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9fb1f12e-0b01-4df4-81cb-bad3a26f5d3b | partitioning-guided-k-means-extreme-empty | 2306.14031 | null | https://arxiv.org/abs/2306.14031v1 | https://arxiv.org/pdf/2306.14031v1.pdf | Partitioning-Guided K-Means: Extreme Empty Cluster Resolution for Extreme Model Compression | Compactness in deep learning can be critical to a model's viability in low-resource applications, and a common approach to extreme model compression is quantization. We consider Iterative Product Quantization (iPQ) with Quant-Noise to be state-of-the-art in this area, but this quantization framework suffers from preven... | ['Lizhong Chen', 'Victor Agostinelli', 'Tianhong Huang'] | 2023-06-24 | null | null | null | null | ['quantization', 'model-compression'] | ['methodology', 'methodology'] | [ 5.66699505e-02 3.10177393e-02 -2.42929116e-01 -3.20869476e-01
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-6.52832612e-02 6.22199953e-01 3.15880895e-01 8.50527138... | [8.595242500305176, 3.1163887977600098] |
e4a7732c-44d5-4d8d-ae4d-d9552c689b1e | magnification-invariant-medical-image | 2302.11488 | null | https://arxiv.org/abs/2302.11488v1 | https://arxiv.org/pdf/2302.11488v1.pdf | Magnification Invariant Medical Image Analysis: A Comparison of Convolutional Networks, Vision Transformers, and Token Mixers | Convolution Neural Networks (CNNs) are widely used in medical image analysis, but their performance degrade when the magnification of testing images differ from the training images. The inability of CNNs to generalize across magnification scales can result in sub-optimal performance on external datasets. This study aim... | ['Amit Sethi', 'Nikhil Cherian Kurian', 'Pranav Jeevan'] | 2023-02-22 | null | null | null | null | ['breast-cancer-histology-image-classification'] | ['medical'] | [ 1.67939380e-01 -1.26707211e-01 -1.51003376e-02 -3.37066829e-01
-4.80066985e-01 -3.79693657e-01 3.59056741e-01 8.02406594e-02
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-1.38175383e-01 1.81015596e-01 3.73501867e-01 -1.81422189... | [15.02774715423584, -2.6805691719055176] |
0cc56657-fe4c-4506-ba31-ef750c4bd9b1 | efficient-re-parameterization-residual | 2109.05479 | null | https://arxiv.org/abs/2109.05479v2 | https://arxiv.org/pdf/2109.05479v2.pdf | Efficient Re-parameterization Residual Attention Network For Nonhomogeneous Image Dehazing | This paper proposes an end-to-end Efficient Re-parameterizationResidual Attention Network(ERRA-Net) to directly restore the nonhomogeneous hazy image. The contribution of this paper mainly has the following three aspects: 1) A novel Multi-branch Attention (MA) block. The spatial attention mechanism better reconstructs ... | ['Peng Chen', 'XinRui Huang', 'ErKang Chen', 'Tian Ye'] | 2021-09-12 | null | null | null | null | ['image-dehazing'] | ['computer-vision'] | [-1.13123149e-01 -3.41936052e-01 3.08752984e-01 -2.75053620e-01
-6.85722530e-01 3.00148875e-01 -1.33825503e-02 -5.07698298e-01
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8.76660496e-02 -3.16198438e-01 3.83475572e-01 -2.94899702... | [10.983869552612305, -3.081284761428833] |
ca23d85a-b457-4185-9a49-139cf591ddea | fact-or-factitious-contextualized-opinion-1 | 2010.15296 | null | https://arxiv.org/abs/2010.15296v1 | https://arxiv.org/pdf/2010.15296v1.pdf | Fact or Factitious? Contextualized Opinion Spam Detection | In this paper we perform an analytic comparison of a number of techniques used to detect fake and deceptive online reviews. We apply a number machine learning approaches found to be effective, and introduce our own approach by fine-tuning state of the art contextualised embeddings. The results we obtain show the potent... | ['Andrew McCarren', 'Jennifer Foster', 'Kirils Sloka', 'Niall Walsh', 'Stefan Kennedy'] | 2020-10-29 | fact-or-factitious-contextualized-opinion | https://aclanthology.org/P19-2048 | https://aclanthology.org/P19-2048.pdf | acl-2019-7 | ['spam-detection'] | ['natural-language-processing'] | [-7.26902336e-02 3.09105635e-01 -5.91785312e-01 -4.30684179e-01
-4.85332221e-01 -4.54278409e-01 1.14539695e+00 4.73653257e-01
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-5.43905199e-02 2.29865804e-01 2.26794332e-01 -7.23393977... | [8.15803050994873, 10.246224403381348] |
971e938a-d386-4389-9c73-845ceac8dc46 | the-politics-of-deceptive-borders-biomarkers | 1911.09156 | null | https://arxiv.org/abs/1911.09156v4 | https://arxiv.org/pdf/1911.09156v4.pdf | The politics of deceptive borders: 'biomarkers of deceit' and the case of iBorderCtrl | This paper critically examines a recently developed proposal for a border control system called iBorderCtrl, designed to detect deception based on facial recognition technology and the measurement of micro-expressions, termed 'biomarkers of deceit'. Funded under the European Commission's Horizon 2020 programme, we situ... | ['Javier Sánchez-Monedero', 'Lina Dencik'] | 2019-11-20 | null | null | null | null | ['deception-detection'] | ['miscellaneous'] | [ 2.65877813e-01 5.42785883e-01 -8.74228328e-02 -5.19892693e-01
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1.22488342e-01 -9.40874368e-02 -3.85600448e-01 -3.34426343... | [9.025540351867676, 6.324026107788086] |
de30de09-ac32-4568-9079-a4f8a132efa6 | cope-conditional-image-generation-using | 2104.05077 | null | https://arxiv.org/abs/2104.05077v3 | https://arxiv.org/pdf/2104.05077v3.pdf | CoPE: Conditional image generation using Polynomial Expansions | Generative modeling has evolved to a notable field of machine learning. Deep polynomial neural networks (PNNs) have demonstrated impressive results in unsupervised image generation, where the task is to map an input vector (i.e., noise) to a synthesized image. However, the success of PNNs has not been replicated in con... | ['Yannis Panagakis', 'Markos Georgopoulos', 'Grigorios G Chrysos'] | 2021-04-11 | null | null | null | null | ['conditional-image-generation'] | ['computer-vision'] | [ 6.68916464e-01 2.36052260e-01 1.13475986e-01 -2.07159951e-01
-9.63380694e-01 -3.39257598e-01 6.90006733e-01 -4.63494033e-01
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1.70482561e-01 5.46381533e-01 -2.52167612e-01 -1.81467533... | [11.537797927856445, -0.3645278513431549] |
ebd66b05-df6c-4849-9968-f766b80b663e | mirrorwic-on-eliciting-word-in-context | 2109.09237 | null | https://arxiv.org/abs/2109.09237v1 | https://arxiv.org/pdf/2109.09237v1.pdf | MirrorWiC: On Eliciting Word-in-Context Representations from Pretrained Language Models | Recent work indicated that pretrained language models (PLMs) such as BERT and RoBERTa can be transformed into effective sentence and word encoders even via simple self-supervised techniques. Inspired by this line of work, in this paper we propose a fully unsupervised approach to improving word-in-context (WiC) represen... | ['Ivan Vulić', 'Anna Korhonen', 'Nigel Collier', 'Fangyu Liu', 'Qianchu Liu'] | 2021-09-19 | null | https://aclanthology.org/2021.conll-1.44 | https://aclanthology.org/2021.conll-1.44.pdf | conll-emnlp-2021-11 | ['contextualised-word-representations'] | ['natural-language-processing'] | [ 4.30042952e-01 2.12134242e-01 -4.37940806e-01 -5.96489668e-01
-1.23556638e+00 -8.07562709e-01 8.67661953e-01 3.71091366e-01
-9.20314312e-01 7.60041535e-01 7.38187015e-01 -3.81945491e-01
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-1.75871309e-02 4.51462805e-01 -1.07289050e-02 -7.37249374... | [10.875801086425781, 9.536581993103027] |
7ba6769a-3117-4da0-a38c-b6bf6c154607 | the-effect-of-counterfactuals-on-reading | 2304.00487 | null | https://arxiv.org/abs/2304.00487v1 | https://arxiv.org/pdf/2304.00487v1.pdf | The Effect of Counterfactuals on Reading Chest X-rays | This study evaluates the effect of counterfactual explanations on the interpretation of chest X-rays. We conduct a reader study with two radiologists assessing 240 chest X-ray predictions to rate their confidence that the model's prediction is correct using a 5 point scale. Half of the predictions are false positives. ... | ['Akshay Chaudhari', 'Matthew Lungren', 'Anuj Pareek', 'Evan Zucker', 'Sovann En', 'Rupert Brooks', 'Joseph Paul Cohen'] | 2023-04-02 | null | null | null | null | ['counterfactual-explanation'] | ['miscellaneous'] | [ 2.34008178e-01 1.18548381e+00 -7.33608067e-01 -5.09336174e-01
-7.88789392e-01 -3.77650172e-01 5.25238931e-01 5.73322415e-01
-4.84888911e-01 1.19766736e+00 5.31498849e-01 -1.07287407e+00
-2.67513663e-01 -4.33333755e-01 -7.51774728e-01 -2.92105436e-01
2.88865399e-02 5.60144007e-01 4.47238907e-02 2.25942895... | [8.522860527038574, 5.588902473449707] |
5c097825-a2cb-4fd3-ac51-f390f76d7b7d | predicting-solar-flares-with-remote-sensing | 2110.07658 | null | https://arxiv.org/abs/2110.07658v1 | https://arxiv.org/pdf/2110.07658v1.pdf | Predicting Solar Flares with Remote Sensing and Machine Learning | High energy solar flares and coronal mass ejections have the potential to destroy Earth's ground and satellite infrastructures, causing trillions of dollars in damage and mass human suffering. Destruction of these critical systems would disable power grids and satellites, crippling communications and transportation. Th... | ['Erik Larsen'] | 2021-10-14 | null | null | null | null | ['solar-flare-prediction'] | ['time-series'] | [ 3.59095871e-01 1.83645785e-01 -1.92004681e-01 -6.41505048e-02
-3.15464623e-02 -6.58352852e-01 4.54908937e-01 1.27873495e-01
-1.51152909e-01 9.93760169e-01 -2.54566878e-01 -6.28654599e-01
-2.43278638e-01 -1.04763103e+00 -4.29777741e-01 -7.12128043e-01
-2.00078115e-01 2.76050240e-01 2.16423869e-01 -5.57311058... | [6.64409875869751, 2.8139593601226807] |
ec8298d1-4e4a-43ce-a8b1-ded3befc0818 | scalable-coupling-of-deep-learning-with | 2305.07617 | null | https://arxiv.org/abs/2305.07617v1 | https://arxiv.org/pdf/2305.07617v1.pdf | Scalable Coupling of Deep Learning with Logical Reasoning | In the ongoing quest for hybridizing discrete reasoning with neural nets, there is an increasing interest in neural architectures that can learn how to solve discrete reasoning or optimization problems from natural inputs. In this paper, we introduce a scalable neural architecture and loss function dedicated to learnin... | ['Thomas Schiex', 'Sophie Barbe', 'Marianne Defresne'] | 2023-05-12 | null | null | null | null | ['protein-design', 'logical-reasoning'] | ['medical', 'reasoning'] | [ 4.50032860e-01 6.69104218e-01 -8.81962031e-02 -7.61563182e-01
-8.39698613e-01 -5.93241930e-01 2.63513118e-01 2.38315076e-01
-4.66744602e-01 1.20384765e+00 -2.39016458e-01 -8.25339913e-01
-6.99833274e-01 -9.68442261e-01 -1.07908869e+00 -5.21252513e-01
-2.40031481e-01 9.93730426e-01 -2.83337057e-01 -8.88070166... | [8.69253921508789, 6.9519362449646] |
0e3d31fa-1cea-49fa-83c4-da783b25ad89 | systematic-assessment-of-the-quality-of-fit | 2201.01658 | null | https://arxiv.org/abs/2201.01658v1 | https://arxiv.org/pdf/2201.01658v1.pdf | Systematic assessment of the quality of fit of the stochastic block model for empirical networks | We perform a systematic analysis of the quality of fit of the stochastic block model (SBM) for 275 empirical networks spanning a wide range of domains and orders of size magnitude. We employ posterior predictive model checking as a criterion to assess the quality of fit, which involves comparing networks generated by t... | ['Tiago P. Peixoto', 'Felipe Vaca-Ramírez'] | 2022-01-05 | null | null | null | null | ['stochastic-block-model'] | ['graphs'] | [ 3.72118413e-01 4.10125613e-01 -3.78807783e-01 -5.67994602e-02
-1.63743615e-01 -6.76526725e-01 1.04759026e+00 1.96246088e-01
6.35656863e-02 8.10819685e-01 -2.19184995e-01 -8.51385176e-01
-6.56094551e-01 -1.10220265e+00 -6.36648297e-01 -7.06952453e-01
-3.46221685e-01 1.06753218e+00 7.25833774e-01 -1.27812117... | [6.910609245300293, 5.3136420249938965] |
844f362e-5cfd-4728-97ff-670605661c83 | topological-guided-actor-critic-modular | 2304.10041 | null | https://arxiv.org/abs/2304.10041v1 | https://arxiv.org/pdf/2304.10041v1.pdf | Topological Guided Actor-Critic Modular Learning of Continuous Systems with Temporal Objectives | This work investigates the formal policy synthesis of continuous-state stochastic dynamic systems given high-level specifications in linear temporal logic. To learn an optimal policy that maximizes the satisfaction probability, we take a product between a dynamic system and the translated automaton to construct a produ... | ['Zhentian Qian', 'Lening Li'] | 2023-04-20 | null | null | null | null | ['motion-planning'] | ['robots'] | [ 1.06921516e-01 5.19552886e-01 -8.77864003e-01 5.30283339e-02
-8.15700293e-01 -6.20417118e-01 4.19786960e-01 -1.38724893e-01
-2.48991609e-01 8.36565912e-01 9.02341008e-02 -7.46297061e-01
-4.05975401e-01 -6.99191451e-01 -1.02643192e+00 -5.70911109e-01
-4.36779946e-01 4.44963276e-01 5.51369153e-02 -3.84341687... | [4.449658393859863, 2.07556414604187] |
a432dc28-6673-4405-b137-7926ee312333 | variational-autoencoder-with-disentanglement | 2202.13363 | null | https://arxiv.org/abs/2202.13363v3 | https://arxiv.org/pdf/2202.13363v3.pdf | Variational Autoencoder with Disentanglement Priors for Low-Resource Task-Specific Natural Language Generation | In this paper, we propose a variational autoencoder with disentanglement priors, VAE-DPRIOR, for task-specific natural language generation with none or a handful of task-specific labeled examples. In order to tackle compositional generalization across tasks, our model performs disentangled representation learning by in... | ['Gholamreza Haffari', 'Tianyang Zhan', 'Tongtong Wu', 'Qiongkai Xu', 'Lizhen Qu', 'Zhuang Li'] | 2022-02-27 | null | null | null | null | ['text-style-transfoer'] | ['natural-language-processing'] | [ 4.65411842e-01 2.79013097e-01 -2.04988822e-01 -2.55721390e-01
-1.01185143e+00 -4.39174980e-01 1.00989389e+00 -5.11051655e-01
-3.80675077e-01 9.54918563e-01 7.56718040e-01 3.33895385e-02
1.97029501e-01 -7.60146618e-01 -5.42060912e-01 -7.74429917e-01
7.04171062e-01 8.34912956e-01 -3.82564694e-01 -3.70403320... | [11.808056831359863, 9.090681076049805] |
