911762e-01 2.18194544e-01 -3.32436442e-01 -3.28178018... | [7.936130046844482, 7.586585521697998] |
1448a455-da47-4d66-ab80-5fc2d85bd148 | leveraging-long-and-short-term-information-in-1 | null | null | http://dx.doi.org/10.1109/tcyb.2019.2896766 | http://dx.doi.org/10.1109/tcyb.2019.2896766 | Leveraging Long and Short-Term Information in Content-Aware Movie Recommendation via Adversarial Training | Movie recommendation systems provide users with ranked lists of movies based on individual’s preferences and constraints. Two types of models are commonly used to generate ranking results: 1) long-term models and 2) session-based models. The long-term-based models represent the interactions between users and movies tha... | ['and Ying Shen', 'Xiaojun Chen', 'Zhou Zhao', 'Jianbo Ye', 'Min Yang', 'Benyou Wang', 'Wei Zhao'] | 2020-01-01 | null | null | null | ieee-transactions-on-cybernetics-2020-1 | ['movie-recommendation'] | ['miscellaneous'] | [-6.85567632e-02 -6.38075233e-01 -2.62416989e-01 -7.34150231e-01
-4.63049948e-01 -9.78280842e-01 6.37006998e-01 -3.86081278e-01
-2.86346227e-01 7.07502246e-01 4.66632426e-01 -6.21009618e-02
-3.68102103e-01 -9.43136573e-01 -6.97927833e-01 -6.89028382e-01
-2.13418230e-01 3.77417207e-01 2.71685570e-01 -6.97552204... | [10.120061874389648, 5.596893787384033] |
03b69d89-e27c-4214-baf9-481e1c324bd7 | interpretable-edge-enhancement-and | 2209.09483 | null | https://arxiv.org/abs/2209.09483v1 | https://arxiv.org/pdf/2209.09483v1.pdf | Interpretable Edge Enhancement and Suppression Learning for 3D Point Cloud Segmentation | 3D point clouds can flexibly represent continuous surfaces and can be used for various applications; however, the lack of structural information makes point cloud recognition challenging. Recent edge-aware methods mainly use edge information as an extra feature that describes local structures to facilitate learning. Al... | ['Masashi Matsuoka', 'Qiong Chang', 'Takayuki Shinohara', 'Kyoung-Sook Kim', 'Weimin WANG', 'Xin Liu', 'Haoyi Xiu'] | 2022-09-20 | null | null | null | null | ['scene-segmentation', 'point-cloud-segmentation'] | ['computer-vision', 'computer-vision'] | [-1.49552645e-02 1.15539849e-01 -4.00406688e-01 -3.61830115e-01
-2.33496383e-01 -6.20112658e-01 2.45996252e-01 3.45432684e-02
2.71243244e-01 2.31056958e-01 3.53617892e-02 -4.62908387e-01
-1.19695559e-01 -7.93358564e-01 -1.10396111e+00 -4.63151336e-01
-7.08788112e-02 2.83927232e-01 3.40717852e-01 -7.47794658... | [7.928774833679199, -3.3069980144500732] |
b9e060ef-0094-4084-94d8-190dc926b141 | color-constancy-by-reweighting-image-feature | 1806.09248 | null | https://arxiv.org/abs/1806.09248v3 | https://arxiv.org/pdf/1806.09248v3.pdf | Color Constancy by Reweighting Image Feature Maps | In this study, a novel illuminant color estimation framework is proposed for computational color constancy, which incorporates the high representational capacity of deep-learning-based models and the great interpretability of assumption-based models. The well-designed building block, feature map reweight unit (ReWU), h... | ['Zhengnan Ye', 'Jueqin Qiu', 'Haisong Xu'] | 2018-06-25 | null | null | null | null | ['color-constancy'] | ['computer-vision'] | [-3.99200976e-01 -4.13522333e-01 -1.17878713e-01 -3.78583342e-01
-7.05618739e-01 -2.47394249e-01 4.88884151e-01 -2.21757352e-01
-1.24959543e-01 8.36297333e-01 -1.52605459e-01 -2.06027806e-01
4.84553762e-02 -7.22761333e-01 -6.78423703e-01 -8.67603660e-01
1.44506022e-01 -3.56444158e-02 -6.65687099e-02 1.45988300... | [10.396772384643555, -2.592088222503662] |
4d6d3fc4-c1f5-4908-a6d7-6a4e57bed9e9 | identification-of-conditional-causal-effects | null | null | http://papers.nips.cc/paper/9327-identification-of-conditional-causal-effects-under-markov-equivalence | http://papers.nips.cc/paper/9327-identification-of-conditional-causal-effects-under-markov-equivalence.pdf | Identification of Conditional Causal Effects under Markov Equivalence | Causal identification is the problem of deciding whether a post-interventional distribution is computable from a combination of qualitative knowledge about the data-generating process, which is encoded in a causal diagram, and an observational distribution. A generalization of this problem restricts the qualitative kno... | ['Amin Jaber', 'Jiji Zhang', 'Elias Bareinboim'] | 2019-12-01 | null | null | null | neurips-2019-12 | ['causal-identification'] | ['reasoning'] | [ 4.91395324e-01 5.44445872e-01 -9.10477757e-01 -4.72438008e-01
-3.76409441e-01 -7.79941559e-01 9.14141476e-01 6.05020523e-01
1.13637961e-01 1.16685593e+00 6.59044325e-01 -1.15327263e+00
-8.42263103e-01 -9.71412003e-01 -8.34571719e-01 -3.55014235e-01
-4.84115392e-01 5.06332874e-01 1.29943177e-01 4.63761270... | [8.061615943908691, 5.559758186340332] |
4e78ba88-15f5-48af-82ac-1b79ffbf8bcd | audio-captioning-with-composition-of-acoustic | 2105.06355 | null | https://arxiv.org/abs/2105.06355v1 | https://arxiv.org/pdf/2105.06355v1.pdf | Audio Captioning with Composition of Acoustic and Semantic Information | Generating audio captions is a new research area that combines audio and natural language processing to create meaningful textual descriptions for audio clips. To address this problem, previous studies mostly use the encoder-decoder based models without considering semantic information. To fill this gap, we present a n... | ['Mustafa Sert', 'Ayşegül Özkaya Eren'] | 2021-05-13 | null | null | null | null | ['audio-captioning'] | ['audio'] | [ 4.09102112e-01 2.52992123e-01 1.27667580e-02 -4.07948971e-01
-1.39025557e+00 -2.61631548e-01 2.22988829e-01 5.85669391e-02
-1.73645634e-02 6.35577738e-01 1.13884616e+00 3.50377917e-01
2.78389513e-01 -4.78988707e-01 -9.96902585e-01 -2.44398922e-01
-1.27846450e-01 1.57217041e-01 -1.62098687e-02 -2.14169040... | [15.290803909301758, 4.910626411437988] |
c93c6553-4c54-431f-8f83-728749f4c115 | improved-descriptors-for-patch-matching-and | 1701.06854 | null | http://arxiv.org/abs/1701.06854v4 | http://arxiv.org/pdf/1701.06854v4.pdf | Improved Descriptors for Patch Matching and Reconstruction | We propose a convolutional neural network (ConvNet) based approach for
learning local image descriptors which can be used for significantly improved
patch matching and 3D reconstructions. A multi-resolution ConvNet is used for
learning keypoint descriptors. We also propose a new dataset consisting of an
order of magnit... | ['Sharat Chandran', 'Rahul Mitra', 'Arjun Jain', 'Shuaib Ahmed', 'Sanath Narayan', 'Jiakai Zhang'] | 2017-01-24 | null | null | null | null | ['patch-matching'] | ['computer-vision'] | [-8.97324234e-02 -6.59134209e-01 -2.52125598e-02 -4.99077857e-01
-9.61827636e-01 -5.44473946e-01 9.52063799e-01 9.62970331e-02
-4.82379347e-01 3.90084386e-01 3.25625122e-01 4.39295769e-01
-2.58734912e-01 -8.23141456e-01 -6.81562483e-01 -6.13672197e-01
-4.25338484e-02 2.97727466e-01 4.58093137e-01 -4.63985741... | [8.198591232299805, -1.9473379850387573] |
489fae3d-112f-4433-bcf3-f6e361c01b05 | interpretable-clustering-on-dynamic-graphs | 2012.08740 | null | https://arxiv.org/abs/2012.08740v2 | https://arxiv.org/pdf/2012.08740v2.pdf | Interpretable Clustering on Dynamic Graphs with Recurrent Graph Neural Networks | We study the problem of clustering nodes in a dynamic graph, where the connections between nodes and nodes' cluster memberships may change over time, e.g., due to community migration. We first propose a dynamic stochastic block model that captures these changes, and a simple decay-based clustering algorithm that cluste... | ['Carlee Joe-Wong', 'Yuhang Yao'] | 2020-12-16 | null | null | null | null | ['stochastic-block-model'] | ['graphs'] | [-2.45373935e-01 8.59278664e-02 -2.14384109e-01 -1.22167915e-01
-2.09778883e-02 -6.65967405e-01 4.69281435e-01 4.15895790e-01
-3.02033335e-01 2.24820837e-01 1.49590537e-01 -2.62614399e-01
-1.95359781e-01 -8.83786321e-01 -6.67764425e-01 -9.12727594e-01
-7.08786905e-01 9.88968134e-01 4.74994451e-01 -1.79699913... | [7.131778717041016, 5.782120704650879] |
9c8634fd-ccd5-460e-8fe7-e44c256dbb84 | clas-coordinating-multi-robot-manipulation | 2211.15824 | null | https://arxiv.org/abs/2211.15824v1 | https://arxiv.org/pdf/2211.15824v1.pdf | CLAS: Coordinating Multi-Robot Manipulation with Central Latent Action Spaces | Multi-robot manipulation tasks involve various control entities that can be separated into dynamically independent parts. A typical example of such real-world tasks is dual-arm manipulation. Learning to naively solve such tasks with reinforcement learning is often unfeasible due to the sample complexity and exploration... | ['Patrick van der Smagt', 'Maximilian Karl', 'Elie Aljalbout'] | 2022-11-28 | null | null | null | null | ['robot-manipulation'] | ['robots'] | [ 5.21924347e-02 1.76523343e-01 -1.66325778e-01 4.84904312e-02
