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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
f4a37d10-3b56-4856-b932-e72e7018eaa0 | optimizing-readability-using-genetic | 2301.00374 | null | https://arxiv.org/abs/2301.00374v1 | https://arxiv.org/pdf/2301.00374v1.pdf | Optimizing Readability Using Genetic Algorithms | This research presents ORUGA, a method that tries to automatically optimize the readability of any text in English. The core idea behind the method is that certain factors affect the readability of a text, some of which are quantifiable (number of words, syllables, presence or absence of adverbs, and so on). The nature... | ['Jorge Martinez-Gil'] | 2023-01-01 | null | null | null | null | ['readability-optimization'] | ['natural-language-processing'] | [ 1.73193276e-01 2.28036389e-01 -1.65111169e-01 -9.55327824e-02
-2.31281370e-01 -6.40375376e-01 4.20259595e-01 5.41396201e-01
-4.29936737e-01 7.12398589e-01 5.18135548e-01 -2.92450428e-01
-3.11202049e-01 -8.43926251e-01 -4.28842455e-01 -4.04438347e-01
6.16595089e-01 1.65184200e-01 -9.72711071e-02 -5.46041429... | [11.0912446975708, 10.106895446777344] |
5c3dfb49-ca42-4e9a-8fc5-d86275b85f2e | an-efficient-transformer-for-simultaneous | 2306.04927 | null | https://arxiv.org/abs/2306.04927v1 | https://arxiv.org/pdf/2306.04927v1.pdf | An Efficient Transformer for Simultaneous Learning of BEV and Lane Representations in 3D Lane Detection | Accurately detecting lane lines in 3D space is crucial for autonomous driving. Existing methods usually first transform image-view features into bird-eye-view (BEV) by aid of inverse perspective mapping (IPM), and then detect lane lines based on the BEV features. However, IPM ignores the changes in road height, leading... | ['Mingming Gong', 'Guoqi Qian', 'Bo Du', 'Kate Smith-Miles', 'Ziye Chen'] | 2023-06-08 | null | null | null | null | ['3d-lane-detection', 'lane-detection'] | ['computer-vision', 'computer-vision'] | [-2.96471845e-02 1.16168462e-01 -1.35128379e-01 -7.15233266e-01
-5.43568611e-01 -4.26543742e-01 7.11569071e-01 -4.14366513e-01
-1.85953110e-01 2.97879726e-01 -8.26053508e-03 -5.31821311e-01
2.66958326e-01 -1.00760818e+00 -9.26992834e-01 -5.54001451e-01
5.79871833e-01 2.15945140e-01 6.27868414e-01 -4.57186520... | [8.02640438079834, -1.7432641983032227] |
e740e720-7b47-458c-8b75-3b613df02073 | robustness-disparities-in-face-detection | 2211.15937 | null | https://arxiv.org/abs/2211.15937v1 | https://arxiv.org/pdf/2211.15937v1.pdf | Robustness Disparities in Face Detection | Facial analysis systems have been deployed by large companies and critiqued by scholars and activists for the past decade. Many existing algorithmic audits examine the performance of these systems on later stage elements of facial analysis systems like facial recognition and age, emotion, or perceived gender prediction... | ['John P. Dickerson', 'Tom Goldstein', 'George Z. Wei', 'Samuel Dooley'] | 2022-11-29 | null | null | null | null | ['face-detection', 'gender-prediction'] | ['computer-vision', 'computer-vision'] | [ 1.50319338e-01 1.58655345e-01 7.46506527e-02 -5.44999480e-01
-1.97855100e-01 -6.16443753e-01 5.17427206e-01 -1.97246760e-01
-2.55017936e-01 4.15905356e-01 -1.99372336e-01 -1.89775378e-01
1.10911705e-01 -5.41961670e-01 -2.96653718e-01 -6.77364469e-01