3cf4b375-e285-4e51-b84b-aec61a6fa69c | naist-at-the-nli-2013-shared-task | null | null | https://aclanthology.org/W13-1717 | https://aclanthology.org/W13-1717.pdf | NAIST at the NLI 2013 Shared Task | null | ['Yuji Matsumoto', 'Keisuke Sakaguchi', 'Tomoya Mizumoto', 'Yuta Hayashibe', 'Mamoru Komachi'] | 2013-06-01 | null | null | null | ws-2013-6 | ['grammatical-error-detection'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
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-3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01
-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.198925495147705, 3.846264600753784] |
5932606e-21ba-4a5e-8726-49723a9f7cc9 | hignn-hierarchical-informative-graph-neural | 2208.13994 | null | https://arxiv.org/abs/2208.13994v1 | https://arxiv.org/pdf/2208.13994v1.pdf | HiGNN: Hierarchical Informative Graph Neural Networks for Molecular Property Prediction Equipped with Feature-Wise Attention | Elucidating and accurately predicting the druggability and bioactivities of molecules plays a pivotal role in drug design and discovery and remains an open challenge. Recently, graph neural networks (GNN) have made remarkable advancements in graph-based molecular property prediction. However, current graph-based deep l... | ['Ling Wang', 'Jianrong Xu', 'Duancheng Zhao', 'Yi Zhang', 'Weimin Zhu'] | 2022-08-30 | null | null | null | null | ['molecular-property-prediction'] | ['miscellaneous'] | [ 2.87702858e-01 -1.18487708e-01 -6.54132545e-01 -2.82791138e-01
-4.40132231e-01 -4.04723644e-01 2.30316013e-01 6.86418355e-01
2.25598976e-01 9.85686898e-01 7.52946138e-02 -7.65309215e-01
-4.37016696e-01 -9.28794026e-01 -9.15562689e-01 -9.39336717e-01
-2.90138096e-01 2.77233899e-01 -1.64275542e-01 -1.35806903... | [5.15496301651001, 5.8891496658325195] |
e1290e57-20f3-4255-a3a9-c6e34ae82095 | a-self-supervised-automatic-post-editing-data | 2111.12284 | null | https://arxiv.org/abs/2111.12284v2 | https://arxiv.org/pdf/2111.12284v2.pdf | A Self-Supervised Automatic Post-Editing Data Generation Tool | Data building for automatic post-editing (APE) requires extensive and expert-level human effort, as it contains an elaborate process that involves identifying errors in sentences and providing suitable revisions. Hence, we develop a self-supervised data generation tool, deployable as a web application, that minimizes h... | ['Heuiseok Lim', 'Seungjun Lee', 'Jaehyung Seo', 'Sugyeong Eo', 'Chanjun Park', 'Hyeonseok Moon'] | 2021-11-24 | null | null | null | null | ['automatic-post-editing', 'automatic-post-editing'] | ['computer-vision', 'natural-language-processing'] | [ 2.54641414e-01 3.81219923e-01 -6.57731481e-03 -4.50182050e-01
-7.76415884e-01 -5.05111098e-01 7.12332904e-01 6.43228590e-01
-6.41430736e-01 9.16405320e-01 3.80465090e-01 -4.76509482e-01
-2.70237643e-02 -4.80194956e-01 -5.08429289e-01 2.64518499e-01
3.45968783e-01 7.28173077e-01 2.98214942e-01 -7.37068415... | [11.227923393249512, 10.35571575164795] |
9f066268-2d7f-41ee-be35-718fe6b257b5 | machine-learning-with-guarantees-using | 1609.02664 | null | http://arxiv.org/abs/1609.02664v1 | http://arxiv.org/pdf/1609.02664v1.pdf | Machine Learning with Guarantees using Descriptive Complexity and SMT Solvers | Machine learning is a thriving part of computer science. There are many
efficient approaches to machine learning that do not provide strong theoretical
guarantees, and a beautiful general learning theory. Unfortunately, machine
learning approaches that give strong theoretical guarantees have not been
efficient enough t... | ['Łukasz Kaiser', 'Charles Jordan'] | 2016-09-09 | null | null | null | null | ['board-games'] | ['playing-games'] | [ 1.88294113e-01 8.18030119e-01 -4.66429085e-01 -5.04050374e-01
-6.55420959e-01 -4.68458474e-01 2.93717414e-01 -1.90762721e-03
-2.03055173e-01 9.15582776e-01 -4.14532304e-01 -9.26771283e-01
-5.44729948e-01 -1.19476652e+00 -1.00270391e+00 -4.22052592e-01
-2.80635923e-01 7.29446113e-01 4.80866313e-01 -1.78552002... | [8.757682800292969, 7.010929584503174] |
b8678a73-deb5-4f7e-91f1-44c9adedded7 | superquadric-object-representation-for | 2109.09627 | null | https://arxiv.org/abs/2109.09627v1 | https://arxiv.org/pdf/2109.09627v1.pdf | Superquadric Object Representation for Optimization-based Semantic SLAM | Introducing semantically meaningful objects to visual Simultaneous Localization And Mapping (SLAM) has the potential to improve both the accuracy and reliability of pose estimates, especially in challenging scenarios with significant view-point and appearance changes. However, how semantic objects should be represented... | ['Cesar Cadena', 'Roland Siegwart', 'Juan Nieto', 'Florian Tschopp'] | 2021-09-20 | null | null | null | null | ['semantic-slam'] | ['computer-vision'] | [ 8.33154321e-02 -5.06509542e-01 -2.92705577e-02 -5.48146665e-01
-8.16463292e-01 -8.71401906e-01 6.43153787e-01 6.41928613e-02
-3.39713424e-01 4.08839285e-01 -1.89416677e-01 9.66990367e-02
-8.24762583e-02 -3.30124289e-01 -8.63990843e-01 -3.62191975e-01
2.39356160e-01 1.17422664e+00 3.83637130e-01 -2.47815132... | [7.378938674926758, -2.4020793437957764] |
763f258d-6706-4e68-be89-6064ff204f4b | a-hybrid-learning-rule-for-efficient-and | 1907.01167 | null | https://arxiv.org/abs/1907.01167v3 | https://arxiv.org/pdf/1907.01167v3.pdf | A Tandem Learning Rule for Effective Training and Rapid Inference of Deep Spiking Neural Networks | Spiking neural networks (SNNs) represent the most prominent biologically inspired computing model for neuromorphic computing (NC) architectures. However, due to the non-differentiable nature of spiking neuronal functions, the standard error back-propagation algorithm is not directly applicable to SNNs. In this work, we... | ['Haizhou Li', 'Guoqi Li', 'Malu Zhang', 'Jibin Wu', 'Yansong Chua', 'Kay Chen Tan'] | 2019-07-02 | null | null | null | null | ['event-based-vision'] | ['computer-vision'] | [ 5.11088848e-01 -6.21423602e-01 6.06077671e-01 -2.13927448e-01
7.29286075e-02 -1.40955806e-01 4.09621596e-01 1.05866984e-01
-9.56901431e-01 1.00197887e+00 -7.58962810e-01 -2.96950992e-02
9.67909545e-02 -9.80341792e-01 -1.00489783e+00 -1.09254646e+00
3.95692050e-01 1.41767174e-01 7.38173664e-01 6.94935173... | [8.218690872192383, 2.487032413482666] |
e6fb963b-f52d-466e-b0e9-a4de03517e97 | a-time-series-analysis-based-stock-price | 2004.11697 | null | https://arxiv.org/abs/2004.11697v2 | https://arxiv.org/pdf/2004.11697v2.pdf | A Time Series Analysis-Based Stock Price Prediction Using Machine Learning and Deep Learning Models | Prediction of future movement of stock prices has always been a challenging task for the researchers. While the advocates of the efficient market hypothesis (EMH) believe that it is impossible to design any predictive framework that can accurately predict the movement of stock prices, there are seminal work in the lite... | ['Sidra Mehtab', 'Jaydip Sen'] | 2020-04-17 | null | null | null | null | ['stock-price-prediction'] | ['time-series'] | [-8.29804599e-01 -4.82070595e-01 -8.16525295e-02 -2.92576641e-01
-4.54644829e-01 -5.82134962e-01 6.47288859e-01 -2.07810655e-01
-1.39989272e-01 8.14838052e-01 1.98847279e-01 -9.00751352e-01
-3.33742768e-01 -1.10681617e+00 -5.25464833e-01 -7.24679530e-01
-3.84892792e-01 6.23799562e-01 -1.31405313e-02 -4.10320163... | [4.4752936363220215, 4.225456237792969] |
91c90b3a-7b2d-4144-ae0a-e89be5c0c5d2 | a-comparative-study-on-e-branchformer-vs | 2305.11073 | null | https://arxiv.org/abs/2305.11073v1 | https://arxiv.org/pdf/2305.11073v1.pdf | A Comparative Study on E-Branchformer vs Conformer in Speech Recognition, Translation, and Understanding Tasks | Conformer, a convolution-augmented Transformer variant, has become the de facto encoder architecture for speech processing due to its superior performance in various tasks, including automatic speech recognition (ASR), speech translation (ST) and spoken language understanding (SLU). Recently, a new encoder called E-Bra... | ['Shinji Watanabe', 'Prashant Sridhar', 'Suwon Shon', 'Jiyang Tang', 'William Chen', 'Siddhant Arora', 'Brian Yan', 'Felix Wu', 'Kwangyoun Kim', 'Yifan Peng'] | 2023-05-18 | null | null | null | null | ['spoken-language-understanding', 'spoken-language-understanding'] | ['natural-language-processing', 'speech'] | [ 4.04210538e-01 -1.19742438e-01 -1.33864582e-01 -6.44858778e-01
-1.02177203e+00 -5.74308276e-01 7.14937329e-01 -2.34242886e-01
-6.06956661e-01 4.44813281e-01 7.01283097e-01 -8.77462506e-01
3.73714149e-01 -1.73055261e-01 -5.94081521e-01 -2.31899723e-01
-7.62889087e-02 5.02408743e-01 1.97891165e-02 -3.62025112... | [14.47826099395752, 6.956158638000488] |
9ce6ee86-d9b9-4c4f-9e68-efa365a40dd5 | an-image-is-worth-16x16-words-transformers-1 | 2010.11929 | null | https://arxiv.org/abs/2010.11929v2 | https://arxiv.org/pdf/2010.11929v2.pdf | An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale | While the Transformer architecture has become the de-facto standard for natural language processing tasks, its applications to computer vision remain limited. In vision, attention is either applied in conjunction with convolutional networks, or used to replace certain components of convolutional networks while keeping ... | ['Neil Houlsby', 'Jakob Uszkoreit', 'Sylvain Gelly', 'Georg Heigold', 'Matthias Minderer', 'Mostafa Dehghani', 'Thomas Unterthiner', 'Xiaohua Zhai', 'Dirk Weissenborn', 'Alexander Kolesnikov', 'Lucas Beyer', 'Alexey Dosovitskiy'] | 2020-10-22 | an-image-is-worth-16x16-words-transformers | https://openreview.net/forum?id=YicbFdNTTy | https://openreview.net/pdf?id=YicbFdNTTy | iclr-2021-1 | ['document-image-classification'] | ['computer-vision'] | [ 3.18294197e-01 -1.40647992e-01 5.97956143e-02 -2.50711054e-01
-3.29235792e-01 -4.66240525e-01 7.23752737e-01 -2.70823568e-01
-7.77436078e-01 4.40601468e-01 -2.65250951e-01 -6.72875702e-01
3.14485699e-01 -7.76778162e-01 -7.09852099e-01 -5.83785295e-01
3.24158132e-01 2.87286639e-01 5.70572019e-01 -2.67358452... | [9.390957832336426, 1.7169214487075806] |
fa2890df-11ce-4dd2-a145-dc2fafe8f359 | a-multimodal-framework-for-video-ads | 2108.12868 | null | https://arxiv.org/abs/2108.12868v1 | https://arxiv.org/pdf/2108.12868v1.pdf | A Multimodal Framework for Video Ads Understanding | There is a growing trend in placing video advertisements on social platforms for online marketing, which demands automatic approaches to understand the contents of advertisements effectively. Taking the 2021 TAAC competition as an opportunity, we developed a multimodal system to improve the ability of structured analys... | ['Yu-Gang Jiang', 'Zuxuan Wu', 'Rui Wang', 'Lingchen Meng', 'Zejia Weng'] | 2021-08-29 | null | null | null | null | ['scene-segmentation'] | ['computer-vision'] | [ 5.32959282e-01 -9.42679718e-02 -1.97089717e-01 -6.09862328e-01
-1.16978419e+00 -6.88059092e-01 6.37198269e-01 1.90036878e-01
-5.05092919e-01 9.33095813e-02 3.08708489e-01 -2.12490946e-01
1.16002165e-01 -3.68710756e-01 -7.05135584e-01 -4.76673365e-01
-1.28631249e-01 -1.44080166e-03 4.22918290e-01 1.54327052... | [9.998601913452148, 0.6448584198951721] |
f8f1acd0-420e-4a76-981a-7e92d8f81857 | relational-message-passing-for-fully | 2210.03994 | null | https://arxiv.org/abs/2210.03994v2 | https://arxiv.org/pdf/2210.03994v2.pdf | Relational Message Passing for Fully Inductive Knowledge Graph Completion | In knowledge graph completion (KGC), predicting triples involving emerging entities and/or relations, which are unseen when the KG embeddings are learned, has become a critical challenge. Subgraph reasoning with message passing is a promising and popular solution. Some recent methods have achieved good performance, but... | ['Jeff Z. Pan', 'Song Jiang', 'Huajun Chen', 'Mingyang Chen', 'Wen Zhang', 'Jiaoyan Chen', 'Yuxia Geng'] | 2022-10-08 | null | null | null | null | ['inductive-knowledge-graph-completion'] | ['knowledge-base'] | [ 1.74425095e-02 6.35560930e-01 -4.18839008e-01 -2.44172692e-01
-3.60701978e-01 -2.73340970e-01 5.52441597e-01 6.35676324e-01
-1.62660152e-01 9.16857481e-01 2.67806888e-01 -3.62435013e-01
-4.04730976e-01 -1.49478817e+00 -1.03106034e+00 -2.83875197e-01
-5.51904798e-01 8.49087596e-01 6.03894532e-01 -4.27029371... | [8.854522705078125, 7.949342727661133] |
4be333e8-0427-40ed-a7e3-e4d70080bb4d | an-edge-map-based-ensemble-solution-to-detect | 2201.06098 | null | https://arxiv.org/abs/2201.06098v1 | https://arxiv.org/pdf/2201.06098v1.pdf | An Edge Map based Ensemble Solution to Detect Water Level in Stream | Flooding is one of the most dangerous weather events today. Between $2015-2019$, on average, flooding has caused more than $130$ deaths every year in the USA alone. The devastating nature of flood necessitates the continuous monitoring of water level in the rivers and streams to detect the incoming flood. In this work,... | ['David Koop', 'Michael. E. Papka', 'Priyanjani Chandra', 'Pratool Bharti'] | 2022-01-16 | null | null | null | null | ['template-matching'] | ['computer-vision'] | [-2.08640665e-01 -8.62248316e-02 6.47813618e-01 -3.45600307e-01
-4.03086990e-01 -4.70391929e-01 1.09720312e-01 4.11860436e-01
-8.26734662e-01 6.58613086e-01 -2.17158169e-01 -2.89206028e-01
4.17817049e-02 -1.42419708e+00 -2.42405042e-01 -7.03377724e-01
-4.98918861e-01 -1.75440148e-01 1.77360207e-01 -5.04964173... | [9.410316467285156, -1.4031308889389038] |
66eebde2-7613-432f-ab0a-647e6e231407 | exclusive-independent-probability-estimation | 1809.08168 | null | http://arxiv.org/abs/1809.08168v3 | http://arxiv.org/pdf/1809.08168v3.pdf | Exclusive Independent Probability Estimation using Deep 3D Fully Convolutional DenseNets: Application to IsoIntense Infant Brain MRI Segmentation | The most recent fast and accurate image segmentation methods are built upon
fully convolutional deep neural networks. In this paper, we propose new deep
learning strategies for DenseNets to improve segmenting images with subtle
differences in intensity values and features. We aim to segment brain tissue on
infant brain... | ['Seyed Raein Hashemi', 'Ali Gholipour', 'Simon K. Warfield', 'Sanjay P. Prabhu'] | 2018-09-21 | null | null | null | null | ['infant-brain-mri-segmentation'] | ['medical'] | [ 3.95304769e-01 3.49817336e-01 -5.07746786e-02 -6.09219551e-01
-5.63309133e-01 -4.15932626e-01 4.73606773e-02 3.84801239e-01