-9.11391675e-01 -8.34116757e-01 6.87765002e-01 3.76215093e-02
-6.87850356e-01 1.07922292e+00 4.90132086e-02 -1.20734744e-01
-4.45411503e-01 -5.70553184e-01 -8.17681372e-01 -6.33197188e-01
-5.22099495e-01 1.06937456e+00 1.86911479e-01 -3.55255991... | [4.292304515838623, 1.3393892049789429] |
c5597686-3675-48f7-bb88-a3ee90e1f9b5 | evaluating-copy-blend-augmentation-for-low | 2103.05889 | null | https://arxiv.org/abs/2103.05889v1 | https://arxiv.org/pdf/2103.05889v1.pdf | Evaluating COPY-BLEND Augmentation for Low Level Vision Tasks | Region modification-based data augmentation techniques have shown to improve performance for high level vision tasks (object detection, semantic segmentation, image classification, etc.) by encouraging underlying algorithms to focus on multiple discriminative features. However, as these techniques destroy spatial relat... | ['Kyung-Soo Kim', 'Kuk-Jin Yoon', 'Sandeep Singh Sengar', 'Pranjay Shyam'] | 2021-03-10 | null | null | null | null | ['image-dehazing'] | ['computer-vision'] | [ 1.08434224e+00 -9.20387730e-02 2.96733111e-01 -2.46491641e-01
-7.44118392e-01 -4.61889118e-01 7.27636755e-01 2.56432444e-01
-6.47735596e-01 5.28761506e-01 1.26463294e-01 -7.31422380e-02
1.41219541e-01 -4.66410786e-01 -8.49494874e-01 -1.00647712e+00
2.06089392e-01 -2.44956240e-01 5.84410369e-01 -7.23588243... | [10.983824729919434, -1.9942022562026978] |
416c7640-9a9d-4670-9501-66afb82a3511 | enhancing-deep-knowledge-tracing-with | 2302.07942 | null | https://arxiv.org/abs/2302.07942v1 | https://arxiv.org/pdf/2302.07942v1.pdf | Enhancing Deep Knowledge Tracing with Auxiliary Tasks | Knowledge tracing (KT) is the problem of predicting students' future performance based on their historical interactions with intelligent tutoring systems. Recent studies have applied multiple types of deep neural networks to solve the KT problem. However, there are two important factors in real-world educational data t... | ['Jian Weng', 'Weiqi Luo', 'Boyu Gao', 'Shuyan Huang', 'Jiahao Chen', 'Qiongqiong Liu', 'Zitao Liu'] | 2023-02-14 | null | null | null | null | ['auxiliary-learning', 'knowledge-tracing'] | ['methodology', 'miscellaneous'] | [ 6.14603758e-02 1.06704324e-01 -2.02595428e-01 -4.15224731e-01
-3.53687465e-01 -6.14387453e-01 3.44799012e-01 3.94820273e-01
-3.91765893e-01 8.16967547e-01 1.90621048e-01 -7.12998450e-01
-6.36034489e-01 -9.38704610e-01 -7.15387166e-01 -2.44056568e-01
3.12314957e-01 1.55108392e-01 5.26509166e-01 -4.83853102... | [10.11495590209961, 7.153869152069092] |
093f747e-2218-409e-b73a-0ef6a8fad3f2 | investigating-lstms-for-joint-extraction-of | null | null | https://aclanthology.org/P16-1087 | https://aclanthology.org/P16-1087.pdf | Investigating LSTMs for Joint Extraction of Opinion Entities and Relations | null | ['Arzoo Katiyar', 'Claire Cardie'] | 2016-08-01 | null | null | null | acl-2016-8 | ['fine-grained-opinion-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... | [-7.44317626953125, 3.5877878665924072] |
a409b00c-60ec-4452-a177-3bbf6715daae | t-cell-receptor-protein-sequences-and-sparse | 2304.13145 | null | https://arxiv.org/abs/2304.13145v1 | https://arxiv.org/pdf/2304.13145v1.pdf | T Cell Receptor Protein Sequences and Sparse Coding: A Novel Approach to Cancer Classification | Cancer is a complex disease characterized by uncontrolled cell growth and proliferation. T cell receptors (TCRs) are essential proteins for the adaptive immune system, and their specific recognition of antigens plays a crucial role in the immune response against diseases, including cancer. The diversity and specificity... | ['Murray Patterson', 'Taslim Murad', 'Prakash Chourasia', 'Sarwan Ali', 'Zahra Tayebi'] | 2023-04-25 | null | null | null | null | ['specificity'] | ['natural-language-processing'] | [ 5.67733645e-01 -5.15963614e-01 -5.64350307e-01 -1.83490783e-01
-7.78674483e-01 -5.72798193e-01 4.06040579e-01 7.54170179e-01
-4.24272031e-01 7.51750827e-01 4.20546949e-01 -2.79223740e-01
-8.20541903e-02 -6.77357137e-01 -2.78743953e-01 -1.28459346e+00
1.34321228e-01 6.31421804e-01 1.16251968e-02 -1.97120860... | [4.8468098640441895, 5.58116340637207] |
dab2d799-bf87-41b9-bd0f-c2d5cb317f30 | impact-of-asr-on-alzheimers-disease-detection | 1904.01684 | null | https://arxiv.org/abs/1904.01684v3 | https://arxiv.org/pdf/1904.01684v3.pdf | Impact of ASR on Alzheimer's Disease Detection: All Errors are Equal, but Deletions are More Equal than Others | Automatic Speech Recognition (ASR) is a critical component of any fully-automated speech-based dementia detection model. However, despite years of speech recognition research, little is known about the impact of ASR accuracy on dementia detection. In this paper, we experiment with controlled amounts of artificially gen... | ['Ksenia Shkaruta', 'Jekaterina Novikova', 'Aparna Balagopalan'] | 2019-04-02 | null | null | null | null | ['alzheimer-s-disease-detection'] | ['medical'] | [ 4.77382779e-01 2.15779260e-01 2.79773593e-01 -4.37416762e-01
-8.75736833e-01 -1.57200128e-01 7.67287433e-01 3.65513474e-01
-8.48176241e-01 4.80423868e-01 1.01005137e+00 -5.49296200e-01
-2.28292510e-01 -5.17969489e-01 -1.87195942e-01 -6.68862239e-02
1.23500608e-01 3.43126565e-01 3.57000768e-01 -2.05661699... | [13.963807106018066, 5.457158088684082] |
9db86e7f-bb4b-4d6b-a1a5-c18854b9f5f3 | examining-temporalities-on-stance-detection | 2304.04806 | null | https://arxiv.org/abs/2304.04806v2 | https://arxiv.org/pdf/2304.04806v2.pdf | Examining Temporalities on Stance Detection towards COVID-19 Vaccination | Previous studies have highlighted the importance of vaccination as an effective strategy to control the transmission of the COVID-19 virus. It is crucial for policymakers to have a comprehensive understanding of the public's stance towards vaccination on a large scale. However, attitudes towards COVID-19 vaccination, s... | ['Xingyi Song', 'Kalina Bontcheva', 'Mali Jin', 'Yida Mu'] | 2023-04-10 | null | null | null | null | ['stance-detection'] | ['natural-language-processing'] | [ 7.67584816e-02 -5.95881268e-02 -5.50779998e-01 -4.11377549e-01
-4.10309941e-01 -7.89348662e-01 1.07889581e+00 9.50832665e-01
-7.96152890e-01 5.88417590e-01 6.28147840e-01 -7.81937480e-01
9.28633958e-02 -8.71141493e-01 -6.03005290e-01 -4.84332234e-01
-1.10451784e-02 5.85563600e-01 2.99523890e-01 -5.35821259... | [8.623737335205078, 9.809857368469238] |
bc074a6e-d578-4b88-815d-ca6ddca1e8ef | enhancement-of-seismic-imaging-an-innovative | 1909.06016 | null | http://arxiv.org/abs/1909.06016v1 | http://arxiv.org/pdf/1909.06016v1.pdf | Enhancement of seismic imaging: An innovative deep learning approach | Enhancing the frequency bandwidth of the seismic data is always the pursuance
at the geophysical community. High resolution of seismic data provides the key
resource to extract detailed stratigraphic knowledge. Here, a novel approach,
based on deep learning model, is introduced by extracting reflections from well
log d... | [] | 2019-09-13 | null | null | null | null | ['seismic-imaging'] | ['miscellaneous'] | [-1.67547569e-01 1.58593226e-02 2.67763406e-01 -2.71848440e-01
-1.09144902e+00 -2.81670153e-01 3.66078973e-01 4.14560474e-02
-3.87384385e-01 9.59106982e-01 4.72304821e-01 -3.09122843e-03
-7.54164577e-01 -1.27104211e+00 -7.16187179e-01 -1.09406447e+00
-6.04836047e-01 1.34035423e-01 1.95540071e-01 -3.26216161... | [6.8802289962768555, 2.5773119926452637] |
1a5875cd-de89-4a07-a0dd-e14e738d422d | a-corpus-of-tables-in-full-text-biomedical | null | null | https://aclanthology.org/W16-5108 | https://aclanthology.org/W16-5108.pdf | A Corpus of Tables in Full-Text Biomedical Research Publications | The development of text mining techniques for biomedical research literature has received increased attention in recent times. However, most of these techniques focus on prose, while much important biomedical data reside in tables. In this paper, we present a corpus created to serve as a gold standard for the developme... | ['Tatyana Shmanina', 'Ai Lee Cheam', 'Lawrence Cavedon', 'Thomas Bochynek', 'Ingrid Zukerman'] | 2016-12-01 | null | null | null | ws-2016-12 | ['table-annotation', 'table-annotation'] | ['knowledge-base', 'natural-language-processing'] | [ 3.04343551e-01 1.48653418e-01 -4.90695715e-01 -4.99313802e-01
-9.33251083e-01 -4.66062248e-01 1.72120571e-01 9.90784645e-01
-4.89869237e-01 1.08010650e+00 4.65128362e-01 -5.80905259e-01
-1.63743809e-01 -5.29045045e-01 -2.64596224e-01 -4.97790962e-01
4.25724149e-01 5.72543442e-01 4.15190160e-02 -7.39772245... | [8.574207305908203, 8.673831939697266] |