-1.54867098e-01 -2.21220683e-02 -3.51509333e-01 -1.05800807... | [13.038195610046387, 1.2085245847702026] |
1623b724-cf1f-4fed-86ea-56b0150f31c1 | ace-vc-adaptive-and-controllable-voice | 2302.08137 | null | https://arxiv.org/abs/2302.08137v1 | https://arxiv.org/pdf/2302.08137v1.pdf | ACE-VC: Adaptive and Controllable Voice Conversion using Explicitly Disentangled Self-supervised Speech Representations | In this work, we propose a zero-shot voice conversion method using speech representations trained with self-supervised learning. First, we develop a multi-task model to decompose a speech utterance into features such as linguistic content, speaker characteristics, and speaking style. To disentangle content and speaker ... | ['Boris Ginsburg', 'Jason Li', 'Jocelyn Huang', 'Paarth Neekhara', 'Shehzeen Hussain'] | 2023-02-16 | null | null | null | null | ['voice-conversion', 'voice-conversion', 'speaker-verification'] | ['audio', 'speech', 'speech'] | [ 2.19161838e-01 2.85996556e-01 -6.23819195e-02 -4.69410449e-01
-1.29486048e+00 -5.15698612e-01 3.99865210e-01 -1.79527014e-01
6.36798143e-02 3.74721229e-01 7.14828789e-01 -1.79198518e-01
1.97601169e-01 -3.82724941e-01 -4.77381319e-01 -5.28170824e-01
1.24101363e-01 1.57217532e-01 -2.79489517e-01 -3.59685093... | [14.950693130493164, 6.564146995544434] |
08e8fc03-9d2b-4215-bf9c-ad6574eb55eb | a-topic-coverage-approach-to-evaluation-of | 2012.06274 | null | https://arxiv.org/abs/2012.06274v3 | https://arxiv.org/pdf/2012.06274v3.pdf | A Topic Coverage Approach to Evaluation of Topic Models | Topic models are widely used unsupervised models capable of learning topics - weighted lists of words and documents - from large collections of text documents. When topic models are used for discovery of topics in text collections, a question that arises naturally is how well the model-induced topics correspond to topi... | ['Jan Šnajder', 'Jelena Repar', 'Strahil Ristov', 'Damir Korenčić'] | 2020-12-11 | a-topic-coverage-approach-to-evaluation-of-1 | https://ieeexplore.ieee.org/abstract/document/9526605 | https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9526605 | null | ['topic-coverage'] | ['natural-language-processing'] | [ 1.08578138e-01 5.42227983e-01 -5.85756242e-01 -5.31379044e-01
-1.28403533e+00 -4.46958482e-01 1.01939917e+00 6.97468340e-01
-1.12817474e-01 6.63099468e-01 4.66381341e-01 -1.62811890e-01
-5.70855916e-01 -1.06275773e+00 -3.68735105e-01 -5.19643366e-01
-2.75004029e-01 1.10743558e+00 4.53789532e-01 1.36420548... | [10.374818801879883, 7.102914333343506] |
00986646-9f08-4268-a63f-eff289a218c6 | progressive-bilateral-context-driven-model | 2009.03098 | null | https://arxiv.org/abs/2009.03098v1 | https://arxiv.org/pdf/2009.03098v1.pdf | Progressive Bilateral-Context Driven Model for Post-Processing Person Re-Identification | Most existing person re-identification methods compute pairwise similarity by extracting robust visual features and learning the discriminative metric. Owing to visual ambiguities, these content-based methods that determine the pairwise relationship only based on the similarity between them, inevitably produce a subopt... | ['Silong Peng', 'Arjan Kuijper', 'Xiyuan Hu', 'Min Cao', 'Hao Dou', 'Chen Chen'] | 2020-09-07 | null | null | null | null | ['large-scale-person-re-identification'] | ['computer-vision'] | [-5.79666197e-02 -6.42739415e-01 1.51317388e-01 -4.78678912e-01