-8.58200371e-01 5.34125149e-01 -4.54370558e-01 -2.62044936e-01
-1.94917023e-01 -5.36031544e-01 -8.13940823e-01 -5.72709143e-01
-2.05992535e-01 8.38626564e-01 6.66699469e-01 1.16928816... | [14.21423053741455, -2.357029676437378] |
3282f1c0-72bb-4d36-a34c-15a8a649a4d5 | continual-learning-in-task-oriented-dialogue | 2012.15504 | null | https://arxiv.org/abs/2012.15504v1 | https://arxiv.org/pdf/2012.15504v1.pdf | Continual Learning in Task-Oriented Dialogue Systems | Continual learning in task-oriented dialogue systems can allow us to add new domains and functionalities through time without incurring the high cost of a whole system retraining. In this paper, we propose a continual learning benchmark for task-oriented dialogue systems with 37 domains to be learned continuously in fo... | ['Zhiguang Wang', 'Eunjoon Cho', 'Zhou Yu', 'Bing Liu', 'Paul Crook', 'Seungwhan Moon', 'Zhenpeng Zhou', 'Zhaojiang Lin', 'Andrea Madotto'] | 2020-12-31 | null | https://aclanthology.org/2021.emnlp-main.590 | https://aclanthology.org/2021.emnlp-main.590.pdf | emnlp-2021-11 | ['intent-recognition'] | ['natural-language-processing'] | [ 1.24131851e-01 2.29453370e-01 -1.04788905e-02 -4.60623860e-01
-8.66838098e-01 -9.02381122e-01 1.08651614e+00 -2.07177568e-02
-5.37090421e-01 9.28496361e-01 2.64691293e-01 -5.82879484e-01
3.99749801e-02 -1.57354981e-01 -3.31284374e-01 -4.44171101e-01
-1.65038958e-01 8.00236166e-01 4.92708653e-01 -8.50123882... | [12.856510162353516, 8.003944396972656] |
cab2fae5-9cc1-4553-9531-e89ab4e8f6b1 | training-deep-spiking-neural-networks-using | 1608.08782 | null | http://arxiv.org/abs/1608.08782v1 | http://arxiv.org/pdf/1608.08782v1.pdf | Training Deep Spiking Neural Networks using Backpropagation | Deep spiking neural networks (SNNs) hold great potential for improving the
latency and energy efficiency of deep neural networks through event-based
computation. However, training such networks is difficult due to the
non-differentiable nature of asynchronous spike events. In this paper, we
introduce a novel technique,... | ['Tobi Delbruck', 'Michael Pfeiffer', 'Jun Haeng Lee'] | 2016-08-31 | null | null | null | null | ['event-based-vision'] | ['computer-vision'] | [ 7.57435501e-01 -5.13982594e-01 3.58770013e-01 -9.19274986e-02
-2.66752273e-01 -6.82494938e-01 6.41578853e-01 3.32078040e-01
-8.68768573e-01 1.07746947e+00 -4.09258306e-01 5.39862551e-03
-5.25321178e-02 -1.09316289e+00 -1.20509207e+00 -1.06429076e+00
-7.13985413e-02 1.03132360e-01 5.64907789e-01 1.87719297... | [8.201155662536621, 2.483377456665039] |
bb5f3b2e-c87f-41b8-85b5-e0c003facb9f | triplettrack-3d-object-tracking-using-triplet | 2210.16204 | null | https://arxiv.org/abs/2210.16204v1 | https://arxiv.org/pdf/2210.16204v1.pdf | TripletTrack: 3D Object Tracking using Triplet Embeddings and LSTM | 3D object tracking is a critical task in autonomous driving systems. It plays an essential role for the system's awareness about the surrounding environment. At the same time there is an increasing interest in algorithms for autonomous cars that solely rely on inexpensive sensors, such as cameras. In this paper we inve... | ['Luc van Gool', 'Marc Proesmans', 'Nicola Marinello'] | 2022-10-28 | null | null | null | null | ['3d-object-tracking'] | ['computer-vision'] | [-4.44709241e-01 -3.55284512e-01 -1.64848208e-01 -3.59530509e-01
-3.68538588e-01 -7.54877985e-01 6.79409444e-01 1.07428104e-01
-7.15044320e-01 8.30438957e-02 -3.06670010e-01 -1.02968693e-01
1.76662147e-01 -5.80400646e-01 -7.45056570e-01 -6.91454530e-01
6.15833998e-02 5.14756143e-01 1.10587919e+00 -1.59668431... | [6.682973384857178, -2.1865553855895996] |
9f5042de-b6fd-4962-9b63-98022abaea28 | pretrain-kges-learning-knowledge | null | null | https://openreview.net/forum?id=HJlv-Fz-pS | https://openreview.net/pdf?id=HJlv-Fz-pS | Pretrain-KGEs: Learning Knowledge Representation from Pretrained Models for Knowledge Graph Embeddings | Learning knowledge graph embeddings (KGEs) is an efficient approach to knowledge graph completion. Conventional KGEs often suffer from limited knowledge representation, which causes less accuracy especially when training on sparse knowledge graphs. To remedy this, we present Pretrain-KGEs, a training framework for lear... | ['Bin He', 'Xu sun', 'Qi Su', 'Yi Zhang', 'Xiaoqian Liu', 'Zhiyuan Zhang'] | 2019-12-01 | null | null | null | null | ['knowledge-graph-embeddings', 'knowledge-graph-embeddings'] | ['graphs', 'methodology'] | [-2.95004159e-01 2.65888095e-01 -6.91613376e-01 -1.80136517e-01
-2.53851146e-01 -3.53174120e-01 3.88393641e-01 3.68733943e-01
-3.91201049e-01 6.82807446e-01 1.67177990e-01 -3.01373571e-01
-3.94088715e-01 -1.04425728e+00 -9.07327473e-01 -1.74304232e-01
-1.38611585e-01 6.59951150e-01 1.57494739e-01 -2.66316026... | [8.791321754455566, 7.90716552734375] |
10003c79-8a49-42f8-98a5-cd9d0f1dc2ec | qt30-a-corpus-of-argument-and-conflict-in | null | null | https://aclanthology.org/2022.lrec-1.352 | https://aclanthology.org/2022.lrec-1.352.pdf | QT30: A Corpus of Argument and Conflict in Broadcast Debate | Broadcast political debate is a core pillar of democracy: it is the public’s easiest access to opinions that shape policies and enables the general public to make informed choices. With QT30, we present the largest corpus of analysed dialogical argumentation ever created (19,842 utterances, 280,000 words) and also the ... | ['Chris Reed', 'Ray Becker', 'Kamila Gorska', 'Wassiliki Siskou', 'Zlata Kikteva', 'Annette Hautli-Janisz'] | null | null | null | null | lrec-2022-6 | ['argument-mining'] | ['natural-language-processing'] | [-6.86563328e-02 1.05774009e+00 -4.57070231e-01 -3.99115324e-01
-1.13775909e+00 -1.39956760e+00 1.59510446e+00 7.30763912e-01
-4.31763917e-01 1.15426266e+00 1.70222139e+00 -1.30325091e+00
-2.49101669e-01 -6.26128495e-01 -3.58741581e-01 -4.36321527e-01
5.44329405e-01 8.14166367e-01 5.35526536e-02 -1.05514371... | [9.072022438049316, 9.833664894104004] |
d9c721a2-acbd-43a6-abae-1651b3b5d8fd | clustering-for-graph-datasets-via-gumbel | 2005.02372 | null | https://arxiv.org/abs/2005.02372v2 | https://arxiv.org/pdf/2005.02372v2.pdf | Community Detection Clustering via Gumbel Softmax | Recently, in many systems such as speech recognition and visual processing, deep learning has been widely implemented. In this research, we are exploring the possibility of using deep learning in community detection among the graph datasets. Graphs have gained growing traction in different fields, including social netw... | ['Deepak Bhaskar Acharya', 'Huaming Zhang'] | 2020-05-05 | null | null | null | null | ['miscellaneous'] | ['miscellaneous'] | [-4.06199276e-01 9.24379304e-02 -1.99176893e-02 -1.54931858e-01
4.72420573e-01 -5.04067838e-01 6.65118098e-01 4.57236588e-01
-3.08211923e-01 4.62464541e-01 2.17024043e-01 -3.77519190e-01
-5.99506319e-01 -9.89902556e-01 -3.43957514e-01 -6.60084069e-01
-9.57526803e-01 7.91978598e-01 1.42170340e-01 -3.23385268... | [7.075381755828857, 5.925144195556641] |
a9a1da19-6aa5-4a54-be8e-f2c6280e47f5 | mia-2022-shared-task-submission-leveraging | 2207.01940 | null | https://arxiv.org/abs/2207.01940v3 | https://arxiv.org/pdf/2207.01940v3.pdf | MIA 2022 Shared Task Submission: Leveraging Entity Representations, Dense-Sparse Hybrids, and Fusion-in-Decoder for Cross-Lingual Question Answering | We describe our two-stage system for the Multilingual Information Access (MIA) 2022 Shared Task on Cross-Lingual Open-Retrieval Question Answering. The first stage consists of multilingual passage retrieval with a hybrid dense and sparse retrieval strategy. The second stage consists of a reader which outputs the answer... | ['Sarguna Janani Padmanabhan', 'Zhucheng Tu'] | 2022-07-05 | null | https://aclanthology.org/2022.mia-1.10 | https://aclanthology.org/2022.mia-1.10.pdf | naacl-mia-2022-7 | ['cross-lingual-question-answering', 'passage-retrieval'] | ['natural-language-processing', 'natural-language-processing'] | [-4.82946217e-01 -1.53909037e-02 9.87420157e-02 -1.69158444e-01
-2.37660766e+00 -7.67518759e-01 6.35297000e-01 2.89769232e-01
-9.21204150e-01 8.33236039e-01 4.62116688e-01 -3.08052450e-01
-1.18409604e-01 -4.70333934e-01 -1.01709640e+00 -2.38600671e-01
1.55126810e-01 8.76250625e-01 1.56034261e-01 -7.21709430... | [11.451630592346191, 8.320489883422852] |
a469f09b-dd08-4397-959a-aed5da1bca46 | gaining-and-losing-influence-in-online | null | null | https://aclanthology.org/L18-1110 | https://aclanthology.org/L18-1110.pdf | Gaining and Losing Influence in Online Conversation | null | ['Arun Sharma', 'Tomek Strzalkowski'] | 2018-05-01 | gaining-and-losing-influence-in-online-1 | https://aclanthology.org/L18-1110 | https://aclanthology.org/L18-1110.pdf | lrec-2018-5 | ['dialogue-understanding'] | ['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... | [-7.370425224304199, 3.736316680908203] |
6673d2ae-adba-42f0-8701-ac822d49d7fd | improving-few-shot-user-specific-gaze | 1904.10638 | null | http://arxiv.org/abs/1904.10638v1 | http://arxiv.org/pdf/1904.10638v1.pdf | Improving Few-Shot User-Specific Gaze Adaptation via Gaze Redirection Synthesis | As an indicator of human attention gaze is a subtle behavioral cue which can
be exploited in many applications. However, inferring 3D gaze direction is
challenging even for deep neural networks given the lack of large amount of
data (groundtruthing gaze is expensive and existing datasets use different
setups) and the i... | ['Jean-Marc Odobez', 'Yu Yu', 'Gang Liu'] | 2019-04-24 | improving-few-shot-user-specific-gaze-1 | http://openaccess.thecvf.com/content_CVPR_2019/html/Yu_Improving_Few-Shot_User-Specific_Gaze_Adaptation_via_Gaze_Redirection_Synthesis_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Yu_Improving_Few-Shot_User-Specific_Gaze_Adaptation_via_Gaze_Redirection_Synthesis_CVPR_2019_paper.pdf | cvpr-2019-6 | ['gaze-redirection'] | ['computer-vision'] | [ 3.15726191e-01 6.97242990e-02 -7.30171055e-02 -6.67567432e-01
-2.00601950e-01 -4.21626747e-01 5.01728058e-01 -4.60056603e-01
-3.09288681e-01 8.30092192e-01 2.10626647e-01 7.64558837e-02
5.32864965e-03 -2.61120796e-01 -8.18953574e-01 -4.82271105e-01
3.66990179e-01 -4.59770346e-03 7.31591955e-02 -2.31769472... | [14.12009048461914, 0.0506814680993557] |
03e96ef4-913b-433b-a664-26bb0ad18a3b | indoor-positioning-via-gradient-boosting | 2211.08752 | null | https://arxiv.org/abs/2211.08752v1 | https://arxiv.org/pdf/2211.08752v1.pdf | Indoor Positioning via Gradient Boosting Enhanced with Feature Augmentation using Deep Learning | With the emerge of the Internet of Things (IoT), localization within indoor environments has become inevitable and has attracted a great deal of attention in recent years. Several efforts have been made to cope with the challenges of accurate positioning systems in the presence of signal interference. In this paper, we... | ['Pedro H. J. Nardelli', 'Mehdi Rasti', 'Jaber Babaki', 'Ashkan Goharfar'] | 2022-11-16 | null | null | null | null | ['indoor-localization'] | ['computer-vision'] | [-6.76917732e-02 -3.50959808e-01 1.61057517e-01 -6.98267996e-01
-4.58683580e-01 -7.79180005e-02 3.26519161e-01 6.45237043e-02
-6.40184104e-01 1.26957881e+00 -7.03958645e-02 -5.60300648e-01
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-1.14942141e-01 1.73128828e-01 -4.69179451e-02 -1.99038282... | [6.427677631378174, 0.9303526878356934] |
4c13f85b-15e6-461a-b0f4-791bb9f557cc | improving-rgb-d-point-cloud-registration-by | 2208.14893 | null | https://arxiv.org/abs/2208.14893v2 | https://arxiv.org/pdf/2208.14893v2.pdf | Improving RGB-D Point Cloud Registration by Learning Multi-scale Local Linear Transformation | Point cloud registration aims at estimating the geometric transformation between two point cloud scans, in which point-wise correspondence estimation is the key to its success. In addition to previous methods that seek correspondences by hand-crafted or learnt geometric features, recent point cloud registration methods... | ['Dong Xu', 'Lu Sheng', 'Jing Zhang', 'Zhenghao Chen', 'Xiaoliang Huo', 'ZiMing Wang'] | 2022-08-31 | null | null | null | null | ['point-cloud-registration'] | ['computer-vision'] | [-1.68288335e-01 -4.04187828e-01 -1.20455259e-02 -5.13754189e-01
-9.52992022e-01 -4.78497624e-01 6.00722730e-01 2.13706002e-01
-4.14361566e-01 5.59063479e-02 -7.28115514e-02 4.44164798e-02
-1.43625826e-01 -9.15180206e-01 -6.47994936e-01 -6.67591214e-01
2.91611493e-01 5.48707485e-01 1.86901495e-01 -2.32261375... | [7.678435802459717, -3.014394998550415] |
62e5215d-11b6-4037-8e67-865f710cebb3 | an-upper-bound-for-the-distribution-overlap | 2212.08701 | null | https://arxiv.org/abs/2212.08701v2 | https://arxiv.org/pdf/2212.08701v2.pdf | An Upper Bound for the Distribution Overlap Index and Its Applications | This paper proposes an easy-to-compute upper bound for the overlap index between two probability distributions without requiring any knowledge of the distribution models. The computation of our bound is time-efficient and memory-efficient and only requires finite samples. The proposed bound shows its value in one-class... | ['Farshad Khorrami', 'Siddharth Garg', 'Prashanth Krishnamurthy', 'Hao Fu'] | 2022-12-16 | null | null | null | null | ['one-class-classifier', 'one-class-classification'] | ['methodology', 'miscellaneous'] | [ 4.20571893e-01 -2.65275210e-01 -5.30236244e-01 -5.43047488e-01
-8.78472686e-01 -7.62001812e-01 3.37788194e-01 4.02617604e-01
-3.21096808e-01 8.69444072e-01 -7.60579646e-01 -5.42411745e-01
-4.42051649e-01 -8.81233633e-01 -5.15147567e-01 -8.12275589e-01
-1.03989281e-01 7.54326582e-01 7.04744399e-01 3.25235903... | [8.22064208984375, 4.164764881134033] |
bba063a4-7a8f-41b3-849d-5268555a70d4 | a-convex-and-feature-rich-discriminative | null | null | https://aclanthology.org/P15-1133 | https://aclanthology.org/P15-1133.pdf | A convex and feature-rich discriminative approach to dependency grammar induction | null | ["No{\\'e}mie Elhadad", "{\\'E}douard Grave"] | 2015-07-01 | a-convex-and-feature-rich-discriminative-1 | https://aclanthology.org/P15-1133 | https://aclanthology.org/P15-1133.pdf | ijcnlp-2015-7 | ['dependency-grammar-induction'] | ['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... | [-7.275077819824219, 3.8479018211364746] |