42165d23-834d-4bf7-acd8-3746d80c4b42 | correntropy-based-logistic-regression-with | 2207.09693 | null | https://arxiv.org/abs/2207.09693v1 | https://arxiv.org/pdf/2207.09693v1.pdf | Correntropy-Based Logistic Regression with Automatic Relevance Determination for Robust Sparse Brain Activity Decoding | Recent studies have utilized sparse classifications to predict categorical variables from high-dimensional brain activity signals to expose human's intentions and mental states, selecting the relevant features automatically in the model training process. However, existing sparse classification models will likely be pro... | ['Yasuharu Koike', 'Natsue Yoshimura', 'Yuxi Shi', 'Badong Chen', 'Yuanhao Li'] | 2022-07-20 | null | null | null | null | ['brain-decoding', 'brain-decoding'] | ['medical', 'miscellaneous'] | [ 5.17841041e-01 -3.75944555e-01 2.05425352e-01 -4.92727816e-01
-2.10809618e-01 1.32703006e-01 3.90453041e-01 -6.64706826e-02
-2.28712827e-01 8.55376363e-01 4.46095288e-01 3.07235777e-01
-4.81125653e-01 -4.80463415e-01 -3.40499580e-01 -7.93999314e-01
-1.61772609e-01 -1.16845131e-01 -2.82332301e-01 1.86320335... | [13.023978233337402, 3.461026191711426] |
f6606a03-ba05-4c7f-84c7-676dfcb6cd90 | no-intruder-no-validity-evaluation-criteria | 2103.09263 | null | https://arxiv.org/abs/2103.09263v1 | https://arxiv.org/pdf/2103.09263v1.pdf | No Intruder, no Validity: Evaluation Criteria for Privacy-Preserving Text Anonymization | For sensitive text data to be shared among NLP researchers and practitioners, shared documents need to comply with data protection and privacy laws. There is hence a growing interest in automated approaches for text anonymization. However, measuring such methods' performance is challenging: missing a single identifying... | ['Bennett Kleinberg', 'Maximilian Mozes'] | 2021-03-16 | null | null | null | null | ['text-anonymization'] | ['natural-language-processing'] | [ 2.38688067e-01 1.57967985e-01 -1.18131965e-01 -6.64624333e-01
-8.43331993e-01 -1.09615278e+00 5.96830308e-01 7.95703590e-01
-4.79095727e-01 9.11794066e-01 6.20611966e-01 -1.09957382e-01
-1.36716038e-01 -7.52588689e-01 -2.74955899e-01 -2.45221749e-01
4.01672721e-01 4.59213436e-01 -3.27505767e-01 3.57188940... | [6.152437210083008, 6.914242267608643] |
f822b1c7-ae01-4ec6-91be-2a751abfd6fe | an-investigation-for-implicatures-in-chinese | null | null | https://aclanthology.org/W14-2603 | https://aclanthology.org/W14-2603.pdf | An Investigation for Implicatures in Chinese : Implicatures in Chinese and in English are similar ! | null | ['Janyce Wiebe', 'Lingjia Deng'] | 2014-06-01 | null | null | null | ws-2014-6 | ['implicatures'] | ['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... | [-7.469485759735107, 3.5449655055999756] |
c36be268-93c8-44ed-9130-2a5e03a1d5e7 | skit-s2i-an-indian-accented-speech-to-intent | 2212.13015 | null | https://arxiv.org/abs/2212.13015v1 | https://arxiv.org/pdf/2212.13015v1.pdf | Skit-S2I: An Indian Accented Speech to Intent dataset | Conventional conversation assistants extract text transcripts from the speech signal using automatic speech recognition (ASR) and then predict intent from the transcriptions. Using end-to-end spoken language understanding (SLU), the intents of the speaker are predicted directly from the speech signal without requiring ... | ['Kumarmanas Nethil', 'Swaraj Dalmia', 'Shangeth Rajaa'] | 2022-12-26 | null | null | null | null | ['spoken-language-understanding', 'intent-classification', 'spoken-language-understanding'] | ['natural-language-processing', 'natural-language-processing', 'speech'] | [ 1.96902335e-01 3.91919643e-01 -7.44040012e-02 -1.13093841e+00
-1.32485068e+00 -6.58917844e-01 2.72437215e-01 -4.74904805e-01
-2.74603993e-01 4.09999400e-01 1.10261810e+00 -5.27739644e-01
4.15150642e-01 -4.58996743e-02 -5.07531166e-01 -2.70403355e-01
1.58929333e-01 7.18714833e-01 -4.56629723e-01 -5.12628257... | [14.070131301879883, 6.988542556762695] |
502920b0-1814-41ea-8961-713e30705432 | improving-accuracy-and-explainability-of | 2209.09102 | null | https://arxiv.org/abs/2209.09102v1 | https://arxiv.org/pdf/2209.09102v1.pdf | Improving Accuracy and Explainability of Online Handwriting Recognition | Handwriting recognition technology allows recognizing a written text from a given data. The recognition task can target letters, symbols, or words, and the input data can be a digital image or recorded by various sensors. A wide range of applications from signature verification to electronic document processing can be ... | ['Koray Karabina', 'Jonathan Gold', 'Steven Chang', 'Hilda Azimi'] | 2022-09-14 | null | null | null | null | ['handwriting-recognition'] | ['computer-vision'] | [ 3.35289359e-01 -4.43926424e-01 -3.14161062e-01 -5.57366192e-01
-3.39338928e-01 -6.66194201e-01 4.76771027e-01 -4.01704729e-01
-1.65800944e-01 4.12159294e-01 -8.46336782e-02 -3.68378133e-01
-3.39444906e-01 -9.07268763e-01 -5.44133306e-01 -7.37741709e-01
8.97204354e-02 2.54390121e-01 -1.60183147e-01 -1.68069929... | [11.869140625, 2.530611991882324] |
77dc80cb-be9a-44ef-ae7e-60b6437e5183 | dynamic-quantized-consensus-under-dos-attacks | 2306.00279 | null | https://arxiv.org/abs/2306.00279v1 | https://arxiv.org/pdf/2306.00279v1.pdf | Dynamic quantized consensus under DoS attacks: Towards a tight zooming-out factor | This paper deals with dynamic quantized consensus of dynamical agents in a general form under packet losses induced by Denial-of-Service (DoS) attacks. The communication channel has limited bandwidth and hence the transmitted signals over the network are subject to quantization. To deal with agent's output, an observer... | ['Shengyuan Xu', 'Hideaki Ishii', 'Maopeng Ran', 'Shuai Feng'] | 2023-06-01 | null | null | null | null | ['quantization'] | ['methodology'] | [-1.21184856e-01 2.26287335e-01 -2.29611710e-01 1.62369147e-01
-3.74616951e-01 -6.84273362e-01 3.32492292e-01 1.85174704e-01
-5.65568388e-01 7.23132312e-01 -4.46445763e-01 -3.21362793e-01
1.01797633e-01 -1.06156528e+00 -3.59641612e-01 -1.21699846e+00
-6.13127589e-01 2.41333842e-01 4.99954760e-01 -4.60655749... | [5.218894958496094, 2.6680984497070312] |
943b3a8e-518b-427b-ad2b-385a22465111 | magic123-one-image-to-high-quality-3d-object | 2306.17843 | null | https://arxiv.org/abs/2306.17843v1 | https://arxiv.org/pdf/2306.17843v1.pdf | Magic123: One Image to High-Quality 3D Object Generation Using Both 2D and 3D Diffusion Priors | We present Magic123, a two-stage coarse-to-fine approach for high-quality, textured 3D meshes generation from a single unposed image in the wild using both2D and 3D priors. In the first stage, we optimize a neural radiance field to produce a coarse geometry. In the second stage, we adopt a memory-efficient differentiab... | ['Bernard Ghanem', 'Sergey Tulyakov', 'Peter Wonka', 'Ivan Skorokhodov', 'Hsin-Ying Lee', 'Bing Li', 'Aliaksandr Siarohin', 'Jian Ren', 'Abdullah Hamdi', 'Jinjie Mai', 'Guocheng Qian'] | 2023-06-30 | null | null | null | null | ['image-to-3d'] | ['computer-vision'] | [ 2.28162020e-01 1.10774867e-01 2.16134638e-01 -2.38279641e-01
-8.02912414e-01 -5.35662651e-01 6.38211191e-01 -2.58508086e-01
1.67186305e-01 6.61245465e-01 9.90123823e-02 1.78538635e-03
4.16525491e-02 -9.71410453e-01 -1.02011776e+00 -6.13301814e-01
1.84663162e-01 3.84151608e-01 6.15215562e-02 -1.15073472... | [9.28408432006836, -3.1358799934387207] |
9231585c-fe6b-4fe4-9095-eb599b46b225 | story-cloze-ending-selection-baselines-and | 1703.04330 | null | http://arxiv.org/abs/1703.04330v1 | http://arxiv.org/pdf/1703.04330v1.pdf | Story Cloze Ending Selection Baselines and Data Examination | This paper describes two supervised baseline systems for the Story Cloze Test
Shared Task (Mostafazadeh et al., 2016a). We first build a classifier using
features based on word embeddings and semantic similarity computation. We
further implement a neural LSTM system with different encoding strategies that
try to model ... | ['Anette Frank', 'Todor Mihaylov'] | 2017-03-13 | story-cloze-ending-selection-baselines-and-1 | https://aclanthology.org/W17-0913 | https://aclanthology.org/W17-0913.pdf | ws-2017-4 | ['cloze-test'] | ['natural-language-processing'] | [-1.52577534e-01 -5.52315190e-02 -3.02671313e-01 -4.96335566e-01
-9.85430300e-01 -3.35719287e-01 6.74281895e-01 4.99314696e-01
-6.16847277e-01 2.90794760e-01 9.30098295e-01 3.91803496e-02
-1.75562158e-01 -7.75983751e-01 -3.74339104e-01 -4.18499112e-01
-1.97964489e-01 4.05712992e-01 -1.95622653e-01 -4.38009739... | [11.279988288879395, 8.909173011779785] |