-4.73904163e-01 -6.13912582e-01 6.49079859e-01 4.13206488e-01
-7.80497313e-01 2.93381125e-01 3.71983767e-01 1.71955660e-01
-1.53524175e-01 -5.70505500e-01 -1.29816920e-01 -6.62749112e-01
4.78929393e-02 4.81683582e-01 1.53139666e-01 -9.03593823... | [14.783207893371582, 1.0392581224441528] |
56a521f2-3d96-46c8-b8be-61686feeb509 | learning-to-adapt-multi-view-stereo-by-self | 2009.13278 | null | https://arxiv.org/abs/2009.13278v1 | https://arxiv.org/pdf/2009.13278v1.pdf | Learning to Adapt Multi-View Stereo by Self-Supervision | 3D scene reconstruction from multiple views is an important classical problem in computer vision. Deep learning based approaches have recently demonstrated impressive reconstruction results. When training such models, self-supervised methods are favourable since they do not rely on ground truth data which would be need... | ['Jörg Stückler', 'Arijit Mallick', 'Hendrik Lensch'] | 2020-09-28 | null | null | null | null | ['3d-scene-reconstruction'] | ['computer-vision'] | [ 2.54274637e-01 -1.54490396e-01 4.05447856e-02 -6.05454445e-01
-6.56749904e-01 -4.09956694e-01 7.43179321e-01 -1.50313869e-01
-4.36940223e-01 7.09764779e-01 6.20421357e-02 2.28059828e-01
-7.51852021e-02 -9.01401401e-01 -1.02562916e+00 -6.28203452e-01
3.76228124e-01 9.07494068e-01 4.70088720e-01 -3.94983888... | [8.665449142456055, -2.428774356842041] |
cdb6432b-c164-4196-8bf3-efa9fbeaf03b | stochastic-package-queries-in-probabilistic | 2103.06784 | null | https://arxiv.org/abs/2103.06784v1 | https://arxiv.org/pdf/2103.06784v1.pdf | Stochastic Package Queries in Probabilistic Databases | We provide methods for in-database support of decision making under uncertainty. Many important decision problems correspond to selecting a package (bag of tuples in a relational database) that jointly satisfy a set of constraints while minimizing some overall cost function; in most real-world problems, the data is unc... | ['Alexandra Meliou', 'Peter J. Haas', 'Azza Abouzied', 'Nishant Yadav', 'Matteo Brucato'] | 2021-03-11 | null | null | null | null | ['decision-making-under-uncertainty', 'decision-making-under-uncertainty'] | ['medical', 'reasoning'] | [-1.75503656e-01 1.58824712e-01 -3.96524407e-02 -1.05279160e+00
-1.47484660e+00 -8.50335538e-01 6.69079646e-02 4.67133552e-01
-1.37022678e-02 8.90578032e-01 1.16715722e-01 -6.38434589e-01
-3.26437354e-01 -1.07209134e+00 -1.03780949e+00 -3.34174156e-01
-1.46876961e-01 1.23286510e+00 4.69979905e-02 1.48251757... | [5.73463773727417, 3.6269540786743164] |
84c53a12-8307-4439-ac09-d05d84c748bb | how-does-it-detect-a-malicious-app-explaining | 2111.05108 | null | https://arxiv.org/abs/2111.05108v1 | https://arxiv.org/pdf/2111.05108v1.pdf | "How Does It Detect A Malicious App?" Explaining the Predictions of AI-based Android Malware Detector | AI methods have been proven to yield impressive performance on Android malware detection. However, most AI-based methods make predictions of suspicious samples in a black-box manner without transparency on models' inference. The expectation on models' explainability and transparency by cyber security and AI practitione... | ['Vrizlynn L. L. Thing', 'Zhi Lu'] | 2021-11-06 | null | null | null | null | ['android-malware-detection'] | ['miscellaneous'] | [ 5.89878440e-01 7.20282137e-01 -2.46549487e-01 -1.92952976e-01