f7b08aec-efdc-4efe-8caf-41c3e53e746f | sas-video-qa-self-adaptive-sampling-for | 2307.04192 | null | https://arxiv.org/abs/2307.04192v1 | https://arxiv.org/pdf/2307.04192v1.pdf | SAS Video-QA: Self-Adaptive Sampling for Efficient Video Question-Answering | Video question--answering is a fundamental task in the field of video understanding. Although current vision--language models (VLMs) equipped with Video Transformers have enabled temporal modeling and yielded superior results, they are at the cost of huge computational power and thus too expensive to deploy in real-tim... | ['Soujanya Poria', 'Min-Yen Kan', 'Hui Chen', 'Wei Han'] | 2023-07-09 | null | null | null | null | ['visual-question-answering', 'video-question-answering', 'video-understanding', 'question-answering'] | ['computer-vision', 'computer-vision', 'computer-vision', 'natural-language-processing'] | [ 2.68434018e-01 -4.26008590e-02 -3.65560472e-01 -3.26964080e-01
-7.73405552e-01 -3.97838712e-01 3.55406791e-01 -1.56033188e-01
-4.30719525e-01 3.92046720e-01 1.97168753e-01 -3.27459365e-01
1.76431984e-01 -6.00500584e-01 -1.01067960e+00 -7.04118609e-01
1.90210477e-01 1.88412234e-01 4.45624799e-01 -6.94115739... | [10.098535537719727, 0.7492615580558777] |
02d0c090-6205-4747-8329-39e346790d6a | deep-autoregressive-models-for-the-efficient | 1902.04057 | null | https://arxiv.org/abs/1902.04057v3 | https://arxiv.org/pdf/1902.04057v3.pdf | Deep autoregressive models for the efficient variational simulation of many-body quantum systems | Artificial Neural Networks were recently shown to be an efficient representation of highly-entangled many-body quantum states. In practical applications, neural-network states inherit numerical schemes used in Variational Monte Carlo, most notably the use of Markov-Chain Monte-Carlo (MCMC) sampling to estimate quantum ... | ['Yoav Levine', 'Or Sharir', 'Noam Wies', 'Amnon Shashua', 'Giuseppe Carleo'] | 2019-02-11 | null | null | null | null | ['variational-monte-carlo'] | ['miscellaneous'] | [ 1.69743359e-01 1.64092090e-02 -5.86856417e-02 -1.89323246e-01
-7.11735427e-01 -2.02409506e-01 7.70699322e-01 -3.23595285e-01
-7.47620046e-01 1.28501225e+00 4.66376124e-03 -4.28618670e-01
-4.00115103e-02 -1.15113640e+00 -6.14536226e-01 -1.01818621e+00
-7.86706656e-02 1.14968348e+00 9.79849845e-02 -7.85113573... | [5.614297866821289, 4.909027099609375] |
a05462ae-955a-42be-8e60-835ab0b85678 | hi-fi-hierarchical-feature-integration-for | 1801.01849 | null | http://arxiv.org/abs/1801.01849v4 | http://arxiv.org/pdf/1801.01849v4.pdf | Hi-Fi: Hierarchical Feature Integration for Skeleton Detection | In natural images, the scales (thickness) of object skeletons may
dramatically vary among objects and object parts, making object skeleton
detection a challenging problem. We present a new convolutional neural network
(CNN) architecture by introducing a novel hierarchical feature integration
mechanism, named Hi-Fi, to ... | ['Shang-Hua Gao', 'Ming-Ming Cheng', 'Wei Shen', 'Dandan Li', 'Kai Zhao'] | 2018-01-05 | null | null | null | null | ['object-skeleton-detection'] | ['computer-vision'] | [ 1.22782141e-01 -1.88469052e-01 -1.92041442e-01 -2.51712829e-01
-5.45843542e-01 -8.60934630e-02 4.27608818e-01 6.95679933e-02
-2.67381549e-01 2.93663442e-01 2.85062939e-01 3.86592418e-01
-1.60018995e-01 -1.02567172e+00 -5.97266078e-01 -5.67183495e-01
-1.88178360e-01 9.44269598e-02 1.14502823e+00 -1.60485595... | [9.558755874633789, -0.5878788828849792] |
643002ca-711e-43a1-b8f4-aca90a83d4c2 | mixture-encoder-for-joint-speech-separation | 2306.12173 | null | https://arxiv.org/abs/2306.12173v1 | https://arxiv.org/pdf/2306.12173v1.pdf | Mixture Encoder for Joint Speech Separation and Recognition | Multi-speaker automatic speech recognition (ASR) is crucial for many real-world applications, but it requires dedicated modeling techniques. Existing approaches can be divided into modular and end-to-end methods. Modular approaches separate speakers and recognize each of them with a single-speaker ASR system. End-to-en... | ['Reinhold Haeb-Umbach', 'Ralf Schlüter', 'Christoph Boeddeker', 'Peter Vieting', 'Simon Berger'] | 2023-06-21 | null | null | null | null | ['speech-separation', 'automatic-speech-recognition'] | ['speech', 'speech'] | [ 3.51740360e-01 1.77357361e-01 8.48883316e-02 -6.68003738e-01
-1.39003694e+00 -4.25812483e-01 5.71321487e-01 -1.94522902e-01
-5.08947849e-01 6.85526803e-02 3.64238977e-01 -7.63406038e-01
4.60416794e-01 1.34330034e-01 -4.90294576e-01 -4.80186164e-01
1.98825523e-01 4.48778838e-01 2.35137120e-01 -4.63438034... | [14.599628448486328, 6.335084915161133] |
7e739eb6-4649-47df-98dc-47960639b459 | shifted-diffusion-for-text-to-image | 2211.15388 | null | https://arxiv.org/abs/2211.15388v2 | https://arxiv.org/pdf/2211.15388v2.pdf | Shifted Diffusion for Text-to-image Generation | We present Corgi, a novel method for text-to-image generation. Corgi is based on our proposed shifted diffusion model, which achieves better image embedding generation from input text. Unlike the baseline diffusion model used in DALL-E 2, our method seamlessly encodes prior knowledge of the pre-trained CLIP model in it... | ['Jinhui Xu', 'Changyou Chen', 'Xiao Yang', 'Yizhe Zhu', 'Bingchen Liu', 'Yufan Zhou'] | 2022-11-24 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Zhou_Shifted_Diffusion_for_Text-to-Image_Generation_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Zhou_Shifted_Diffusion_for_Text-to-Image_Generation_CVPR_2023_paper.pdf | cvpr-2023-1 | ['zero-shot-text-to-image-generation'] | ['natural-language-processing'] | [ 3.56932908e-01 3.68184358e-01 -1.35657728e-01 1.32890731e-01
-9.58450079e-01 -6.03850424e-01 1.17908406e+00 -4.13303941e-01
-3.66239876e-01 6.98052824e-01 4.49411482e-01 -1.88721761e-01
4.80244637e-01 -8.93036008e-01 -8.12742710e-01 -6.20864511e-01
3.46564978e-01 5.36176085e-01 9.41240042e-02 -3.30045491... | [11.294049263000488, 0.06761688739061356] |
c2b45b6a-4893-4d63-9f87-a17d651f3abf | decomposing-cryptocurrency-dynamics-into | 2306.17095 | null | https://arxiv.org/abs/2306.17095v1 | https://arxiv.org/pdf/2306.17095v1.pdf | Decomposing cryptocurrency dynamics into recurring and noisy components | This paper investigates the temporal patterns of activity in the cryptocurrency market with a focus on bitcoin, ether, dogecoin, and winklink from January 2020 to December 2022. Market activity measures - logarithmic returns, volume, and transaction number, sampled every 10 seconds, were divided into intraday and intra... | ['Stanisław Drożdż', 'Jarosław Kwapień', 'Maria Skupień', 'Marcin Wątorek'] | 2023-06-29 | null | null | null | null | ['algorithmic-trading'] | ['time-series'] | [-6.84230268e-01 -1.88585758e-01 -1.05075292e-01 3.20706964e-01
-1.95627615e-01 -1.09746468e+00 1.14411676e+00 8.68854448e-02
-8.84509161e-02 8.55654180e-01 4.00942922e-01 -5.32544255e-01
-4.56009209e-01 -6.41467810e-01 -1.78694814e-01 -7.30460286e-01
-7.08951712e-01 1.92049369e-01 3.63136083e-02 -4.45274621... | [4.704764366149902, 4.124517440795898] |
ea1f46ee-d771-40a6-9c37-688f6d536708 | gallery-sampling-for-robust-and-fast-face | 2305.07495 | null | https://arxiv.org/abs/2305.07495v1 | https://arxiv.org/pdf/2305.07495v1.pdf | Gallery Sampling for Robust and Fast Face Identification | Deep learning methods have been achieved brilliant results in face recognition. One of the important tasks to improve the performance is to collect and label images as many as possible. However, labeling identities and checking qualities of large image data are difficult task and mistakes cannot be avoided in processin... | ['Jongju Shin', 'Pyoung-gang Lim', 'Myung-Cheol Roh'] | 2023-05-12 | null | null | null | null | ['face-recognition', 'face-identification'] | ['computer-vision', 'computer-vision'] | [-3.02579015e-01 -4.39243972e-01 2.62574136e-01 -5.56311727e-01
-7.25506604e-01 -4.31750536e-01 3.55355233e-01 -4.34278011e-01
-4.62403744e-01 8.26780796e-01 -2.83905685e-01 2.64045507e-01
-1.27437323e-01 -8.49647999e-01 -5.74405372e-01 -7.81892180e-01
-2.18898393e-02 3.34396750e-01 -2.32604340e-01 3.53689156... | [13.140542984008789, 0.8133897185325623] |
31d2ef92-eec4-4a2a-8eaa-3559cb4f0670 | narrationbot-and-infobot-a-hybrid-system-for | 2111.03994 | null | https://arxiv.org/abs/2111.03994v2 | https://arxiv.org/pdf/2111.03994v2.pdf | NarrationBot and InfoBot: A Hybrid System for Automated Video Description | Video accessibility is crucial for blind and low vision users for equitable engagements in education, employment, and entertainment. Despite the availability of professional and amateur services and tools, most human-generated descriptions are expensive and time consuming. Moreover, the rate of human-generated descript... | ['Pooyan Fazli', 'Ilmi Yoon', 'Abhishek Das', 'Yash Kant', 'Jose M. Castanon', 'Lothar Narins', 'Aditya Bodi', 'Yue-Ting Siu', 'Shasta Ihorn'] | 2021-11-07 | null | null | null | null | ['video-description'] | ['computer-vision'] | [-7.35892951e-02 -9.34531353e-03 -6.07381836e-02 -3.90350848e-01
-8.51603985e-01 -6.43205523e-01 2.09589958e-01 -1.19405471e-01
-6.24926925e-01 7.55517304e-01 8.52767587e-01 -2.89184868e-01
-4.75400686e-02 -2.59542406e-01 -3.80134463e-01 -4.59738187e-02
3.81176263e-01 -1.65436476e-01 3.86527419e-01 -1.48298576... | [10.964506149291992, 0.761188805103302] |
8fb767e6-463a-402d-9d5a-989a9d0abf49 | pr-net-preference-reasoning-for-personalized | 2109.01799 | null | https://arxiv.org/abs/2109.01799v1 | https://arxiv.org/pdf/2109.01799v1.pdf | PR-Net: Preference Reasoning for Personalized Video Highlight Detection | Personalized video highlight detection aims to shorten a long video to interesting moments according to a user's preference, which has recently raised the community's attention. Current methods regard the user's history as holistic information to predict the user's preference but negating the inherent diversity of the ... | ['Wenping Wang', 'Xing Sun', 'Pai Peng', 'Nenglun Chen', 'Wenzhe Wang', 'Penghao Zhou', 'Runnan Chen'] | 2021-09-04 | null | http://openaccess.thecvf.com//content/ICCV2021/html/Chen_PR-Net_Preference_Reasoning_for_Personalized_Video_Highlight_Detection_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Chen_PR-Net_Preference_Reasoning_for_Personalized_Video_Highlight_Detection_ICCV_2021_paper.pdf | iccv-2021-1 | ['highlight-detection'] | ['computer-vision'] | [ 8.63325670e-02 -2.16735020e-01 -4.84986573e-01 -5.43327034e-01
-8.80498350e-01 -3.63962919e-01 4.02094364e-01 2.74335712e-01
-3.39760154e-01 4.50818896e-01 6.59828365e-01 4.31422234e-01
-2.64635831e-01 -5.35480797e-01 -2.88432747e-01 -7.20086157e-01
3.76027897e-02 -1.14724934e-01 5.16191781e-01 -1.23412035... | [10.013283729553223, 0.4398844838142395] |
cb60b793-5f45-4c87-9fa0-e16b0cb8dd56 | degree-of-linear-polarization-based-color | null | null | http://openaccess.thecvf.com//content/CVPR2022/html/Ono_Degree-of-Linear-Polarization-Based_Color_Constancy_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Ono_Degree-of-Linear-Polarization-Based_Color_Constancy_CVPR_2022_paper.pdf | Degree-of-Linear-Polarization-Based Color Constancy | Color constancy is an essential function in digital photography and a fundamental process for many computer vision applications. Accordingly, many methods have been proposed, and some recent ones have used deep neural networks to handle more complex scenarios. However, both the traditional and latest methods still ... | ['Yusuke Moriuchi', 'Teppei Kurita', 'Legong Sun', 'Yuhi Kondo', 'Taishi Ono'] | 2022-01-01 | null | null | null | cvpr-2022-1 | ['color-constancy'] | ['computer-vision'] | [ 1.24920912e-01 -7.36961722e-01 8.37618113e-02 -3.18087488e-01
-1.07311860e-01 -4.19396758e-01 1.90995619e-01 -5.82875609e-01
-2.95118511e-01 8.02196801e-01 -3.87647092e-01 -1.83480352e-01
1.29708856e-01 -9.44135964e-01 -5.72335184e-01 -1.15628505e+00
3.28135580e-01 -2.39757359e-01 3.14678073e-01 -1.10285930... | [10.573589324951172, -2.6102418899536133] |
8d3dcac7-5e61-434c-a2d8-50571f7754df | graph-free-multi-hop-reading-comprehension-a | 2107.11823 | null | https://arxiv.org/abs/2107.11823v1 | https://arxiv.org/pdf/2107.11823v1.pdf | Graph-free Multi-hop Reading Comprehension: A Select-to-Guide Strategy | Multi-hop reading comprehension (MHRC) requires not only to predict the correct answer span in the given passage, but also to provide a chain of supporting evidences for reasoning interpretability. It is natural to model such a process into graph structure by understanding multi-hop reasoning as jumping over entity nod... | ['Hai Zhao', 'Zhuosheng Zhang', 'Bohong Wu'] | 2021-07-25 | null | null | null | null | ['multi-hop-reading-comprehension'] | ['natural-language-processing'] | [ 1.36366218e-01 8.37745130e-01 -1.75276734e-02 -2.74655223e-01
-7.84682572e-01 -5.92435956e-01 5.24878323e-01 8.58053744e-01
-1.95085660e-01 7.86568046e-01 6.47313237e-01 -9.10715461e-01
-3.88339847e-01 -1.14808393e+00 -1.17953992e+00 6.08290685e-03
1.56301022e-01 7.19720721e-01 5.41144788e-01 -6.80781484... | [10.807378768920898, 7.950563430786133] |
3dae4b6c-0e1c-4c97-bcb3-0b8d7d02e825 | sparse-random-hypergraphs-non-backtracking | 2203.07346 | null | https://arxiv.org/abs/2203.07346v3 | https://arxiv.org/pdf/2203.07346v3.pdf | Sparse random hypergraphs: Non-backtracking spectra and community detection | We consider the community detection problem in a sparse $q$-uniform hypergraph $G$, assuming that $G$ is generated according to the Hypergraph Stochastic Block Model (HSBM). We prove that a spectral method based on the non-backtracking operator for hypergraphs works with high probability down to the generalized Kesten-... | ['Yizhe Zhu', 'Ludovic Stephan'] | 2022-03-14 | null | null | null | null | ['stochastic-block-model'] | ['graphs'] | [ 2.09424466e-01 5.63433886e-01 -2.30651572e-01 4.39763606e-01
-4.60083187e-01 -6.69447839e-01 1.65171232e-02 -5.06625623e-02
1.07403249e-01 4.98126119e-01 -1.76712364e-01 -6.09416485e-01
-5.42901695e-01 -1.20146513e+00 -8.35387349e-01 -1.04531229e+00
-6.85022652e-01 9.28913593e-01 1.81893229e-01 -1.48618951... | [6.854725360870361, 5.132497787475586] |