2749e795-e271-47e5-aec7-f1225fe9b9b0 | class-aware-visual-prompt-tuning-for-vision | 2208.08340 | null | https://arxiv.org/abs/2208.08340v4 | https://arxiv.org/pdf/2208.08340v4.pdf | Dual Modality Prompt Tuning for Vision-Language Pre-Trained Model | With the emergence of large pre-trained vison-language model like CLIP, transferable representations can be adapted to a wide range of downstream tasks via prompt tuning. Prompt tuning tries to probe the beneficial information for downstream tasks from the general knowledge stored in the pre-trained model. A recently p... | ['Yanning Zhang', 'Peng Wang', 'Guoqiang Liang', 'Shizhou Zhang', 'De Cheng', 'Qirui Wu', 'Yinghui Xing'] | 2022-08-17 | null | null | null | null | ['general-knowledge'] | ['miscellaneous'] | [ 1.65061489e-01 -8.93212408e-02 -2.54732013e-01 -4.72446591e-01
-7.66721785e-01 -5.59047401e-01 8.20236742e-01 1.12352304e-01
-3.34362328e-01 4.56401646e-01 5.18278539e-01 -5.89939989e-02
-4.49724495e-03 -5.62729776e-01 -8.26291025e-01 -9.65617776e-01
4.40621048e-01 -8.10492039e-02 1.01306975e-01 -1.64663240... | [10.192989349365234, 1.8232231140136719] |
f896ed91-bc0e-4890-86f2-d4e9d8a72e00 | multi-level-and-multi-scale-feature | 1703.01793 | null | http://arxiv.org/abs/1703.01793v2 | http://arxiv.org/pdf/1703.01793v2.pdf | Multi-Level and Multi-Scale Feature Aggregation Using Pre-trained Convolutional Neural Networks for Music Auto-tagging | Music auto-tagging is often handled in a similar manner to image
classification by regarding the 2D audio spectrogram as image data. However,
music auto-tagging is distinguished from image classification in that the tags
are highly diverse and have different levels of abstractions. Considering this
issue, we propose a ... | ['Juhan Nam', 'Jongpil Lee'] | 2017-03-06 | null | null | null | null | ['music-auto-tagging'] | ['music'] | [ 2.30117619e-01 -3.79888892e-01 7.94343278e-02 -2.96342790e-01
-8.43176126e-01 -6.76588655e-01 4.02998894e-01 -3.11324373e-03
-5.42034626e-01 3.31744105e-01 3.64960790e-01 3.00769240e-01
-8.09169337e-02 -7.71849573e-01 -8.17069232e-01 -4.40903604e-01
-2.37837538e-01 1.23288296e-01 1.85845390e-01 1.62498370... | [15.722698211669922, 5.206240177154541] |
4a343228-9587-4284-858a-0da1390f9d69 | random-access-neural-compression-of-material | 2305.17105 | null | https://arxiv.org/abs/2305.17105v1 | https://arxiv.org/pdf/2305.17105v1.pdf | Random-Access Neural Compression of Material Textures | The continuous advancement of photorealism in rendering is accompanied by a growth in texture data and, consequently, increasing storage and memory demands. To address this issue, we propose a novel neural compression technique specifically designed for material textures. We unlock two more levels of detail, i.e., 16x ... | ['Aaron Lefohn', 'Pontus Ebelin', 'Tomas Akenine-Möller', 'Bartlomiej Wronski', 'Marco Salvi', 'Karthik Vaidyanathan'] | 2023-05-26 | null | null | null | null | ['image-compression'] | ['computer-vision'] | [ 5.16497850e-01 -6.75910041e-02 1.08459052e-02 1.41790286e-01
-3.68743747e-01 -2.59938955e-01 4.77802813e-01 2.42644578e-01
-2.78017521e-01 4.16096509e-01 7.63662830e-02 -5.34428477e-01
2.15588942e-01 -1.25651848e+00 -9.19688344e-01 -6.77317202e-01
-4.21335287e-02 4.18340951e-01 5.26316822e-01 -2.08306387... | [11.259970664978027, -1.3943321704864502] |
c4a7aeae-511b-4c9d-8ac5-b956792ba283 | towards-reducing-aleatoric-uncertainty-for | 2110.11012 | null | https://arxiv.org/abs/2110.11012v2 | https://arxiv.org/pdf/2110.11012v2.pdf | Towards Reducing Aleatoric Uncertainty for Medical Imaging Tasks | In safety-critical applications like medical diagnosis, certainty associated with a model's prediction is just as important as its accuracy. Consequently, uncertainty estimation and reduction play a crucial role. Uncertainty in predictions can be attributed to noise or randomness in data (aleatoric) and incorrect model... | ['Deepti R. Bathula', 'Narayanan C. Krishnan', 'Abhishek Singh Sambyal'] | 2021-10-21 | null | null | null | null | ['decision-making-under-uncertainty', 'decision-making-under-uncertainty'] | ['medical', 'reasoning'] | [ 6.30678356e-01 7.83877671e-01 1.63297839e-02 -8.30921948e-01
-1.10126901e+00 -2.35155508e-01 5.36499977e-01 6.55809045e-01
-6.11428440e-01 9.91097331e-01 1.18493229e-01 -3.46953005e-01
-2.70465255e-01 -5.64602196e-01 -8.49239588e-01 -7.38806963e-01
4.12357807e-01 7.39308119e-01 2.02866495e-01 5.07724404... | [14.380502700805664, -2.0491151809692383] |
d6faf967-0e76-4a96-ac0d-f5d9367f16a2 | sparse-range-constrained-learning-and-its | 1807.10571 | null | http://arxiv.org/abs/1807.10571v1 | http://arxiv.org/pdf/1807.10571v1.pdf | Sparse Range-constrained Learning and Its Application for Medical Image Grading | Sparse learning has been shown to be effective in solving many real-world
problems. Finding sparse representations is a fundamentally important topic in
many fields of science including signal processing, computer vision, genome
study and medical imaging. One important issue in applying sparse
representation is to find... | ['Cheng Jun'] | 2018-07-11 | null | null | null | null | ['sparse-learning'] | ['methodology'] | [ 2.92559117e-01 -2.98622489e-01 -1.34817094e-01 -3.16301674e-01
-6.93243980e-01 -1.75714791e-01 -2.25154217e-02 2.76188016e-01
-1.40267879e-01 6.03759527e-01 3.76853317e-01 1.45858064e-01
-5.40531576e-01 -7.51130283e-01 -1.38421580e-01 -8.51877213e-01
1.52377069e-01 4.67245609e-01 1.88255683e-01 5.28833903... | [12.465533256530762, 0.35658663511276245] |
2648aab0-4c54-4188-a79b-72bdda4c9ef0 | generalizability-of-deep-adult-lung | 2211.02475 | null | https://arxiv.org/abs/2211.02475v2 | https://arxiv.org/pdf/2211.02475v2.pdf | Generalizability of Deep Adult Lung Segmentation Models to the Pediatric Population: A Retrospective Study | Lung segmentation in chest X-rays (CXRs) is an important prerequisite for improving the specificity of diagnoses of cardiopulmonary diseases in a clinical decision support system. Current deep learning models for lung segmentation are trained and evaluated on CXR datasets in which the radiographic projections are captu... | ['Sameer Antani', 'Zhiyun Xue', 'Ghada Zamzmi', 'Feng Yang', 'Sivaramakrishnan Rajaraman'] | 2022-11-04 | null | null | null | null | ['ms-ssim'] | ['computer-vision'] | [ 9.17315111e-02 -3.88728678e-02 -1.38134003e-01 -3.66274953e-01
-7.40402222e-01 -6.38161480e-01 3.09773266e-01 3.56491774e-01
-4.33667094e-01 5.45318127e-01 1.40938535e-01 -6.36303067e-01
-3.24261427e-01 -6.94292247e-01 -3.43680203e-01 -7.02967346e-01
-1.63901551e-03 7.80291557e-01 4.97276247e-01 2.15764359... | [15.158475875854492, -2.086452007293701] |
a0d61da0-eb62-4414-b032-30c774729015 | hybrid-quantum-neural-network-for-drug | 2211.05777 | null | https://arxiv.org/abs/2211.05777v2 | https://arxiv.org/pdf/2211.05777v2.pdf | Hybrid quantum neural network for drug response prediction | Cancer is one of the leading causes of death worldwide. It is caused by a variety of genetic mutations, which makes every instance of the disease unique. Since chemotherapy can have extremely severe side effects, each patient requires a personalized treatment plan. Finding the dosages that maximize the beneficial effec... | ['Alexey Melnikov', 'Tatiana Tomashuk', 'Daria Kosichkina', 'Nurbolat Kenbayev', 'Mohammad Kordzanganeh', 'Asel Sagingalieva'] | 2022-11-10 | null | null | null | null | ['drug-response-prediction'] | ['medical'] | [ 3.80943954e-01 -2.13778481e-01 -4.48375195e-01 -2.28889957e-01
-8.15735519e-01 -4.22152728e-01 3.28869708e-02 7.93225408e-01
-4.64381903e-01 9.67835426e-01 -1.85887948e-01 -5.13303041e-01
-1.61862209e-01 -1.38851142e+00 -6.09898031e-01 -1.10412335e+00
1.00342825e-01 6.29318297e-01 -7.79657289e-02 -5.58602393... | [5.342650413513184, 5.396031379699707] |
a452f749-191c-451c-931c-2b30cab23f82 | multi-temporal-sentinel-1-and-2-data-fusion | 1807.09954 | null | http://arxiv.org/abs/1807.09954v1 | http://arxiv.org/pdf/1807.09954v1.pdf | Multi-temporal Sentinel-1 and -2 Data Fusion for Optical Image Simulation | In this paper, we present the optical image simulation from a synthetic
aperture radar (SAR) data using deep learning based methods. Two models, i.e.,
optical image simulation directly from the SAR data and from multi-temporal
SARoptical data, are proposed to testify the possibilities. The deep learning
based methods t... | ['Naoto Yokoya', 'Wei He'] | 2018-07-26 | null | null | null | null | ['cloud-removal'] | ['computer-vision'] | [ 3.10029328e-01 -3.73084724e-01 5.26840448e-01 -3.19559753e-01
-6.78829193e-01 -5.77193081e-01 6.75204694e-01 -8.63803148e-01
-3.01567852e-01 9.67033386e-01 -7.49529451e-02 -4.33702976e-01
-1.78456366e-01 -1.08338141e+00 -6.03881001e-01 -1.10007870e+00
-1.63814351e-01 3.14038754e-01 1.70911476e-01 -3.34426910... | [10.053825378417969, -2.0603463649749756] |