-2.84466952e-01 -6.80235505e-01 9.93719637e-01 -2.69095600e-01
3.75702202e-01 6.28691018e-01 -1.09858088e-01 -4.49248761e-01
-4.97438349e-02 -5.31802535e-01 -9.68160331e-01 -3.90882164e-01
-2.52013624e-01 1.81139991e-01 5.14624314e-03 -2.13158906... | [14.32205581665039, 9.63951587677002] |
26e10d25-83e9-45f4-b2eb-f956318244f1 | latent-dirichlet-allocation | null | null | https://dl.acm.org/doi/10.5555/944919.944937#d7400906e1 | https://www.jmlr.org/papers/volume3/blei03a/blei03a.pdf | Latent Dirichlet Allocation | We describe latent Dirichlet allocation (LDA), a generative probabilistic model for collections of
discrete data such as text corpora. LDA is a three-level hierarchical Bayesian model, in which each
item of a collection is modeled as a finite mixture over an underlying set of topics. Each topic is, in
turn, modeled ... | ['Michael I. Jordan', 'Andrew Y. Ng', 'David M. Blei'] | 2003-01-01 | null | null | null | null | ['text-categorization'] | ['natural-language-processing'] | [-2.22093254e-01 1.57873750e-01 -3.62712145e-01 -4.85418946e-01
-8.20491433e-01 -4.48432833e-01 1.20698082e+00 1.38445109e-01
-6.67977259e-02 5.74205279e-01 6.04583323e-01 -8.11698735e-02
-9.37932432e-02 -1.09276378e+00 -2.72778600e-01 -6.90747380e-01
-2.96553094e-02 1.37073529e+00 1.06018916e-01 2.92366683... | [10.333670616149902, 6.902393817901611] |
cd11aebe-6d64-4685-9142-0c58af1ed07e | complex-politics-a-quantitative-semantic-and | 1510.03797 | null | http://arxiv.org/abs/1510.03797v1 | http://arxiv.org/pdf/1510.03797v1.pdf | Complex Politics: A Quantitative Semantic and Topological Analysis of UK House of Commons Debates | This study is a first, exploratory attempt to use quantitative semantics
techniques and topological analysis to analyze systemic patterns arising in a
complex political system. In particular, we use a rich data set covering all
speeches and debates in the UK House of Commons between 1975 and 2014. By the
use of dynamic... | ['Bahattin Tolga Oztan', 'María Pereda', 'Stefano Gurciullo', 'Slava Mikhaylov', 'Federico Battiston', 'Sebastian Poledna', 'Michael Smallegan', 'Alice Patania', 'Alexander Herzog', 'Peter John', 'Daniel Hedblom'] | 2015-10-13 | null | null | null | null | ['dynamic-topic-modeling'] | ['natural-language-processing'] | [-1.90341979e-01 4.27472085e-01 -2.85383672e-01 -4.12537828e-02
-2.28890821e-01 -9.72862601e-01 1.58391941e+00 8.50200534e-01
-1.09294221e-01 4.26923186e-01 1.11070716e+00 -9.01490390e-01
-9.15520608e-01 -9.08041239e-01 -1.61045745e-01 -3.81777197e-01
-5.05726814e-01 6.85201943e-01 4.93505061e-01 -6.90090001... | [8.888388633728027, 9.848833084106445] |
17f3f17a-2897-433a-94a5-2576cf3f6f34 | topics-in-the-haystack-extracting-and | 2303.17324 | null | https://arxiv.org/abs/2303.17324v1 | https://arxiv.org/pdf/2303.17324v1.pdf | Topics in the Haystack: Extracting and Evaluating Topics beyond Coherence | Extracting and identifying latent topics in large text corpora has gained increasing importance in Natural Language Processing (NLP). Most models, whether probabilistic models similar to Latent Dirichlet Allocation (LDA) or neural topic models, follow the same underlying approach of topic interpretability and topic ext... | ['Benjamin Säfken', 'Elisabeth Bergherr', 'Arik Reuter', 'Quentin Seifert', 'Anton Thielmann'] | 2023-03-30 | null | null | null | null | ['topic-models'] | ['natural-language-processing'] | [-2.57480234e-01 9.35675129e-02 -3.04572374e-01 -3.39610457e-01