8ddfceb3-aa94-4c54-adaf-756ce8d352d4 | instance-weighted-central-similarity-for | 2108.05274 | null | https://arxiv.org/abs/2108.05274v5 | https://arxiv.org/pdf/2108.05274v5.pdf | Instance-weighted Central Similarity for Multi-label Image Retrieval | Deep hashing has been widely applied to large-scale image retrieval by encoding high-dimensional data points into binary codes for efficient retrieval. Compared with pairwise/triplet similarity based hash learning, central similarity based hashing can more efficiently capture the global data distribution. For multi-lab... | ['Hanyu Peng', 'Zhiwei Zhang'] | 2021-08-11 | null | null | null | null | ['multi-label-image-retrieval'] | ['computer-vision'] | [-8.43074620e-02 -4.68930334e-01 -4.51755822e-01 -4.13748711e-01
-1.25260055e+00 -3.64076704e-01 3.96487266e-01 6.20809257e-01
-6.47393107e-01 2.85208791e-01 3.58304530e-02 1.91188768e-01
-1.58937529e-01 -8.63989055e-01 -7.06959724e-01 -1.14618862e+00
-1.28367422e-02 6.67537153e-01 2.76282132e-01 1.58859998... | [11.310572624206543, 0.9475767612457275] |
bba14efc-880c-44e4-9691-1bc104cbc524 | bertgen-multi-task-generation-through-bert | 2106.03484 | null | https://arxiv.org/abs/2106.03484v1 | https://arxiv.org/pdf/2106.03484v1.pdf | BERTGEN: Multi-task Generation through BERT | We present BERTGEN, a novel generative, decoder-only model which extends BERT by fusing multimodal and multilingual pretrained models VL-BERT and M-BERT, respectively. BERTGEN is auto-regressively trained for language generation tasks, namely image captioning, machine translation and multimodal machine translation, und... | ['Lucia Specia', 'Pranava Madhyastha', 'Ozan Caglayan', 'Faidon Mitzalis'] | 2021-06-07 | null | https://aclanthology.org/2021.acl-long.503 | https://aclanthology.org/2021.acl-long.503.pdf | acl-2021-5 | ['multimodal-machine-translation'] | ['natural-language-processing'] | [ 3.07391346e-01 4.50663537e-01 -2.98495412e-01 -1.93071038e-01
-1.81289244e+00 -4.79783535e-01 1.23491549e+00 -5.68097711e-01
-3.45900267e-01 1.14613032e+00 5.05871654e-01 -4.52707559e-01
8.17514777e-01 -3.87358904e-01 -1.23636293e+00 -3.86644304e-01
2.75048286e-01 1.02779305e+00 -4.81753170e-01 -3.01208436... | [11.30931282043457, 1.4399302005767822] |
f0aec122-d3b3-44a7-9931-2aad4d56a9b6 | video-mask-transfiner-for-high-quality-video | 2207.14012 | null | https://arxiv.org/abs/2207.14012v1 | https://arxiv.org/pdf/2207.14012v1.pdf | Video Mask Transfiner for High-Quality Video Instance Segmentation | While Video Instance Segmentation (VIS) has seen rapid progress, current approaches struggle to predict high-quality masks with accurate boundary details. Moreover, the predicted segmentations often fluctuate over time, suggesting that temporal consistency cues are neglected or not fully utilized. In this paper, we set... | ['Fisher Yu', 'Chi-Keung Tang', 'Yu-Wing Tai', 'Martin Danelljan', 'Henghui Ding', 'Lei Ke'] | 2022-07-28 | null | null | null | null | ['video-instance-segmentation'] | ['computer-vision'] | [ 2.56021857e-01 -3.52403164e-01 -2.82471627e-01 -1.08597897e-01
-7.85285950e-01 -5.22491634e-01 4.21812624e-01 -1.18524238e-01
-3.86136651e-01 7.42869377e-01 6.96476176e-02 1.88844487e-01
6.70180172e-02 -2.67354041e-01 -6.50439620e-01 -4.04661059e-01
-2.07335949e-02 5.64841926e-01 1.07366478e+00 -7.11042210... | [9.138483047485352, -0.10466591268777847] |
ab968d58-4cda-43c3-9f87-1bc386b6eb2d | stance-in-depth-deep-neural-approach-to | null | null | https://www.sciencedirect.com/science/article/pii/S1877050918308640 | https://ac.els-cdn.com/S1877050918308640/1-s2.0-S1877050918308640-main.pdf?_tid=556a1792-f871-4058-97de-e3cc8bc4ed63&acdnat=1551979346_642c1f818cbfdeb437938a5298dc364f | Stance-In-Depth Deep Neural Approach to Stance Classification | Understanding the user intention from text is a problem of growing interest. The social media like Twitter, Facebook etc. extract
user intention to analyze the behaviour of a user which in turn is employed for bot recognition, satire detection, fake news
detection etc.. The process of identifying stance of a user fro... | ['Bhadrachalam Chitturi', 'Gayathri Rajendran', 'Prabaharan Poornachandran'] | 2018-06-08 | null | null | null | international-conference-on-computational | ['satire-detection'] | ['natural-language-processing'] | [ 1.69701397e-01 7.01629519e-02 -5.67802668e-01 -2.48919353e-01
-2.79432237e-01 -5.60625553e-01 1.08976138e+00 3.48028123e-01
-4.32662129e-01 7.94367313e-01 6.86377466e-01 -5.43905020e-01
5.84297299e-01 -8.48323226e-01 -2.74647355e-01 -5.13276339e-01
6.83621824e-01 5.42261004e-01 2.99002409e-01 -6.04764223... | [8.316808700561523, 10.119523048400879] |
b735bf1e-22a0-4cac-9229-e875b735342b | towards-interpretable-sleep-stage | 2208.06991 | null | https://arxiv.org/abs/2208.06991v2 | https://arxiv.org/pdf/2208.06991v2.pdf | Towards Interpretable Sleep Stage Classification Using Cross-Modal Transformers | Accurate sleep stage classification is significant for sleep health assessment. In recent years, several machine-learning based sleep staging algorithms have been developed, and in particular, deep-learning based algorithms have achieved performance on par with human annotation. Despite the improved performance, a limi... | ['Chamira U. S. Edussooriya', 'Anjula C. De Silva', 'Simon L. Kappel', 'Dhinesh Suntharalingham', 'Vinith Kugathasan', 'Mithunjha Anandakumar', 'Jathurshan Pradeepkumar'] | 2022-08-15 | null | null | null | null | ['sleep-stage-detection', 'multimodal-sleep-stage-detection', 'sleep-staging', 'automatic-sleep-stage-classification', 'classification'] | ['medical', 'medical', 'medical', 'medical', 'methodology'] | [-4.27213572e-02 2.00503320e-02 -3.52702290e-01 -4.58751917e-01
-8.22834730e-01 -1.10089101e-01 6.59931302e-02 1.30703062e-01
-5.21635711e-01 5.27649701e-01 1.89617500e-01 -4.09308910e-01
5.12925610e-02 -4.98099208e-01 -1.40862435e-01 -5.97806752e-01
2.47205481e-01 4.50743377e-01 2.49973059e-01 -2.58257408... | [13.524077415466309, 3.5212693214416504] |
807d086e-6946-4818-864e-f872338f472d | the-magnitude-vector-of-images-1 | 2110.15188 | null | https://arxiv.org/abs/2110.15188v2 | https://arxiv.org/pdf/2110.15188v2.pdf | The magnitude vector of images | The magnitude of a finite metric space has recently emerged as a novel invariant quantity, allowing to measure the effective size of a metric space. Despite encouraging first results demonstrating the descriptive abilities of the magnitude, such as being able to detect the boundary of a metric space, the potential use ... | ["Leslie O'Bray", 'Edward De Brouwer', 'Bastian Rieck', 'Michael F. Adamer'] | 2021-10-28 | the-magnitude-vector-of-images | https://openreview.net/forum?id=-3Qj7Jl6UP5 | https://openreview.net/pdf?id=-3Qj7Jl6UP5 | null | ['boundary-detection'] | ['computer-vision'] | [ 4.90160584e-01 1.93183005e-01 -2.05240794e-03 -2.24769026e-01
-3.05771023e-01 -4.84307766e-01 6.15355849e-01 3.25238854e-01
-5.33590019e-01 3.30899954e-01 -3.25134955e-02 -1.43383771e-01
-4.87045854e-01 -8.70174527e-01 -5.09933352e-01 -7.54610300e-01
-5.80220163e-01 -1.86822519e-01 2.80245375e-02 -1.22732744... | [7.544579029083252, 4.066821098327637] |
27733ef2-1b0f-4df0-9610-0cbf8043b203 | dockstring-easy-molecular-docking-yields | 2110.15486 | null | https://arxiv.org/abs/2110.15486v1 | https://arxiv.org/pdf/2110.15486v1.pdf | DOCKSTRING: easy molecular docking yields better benchmarks for ligand design | The field of machine learning for drug discovery is witnessing an explosion of novel methods. These methods are often benchmarked on simple physicochemical properties such as solubility or general druglikeness, which can be readily computed. However, these properties are poor representatives of objective functions in d... | ['Sergio Bacallado', 'Andreas Bender', 'José Miguel Hernández-Lobato', 'Austin J. Tripp', 'Gregor N. C. Simm', 'Miguel García-Ortegón'] | 2021-10-29 | null | null | null | null | ['molecular-docking'] | ['medical'] | [ 4.90053333e-02 -4.66371387e-01 -5.03348351e-01 -1.97086796e-01
-1.12383258e+00 -8.77659261e-01 4.14400935e-01 6.72596097e-01
-5.41414797e-01 1.35039008e+00 -2.04739481e-01 -6.30549431e-01
-2.20379546e-01 -3.97907853e-01 -8.13644350e-01 -9.22575712e-01
-3.38164002e-01 6.80334985e-01 -8.59400164e-03 -2.47600198... | [4.973506927490234, 5.558060646057129] |
7e6ed829-2fe6-48e2-8632-0a5e11432d18 | global-wheat-challenge-2020-analysis-of-the | 2105.06182 | null | https://arxiv.org/abs/2105.06182v1 | https://arxiv.org/pdf/2105.06182v1.pdf | Global Wheat Challenge 2020: Analysis of the competition design and winning models | Data competitions have become a popular approach to crowdsource new data analysis methods for general and specialized data science problems. In plant phenotyping, data competitions have a rich history, and new outdoor field datasets have potential for new data competitions. We developed the Global Wheat Challenge as a ... | ['Ian Stavness', 'Frederic Baret', 'Wei Guo', 'Franklin Ogidi', 'Etienne David'] | 2021-05-13 | null | null | null | null | ['head-detection', 'plant-phenotyping'] | ['computer-vision', 'computer-vision'] | [-1.05040431e-01 -1.22294940e-01 -9.77268219e-02 -4.50231701e-01
-5.00322998e-01 -1.17435658e+00 2.51141489e-01 7.73636162e-01
-2.75123924e-01 4.38809216e-01 1.75404653e-01 -5.07864296e-01
-1.76142737e-01 -8.56112778e-01 -6.86120450e-01 -3.34719747e-01
1.24993213e-01 5.83412409e-01 5.33603072e-01 -6.24300599... | [9.219963073730469, -1.5273334980010986] |
0845cbec-6009-4aa1-a49f-22163d35d220 | submodboxes-near-optimal-search-for-a-set-of | null | null | http://papers.nips.cc/paper/5779-submodboxes-near-optimal-search-for-a-set-of-diverse-object-proposals | http://papers.nips.cc/paper/5779-submodboxes-near-optimal-search-for-a-set-of-diverse-object-proposals.pdf | SubmodBoxes: Near-Optimal Search for a Set of Diverse Object Proposals | This paper formulates the search for a set of bounding boxes (as needed in object proposal generation) as a monotone submodular maximization problem over the space of all possible bounding boxes in an image. Since the number of possible bounding boxes in an image is very large $O(#pixels^2)$, even a single linear scan ... | ['Dhruv Batra', 'Qing Sun'] | 2015-12-01 | null | null | null | neurips-2015-12 | ['object-proposal-generation'] | ['computer-vision'] | [ 3.79063815e-01 4.07862782e-01 -4.61551636e-01 -2.73407072e-01
-9.16976988e-01 -6.02789938e-01 1.98793769e-01 1.62720263e-01
-4.12029713e-01 8.63960385e-01 -3.02613556e-01 -1.86268568e-01
-1.17512442e-01 -8.84809554e-01 -7.17141092e-01 -7.97117531e-01
-1.41818970e-01 7.23251283e-01 6.71141207e-01 -1.46196455... | [9.471566200256348, 0.22436439990997314] |
3297a909-c700-44e1-970b-d1a0ae100821 | rand-robustness-aware-norm-decay-for | 2305.15536 | null | https://arxiv.org/abs/2305.15536v1 | https://arxiv.org/pdf/2305.15536v1.pdf | RAND: Robustness Aware Norm Decay For Quantized Seq2seq Models | With the rapid increase in the size of neural networks, model compression has become an important area of research. Quantization is an effective technique at decreasing the model size, memory access, and compute load of large models. Despite recent advances in quantization aware training (QAT) technique, most papers pr... | ['Yanzhang He', 'Oleg Rybakov', 'Shaojin Ding', 'David Rim', 'David Qiu'] | 2023-05-24 | null | null | null | null | ['model-compression'] | ['methodology'] | [ 5.02883673e-01 -1.72203153e-01 -2.27618337e-01 -4.07036513e-01
-9.50530171e-01 -3.03678662e-01 5.34480155e-01 -6.23191334e-03
-7.52660155e-01 6.86343133e-01 1.94571558e-02 -5.23630381e-01
-1.65886372e-01 -4.34280336e-01 -8.22615504e-01 -6.60932660e-01
-1.50819078e-01 4.53720123e-01 3.03201586e-01 -8.42428133... | [14.040766716003418, 6.425772666931152] |
d7647134-512e-417e-981b-9d609a68f6c9 | are-3d-face-shapes-expressive-enough-for | 2207.01113 | null | https://arxiv.org/abs/2207.01113v2 | https://arxiv.org/pdf/2207.01113v2.pdf | Are 3D Face Shapes Expressive Enough for Recognising Continuous Emotions and Action Unit Intensities? | Recognising continuous emotions and action unit (AU) intensities from face videos requires a spatial and temporal understanding of expression dynamics. Existing works primarily rely on 2D face appearances to extract such dynamics. This work focuses on a promising alternative based on parametric 3D face shape alignment ... | ['Michel Valstar', 'Timo Giesbrecht', 'Elisabeth André', 'Björn W. Schuller', 'Ömer Sümer', 'Mani Kumar Tellamekala'] | 2022-07-03 | null | null | null | null | ['face-alignment'] | ['computer-vision'] | [-3.06586266e-01 -7.09972084e-02 7.74323493e-02 -7.63849676e-01
-1.41985908e-01 -6.56681001e-01 6.85510874e-01 -4.59577054e-01
-1.52016506e-01 3.45551163e-01 -1.05063496e-02 4.72599357e-01
8.99743568e-03 -2.87837386e-01 -2.34752968e-01 -7.56574392e-01
-3.23768914e-01 2.50557035e-01 -7.40778625e-01 -4.97208685... | [13.534074783325195, 1.8079111576080322] |
5c6da70f-41fe-4763-bdcb-234359b9e519 | reconstruction-and-membership-inference | 1906.03006 | null | https://arxiv.org/abs/1906.03006v1 | https://arxiv.org/pdf/1906.03006v1.pdf | Reconstruction and Membership Inference Attacks against Generative Models | We present two information leakage attacks that outperform previous work on membership inference against generative models. The first attack allows membership inference without assumptions on the type of the generative model. Contrary to previous evaluation metrics for generative models, like Kernel Density Estimation,... | ['Martin Härterich', 'Daniel Bernau', 'Benjamin Hilprecht'] | 2019-06-07 | null | null | null | null | ['membership-inference-attack'] | ['computer-vision'] | [ 3.30926210e-01 6.52217925e-01 -1.11734375e-01 -3.59586298e-01
-1.05569577e+00 -1.18630719e+00 9.43439364e-01 -7.09092841e-02
-2.87245095e-01 6.84385300e-01 -1.15521932e-02 -8.34826946e-01
-1.78579781e-02 -1.14802527e+00 -1.11144137e+00 -6.72841489e-01
2.32517019e-01 5.81513286e-01 -2.05854222e-01 2.71877885... | [5.939434051513672, 7.18695592880249] |