50bd7629-0f7f-4232-a59c-e082d68a777c | the-economics-of-recommender-systems-evidence | 2211.14219 | null | https://arxiv.org/abs/2211.14219v1 | https://arxiv.org/pdf/2211.14219v1.pdf | The Economics of Recommender Systems: Evidence from a Field Experiment on MovieLens | We conduct a field experiment on a movie-recommendation platform to identify if and how recommendations affect consumption. We use within-consumer randomization at the good level and elicit beliefs about unconsumed goods to disentangle exposure from informational effects. We find recommendations increase consumption be... | ['Joseph Konstan', 'Ruoyan Kong', 'Daniel Kluver', 'Duarte Goncalves', 'Guy Aridor'] | 2022-11-25 | null | null | null | null | ['movie-recommendation'] | ['miscellaneous'] | [-3.83171707e-01 3.09230268e-01 -1.16517448e+00 -3.45964342e-01
-2.17781335e-01 -1.09738290e+00 5.13034940e-01 3.89735818e-01
-5.43624997e-01 9.17361304e-02 1.04176676e+00 -9.92380381e-01
-1.37400748e-02 -1.11665380e+00 -8.97502482e-01 -5.61022460e-02
2.19233617e-01 -4.24389988e-01 -3.19104105e-01 -8.62049088... | [9.54659366607666, 5.619514465332031] |
7e40f644-9fc6-45d6-96b8-80b6c1e83f22 | vision-transformer-based-video-hashing | 2112.08117 | null | https://arxiv.org/abs/2112.08117v2 | https://arxiv.org/pdf/2112.08117v2.pdf | Vision Transformer Based Video Hashing Retrieval for Tracing the Source of Fake Videos | In recent years, the spread of fake videos has brought great influence on individuals and even countries. It is important to provide robust and reliable results for fake videos. The results of conventional detection methods are not reliable and not robust for unseen videos. Another alternative and more effective way is... | ['Xuyuan Lai', 'Jinchuan Li', 'Yun Cao', 'Xianfeng Zhao', 'Pengfei Pei'] | 2021-12-15 | null | null | null | null | ['video-inpainting'] | ['computer-vision'] | [-1.44351050e-01 -4.87557441e-01 -2.72777323e-02 -1.42930165e-01
-5.61409414e-01 -5.88040471e-01 3.85686129e-01 -2.52490103e-01
-2.08078131e-01 9.19256985e-01 -2.87262835e-02 -2.30563179e-01
3.10426444e-01 -6.29827142e-01 -8.61785591e-01 -4.79977667e-01
-9.10018384e-03 7.14100450e-02 3.23601812e-01 -2.10674524... | [12.521074295043945, 1.0634490251541138] |
5208e290-4c74-4820-b155-8edafcfbe4a2 | deep-attention-fusion-feature-for-speech | 2003.07544 | null | https://arxiv.org/abs/2003.07544v1 | https://arxiv.org/pdf/2003.07544v1.pdf | Deep Attention Fusion Feature for Speech Separation with End-to-End Post-filter Method | In this paper, we propose an end-to-end post-filter method with deep attention fusion features for monaural speaker-independent speech separation. At first, a time-frequency domain speech separation method is applied as the pre-separation stage. The aim of pre-separation stage is to separate the mixture preliminarily. ... | ['Jian-Hua Tao', 'Cunhang Fan', 'Bin Liu', 'Zhengqi Wen', 'Xuefei Liu', 'Jiangyan Yi'] | 2020-03-17 | null | null | null | null | ['deep-attention', 'deep-attention'] | ['computer-vision', 'natural-language-processing'] | [-3.42408232e-02 -4.13219035e-01 4.79670197e-01 -1.28541216e-01
-1.04078424e+00 -1.44983843e-01 4.78414185e-02 -1.06827691e-01
-3.80211115e-01 3.79147828e-01 4.46006149e-01 -6.24125935e-02
-3.01415801e-01 -3.41955841e-01 -2.25315556e-01 -1.07643747e+00
7.43346065e-02 -3.75467032e-01 8.87742937e-02 -2.62330890... | [14.905877113342285, 5.8133392333984375] |
c117e2cf-deee-4141-b778-91d3f2984a45 | an-artificial-life-simulation-library-based | 2304.13520 | null | https://arxiv.org/abs/2304.13520v1 | https://arxiv.org/pdf/2304.13520v1.pdf | An Artificial Life Simulation Library Based on Genetic Algorithm, 3-Character Genetic Code and Biological Hierarchy | Genetic algorithm (GA) is inspired by biological evolution of genetic organisms by optimizing the genotypic combinations encoded within each individual with the help of evolutionary operators, suggesting that GA may be a suitable model for studying real-life evolutionary processes. This paper describes the design of a ... | ['Maurice HT Ling'] | 2023-02-19 | null | null | null | null | ['artificial-life'] | ['miscellaneous'] | [ 4.62616161e-02 -3.56540173e-01 3.98417920e-01 2.90860441e-02
1.08561397e+00 -5.39372981e-01 4.64070380e-01 1.61102369e-01
-4.67836797e-01 9.50007379e-01 -3.23478520e-01 -4.58593428e-01
2.20917702e-01 -1.14031172e+00 -6.54276848e-01 -8.98098290e-01
-4.30217564e-01 1.26416549e-01 2.32633606e-01 -5.21224678... | [5.625804901123047, 4.136998653411865] |
87cf1212-7a0f-4fcb-b6a9-40fd8ed5d1d6 | ghrs-graph-based-hybrid-recommendation-system | 2111.11293 | null | https://arxiv.org/abs/2111.11293v2 | https://arxiv.org/pdf/2111.11293v2.pdf | GHRS: Graph-based Hybrid Recommendation System with Application to Movie Recommendation | Research about recommender systems emerges over the last decade and comprises valuable services to increase different companies' revenue. Several approaches exist in handling paper recommender systems. While most existing recommender systems rely either on a content-based approach or a collaborative approach, there are... | ['Mohammad Hadi Valipour', 'Zahra Zamanzadeh Darban'] | 2021-11-06 | null | null | null | null | ['movie-recommendation'] | ['miscellaneous'] | [-2.52546221e-01 -3.53621036e-01 -1.29933104e-01 -5.00726163e-01
-1.51813537e-01 -3.73159707e-01 5.46595335e-01 2.59404510e-01
-3.00816417e-01 4.13062930e-01 4.30165946e-01 -6.24991655e-02
-7.79653966e-01 -1.10812485e+00 3.65577787e-02 -7.25803971e-01
2.92500168e-01 4.73530084e-01 2.55233735e-01 -6.67327702... | [10.06755256652832, 5.80987548828125] |
ab11203a-16e1-41da-b5bc-04f8eb4be72b | n2dnot-too-deep-clustering-via-clustering-the | 1908.05968 | null | https://arxiv.org/abs/1908.05968v6 | https://arxiv.org/pdf/1908.05968v6.pdf | N2D: (Not Too) Deep Clustering via Clustering the Local Manifold of an Autoencoded Embedding | Deep clustering has increasingly been demonstrating superiority over conventional shallow clustering algorithms. Deep clustering algorithms usually combine representation learning with deep neural networks to achieve this performance, typically optimizing a clustering and non-clustering loss. In such cases, an autoenco... | ['Robert J. Piechocki', 'Raul Santos-Rodriguez', 'Ryan McConville', 'Ian Craddock'] | 2019-08-16 | null | null | null | null | ['time-series-clustering'] | ['time-series'] | [-6.69938505e-01 -1.71119288e-01 1.02249056e-01 -3.65065366e-01
-7.95967340e-01 -5.53484499e-01 5.34590721e-01 8.39790553e-02
-3.61072928e-01 -1.69553041e-01 2.95319349e-01 5.09238690e-02
-3.94834965e-01 -6.29481673e-01 -6.37547553e-01 -1.10419607e+00
-5.36485136e-01 7.45789468e-01 -3.97257805e-01 2.72266418... | [9.106363296508789, 3.2656302452087402] |
812e8389-3224-41a9-bcdf-1b59f0f1fdc1 | predict-to-detect-prediction-guided-3d-object | 2306.08528 | null | https://arxiv.org/abs/2306.08528v1 | https://arxiv.org/pdf/2306.08528v1.pdf | Predict to Detect: Prediction-guided 3D Object Detection using Sequential Images | Recent camera-based 3D object detection methods have introduced sequential frames to improve the detection performance hoping that multiple frames would mitigate the large depth estimation error. Despite improved detection performance, prior works rely on naive fusion methods (e.g., concatenation) or are limited to sta... | ['Dongsuk Kum', 'In-Jae Lee', 'Youngseok Kim', 'Sanmin Kim'] | 2023-06-14 | null | null | null | null | ['3d-object-detection', 'depth-estimation'] | ['computer-vision', 'computer-vision'] | [ 3.43251824e-01 -2.55994290e-01 -1.57873794e-01 -2.14754492e-01
-5.12612522e-01 -4.04808134e-01 7.00445712e-01 -4.57213931e-02
-3.81824821e-01 1.81717232e-01 1.23105645e-01 -1.08493445e-02
1.72230810e-01 -5.53868413e-01 -5.88762224e-01 -5.40045559e-01
-1.34853914e-01 -2.68213600e-01 1.18870509e+00 5.03327101... | [7.982998371124268, -2.1086273193359375] |
aa65a3ec-87a6-4af1-a474-2cc186e388f6 | vatlm-visual-audio-text-pre-training-with | 2211.11275 | null | https://arxiv.org/abs/2211.11275v2 | https://arxiv.org/pdf/2211.11275v2.pdf | VATLM: Visual-Audio-Text Pre-Training with Unified Masked Prediction for Speech Representation Learning | Although speech is a simple and effective way for humans to communicate with the outside world, a more realistic speech interaction contains multimodal information, e.g., vision, text. How to design a unified framework to integrate different modal information and leverage different resources (e.g., visual-audio pairs, ... | ['Furu Wei', 'Jinyu Li', 'Daxin Jiang', 'LiRong Dai', 'Jie Zhang', 'Binxing Jiao', 'Shujie Liu', 'Ziqiang Zhang', 'Long Zhou', 'Qiushi Zhu'] | 2022-11-21 | null | null | null | null | ['audio-visual-speech-recognition'] | ['speech'] | [ 2.00127661e-01 -1.08887956e-01 -2.68366843e-01 -4.51298475e-01
-1.01224124e+00 -5.14224112e-01 8.65064561e-01 -1.48113117e-01
-3.17935497e-01 2.12014526e-01 6.01221979e-01 -3.70647460e-01
4.54268664e-01 -2.07531795e-01 -6.42115831e-01 -4.38406974e-01