-7.06469536e-01 -5.74176729e-01 1.03477001e+00 6.26714408e-01
-2.90485263e-01 2.64822304e-01 7.03026474e-01 -1.93854049e-01
-5.66050224e-02 -7.35597134e-01 7.76521266e-02 -5.34700334e-01
-2.68873066e-01 8.15789461e-01 1.54663548e-01 6.63414970... | [10.393796920776367, 6.996838092803955] |
62e744ca-9b1f-49d6-9e89-098ae5a3ac06 | vuldeepecker-a-deep-learning-based-system-for | 1801.01681 | null | http://arxiv.org/abs/1801.01681v1 | http://arxiv.org/pdf/1801.01681v1.pdf | VulDeePecker: A Deep Learning-Based System for Vulnerability Detection | The automatic detection of software vulnerabilities is an important research
problem. However, existing solutions to this problem rely on human experts to
define features and often miss many vulnerabilities (i.e., incurring high false
negative rate). In this paper, we initiate the study of using deep
learning-based vul... | ['Sujuan Wang', 'Zhijun Deng', 'Yuyi Zhong', 'Xinyu Ou', 'Hai Jin', 'Deqing Zou', 'Zhen Li', 'Shouhuai Xu'] | 2018-01-05 | null | null | null | null | ['vulnerability-detection'] | ['miscellaneous'] | [-5.08490443e-01 -2.43145540e-01 -1.27728134e-01 -2.94567227e-01
-5.44827938e-01 -9.38645601e-01 1.32538527e-01 3.57171863e-01
-8.83490499e-03 1.46870136e-01 -2.08764136e-01 -9.98650491e-01
1.26541823e-01 -1.05862963e+00 -4.60067034e-01 -3.02094072e-01
-3.05154383e-01 -2.06288353e-01 3.61386955e-01 -4.25810784... | [7.054413795471191, 7.778486728668213] |
70f3ac43-877c-4b3f-95e7-97b1dc5e9129 | multi-person-pose-estimation-with-enhanced-1 | 2003.10238 | null | https://arxiv.org/abs/2003.10238v1 | https://arxiv.org/pdf/2003.10238v1.pdf | Multi-Person Pose Estimation with Enhanced Feature Aggregation and Selection | We propose a novel Enhanced Feature Aggregation and Selection network (EFASNet) for multi-person 2D human pose estimation. Due to enhanced feature representation, our method can well handle crowded, cluttered and occluded scenes. More specifically, a Feature Aggregation and Selection Module (FASM), which constructs hie... | ['Xixia Xu', 'Qi Zou', 'Xue Lin'] | 2020-03-20 | null | null | null | null | ['2d-human-pose-estimation'] | ['computer-vision'] | [-2.90761203e-01 -5.18141627e-01 1.43873349e-01 -4.60624337e-01
-7.73175657e-01 1.40424535e-01 4.26180899e-01 -1.08974576e-01
-4.79490489e-01 7.17154980e-01 7.35780895e-01 6.53089225e-01
-1.79935805e-02 -6.92938328e-01 -5.31527579e-01 -4.96797174e-01
1.23053407e-02 4.78109181e-01 5.78139186e-01 -3.47701460... | [7.14971399307251, -0.7900003790855408] |
4028744c-0922-4a0d-b247-3b3dc1c0967a | from-depth-data-to-head-pose-estimation-a | 1703.03624 | null | http://arxiv.org/abs/1703.03624v1 | http://arxiv.org/pdf/1703.03624v1.pdf | From Depth Data to Head Pose Estimation: a Siamese approach | The correct estimation of the head pose is a problem of the great importance
for many applications. For instance, it is an enabling technology in automotive
for driver attention monitoring. In this paper, we tackle the pose estimation
problem through a deep learning network working in regression manner.