44368405-5ce7-4975-9a9b-e91fc225b696 | semantic-frame-induction-with-deep-metric | 2304.14286 | null | https://arxiv.org/abs/2304.14286v1 | https://arxiv.org/pdf/2304.14286v1.pdf | Semantic Frame Induction with Deep Metric Learning | Recent studies have demonstrated the usefulness of contextualized word embeddings in unsupervised semantic frame induction. However, they have also revealed that generic contextualized embeddings are not always consistent with human intuitions about semantic frames, which causes unsatisfactory performance for frame ind... | ['Koichi Takeda', 'Ryohei Sasano', 'Kosuke Yamada'] | 2023-04-27 | null | null | null | null | ['metric-learning', 'metric-learning'] | ['computer-vision', 'methodology'] | [ 1.28125012e-01 4.23877567e-01 -3.99966925e-01 -6.49784267e-01
-9.23300266e-01 -5.52557051e-01 7.74913192e-01 2.83734173e-01
-3.93476993e-01 7.08880424e-01 7.26166368e-01 -1.22392125e-01
-1.03152201e-01 -8.54610503e-01 -6.98386908e-01 -5.26183128e-01
6.29030913e-02 5.68129420e-01 3.53971839e-01 -1.83861777... | [10.143475532531738, 9.182047843933105] |
fb75d6b0-ab36-44ea-b9be-b41069d61f72 | adversarial-attacks-neutralization-via-data | 2306.12161 | null | https://arxiv.org/abs/2306.12161v1 | https://arxiv.org/pdf/2306.12161v1.pdf | Adversarial Attacks Neutralization via Data Set Randomization | Adversarial attacks on deep-learning models pose a serious threat to their reliability and security. Existing defense mechanisms are narrow addressing a specific type of attack or being vulnerable to sophisticated attacks. We propose a new defense mechanism that, while being focused on image-based classifiers, is gener... | ['Roberto Di Pietro', 'Mouna Rabhi'] | 2023-06-21 | null | null | null | null | ['adversarial-attack'] | ['adversarial'] | [ 3.16396177e-01 6.20336607e-02 1.26432240e-01 -2.14236364e-01
-4.73905087e-01 -1.09941137e+00 7.73392022e-01 -2.73581475e-01
-4.15531814e-01 4.21701312e-01 -2.56143868e-01 -5.64061940e-01
-1.16397671e-01 -1.16827440e+00 -7.05342650e-01 -9.74432349e-01
-1.54675961e-01 9.71211195e-02 3.24563086e-01 -3.20884377... | [5.609267234802246, 7.8031511306762695] |
3e14e80a-ffd3-4a47-b2e8-a274081569e3 | on-the-cross-dataset-generalization-for | 2201.00267 | null | https://arxiv.org/abs/2201.00267v4 | https://arxiv.org/pdf/2201.00267v4.pdf | On the Cross-dataset Generalization in License Plate Recognition | Automatic License Plate Recognition (ALPR) systems have shown remarkable performance on license plates (LPs) from multiple regions due to advances in deep learning and the increasing availability of datasets. The evaluation of deep ALPR systems is usually done within each dataset; therefore, it is questionable if such ... | ['David Menotti', 'Valter Estevam', 'Diego R. Lucio', 'Everton V. Cardoso', 'Rayson Laroca'] | 2022-01-02 | null | null | null | null | ['scene-text-recognition', 'license-plate-recognition', 'license-plate-detection'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 8.09843093e-02 -7.50988007e-01 -9.35752988e-02 -5.30031979e-01
-1.14671862e+00 -9.36367273e-01 6.63981676e-01 -2.40284309e-01
-4.25182551e-01 3.39397907e-01 -3.69488180e-01 -4.15875703e-01
1.18597083e-01 -3.44748884e-01 -9.65328991e-01 -5.16472161e-01
1.83790416e-01 7.05321252e-01 5.74334443e-01 -1.91228576... | [9.835419654846191, -4.924130439758301] |
ea4acaba-0674-46bf-9b7e-1f078fd96dc8 | detection-d-objets-dans-les-documents | 2301.11753 | null | https://arxiv.org/abs/2301.11753v1 | https://arxiv.org/pdf/2301.11753v1.pdf | Détection d'Objets dans les documents numérisés par réseaux de neurones profonds | In this thesis, we study multiple tasks related to document layout analysis such as the detection of text lines, the splitting into acts or the detection of the writing support. Thus, we propose two deep neural models following two different approaches. We aim at proposing a model for object detection that considers th... | ['Mélodie Boillet'] | 2023-01-27 | null | null | null | null | ['document-layout-analysis', 'line-detection'] | ['computer-vision', 'computer-vision'] | [ 4.33631837e-01 5.68016805e-02 1.40924439e-01 -2.60234892e-01
-3.11756551e-01 -3.30029160e-01 5.23458898e-01 4.66360897e-01
-4.73987550e-01 4.99802053e-01 -4.90261108e-01 -2.49543354e-01
-1.57840297e-01 -8.35405707e-01 -7.66137123e-01 -4.85709459e-01
2.52586633e-01 7.11977422e-01 5.25261998e-01 8.07895884... | [11.770841598510742, 2.6366260051727295] |
2a0fe972-e9af-4fa6-afae-5f4d21bd9606 | deep-seam-prediction-for-image-stitching | 2302.05027 | null | https://arxiv.org/abs/2302.05027v2 | https://arxiv.org/pdf/2302.05027v2.pdf | Deep Seam Prediction for Image Stitching Based on Selection Consistency Loss | Image stitching is to construct panoramic images with wider field of vision (FOV) from some images captured from different viewing positions. To solve the problem of fusion ghosting in the stitched image, seam-driven methods avoid the misalignment area to fuse images by predicting the best seam. Currently, as standard ... | ['Wenbing Tao', 'Nanjun Yuan', 'Zhi Chen', 'Fan Yang', 'Senmao Cheng'] | 2023-02-10 | null | null | null | null | ['image-stitching'] | ['computer-vision'] | [ 2.56448984e-01 -1.53397113e-01 -4.27330285e-02 -3.36724311e-01
-3.00918728e-01 -1.07169293e-01 4.36456293e-01 -5.23172140e-01
-3.95763844e-01 3.92726600e-01 -1.14784002e-01 -1.26932755e-01
-2.12941527e-01 -6.29624069e-01 -8.90471876e-01 -7.76880383e-01
4.15389329e-01 -3.31902206e-02 6.94794238e-01 -2.56135494... | [9.480511665344238, -2.2328014373779297] |
b0ae44a0-0085-4650-9e38-21e0b18254cc | ray3d-ray-based-3d-human-pose-estimation-for | 2203.11471 | null | https://arxiv.org/abs/2203.11471v3 | https://arxiv.org/pdf/2203.11471v3.pdf | Ray3D: ray-based 3D human pose estimation for monocular absolute 3D localization | In this paper, we propose a novel monocular ray-based 3D (Ray3D) absolute human pose estimation with calibrated camera. Accurate and generalizable absolute 3D human pose estimation from monocular 2D pose input is an ill-posed problem. To address this challenge, we convert the input from pixel space to 3D normalized ray... | ['Wongun Choi', 'Renliang Weng', 'Fenghai Li', 'Yu Zhan'] | 2022-03-22 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Zhan_Ray3D_Ray-Based_3D_Human_Pose_Estimation_for_Monocular_Absolute_3D_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Zhan_Ray3D_Ray-Based_3D_Human_Pose_Estimation_for_Monocular_Absolute_3D_CVPR_2022_paper.pdf | cvpr-2022-1 | ['monocular-3d-human-pose-estimation'] | ['computer-vision'] | [-2.57211089e-01 -3.94003034e-01 -1.60132304e-01 -3.96647364e-01
-6.44259751e-01 -4.64439690e-01 3.84414226e-01 -5.55873036e-01
-5.40032744e-01 5.14546454e-01 3.26709360e-01 2.00447872e-01
2.84767151e-01 -3.09973687e-01 -7.72089183e-01 -3.10244352e-01
2.31350854e-01 4.85655308e-01 2.93697342e-02 -2.04272464... | [7.031741619110107, -0.9647547006607056] |
10cc1164-5f7b-4cdf-8270-091e30ccac40 | co-generation-of-game-levels-and-game-playing | 2007.08497 | null | https://arxiv.org/abs/2007.08497v2 | https://arxiv.org/pdf/2007.08497v2.pdf | Co-generation of game levels and game-playing agents | Open-endedness, primarily studied in the context of artificial life, is the ability of systems to generate potentially unbounded ontologies of increasing novelty and complexity. Engineering generative systems displaying at least some degree of this ability is a goal with clear applications to procedural content generat... | ['L. B. Soros', 'Julian Togelius', 'Aaron Dharna'] | 2020-07-16 | null | null | null | null | ['artificial-life'] | ['miscellaneous'] | [ 1.38538852e-01 5.63001990e-01 5.14968336e-01 3.53652418e-01
-6.41516298e-02 -7.33145833e-01 7.15975225e-01 -3.57787311e-01
2.53329729e-03 8.63239527e-01 5.11356816e-02 -3.37663829e-01
-5.37673712e-01 -1.27882993e+00 -7.50978351e-01 -5.43271780e-01
-2.06064299e-01 4.91512150e-01 1.89972192e-01 -1.05480731... | [3.6364943981170654, 1.5810099840164185] |
8d65851a-4d05-4b27-bff0-afc9f2af7a86 | joint-embedding-in-hierarchical-distance-and | 2303.15655 | null | https://arxiv.org/abs/2303.15655v1 | https://arxiv.org/pdf/2303.15655v1.pdf | Joint embedding in Hierarchical distance and semantic representation learning for link prediction | The link prediction task aims to predict missing entities or relations in the knowledge graph and is essential for the downstream application. Existing well-known models deal with this task by mainly focusing on representing knowledge graph triplets in the distance space or semantic space. However, they can not fully c... | ['Fengyu Zhou', 'Chongfeng Fan', 'Jianye Chen', 'Jin Liu'] | 2023-03-28 | null | null | null | null | ['knowledge-graph-embedding'] | ['graphs'] | [-2.45645568e-01 2.84525901e-01 -4.93696809e-01 -1.42227501e-01
-6.31665811e-02 -5.07892549e-01 5.00350058e-01 4.49892223e-01
-6.68951347e-02 5.75368404e-01 3.11316520e-01 -2.69744396e-01
-7.54800677e-01 -1.18832803e+00 -4.99486357e-01 -4.38162327e-01
-2.07853522e-02 6.01303637e-01 3.91364068e-01 -2.01387942... | [8.676774024963379, 7.850226402282715] |
563a6101-9822-4ddf-8fd3-7bc0644d0eb3 | 3d-object-tracking-with-transformer | 2110.14921 | null | https://arxiv.org/abs/2110.14921v1 | https://arxiv.org/pdf/2110.14921v1.pdf | 3D Object Tracking with Transformer | Feature fusion and similarity computation are two core problems in 3D object tracking, especially for object tracking using sparse and disordered point clouds. Feature fusion could make similarity computing more efficient by including target object information. However, most existing LiDAR-based approaches directly use... | ['Sifan Zhou', 'Zuoxu Gu', 'Jiayao Shan', 'Zheng Fang', 'Yubo Cui'] | 2021-10-28 | null | null | null | null | ['3d-object-tracking'] | ['computer-vision'] | [-4.68478620e-01 -7.26396203e-01 -8.35379586e-02 -3.04524004e-01
-5.86121798e-01 -3.40848476e-01 4.02972758e-01 -5.05165681e-02
-9.56370533e-02 -2.77348906e-02 -1.15908936e-01 1.35014668e-01
3.92603949e-02 -5.93119383e-01 -7.26921916e-01 -5.68092823e-01
1.49677500e-01 5.37323356e-01 6.22184217e-01 -1.26286764... | [6.5990495681762695, -2.380692958831787] |
9d4b9cc1-7e8b-4b05-ba4f-d4f172b1f789 | simulation-intelligence-towards-a-new | 2112.03235 | null | https://arxiv.org/abs/2112.03235v2 | https://arxiv.org/pdf/2112.03235v2.pdf | Simulation Intelligence: Towards a New Generation of Scientific Methods | The original "Seven Motifs" set forth a roadmap of essential methods for the field of scientific computing, where a motif is an algorithmic method that captures a pattern of computation and data movement. We present the "Nine Motifs of Simulation Intelligence", a roadmap for the development and integration of the essen... | ['Johann Brehmer', 'David Krakauer', 'Avi Pfeffer', 'Stephan Zheng', 'Samuel Assefa', 'Manuela Veloso', 'Adi Hanuka', 'Haruko Wainwright', 'Jiaxin Zhang', 'Kyle Cranmer', 'Jakob Macke', 'Peter L. McMahon', 'Erik Peterson', 'Olexandr Isayev', 'Carina Prunkl', 'Atılım Güneş Baydin', 'Kamil Rocki', 'Sanjay Choudry', 'Anim... | 2021-12-06 | null | null | null | null | ['probabilistic-programming'] | ['methodology'] | [ 1.47411689e-01 2.45690495e-02 -8.07166621e-02 2.92987287e-01
-1.29989132e-01 -7.02373326e-01 1.18188381e+00 2.35412776e-01
4.50308919e-02 6.86993420e-01 1.83584020e-01 -9.60734725e-01
-7.15195775e-01 -1.02084041e+00 -8.86224568e-01 -1.01738644e+00
-3.92133921e-01 8.32802117e-01 -1.64429203e-01 -1.92976385... | [6.0532965660095215, 4.12118673324585] |
61390b09-a7c3-4a63-9665-d0d43129c64e | robust-remote-sensing-scene-classification | 2301.05858 | null | https://arxiv.org/abs/2301.05858v1 | https://arxiv.org/pdf/2301.05858v1.pdf | Robust Remote Sensing Scene Classification with Multi-View Voting and Entropy Ranking | Deep convolutional neural networks have been widely used in scene classification of remotely sensed images. In this work, we propose a robust learning method for the task that is secure against partially incorrect categorization of images. Specifically, we remove and correct errors in the labels progressively by iterat... | ['George Hadjichristofi', 'Min Gan', 'Tao Wang', 'Jinyang Wang'] | 2023-01-14 | null | null | null | null | ['scene-classification'] | ['computer-vision'] | [ 3.70508105e-01 4.17441353e-02 -4.22780812e-02 -7.91579485e-01
-6.19418919e-01 -7.29552746e-01 3.52705389e-01 1.77861765e-01
-4.38369185e-01 6.62390530e-01 1.10858455e-01 -3.31882626e-01
-2.28969529e-01 -8.87038231e-01 -5.89738965e-01 -7.93305933e-01
3.08908731e-01 2.28484407e-01 -1.55697256e-01 1.74703047... | [9.529565811157227, 3.819283962249756] |
4101079d-e7cd-478d-a234-8b10251c8a01 | ground-truth-free-meta-learning-for-deep | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Qin_Ground-Truth_Free_Meta-Learning_for_Deep_Compressive_Sampling_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Qin_Ground-Truth_Free_Meta-Learning_for_Deep_Compressive_Sampling_CVPR_2023_paper.pdf | Ground-Truth Free Meta-Learning for Deep Compressive Sampling | Deep learning has become an important tool for reconstructing images in compressive sampling (CS). This paper proposes a ground-truth (GT) free meta-learning method for CS, which leverages both external and internal learning for unsupervised high-quality image reconstruction. The proposed method first trains a deep... | ['Hui Ji', 'Tongyao Pang', 'Yuhui Quan', 'Xinran Qin'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['image-reconstruction', 'unrolling'] | ['computer-vision', 'computer-vision'] | [ 4.82380003e-01 9.79369357e-02 -1.61355674e-01 -2.25202695e-01
-1.35843885e+00 -4.08590212e-02 1.61549792e-01 -3.36275935e-01
-1.97648391e-01 4.70786005e-01 2.90193141e-01 -3.36346895e-01
-2.79347777e-01 -4.83128577e-01 -9.25139010e-01 -9.22383845e-01
-2.66508639e-01 1.72370926e-01 -3.03532928e-01 5.31282872... | [11.28690242767334, -2.2484071254730225] |
ca8f51cb-e166-4743-85b4-48029a1ea26d | effectively-incorporating-weighted-cost-to-go | 2205.11624 | null | https://arxiv.org/abs/2205.11624v4 | https://arxiv.org/pdf/2205.11624v4.pdf | Effective Integration of Weighted Cost-to-go and Conflict Heuristic within Suboptimal CBS | Conflict-Based Search (CBS) is a popular multi-agent path finding (MAPF) solver that employs a low-level single agent planner and a high-level constraint tree to resolve conflicts. The vast majority of modern MAPF solvers focus on improving CBS by reducing the size of this tree through various strategies with few metho... | ['Tushar Kusnur', 'Maxim Likhachev', 'Rishi Veerapaneni'] | 2022-05-23 | null | null | null | null | ['multi-agent-path-finding'] | ['playing-games'] | [ 2.62846977e-01 6.49128675e-01 -5.32768011e-01 8.83324742e-02