4.92761701e-01 3.17492872e-01 5.26022725e-02 -2.27360707... | [14.154187202453613, 5.058468341827393] |
74514ebe-8a15-4472-8d99-43e5a27b05a3 | membership-inference-attacks-and-defenses-in-1 | 2202.03335 | null | https://arxiv.org/abs/2202.03335v2 | https://arxiv.org/pdf/2202.03335v2.pdf | Membership Inference Attacks and Defenses in Neural Network Pruning | Neural network pruning has been an essential technique to reduce the computation and memory requirements for using deep neural networks for resource-constrained devices. Most existing research focuses primarily on balancing the sparsity and accuracy of a pruned neural network by strategically removing insignificant par... | ['Lan Zhang', 'Xiaoyong Yuan'] | 2022-02-07 | null | null | null | null | ['membership-inference-attack'] | ['computer-vision'] | [ 3.92233521e-01 9.61650610e-02 -2.72314698e-01 -4.71866578e-01
1.38876811e-02 -4.69502687e-01 -1.66314647e-01 -6.30074963e-02
-5.23881733e-01 6.91238582e-01 -1.89519092e-01 -6.59831405e-01
-2.79970407e-01 -8.00913155e-01 -8.03513467e-01 -8.54191184e-01
-4.34253365e-03 -3.53923887e-01 3.85804400e-02 1.25393555... | [5.892332553863525, 7.106832504272461] |
41829039-27dc-4526-9450-77054a4747a0 | revision-for-concision-a-constrained | 2210.14257 | null | https://arxiv.org/abs/2210.14257v1 | https://arxiv.org/pdf/2210.14257v1.pdf | Revision for Concision: A Constrained Paraphrase Generation Task | Academic writing should be concise as concise sentences better keep the readers' attention and convey meaning clearly. Writing concisely is challenging, for writers often struggle to revise their drafts. We introduce and formulate revising for concision as a natural language processing task at the sentence level. Revis... | ['Kwan Hui Lim', 'Wenchuan Mu'] | 2022-10-25 | null | null | null | null | ['paraphrase-generation', 'paraphrase-generation'] | ['computer-code', 'natural-language-processing'] | [ 4.85698432e-01 2.02219576e-01 -2.41244689e-01 -5.97731113e-01
-5.90260625e-01 -8.22743058e-01 4.18563932e-01 8.09004903e-01
-6.42659903e-01 1.16248775e+00 5.97926021e-01 -5.99682868e-01
-3.02240670e-01 -5.11152446e-01 -3.90439630e-01 1.92519143e-01
6.01679683e-01 8.44867751e-02 1.05065115e-01 -4.28784579... | [12.033157348632812, 9.462803840637207] |
0073446e-7d9e-4a5a-a1a3-40ccdc6ddb9d | ehrsql-a-practical-text-to-sql-benchmark-for-1 | 2301.07695 | null | https://arxiv.org/abs/2301.07695v4 | https://arxiv.org/pdf/2301.07695v4.pdf | EHRSQL: A Practical Text-to-SQL Benchmark for Electronic Health Records | We present a new text-to-SQL dataset for electronic health records (EHRs). The utterances were collected from 222 hospital staff members, including physicians, nurses, and insurance review and health records teams. To construct the QA dataset on structured EHR data, we conducted a poll at a university hospital and used... | ['Edward Choi', 'Jong-Yeup Kim', 'Minjoon Seo', 'Seongjun Yang', 'Woncheol Shin', 'Yeonsu Kwon', 'Seongsu Bae', 'Hyeonji Hwang', 'Gyubok Lee'] | 2023-01-16 | ehrsql-a-practical-text-to-sql-benchmark-for | https://openreview.net/forum?id=B2W8Vy0rarw | https://openreview.net/pdf?id=B2W8Vy0rarw | neurips-2022-datasets-and-benchmarks-2022-12 | ['text-to-sql'] | ['computer-code'] | [-2.31020465e-01 2.78779775e-01 5.42836823e-02 -9.57520843e-01
-1.60577023e+00 -7.35633910e-01 -2.91323364e-01 8.86325836e-01
-1.53152287e-01 7.09359288e-01 8.61532390e-01 -8.67847502e-01
-2.21268594e-01 -8.99325967e-01 -3.57439071e-01 -9.64618921e-02
-1.89638995e-02 8.86124134e-01 -3.71163875e-01 -1.85012355... | [8.719667434692383, 8.488037109375] |
e5bb5b1e-263e-4209-96e9-d032a4529238 | task-discrepancy-maximization-for-fine-1 | 2207.01376 | null | https://arxiv.org/abs/2207.01376v1 | https://arxiv.org/pdf/2207.01376v1.pdf | Task Discrepancy Maximization for Fine-grained Few-Shot Classification | Recognizing discriminative details such as eyes and beaks is important for distinguishing fine-grained classes since they have similar overall appearances. In this regard, we introduce Task Discrepancy Maximization (TDM), a simple module for fine-grained few-shot classification. Our objective is to localize the class-w... | ['Jae-Pil Heo', 'WonJun Moon', 'SuBeen Lee'] | 2022-07-04 | task-discrepancy-maximization-for-fine | http://openaccess.thecvf.com//content/CVPR2022/html/Lee_Task_Discrepancy_Maximization_for_Fine-Grained_Few-Shot_Classification_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Lee_Task_Discrepancy_Maximization_for_Fine-Grained_Few-Shot_Classification_CVPR_2022_paper.pdf | cvpr-2022-1 | ['few-shot-image-classification'] | ['computer-vision'] | [ 2.65097201e-01 -2.18516991e-01 -3.81033778e-01 -5.56007206e-01
-8.16842139e-01 -1.95875227e-01 6.62262201e-01 1.07732028e-01
-2.21847519e-01 6.72874749e-01 3.52510303e-01 3.19859952e-01
-1.36566922e-01 -7.18096197e-01 -4.57877934e-01 -7.76989162e-01
7.18178749e-02 -7.25587830e-02 5.29503584e-01 6.77312315... | [9.77492618560791, 2.0416064262390137] |
9aff1aac-1e88-4e11-a1c0-e66a8413008b | video-killed-the-hd-map-predicting-driving | 2305.11856 | null | https://arxiv.org/abs/2305.11856v1 | https://arxiv.org/pdf/2305.11856v1.pdf | Video Killed the HD-Map: Predicting Driving Behavior Directly From Drone Images | The development of algorithms that learn behavioral driving models using human demonstrations has led to increasingly realistic simulations. In general, such models learn to jointly predict trajectories for all controlled agents by exploiting road context information such as drivable lanes obtained from manually annota... | ['Frank Wood', 'Adam Ścibior', 'Berend Zwartsenberg', 'Xiaoxuan Liang', 'Dylan Green', 'Setareh Dabiri', 'Justice Sefas', 'Matthew Niedoba', 'Jonathan Wilder Lavington', 'Vasileios Lioutas', 'Yunpeng Liu'] | 2023-05-19 | null | null | null | null | ['trajectory-prediction'] | ['computer-vision'] | [-2.59490401e-01 1.44319190e-02 4.43127975e-02 -7.22241402e-01
-5.76850593e-01 -6.39297068e-01 9.43764031e-01 8.37443769e-02
-5.33075094e-01 8.93532574e-01 8.69014859e-02 -5.20321906e-01
1.04714386e-01 -1.17404819e+00 -9.66299295e-01 -1.99985147e-01
-3.83248210e-01 9.34696913e-01 7.25886345e-01 -5.92180431... | [5.553201675415039, 0.849898099899292] |
a0dca224-70af-43ac-b698-7b4f9d3b9b4a | universal-domain-adaptive-object-detector | 2207.01756 | null | https://arxiv.org/abs/2207.01756v1 | https://arxiv.org/pdf/2207.01756v1.pdf | Universal Domain Adaptive Object Detector | Universal domain adaptive object detection (UniDAOD)is more challenging than domain adaptive object detection (DAOD) since the label space of the source domain may not be the same as that of the target and the scale of objects in the universal scenarios can vary dramatically (i.e, category shift and scale shift). To th... | ['ShiLiang Pu', 'WeiJie Chen', 'Lei Zhang', 'Wenxu Shi'] | 2022-07-05 | null | null | null | null | ['multi-label-learning'] | ['methodology'] | [ 2.19039336e-01 -3.28957111e-01 -2.12030392e-02 -5.70117533e-01
-6.97419822e-01 -7.31878757e-01 3.32541734e-01 -1.03499405e-01
-6.16239488e-01 4.54631120e-01 -2.58443922e-01 1.91137195e-01
1.03457406e-01 -6.34235680e-01 -6.24716759e-01 -8.03314447e-01
1.84234068e-01 4.68460321e-01 1.01736546e+00 -2.22334601... | [9.363673210144043, 1.488131046295166] |
537ec6c5-afde-4709-bad3-e111225f2021 | image-processing-based-scene-text-detection | 2004.08079 | null | https://arxiv.org/abs/2004.08079v1 | https://arxiv.org/pdf/2004.08079v1.pdf | Image Processing Based Scene-Text Detection and Recognition with Tesseract | Text Recognition is one of the challenging tasks of computer vision with considerable practical interest. Optical character recognition (OCR) enables different applications for automation. This project focuses on word detection and recognition in natural images. In comparison to reading text in scanned documents, the t... | ['Bénédicte Bernier', 'Ebin Zacharias', 'Martin Teuchler'] | 2020-04-17 | null | null | null | null | ['scene-text-detection'] | ['computer-vision'] | [ 8.62511694e-01 -2.83617288e-01 2.30136916e-01 -2.03764141e-01
-2.25399777e-01 -6.22177660e-01 7.19821632e-01 -8.20162520e-02
-6.32636487e-01 5.30171096e-01 -5.17255545e-01 -4.18637693e-01
1.18662551e-01 -4.12259072e-01 -3.15116078e-01 -6.59042060e-01
3.48083645e-01 5.29293776e-01 4.12297428e-01 1.09411068... | [11.80321979522705, 2.603389024734497] |