Traditional met... | ['Guido Borghi', 'Roberto Vezzani', 'Rita Cucchiara', 'Marco Venturelli'] | 2017-03-10 | null | null | null | null | ['head-pose-estimation', 'driver-attention-monitoring'] | ['computer-vision', 'computer-vision'] | [-3.05798650e-01 2.29899302e-01 -6.12510741e-02 -8.09688151e-01
-8.21363509e-01 4.81194444e-02 4.99115527e-01 -2.14185581e-01
-1.07166290e+00 6.29857898e-01 1.09655631e-03 5.32880947e-02
5.69321625e-02 -3.93681020e-01 -7.32205331e-01 -6.89011216e-01
1.79084629e-01 5.51534712e-01 1.34690329e-01 -2.38205269... | [13.659453392028809, 0.28994593024253845] |
b34cb75c-4f01-48be-a44c-fdd404ac20e8 | idiapers-causal-news-corpus-2022-extracting | 2209.03891 | null | https://arxiv.org/abs/2209.03891v2 | https://arxiv.org/pdf/2209.03891v2.pdf | IDIAPers @ Causal News Corpus 2022: Extracting Cause-Effect-Signal Triplets via Pre-trained Autoregressive Language Model | In this paper, we describe our shared task submissions for Subtask 2 in CASE-2022, Event Causality Identification with Casual News Corpus. The challenge focused on the automatic detection of all cause-effect-signal spans present in the sentence from news-media. We detect cause-effect-signal spans in a sentence using T5... | ['Pavel Smrz', 'Petr Motlicek', 'Sergio Burdisso', 'Esaú Villatoro-Tello', 'Juan Zuluaga-Gomez', 'Muskaan Singh', 'Martin Fajcik'] | 2022-09-08 | null | null | null | null | ['event-causality-identification'] | ['natural-language-processing'] | [ 2.85323262e-01 1.49598196e-01 -7.44069293e-02 -3.79934788e-01
-1.17667699e+00 -6.03411913e-01 7.41048872e-01 6.24137938e-01
-2.92819530e-01 1.02003789e+00 7.94020772e-01 -4.60338384e-01
-1.21479735e-01 -5.72295666e-01 -1.08683527e+00 -2.79015899e-01
-6.00753367e-01 3.06435049e-01 2.66804665e-01 -6.91470727... | [9.0769624710083, 9.248492240905762] |
e87cdf60-531a-4894-8802-973af69eb7dc | dynamic-scene-graph-generation-via | null | null | http://openaccess.thecvf.com//content/CVPR2022/html/Li_Dynamic_Scene_Graph_Generation_via_Anticipatory_Pre-Training_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Li_Dynamic_Scene_Graph_Generation_via_Anticipatory_Pre-Training_CVPR_2022_paper.pdf | Dynamic Scene Graph Generation via Anticipatory Pre-Training | Humans can not only see the collection of objects in visual scenes, but also identify the relationship between objects. The visual relationship in the scene can be abstracted into the semantic representation of triple <subject, predicate, object> and thus results in a scene graph, which can convey a lot of informat... | ['Changsheng Xu', 'Xiaoshan Yang', 'Yiming Li'] | 2022-01-01 | null | null | null | cvpr-2022-1 | ['scene-graph-generation'] | ['computer-vision'] | [ 4.27037716e-01 -1.65027946e-01 -6.46266416e-02 -5.45008838e-01
8.49744529e-02 -4.55453783e-01 4.27554935e-01 1.92335114e-01
-1.67549908e-01 3.47825110e-01 5.20047426e-01 6.20541312e-02
1.60840526e-01 -7.30848491e-01 -8.89115930e-01 -4.88381237e-01
5.92228808e-02 -4.48209606e-02 7.14926839e-01 -1.31030425... | [9.017190933227539, 0.7610204219818115] |