-7.04297781e-01 -7.14031935e-01 5.61896086e-01 2.04721466e-01
-3.70730817e-01 1.32818675e+00 2.76460469e-01 -5.46092808e-01
-5.86568356e-01 -9.08732533e-01 -3.92978489e-01 -4.53512609e-01
-6.27792358e-01 9.54542279e-01 1.06361818e+00 -7.52392650... | [4.971241474151611, 1.8725779056549072] |
97b536b1-4a68-47a1-af20-618470adea13 | convolutional-recurrent-neural-networks-on | 2001.03538 | null | https://arxiv.org/abs/2001.03538v1 | https://arxiv.org/pdf/2001.03538v1.pdf | Convolutional-Recurrent Neural Networks on Low-Power Wearable Platforms for Cardiac Arrhythmia Detection | Low-power sensing technologies, such as wearables, have emerged in the healthcare domain since they enable continuous and non-invasive monitoring of physiological signals. In order to endow such devices with clinical value, classical signal processing has encountered numerous challenges. However, data-driven methods, s... | ['Ricard Delgado-Gonzalo', 'Antonino Faraone'] | 2020-01-08 | null | null | null | null | ['arrhythmia-detection'] | ['medical'] | [ 4.56042230e-01 -6.75130039e-02 -6.44489303e-02 -5.13040781e-01
-4.93638545e-01 -1.37650385e-01 -4.59369361e-01 3.15694332e-01
-6.14597976e-01 7.15975225e-01 -2.44259447e-01 -6.35726810e-01
-1.26594022e-01 -5.08638620e-01 -2.91670144e-01 -6.66914344e-01
-1.19103715e-01 -2.89850801e-01 -2.45120138e-01 1.19632386... | [13.98394775390625, 3.1576499938964844] |
a8687153-f5e7-4a1f-956f-73aeece4cbb2 | zeromesh-zero-shot-single-view-3d-mesh | 2208.02676 | null | https://arxiv.org/abs/2208.02676v2 | https://arxiv.org/pdf/2208.02676v2.pdf | Single-view 3D Mesh Reconstruction for Seen and Unseen Categories | Single-view 3D object reconstruction is a fundamental and challenging computer vision task that aims at recovering 3D shapes from single-view RGB images. Most existing deep learning based reconstruction methods are trained and evaluated on the same categories, and they cannot work well when handling objects from novel ... | ['Luping Zhou', 'Guosheng Lin', 'Xianghui Yang'] | 2022-08-04 | null | null | null | null | ['3d-object-reconstruction', 'object-reconstruction'] | ['computer-vision', 'computer-vision'] | [-3.94531712e-02 -2.37095891e-03 2.18422189e-01 -5.28598487e-01
-6.35980546e-01 -4.02480990e-01 5.22911131e-01 -4.14129615e-01
3.76110524e-02 2.00394705e-01 -4.34451140e-02 1.37439907e-01
-2.00482216e-02 -9.20580924e-01 -1.08668387e+00 -5.98105073e-01
5.79910159e-01 7.59626031e-01 3.28294873e-01 -1.89052999... | [8.42149543762207, -3.2146761417388916] |
b8998571-5eab-47ae-8b4c-0f3cc21fa030 | avoiding-inference-heuristics-in-few-shot | 2109.04144 | null | https://arxiv.org/abs/2109.04144v1 | https://arxiv.org/pdf/2109.04144v1.pdf | Avoiding Inference Heuristics in Few-shot Prompt-based Finetuning | Recent prompt-based approaches allow pretrained language models to achieve strong performances on few-shot finetuning by reformulating downstream tasks as a language modeling problem. In this work, we demonstrate that, despite its advantages on low data regimes, finetuned prompt-based models for sentence pair classific... | ['Iryna Gurevych', 'Victor Sanh', 'Nafise Sadat Moosavi', 'Prasetya Ajie Utama'] | 2021-09-09 | null | https://aclanthology.org/2021.emnlp-main.713 | https://aclanthology.org/2021.emnlp-main.713.pdf | emnlp-2021-11 | ['sentence-pair-classification'] | ['natural-language-processing'] | [ 4.27272797e-01 2.24716693e-01 -1.67004913e-01 -5.52839279e-01
-9.59413469e-01 -5.08605897e-01 8.06299150e-01 5.22851706e-01
-6.37544751e-01 7.33694136e-01 4.22932535e-01 -6.60513878e-01
-1.55977517e-01 -7.21562266e-01 -7.92459130e-01 -6.06839418e-01
3.22314620e-01 5.77386379e-01 1.82319567e-01 -5.06702960... | [10.809849739074707, 8.48558521270752] |
e6b9f268-7951-44a4-a8ea-ad26aa06d1a7 | learning-representations-for-time-series | null | null | http://papers.nips.cc/paper/8634-learning-representations-for-time-series-clustering | http://papers.nips.cc/paper/8634-learning-representations-for-time-series-clustering.pdf | Learning Representations for Time Series Clustering | Time series clustering is an essential unsupervised technique in cases when category information is not available. It has been widely applied to genome data, anomaly detection, and in general, in any domain where pattern detection is important. Although feature-based time series clustering methods are robust to noise a... | ['Gary W. Cottrell', 'Sen Li', 'Qianli Ma', 'Jiawei Zheng'] | 2019-12-01 | null | null | null | neurips-2019-12 | ['time-series-clustering'] | ['time-series'] | [ 3.06564808e-01 -6.14693820e-01 -7.36672133e-02 -3.33360881e-01
-5.76717675e-01 -5.11710525e-01 4.29104835e-01 1.90419033e-01
-2.90782452e-01 3.26533705e-01 9.05170590e-02 5.01049508e-04
-5.22835195e-01 -5.62167287e-01 -5.48433542e-01 -1.25857770e+00
-3.84543657e-01 2.83781886e-01 1.71779305e-01 -4.89619374... | [7.3092451095581055, 3.3541672229766846] |
9aa080f9-c483-472c-8dc6-a8cbdfda83eb | 3d-object-detection-with-a-self-supervised | 2205.00705 | null | https://arxiv.org/abs/2205.00705v2 | https://arxiv.org/pdf/2205.00705v2.pdf | 3D Object Detection with a Self-supervised Lidar Scene Flow Backbone | State-of-the-art lidar-based 3D object detection methods rely on supervised learning and large labeled datasets. However, annotating lidar data is resource-consuming, and depending only on supervised learning limits the applicability of trained models. Self-supervised training strategies can alleviate these issues by l... | ['Emeç Erçelik', 'Alois Knoll', 'Yılmaz Kaan Çaylı', 'Maximilian Listl', 'Pınar Topçam', 'Hanzhen Zhang', 'Zhijie Yang', 'MingYu Liu', 'Ekim Yurtsever'] | 2022-05-02 | null | null | null | null | ['scene-flow-estimation'] | ['computer-vision'] | [-5.77559359e-02 -2.08380729e-01 -6.44855559e-01 -3.28466952e-01
-6.56161845e-01 -7.41148651e-01 5.90975583e-01 -2.45849583e-02
-6.95748851e-02 1.15265183e-01 -2.10717186e-01 -4.50248331e-01
3.60279202e-01 -7.61609674e-01 -7.86184251e-01 -3.87312204e-01
-9.81621221e-02 5.49389839e-01 8.68920147e-01 1.64332062... | [7.803024768829346, -2.5995724201202393] |
8c3f13a1-2712-4ea2-b26c-10a660178dc4 | multichannel-sleep-spindle-detection-using | null | null | https://doi.org/10.1016/j.jneumeth.2017.06.004 | https://www.researchgate.net/publication/317533757_Multichannel_Sleep_Spindle_Detection_using_Sparse_Low-Rank_Optimization | Multichannel sleep spindle detection using sparse low-rank optimization | BACKGROUND:
Automated single-channel spindle detectors, for human sleep EEG, are blind to the presence of spindles in other recorded channels unlike visual annotation by a human expert.
NEW METHOD:
We propose a multichannel spindle detection method that aims to detect global and local spindle activity in human sle... | ['Indu Ayappa', 'David M. Rapoport', 'Ricardo S.Osorio', 'Ivan W. Selesnick', 'Ankit Parekha', 'Andrew W. Vargad'] | 2017-08-15 | null | null | null | journal-of-neuroscience-methods-volume-288 | ['spindle-detection'] | ['medical'] | [ 3.06834579e-01 -9.83257964e-02 3.26092720e-01 -9.07562822e-02
-5.26618540e-01 -8.10099006e-01 -9.51487869e-02 1.92294285e-01
-4.45290834e-01 1.07395065e+00 -6.28820360e-02 -1.04848996e-01
-1.75238356e-01 2.27023765e-01 -2.69716382e-01 -7.69280374e-01
-1.68219075e-01 -7.54368082e-02 1.42082423e-01 1.24505349... | [13.385936737060547, 3.4607253074645996] |
7fa142fc-58de-4941-a988-e32e0242e632 | sar-image-despeckling-using-quadratic-linear | 1801.04751 | null | http://arxiv.org/abs/1801.04751v1 | http://arxiv.org/pdf/1801.04751v1.pdf | SAR Image Despeckling Using Quadratic-Linear Approximated L1-Norm | Speckle noise, inherent in synthetic aperture radar (SAR) images, degrades
the performance of the various SAR image analysis tasks. Thus, speckle noise
reduction is a critical preprocessing step for smoothing homogeneous regions
while preserving details. This letter proposes a variational despeckling
approach where L1-... | ['Fatih Nar'] | 2018-01-15 | null | null | null | null | ['sar-image-despeckling'] | ['computer-vision'] | [ 5.41757286e-01 -4.01516706e-01 4.62508619e-01 -4.45019186e-01
-8.17992091e-01 -4.88244563e-01 4.21277136e-01 -2.96867400e-01
-5.77857196e-01 8.84890318e-01 1.57031149e-01 -1.73251554e-02
-4.10479635e-01 -5.78664660e-01 -4.82900366e-02 -1.09537661e+00
2.20952183e-01 -1.44175887e-01 1.65066898e-01 6.58911839... | [10.44385814666748, -2.2218053340911865] |
50c46494-87d2-4707-8716-65e4d94450bd | super-nerf-view-consistent-detail-generation | 2304.13518 | null | https://arxiv.org/abs/2304.13518v1 | https://arxiv.org/pdf/2304.13518v1.pdf | Super-NeRF: View-consistent Detail Generation for NeRF super-resolution | The neural radiance field (NeRF) achieved remarkable success in modeling 3D scenes and synthesizing high-fidelity novel views. However, existing NeRF-based methods focus more on the make full use of the image resolution to generate novel views, but less considering the generation of details under the limited input reso... | ['Qionghai Dai', 'Yuwang Wang', 'Xiaohang Yu', 'Tao Yu', 'Yuqi Han'] | 2023-04-26 | null | null | null | null | ['image-super-resolution'] | ['computer-vision'] | [ 3.27391803e-01 2.96826139e-02 4.44462039e-02 -3.62298846e-01
-9.97964501e-01 -2.49769956e-01 4.98564929e-01 -7.35996962e-01
2.63036579e-01 7.12197781e-01 5.45439184e-01 3.21352452e-01
-1.97602719e-01 -1.22897255e+00 -7.94928312e-01 -6.16693497e-01
2.62402594e-01 -5.30664984e-04 1.97253630e-01 -4.64104205... | [10.637517929077148, -2.2724404335021973] |
7ba69009-f29e-4367-bf7c-f7440567b166 | intelligent-3d-network-protocol-for | 2207.11504 | null | https://arxiv.org/abs/2207.11504v1 | https://arxiv.org/pdf/2207.11504v1.pdf | Intelligent 3D Network Protocol for Multimedia Data Classification using Deep Learning | In videos, the human's actions are of three-dimensional (3D) signals. These videos investigate the spatiotemporal knowledge of human behavior. The promising ability is investigated using 3D convolution neural networks (CNNs). The 3D CNNs have not yet achieved high output for their well-established two-dimensional (2D) ... | ['Faisal Jamil', 'Harun Jamil', 'Ammar Muthanna', 'Abid Ali', 'Muhammad Munawar Iqbal', 'Eman A. Aldhahri', 'Arslan Syed'] | 2022-07-23 | null | null | null | null | ['video-classification'] | ['computer-vision'] | [-3.28421950e-01 -4.78329778e-01 -2.39057988e-01 -1.19989261e-01
1.42904609e-01 -1.06076740e-01 6.63044691e-01 -5.95935166e-01
-6.34332776e-01 4.72245276e-01 3.32150698e-01 -9.87740383e-02
-2.02639446e-01 -5.54634988e-01 -5.20307720e-01 -6.11622095e-01
-5.03686130e-01 -1.21586882e-01 2.13696465e-01 -1.07679009... | [7.9291276931762695, 0.4787179231643677] |
bffc2ad4-4a57-4244-a291-585b42a70994 | refine-re-randomization-before-fine-tuning | 2205.05282 | null | https://arxiv.org/abs/2205.05282v3 | https://arxiv.org/pdf/2205.05282v3.pdf | ReFine: Re-randomization before Fine-tuning for Cross-domain Few-shot Learning | Cross-domain few-shot learning (CD-FSL), where there are few target samples under extreme differences between source and target domains, has recently attracted huge attention. Recent studies on CD-FSL generally focus on transfer learning based approaches, where a neural network is pre-trained on popular labeled source ... | ['Se-Young Yun', 'Hwanjun Song', 'Jin-Hwa Kim', 'Namgyu Ho', 'Sungnyun Kim', 'Jaehoon Oh'] | 2022-05-11 | null | null | null | null | ['cross-domain-few-shot', 'cross-domain-few-shot-learning'] | ['computer-vision', 'computer-vision'] | [ 3.83578271e-01 -2.87904650e-01 -5.32169104e-01 -5.49596071e-01
-9.28189397e-01 -4.81069356e-01 6.01891577e-01 -4.83849794e-02
-5.08602738e-01 8.71585846e-01 1.93118319e-01 3.51553440e-01
-4.84042950e-02 -9.61153030e-01 -4.51434523e-01 -6.29867673e-01
3.06834430e-01 7.29162872e-01 7.40249395e-01 -3.05326939... | [10.107619285583496, 3.070810556411743] |
b4250b34-ef4b-4f55-a6a2-15e3a8921daf | enabling-multimodal-generation-on-clip-via-1 | 2203.06386 | null | https://arxiv.org/abs/2203.06386v2 | https://arxiv.org/pdf/2203.06386v2.pdf | Enabling Multimodal Generation on CLIP via Vision-Language Knowledge Distillation | The recent large-scale vision-language pre-training (VLP) of dual-stream architectures (e.g., CLIP) with a tremendous amount of image-text pair data, has shown its superiority on various multimodal alignment tasks. Despite its success, the resulting models are not capable of multimodal generative tasks due to the weak ... | ['Pascale Fung', 'Qun Liu', 'Xin Jiang', 'Lifeng Shang', 'Lu Hou', 'Wenliang Dai'] | 2022-03-12 | null | https://aclanthology.org/2022.findings-acl.187 | https://aclanthology.org/2022.findings-acl.187.pdf | findings-acl-2022-5 | ['multimodal-generation'] | ['natural-language-processing'] | [ 1.58357844e-01 2.70300448e-01 -3.22211236e-02 -1.54201686e-01
-1.12862837e+00 -3.17910433e-01 1.08983207e+00 -1.66890383e-01
-3.47006470e-01 6.74865961e-01 3.43867660e-01 -2.83320397e-01
6.22139156e-01 -7.46342540e-01 -1.06524837e+00 -6.34042442e-01
5.68542540e-01 6.38560772e-01 -2.07525957e-02 -4.29986835... | [10.994552612304688, 1.3175007104873657] |
56b4b5c7-b875-4b43-a07f-0e0b74974dee | fara-future-aware-ranking-algorithm-for | 2305.16637 | null | https://arxiv.org/abs/2305.16637v1 | https://arxiv.org/pdf/2305.16637v1.pdf | FARA: Future-aware Ranking Algorithm for Fairness Optimization | Ranking systems are the key components of modern Information Retrieval (IR) applications, such as search engines and recommender systems. Besides the ranking relevance to users, the exposure fairness to item providers has also been considered an important factor in ranking optimization. Many fair ranking algorithms hav... | ['Qingyao Ai', 'Zhenduo Wang', 'Zhichao Xu', 'Tao Yang'] | 2023-05-26 | null | null | null | null | ['exposure-fairness', 'information-retrieval'] | ['adversarial', 'natural-language-processing'] | [-3.49328309e-01 -2.42883950e-01 -5.64952791e-01 -5.66310763e-01
-8.33824813e-01 -6.30268455e-01 3.62747282e-01 2.13860609e-02