f0bad5c4-3f1c-4d3f-abb7-f43790173192 | authnet-a-deep-learning-based-authentication | 2012.02515 | null | https://arxiv.org/abs/2012.02515v2 | https://arxiv.org/pdf/2012.02515v2.pdf | AuthNet: A Deep Learning based Authentication Mechanism using Temporal Facial Feature Movements | Biometric systems based on Machine learning and Deep learning are being extensively used as authentication mechanisms in resource-constrained environments like smartphones and other small computing devices. These AI-powered facial recognition mechanisms have gained enormous popularity in recent years due to their trans... | ['Sowmya Kamath', 'B R Mukesh', 'Pravan Omprakash', 'Mohit Raghavendra'] | 2020-12-04 | null | null | null | null | ['lip-password-classification'] | ['time-series'] | [ 6.14912324e-02 -1.48250490e-01 -1.77062958e-01 -2.42799237e-01
-2.11486787e-01 -3.36618632e-01 5.61863184e-01 -5.12152493e-01
-7.79172003e-01 6.12105906e-01 -3.00447017e-01 -1.95171893e-01
5.83210737e-02 -3.87763470e-01 -2.95057923e-01 -7.84571826e-01
1.55089587e-01 -1.56052476e-02 -8.29007775e-02 -2.65421197... | [13.303565979003906, 1.1823457479476929] |
3be86768-7c4d-4e28-a335-5e1d9439b5ac | nlatool-an-application-for-enhanced-deep-text | null | null | https://aclanthology.org/C18-2026 | https://aclanthology.org/C18-2026.pdf | NLATool: an Application for Enhanced Deep Text Understanding | Today, we see an ever growing number of tools supporting text annotation. Each of these tools is optimized for specific use-cases such as named entity recognition. However, we see large growing knowledge bases such as Wikipedia or the Google Knowledge Graph. In this paper, we introduce NLATool, a web application develo... | ['Eric H{\\"a}mmerle', 'Markus G{\\"a}rtner', 'Valentin Schwind', 'Sven Mayer', 'Jonas Kuhn', 'Lars Lischke', 'Florin Rheinwald', 'Emine Turcan', 'Gustav Murawski'] | 2018-08-01 | nlatool-an-application-for-enhanced-deep-text-1 | https://aclanthology.org/C18-2026 | https://aclanthology.org/C18-2026.pdf | coling-2018-8 | ['text-annotation'] | ['natural-language-processing'] | [-4.81593639e-01 4.95514899e-01 -4.03116763e-01 -2.41116405e-01
-3.41210812e-01 -8.77701938e-01 4.62376446e-01 7.62629867e-01
-6.04920149e-01 7.48223662e-01 5.41057110e-01 -2.38428757e-01
-1.63313225e-01 -7.98514605e-01 -2.23138295e-02 3.48355561e-01
4.94458258e-01 7.93358207e-01 2.32344747e-01 -2.43068367... | [9.304187774658203, 8.757075309753418] |
1816bbb8-b87e-4cdf-949e-6b9756176e65 | better-modeling-the-programming-world-with | 2201.03346 | null | https://arxiv.org/abs/2201.03346v2 | https://arxiv.org/pdf/2201.03346v2.pdf | Better Modeling the Programming World with Code Concept Graphs-augmented Multi-modal Learning | The progress made in code modeling has been tremendous in recent years thanks to the design of natural language processing learning approaches based on state-of-the-art model architectures. Nevertheless, we believe that the current state-of-the-art does not focus enough on the full potential that data may bring to a le... | ['Bang Liu', 'Houari Sahraoui', 'Martin Weyssow'] | 2022-01-10 | null | null | null | null | ['code-search', 'code-search'] | ['computer-code', 'computer-vision'] | [-8.20106417e-02 4.64907318e-01 -2.47973070e-01 -4.18475956e-01
-4.18220729e-01 -2.69868433e-01 6.89211667e-01 5.44066966e-01
-3.70750166e-02 -1.27109230e-01 3.88808668e-01 -8.62105310e-01
-3.04097801e-01 -7.98859715e-01 -7.75372446e-01 2.20984578e-01
-4.03445989e-01 3.02728534e-01 9.54978764e-02 -3.87354076... | [7.6586737632751465, 7.787206172943115] |
bac28a73-c144-4a47-a4ff-ff12b014011f | semi-supervised-confidence-level-based | 2211.15066 | null | https://arxiv.org/abs/2211.15066v1 | https://arxiv.org/pdf/2211.15066v1.pdf | Semi-Supervised Confidence-Level-based Contrastive Discrimination for Class-Imbalanced Semantic Segmentation | To overcome the data-hungry challenge, we have proposed a semi-supervised contrastive learning framework for the task of class-imbalanced semantic segmentation. First and foremost, to make the model operate in a semi-supervised manner, we proposed the confidence-level-based contrastive learning to achieve instance disc... | ['Kangcheng Liu'] | 2022-11-28 | null | null | null | null | ['road-segementation'] | ['computer-vision'] | [ 5.22056103e-01 3.01265806e-01 -3.59541118e-01 -7.83007264e-01
-1.11006343e+00 -2.22184919e-02 -3.03815864e-02 2.04693094e-01
-3.79890263e-01 6.73094034e-01 -3.95414859e-01 -1.07415058e-01
-2.15513110e-01 -9.17127192e-01 -5.42165518e-01 -8.62433910e-01
3.74587029e-01 3.27569991e-01 3.64757866e-01 2.80826867... | [14.49334716796875, -2.0312771797180176] |
856b036a-45d0-45ff-9e8c-96e8aa437da1 | h-vfi-hierarchical-frame-interpolation-for | 2211.11309 | null | https://arxiv.org/abs/2211.11309v1 | https://arxiv.org/pdf/2211.11309v1.pdf | H-VFI: Hierarchical Frame Interpolation for Videos with Large Motions | Capitalizing on the rapid development of neural networks, recent video frame interpolation (VFI) methods have achieved notable improvements. However, they still fall short for real-world videos containing large motions. Complex deformation and/or occlusion caused by large motions make it an extremely difficult problem ... | ['Yu-Wing Tai', 'Chi-Keung Tang', 'Xin Tao', 'Yanan sun', 'Guangyang Wu', 'Changlin Li'] | 2022-11-21 | null | null | null | null | ['video-frame-interpolation'] | ['computer-vision'] | [ 1.30599057e-02 -4.44184244e-01 -2.30921909e-01 -1.65451020e-01
-6.58860624e-01 -9.51379463e-02 3.10053378e-01 -4.52325553e-01
-2.46612042e-01 8.87457848e-01 1.88317761e-01 1.16007030e-01
6.33922368e-02 -7.01618254e-01 -9.95872200e-01 -7.16270983e-01
-4.00553793e-02 -4.54717614e-02 6.23948812e-01 -1.50025517... | [10.749916076660156, -1.4522819519042969] |
315377bc-3ed6-40fe-a226-9499796ad7de | backpack-language-models | 2305.16765 | null | https://arxiv.org/abs/2305.16765v1 | https://arxiv.org/pdf/2305.16765v1.pdf | Backpack Language Models | We present Backpacks: a new neural architecture that marries strong modeling performance with an interface for interpretability and control. Backpacks learn multiple non-contextual sense vectors for each word in a vocabulary, and represent a word in a sequence as a context-dependent, non-negative linear combination of ... | ['Percy Liang', 'Christopher D. Manning', 'John Thickstun', 'John Hewitt'] | 2023-05-26 | null | null | null | null | ['word-embeddings'] | ['methodology'] | [ 4.28261608e-01 4.23608720e-01 -2.99116224e-01 -5.42709649e-01
-5.62578738e-01 -1.10566986e+00 6.34268820e-01 1.95768729e-01
-6.38349235e-01 3.85165811e-01 6.38785839e-01 -8.11840236e-01
1.84445128e-01 -8.12283576e-01 -7.80095279e-01 -4.16961402e-01
4.48523074e-01 7.65148759e-01 -1.86281487e-01 -9.00736451... | [10.589326858520508, 8.744301795959473] |
b458df83-a112-4661-99e9-f7496884943d | l-seqsleepnet-whole-cycle-long-sequence | 2301.03441 | null | https://arxiv.org/abs/2301.03441v2 | https://arxiv.org/pdf/2301.03441v2.pdf | L-SeqSleepNet: Whole-cycle Long Sequence Modelling for Automatic Sleep Staging | Human sleep is cyclical with a period of approximately 90 minutes, implying long temporal dependency in the sleep data. Yet, exploring this long-term dependency when developing sleep staging models has remained untouched. In this work, we show that while encoding the logic of a whole sleep cycle is crucial to improve s... | ['Maarten De Vos', 'Kaare Mikkelsen', 'Mathias Baumert', 'Alfred Mertins', 'Philipp Koch', 'Minh C. Tran', 'Oliver Y. Chén', 'Elisabeth Heremans', 'Kristian P. Lorenzen', 'Huy Phan'] | 2023-01-09 | null | null | null | null | ['sleep-staging'] | ['medical'] | [ 7.02752396e-02 -9.93761346e-02 9.18762907e-02 -3.25323224e-01
-2.17656150e-01 -2.85923094e-01 1.41594425e-01 -1.66945934e-01
-6.92593873e-01 9.01052952e-01 1.48013353e-01 -3.37306857e-01
-3.85060370e-01 -1.84549615e-01 -3.23108286e-01 -7.43961751e-01
-2.27240041e-01 2.00918183e-01 2.10877523e-01 -3.50047916... | [13.375988006591797, 3.6271185874938965] |
a4e21d6a-8900-4830-9e17-55081baab275 | the-elements-of-end-to-end-deep-face | 2009.13290 | null | https://arxiv.org/abs/2009.13290v4 | https://arxiv.org/pdf/2009.13290v4.pdf | The Elements of End-to-end Deep Face Recognition: A Survey of Recent Advances | Face recognition is one of the most popular and long-standing topics in computer vision. With the recent development of deep learning techniques and large-scale datasets, deep face recognition has made remarkable progress and been widely used in many real-world applications. Given a natural image or video frame as inpu... | ['Xiao-Ping Zhang', 'Hailin Shi', 'Hang Du', 'Dan Zeng', 'Tao Mei'] | 2020-09-28 | null | null | null | null | ['face-alignment'] | ['computer-vision'] | [ 2.26956427e-01 -4.07039076e-01 -1.18516229e-01 -8.64336491e-01
-5.17696083e-01 -2.25692332e-01 3.82607788e-01 -8.43806326e-01
-2.37397075e-01 2.14827791e-01 -1.44820184e-01 2.37460002e-01
-3.32799554e-02 -6.15889668e-01 -4.96755183e-01 -9.50822890e-01
-2.30845045e-02 2.15671927e-01 -5.36428273e-01 -1.02447197... | [13.285872459411621, 0.7376279830932617] |
a38be23d-cfd5-4591-828c-8711a9021872 | a-comparative-study-of-semi-and-self | 2011.08076 | null | https://arxiv.org/abs/2011.08076v2 | https://arxiv.org/pdf/2011.08076v2.pdf | A comparative study of semi- and self-supervised semantic segmentation of biomedical microscopy data | In recent years, Convolutional Neural Networks (CNNs) have become the state-of-the-art method for biomedical image analysis. However, these networks are usually trained in a supervised manner, requiring large amounts of labelled training data. These labelled data sets are often difficult to acquire in the biomedical do... | ['Nico Scherf', 'Ingo Roeder', 'Sebastian Wagner', 'Sebastian Niehaus', 'Alisa Mironenko', 'Nastassya Horlava'] | 2020-11-11 | null | null | null | null | ['self-supervised-image-classification'] | ['computer-vision'] | [ 5.42489111e-01 2.86334038e-01 4.45765555e-02 -7.01943457e-01
-3.31720173e-01 -4.12987322e-01 2.54711956e-01 4.88042891e-01
-1.19017112e+00 9.63090420e-01 -6.04914606e-01 -4.57777202e-01
2.33259946e-01 -7.71427393e-01 -5.87190390e-01 -7.88322926e-01
3.20705980e-01 8.90655398e-01 3.01219523e-01 1.05918065... | [14.461690902709961, -2.874837875366211] |
5320f2c6-6d75-4cb3-b679-ff66c7688545 | machine-translation-with-weakly-paired | null | null | https://openreview.net/forum?id=ryza73R9tQ | https://openreview.net/pdf?id=ryza73R9tQ | Machine Translation With Weakly Paired Bilingual Documents | Neural machine translation, which achieves near human-level performance in some languages, strongly relies on the availability of large amounts of parallel sentences, which hinders its applicability to low-resource language pairs. Recent works explore the possibility of unsupervised machine translation with monolingual... | ['Tie-Yan Liu', 'Jinhua Zhu', 'Fei Gao', 'Di He', 'Xu Tan', 'Tao Qin', 'Lijun Wu'] | null | null | null | null | iclr-2019-5 | ['unsupervised-machine-translation'] | ['natural-language-processing'] | [ 9.65443179e-02 -1.70088708e-01 -5.71160853e-01 -3.99633825e-01
-1.44527876e+00 -7.79954672e-01 8.25355291e-01 1.01032704e-01
-6.69767678e-01 1.25503588e+00 1.31648421e-01 -5.93153059e-01
2.18859389e-01 -6.70679748e-01 -9.89358664e-01 -7.31566608e-01
3.41602087e-01 7.30500460e-01 -1.64356694e-01 -5.00540376... | [11.596829414367676, 10.359140396118164] |
bac1693b-578b-4676-81b6-3298669e839d | understanding-a-class-of-decentralized-and | 2204.12663 | null | https://arxiv.org/abs/2204.12663v2 | https://arxiv.org/pdf/2204.12663v2.pdf | Understanding A Class of Decentralized and Federated Optimization Algorithms: A Multi-Rate Feedback Control Perspective | Distributed algorithms have been playing an increasingly important role in many applications such as machine learning, signal processing, and control. Significant research efforts have been devoted to developing and analyzing new algorithms for various applications. In this work, we provide a fresh perspective to under... | ['Nicola Elia', 'Mingyi Hong', 'Xinwei Zhang'] | 2022-04-27 | null | null | null | null | ['distributed-optimization'] | ['methodology'] | [-1.30111977e-01 -4.48775589e-01 -1.72643095e-01 -1.78377435e-01
-6.42311633e-01 -7.51224697e-01 3.34030420e-01 1.84319466e-01
-2.15651676e-01 8.97455871e-01 1.65966317e-01 -3.29110056e-01
-3.97106469e-01 -6.28690183e-01 -5.82714498e-01 -8.89599919e-01
-2.54280478e-01 -1.09014295e-01 -1.21439286e-01 -2.70651072... | [6.2996697425842285, 4.88827657699585] |
d5945e76-5979-4b93-a19d-bb646fc3f316 | stochastic-model-predictive-control-with-1 | 2305.19262 | null | https://arxiv.org/abs/2305.19262v1 | https://arxiv.org/pdf/2305.19262v1.pdf | Stochastic Model Predictive Control with Dynamic Chance Constraints | In this work, we introduce a stochastic model predictive control scheme for dynamic chance constraints. We consider linear discrete-time systems affected by unbounded additive stochastic disturbance and subject to chance constraints that are defined by time-varying probabilities with a common, fixed lower bound. By uti... | ['Mircea Lazar', 'Sofie Haesaert', 'Maico Hendrikus Wilhelmus Engelaar'] | 2023-05-30 | null | null | null | null | ['stochastic-optimization'] | ['methodology'] | [ 1.93071336e-01 4.35839325e-01 -4.17901754e-01 3.35054904e-01
-8.23032260e-01 -1.01675820e+00 5.64071119e-01 -6.04051054e-02
9.44411159e-02 1.40984631e+00 -6.19560704e-02 -6.67012155e-01
-7.70096123e-01 -8.81839991e-01 -6.63420677e-01 -1.05962706e+00
-2.96782553e-01 4.63724673e-01 9.80147421e-02 -2.43938323... | [4.771667003631592, 2.414045810699463] |
aa172d2b-090d-44c4-892f-c7f6932ced69 | tesla-test-time-self-learning-with-automatic | 2303.09870 | null | https://arxiv.org/abs/2303.09870v1 | https://arxiv.org/pdf/2303.09870v1.pdf | TeSLA: Test-Time Self-Learning With Automatic Adversarial Augmentation | Most recent test-time adaptation methods focus on only classification tasks, use specialized network architectures, destroy model calibration or rely on lightweight information from the source domain. To tackle these issues, this paper proposes a novel Test-time Self-Learning method with automatic Adversarial augmentat... | ['Jean-Philippe Thiran', 'Behzad Bozorgtabar', 'Guillaume Vray', 'Devavrat Tomar'] | 2023-03-17 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Tomar_TeSLA_Test-Time_Self-Learning_With_Automatic_Adversarial_Augmentation_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Tomar_TeSLA_Test-Time_Self-Learning_With_Automatic_Adversarial_Augmentation_CVPR_2023_paper.pdf | cvpr-2023-1 | ['self-learning'] | ['natural-language-processing'] | [ 4.21168685e-01 3.29965740e-01 -3.47006768e-01 -5.01823902e-01
-1.35817492e+00 -7.11323321e-01 3.02935064e-01 1.71999156e-01
-5.77279568e-01 9.55708265e-01 -3.10327441e-01 -2.12126359e-01
-1.54327318e-01 -5.50278842e-01 -9.52650666e-01 -6.94872081e-01
-1.03494644e-01 6.60433769e-01 4.11711901e-01 -1.64051931... | [14.514430046081543, -1.9173448085784912] |
b2b21fd1-871b-4abb-895b-aefd1e684da2 | jiff-jointly-aligned-implicit-face-function | 2204.10549 | null | https://arxiv.org/abs/2204.10549v1 | https://arxiv.org/pdf/2204.10549v1.pdf | JIFF: Jointly-aligned Implicit Face Function for High Quality Single View Clothed Human Reconstruction | This paper addresses the problem of single view 3D human reconstruction. Recent implicit function based methods have shown impressive results, but they fail to recover fine face details in their reconstructions. This largely degrades user experience in applications like 3D telepresence. In this paper, we focus on impro... | ['Kwan-Yee K. Wong', 'Wenqi Yang', 'Kai Han', 'GuanYing Chen', 'Yukang Cao'] | 2022-04-22 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Cao_JIFF_Jointly-Aligned_Implicit_Face_Function_for_High_Quality_Single_View_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Cao_JIFF_Jointly-Aligned_Implicit_Face_Function_for_High_Quality_Single_View_CVPR_2022_paper.pdf | cvpr-2022-1 | ['face-model', '3d-human-reconstruction', 'face-reconstruction'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 1.44630790e-01 8.39185342e-02 1.31338909e-01 -6.59623146e-01
-8.20514143e-01 -2.11903647e-01 5.41034818e-01 -7.29618967e-01
3.07726324e-01 3.68852288e-01 6.06678545e-01 3.13958704e-01
5.22441082e-02 -7.37042546e-01 -6.96725726e-01 -2.42277816e-01
1.87019125e-01 7.49661982e-01 -5.75983524e-02 -3.06248337... | [13.142935752868652, -0.03410336747765541] |
8819b0a4-865b-47b5-9ea4-77d1af258089 | in-defense-of-pseudo-labeling-an-uncertainty-1 | 2101.06329 | null | https://arxiv.org/abs/2101.06329v3 | https://arxiv.org/pdf/2101.06329v3.pdf | In Defense of Pseudo-Labeling: An Uncertainty-Aware Pseudo-label Selection Framework for Semi-Supervised Learning | The recent research in semi-supervised learning (SSL) is mostly dominated by consistency regularization based methods which achieve strong performance. However, they heavily rely on domain-specific data augmentations, which are not easy to generate for all data modalities. Pseudo-labeling (PL) is a general SSL approach... | ['Mubarak Shah', 'Yogesh S Rawat', 'Kevin Duarte', 'Mamshad Nayeem Rizve'] | 2021-01-15 | in-defense-of-pseudo-labeling-an-uncertainty | https://openreview.net/forum?id=-ODN6SbiUU | https://openreview.net/pdf?id=-ODN6SbiUU | iclr-2021-1 | ['semi-supervised-video-classification', 'semi-supervised-medical-image-classification'] | ['computer-vision', 'medical'] | [ 4.84923691e-01 2.36863717e-01 -5.26738405e-01 -8.88580382e-01
-1.24027240e+00 -5.32840967e-01 6.89926505e-01 2.19547957e-01
-5.12419701e-01 1.12504041e+00 -1.50157094e-01 -5.89069165e-02
2.41699532e-01 -4.52714205e-01 -9.23850000e-01 -6.83881938e-01
5.56679010e-01 6.44509733e-01 3.62802036e-02 2.39717424... | [9.50585651397705, 3.924499273300171] |
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