-2.98436046e-01 6.66471779e-01 2.88242787e-01 -3.72489274e-01
-8.43575001e-01 -7.23604858e-01 -3.08689952e-01 -3.40526968e-01
-2.00340390e-01 5.98094344e-01 2.85485722e-02 -3.17487866... | [9.607768058776855, 5.60270881652832] |
51a4b4ee-bbcc-4fb4-85d7-30279fda4824 | simultaneously-color-depth-super-resolution | 1708.09105 | null | http://arxiv.org/abs/1708.09105v3 | http://arxiv.org/pdf/1708.09105v3.pdf | Simultaneously Color-Depth Super-Resolution with Conditional Generative Adversarial Network | Recently, Generative Adversarial Network (GAN) has been found wide
applications in style transfer, image-to-image translation and image
super-resolution. In this paper, a color-depth conditional GAN is proposed to
concurrently resolve the problems of depth super-resolution and color
super-resolution in 3D videos. First... | ['Huihui Bai', 'Bing Zeng', 'Yao Zhao', 'Lijun Zhao', 'Jie Liang', 'Anhong Wang'] | 2017-08-30 | null | null | null | null | ['image-smoothing'] | ['computer-vision'] | [ 4.94491488e-01 5.53561002e-02 2.22799763e-01 -1.87113866e-01
-7.56861687e-01 -2.61847138e-01 2.57830620e-01 -7.90132701e-01
-1.54698923e-01 9.59475458e-01 1.89415477e-02 2.23966748e-01
1.09706692e-01 -1.03875279e+00 -5.66066384e-01 -9.41485047e-01
5.10485888e-01 -7.53439739e-02 1.97657794e-01 -4.09324877... | [11.01171875, -2.033147096633911] |
8e91c718-ec18-44e1-855c-60ee4be66956 | drone-shadow-tracking | 1905.08214 | null | https://arxiv.org/abs/1905.08214v1 | https://arxiv.org/pdf/1905.08214v1.pdf | Drone Shadow Tracking | Aerial videos taken by a drone not too far above the surface may contain the drone's shadow projected on the scene. This deteriorates the aesthetic quality of videos. With the presence of other shadows, shadow removal cannot be directly applied, and the shadow of the drone must be tracked. Tracking a drone's shadow in ... | ['Sabine Süsstrunk', 'Xiaoyan Zou', 'Ruofan Zhou', 'Majed El Helou'] | 2019-05-20 | null | null | null | null | ['shadow-removal', 'shadow-detection'] | ['computer-vision', 'computer-vision'] | [ 5.37459254e-01 -4.35109675e-01 3.44413161e-01 1.79659784e-01
2.71914452e-01 -1.19542134e+00 4.12104070e-01 -1.84909865e-01
-7.43680894e-02 6.99056685e-01 -2.00131878e-01 -1.13333747e-01
1.22620232e-01 -4.29236948e-01 -4.22167361e-01 -7.55704522e-01
-3.56153995e-01 -1.51075134e-02 1.00529563e+00 -1.91384777... | [7.389305591583252, -1.5735526084899902] |
be4bb708-05df-4758-9597-98cb50017349 | semi-supervised-multitask-learning-for | 1704.07156 | null | http://arxiv.org/abs/1704.07156v1 | http://arxiv.org/pdf/1704.07156v1.pdf | Semi-supervised Multitask Learning for Sequence Labeling | We propose a sequence labeling framework with a secondary training objective,
learning to predict surrounding words for every word in the dataset. This
language modeling objective incentivises the system to learn general-purpose
patterns of semantic and syntactic composition, which are also useful for
improving accurac... | ['Marek Rei'] | 2017-04-24 | semi-supervised-multitask-learning-for-1 | https://aclanthology.org/P17-1194 | https://aclanthology.org/P17-1194.pdf | acl-2017-7 | ['grammatical-error-detection'] | ['natural-language-processing'] | [ 2.86464959e-01 3.12904179e-01 -6.72837615e-01 -4.75136071e-01
-6.67575181e-01 -6.76183701e-01 3.67032558e-01 6.10507309e-01
-8.77102613e-01 9.46295857e-01 5.15595078e-01 -6.73554897e-01
3.78949672e-01 -3.83338094e-01 -7.03027129e-01 -2.81056076e-01
-8.52322802e-02 3.92967552e-01 1.50561273e-01 -1.22001313... | [10.723347663879395, 8.802068710327148] |
645510c5-40c7-41c7-aa4c-539e976a9046 | fpconv-learning-local-flattening-for-point | 2002.10701 | null | https://arxiv.org/abs/2002.10701v3 | https://arxiv.org/pdf/2002.10701v3.pdf | FPConv: Learning Local Flattening for Point Convolution | We introduce FPConv, a novel surface-style convolution operator designed for 3D point cloud analysis. Unlike previous methods, FPConv doesn't require transforming to intermediate representation like 3D grid or graph and directly works on surface geometry of point cloud. To be more specific, for each point, FPConv perfo... | ['Zizheng Yan', 'Haibin Huang', 'Yiqun Lin', 'Xiaoguang Han', 'Ligang Liu', 'Shuguang Cui', 'Dong Du'] | 2020-02-25 | fpconv-learning-local-flattening-for-point-1 | http://openaccess.thecvf.com/content_CVPR_2020/html/Lin_FPConv_Learning_Local_Flattening_for_Point_Convolution_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Lin_FPConv_Learning_Local_Flattening_for_Point_Convolution_CVPR_2020_paper.pdf | cvpr-2020-6 | ['3d-object-classification'] | ['computer-vision'] | [-8.59354343e-03 1.01387650e-01 1.13977846e-02 -4.44260985e-01
-3.89338583e-01 -4.60520148e-01 6.16887987e-01 1.83002859e-01
-2.22275257e-01 9.75712985e-02 -3.51767749e-01 -5.91552556e-01
3.63248080e-01 -1.09764338e+00 -9.92428958e-01 -1.11341022e-01
-2.44796321e-01 5.85544705e-01 4.79806811e-01 9.27906558... | [8.031340599060059, -3.692086935043335] |
64355c01-0158-4e27-81f8-d033de32cb65 | prodesign-toward-effective-and-efficient | 2209.12643 | null | https://arxiv.org/abs/2209.12643v4 | https://arxiv.org/pdf/2209.12643v4.pdf | PiFold: Toward effective and efficient protein inverse folding | How can we design protein sequences folding into the desired structures effectively and efficiently? AI methods for structure-based protein design have attracted increasing attention in recent years; however, few methods can simultaneously improve the accuracy and efficiency due to the lack of expressive features and a... | ['Stan Z. Li', 'Pablo Chacón', 'Cheng Tan', 'Zhangyang Gao'] | 2022-09-22 | null | null | null | null | ['protein-design'] | ['medical'] | [ 1.63450629e-01 -7.50503689e-02 -4.72909510e-02 -3.04379314e-01
-7.88188696e-01 -6.30185604e-01 -6.70499206e-02 -1.86194628e-01
-2.12717935e-01 1.10140121e+00 4.61034141e-02 -4.98049349e-01
2.71044999e-01 -4.21357334e-01 -9.94048834e-01 -8.94326985e-01
1.72570333e-01 4.24498707e-01 -6.80910274e-02 -3.18386853... | [4.696526050567627, 5.645231246948242] |
6be0600a-d111-4014-a744-6d81b35e219b | using-satellite-image-classification-and | 1903.04347 | null | http://arxiv.org/abs/1903.04347v1 | http://arxiv.org/pdf/1903.04347v1.pdf | Using satellite image classification and digital terrain modelling to assess forest species distribution on mountain slopes.A case study in Varatec Forest District | The relation between ecological conditions and geomorphological factors is
considered the basis for species distribution in Romania. In this context, the
location of each species within parts of the mountain slopes is difficult on a
medium to brad scale level. The paper presents methodology to combine
vegetation data, ... | [] | 2019-03-11 | null | null | null | null | ['satellite-image-classification'] | ['computer-vision'] | [ 4.98839989e-02 -6.11367106e-01 -1.72359839e-01 -1.28748685e-01
2.22574756e-01 -3.58802289e-01 4.08116668e-01 2.71487176e-01
-7.17671096e-01 1.28774405e+00 -3.28185186e-02 -7.19088376e-01
-2.92605996e-01 -1.38170660e+00 -7.41849095e-02 -4.81597841e-01
-4.39318031e-01 2.71859944e-01 2.33092442e-01 -7.24494874... | [9.408945083618164, -1.599672794342041] |
6683c623-6975-4df2-834d-3e0c46026084 | a-majorization-minimization-algorithm-for-1 | 2204.09741 | null | https://arxiv.org/abs/2204.09741v1 | https://arxiv.org/pdf/2204.09741v1.pdf | A majorization-minimization algorithm for nonnegative binary matrix factorization | This paper tackles the problem of decomposing binary data using matrix factorization. We consider the family of mean-parametrized Bernoulli models, a class of generative models that are well suited for modeling binary data and enables interpretability of the factors. We factorize the Bernoulli parameter and consider an... | ['Cédric Févotte', 'Paul Magron'] | 2022-04-20 | null | null | null | null | ['matrix-completion'] | ['methodology'] | [ 4.35488790e-01 2.95151561e-01 -4.14078355e-01 -5.85759163e-01
-7.87934244e-01 -5.29933393e-01 6.93656921e-01 1.94222294e-02
-4.05502528e-01 5.52692533e-01 1.79823130e-01 -5.84662497e-01
-3.09467942e-01 -5.39437890e-01 -7.41032481e-01 -6.82342112e-01
8.36579874e-03 6.17574811e-01 -1.40928179e-01 4.54760864... | [7.3534111976623535, 4.411067962646484] |
c382e7dd-ddd3-42ea-8bd3-b02a4de7de83 | auc-maximization-for-low-resource-named | 2212.04800 | null | https://arxiv.org/abs/2212.04800v3 | https://arxiv.org/pdf/2212.04800v3.pdf | AUC Maximization for Low-Resource Named Entity Recognition | Current work in named entity recognition (NER) uses either cross entropy (CE) or conditional random fields (CRF) as the objective/loss functions to optimize the underlying NER model. Both of these traditional objective functions for the NER problem generally produce adequate performance when the data distribution is ba... | ['Lan Du', 'Changyou Chen', 'Richard Beare', 'Wray Buntine', 'Wei Tan', 'Ngoc Dang Nguyen'] | 2022-12-09 | null | null | null | null | ['low-resource-named-entity-recognition'] | ['natural-language-processing'] | [-1.76663101e-01 2.74206605e-02 -3.81187797e-01 -5.72708786e-01
-1.06497931e+00 -5.73991477e-01 3.17991555e-01 4.53641355e-01
-1.04237688e+00 8.60812068e-01 1.85206771e-01 -2.28162855e-01
-1.47437751e-01 -6.79865420e-01 -2.98848271e-01 -2.22903982e-01
1.55997686e-02 4.12616849e-01 -5.22095226e-02 1.30438164... | [9.710752487182617, 9.384809494018555] |
d35e5087-b05e-4eed-a8a3-7adcb6cf8367 | the-future-of-chatgpt-enabled-labor-market-a | 2304.09823 | null | https://arxiv.org/abs/2304.09823v2 | https://arxiv.org/pdf/2304.09823v2.pdf | The Future of ChatGPT-enabled Labor Market: A Preliminary Study | As a phenomenal large language model, ChatGPT has achieved unparalleled success in various real-world tasks and increasingly plays an important role in our daily lives and work. However, extensive concerns are also raised about the potential ethical issues, especially about whether ChatGPT-like artificial general intel... | ['HengShu Zhu', 'Meng Chang', 'Yaqi Yang', 'Shiyu Wu', 'Xi Chen', 'Lan Chen'] | 2023-04-14 | null | null | null | null | ['collaborative-filtering'] | ['miscellaneous'] | [-1.11061431e-01 5.50424337e-01 -2.93610036e-01 -1.56755567e-01
4.06792089e-02 -2.53526449e-01 3.14797610e-01 -1.02037288e-01
-4.81483817e-01 5.96086025e-01 3.48564565e-01 -5.00875652e-01
-7.65550673e-01 -9.54756677e-01 -1.21272877e-01 -1.78115204e-01
5.14127016e-01 1.01956618e+00 -3.04523528e-01 -6.77202523... | [9.19057846069336, 6.6236958503723145] |
3cdc8a13-e4bd-47be-850a-6940a93f7cb0 | fastpose-towards-real-time-pose-estimation | 1908.05593 | null | https://arxiv.org/abs/1908.05593v1 | https://arxiv.org/pdf/1908.05593v1.pdf | FastPose: Towards Real-time Pose Estimation and Tracking via Scale-normalized Multi-task Networks | Both accuracy and efficiency are significant for pose estimation and tracking in videos. State-of-the-art performance is dominated by two-stages top-down methods. Despite the leading results, these methods are impractical for real-world applications due to their separated architectures and complicated calculation. This... | ['Wei Zou', 'Jiabin Zhang', 'Yanwei Li', 'Peng Li', 'Hu Su', 'Guan Huang', 'Zheng Zhu'] | 2019-08-15 | null | null | null | null | ['multi-person-pose-estimation-and-tracking'] | ['computer-vision'] | [-2.00477228e-01 -3.61361772e-01 1.24722056e-01 -2.85014361e-01
-5.48712552e-01 -3.32927316e-01 1.06340967e-01 -3.09622020e-01
-7.51157105e-01 5.57075977e-01 1.89519778e-01 5.15193284e-01
2.39544049e-01 -4.46116060e-01 -5.01851618e-01 -3.75962377e-01
-2.52854079e-02 5.30296266e-01 5.60148358e-01 4.96704876... | [7.063582420349121, -0.9057454466819763] |
f79c1507-28c8-4ff0-8c56-59089fdccc21 | florence-a-new-foundation-model-for-computer | 2111.11432 | null | https://arxiv.org/abs/2111.11432v1 | https://arxiv.org/pdf/2111.11432v1.pdf | Florence: A New Foundation Model for Computer Vision | Automated visual understanding of our diverse and open world demands computer vision models to generalize well with minimal customization for specific tasks, similar to human vision. Computer vision foundation models, which are trained on diverse, large-scale dataset and can be adapted to a wide range of downstream tas... | ['Pengchuan Zhang', 'Luowei Zhou', 'Michael Zeng', 'Jianwei Yang', 'Zhen Xiao', 'Bin Xiao', 'JianFeng Wang', 'Lijuan Wang', 'Yu Shi', 'Yumao Lu', 'Zicheng Liu', 'Mengchen Liu', 'Ce Liu', 'Chunyuan Li', 'Boxin Li', 'Xuedong Huang', 'Houdong Hu', 'Jianfeng Gao', 'Xiyang Dai', 'Noel Codella', 'Yi-Ling Chen', 'Dongdong Che... | 2021-11-22 | null | null | null | null | ['zero-shot-transfer-image-classification', 'zero-shot-cross-modal-retrieval'] | ['computer-vision', 'miscellaneous'] | [ 1.93096429e-01 -4.04622912e-01 -3.86307061e-01 -2.46375993e-01
-9.63744044e-01 -5.84758699e-01 7.89671421e-01 -4.09486413e-01
-2.52539575e-01 6.79580033e-01 1.48665294e-01 -1.38034731e-01
1.53273702e-01 -7.40555882e-01 -9.66329157e-01 -5.47148764e-01
2.17511997e-01 4.26808387e-01 5.30981541e-01 -4.02160853... | [10.326523780822754, 1.7392995357513428] |
dc683019-bb0d-405a-a57a-39876af4dc0c | robust-segmentation-models-using-an | 2109.14879 | null | https://arxiv.org/abs/2109.14879v1 | https://arxiv.org/pdf/2109.14879v1.pdf | Robust Segmentation Models using an Uncertainty Slice Sampling Based Annotation Workflow | Semantic segmentation neural networks require pixel-level annotations in large quantities to achieve a good performance. In the medical domain, such annotations are expensive, because they are time-consuming and require expert knowledge. Active learning optimizes the annotation effort by devising strategies to select c... | ['Hans Meine', 'Bram van Ginneken', 'Horst K. Hahn', 'Andrea Schenk', 'Grzegorz Chlebus'] | 2021-09-30 | null | null | null | null | ['liver-segmentation'] | ['medical'] | [ 1.60123467e-01 5.87354898e-01 -4.83739711e-02 -3.70687366e-01
-1.17013371e+00 -5.75112343e-01 2.73181319e-01 6.67358100e-01
-8.04690301e-01 9.24031019e-01 -9.87539515e-02 -3.55507165e-01
-3.53176534e-01 -7.23630607e-01 -5.94141424e-01 -9.62796450e-01
-4.10728455e-01 6.34588301e-01 4.09367800e-01 5.81128776... | [14.426414489746094, -2.4769206047058105] |
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