2c797ca6-4486-44e2-9818-283d7c090348 | human-joint-kinematics-diffusion-refinement | 2210.05976 | null | https://arxiv.org/abs/2210.05976v2 | https://arxiv.org/pdf/2210.05976v2.pdf | Human Joint Kinematics Diffusion-Refinement for Stochastic Motion Prediction | Stochastic human motion prediction aims to forecast multiple plausible future motions given a single pose sequence from the past. Most previous works focus on designing elaborate losses to improve the accuracy, while the diversity is typically characterized by randomly sampling a set of latent variables from the latent... | ['Shengxiang Hu', 'Xiaoning Sun', 'Weiqing Li', 'Jianfeng Lu', 'Bin Li', 'Huaijiang Sun', 'Dong Wei'] | 2022-10-12 | null | null | null | null | ['stochastic-human-motion-prediction'] | ['computer-vision'] | [ 1.19841471e-01 2.49317437e-01 2.37446725e-02 -5.13593331e-02
-5.79064012e-01 -7.22406730e-02 7.98416913e-01 -7.73117840e-01
-2.07260817e-01 7.89858162e-01 6.51382506e-01 1.73709556e-01
2.76476294e-01 -8.89785230e-01 -9.21515822e-01 -1.32532883e+00
3.28479201e-01 6.55279934e-01 3.57472241e-01 -1.10181056... | [7.313658237457275, -0.12049538642168045] |
6109d261-9050-4829-b304-b46f31c29630 | evaluating-and-improving-the-coreference | 2302.08464 | null | https://arxiv.org/abs/2302.08464v1 | https://arxiv.org/pdf/2302.08464v1.pdf | Evaluating and Improving the Coreference Capabilities of Machine Translation Models | Machine translation (MT) requires a wide range of linguistic capabilities, which current end-to-end models are expected to learn implicitly by observing aligned sentences in bilingual corpora. In this work, we ask: \emph{How well do MT models learn coreference resolution from implicit signal?} To answer this question, ... | ['Gabriel Stanovsky', 'Omri Abend', 'Arie Cattan', 'Asaf Yehudai'] | 2023-02-16 | null | null | null | null | ['coreference-resolution'] | ['natural-language-processing'] | [ 2.82856107e-01 3.68356407e-01 -7.01353669e-01 -5.96964777e-01
-1.57146049e+00 -1.15120482e+00 8.54367912e-01 -4.03817862e-01
-4.79450732e-01 1.18618166e+00 6.63855672e-01 -5.00560939e-01
2.43897438e-01 -1.77252859e-01 -7.44201422e-01 -2.00075403e-01
2.95804471e-01 1.47142422e+00 -4.98877019e-02 -6.98547781... | [9.273039817810059, 9.560680389404297] |
a37ec121-9a6e-4966-a405-1d841eee76b3 | hgr-net-a-fusion-network-for-hand-gesture | 1806.05653 | null | https://arxiv.org/abs/1806.05653v3 | https://arxiv.org/pdf/1806.05653v3.pdf | HGR-Net: A Fusion Network for Hand Gesture Segmentation and Recognition | We propose a two-stage convolutional neural network (CNN) architecture for robust recognition of hand gestures, called HGR-Net, where the first stage performs accurate semantic segmentation to determine hand regions, and the second stage identifies the gesture. The segmentation stage architecture is based on the combin... | ['Amirhossein Dadashzadeh', 'Majid Mirmehdi', 'Alireza Tavakoli Targhi', 'Maryam Tahmasbi'] | 2018-06-14 | null | null | null | null | ['hand-segmentation'] | ['computer-vision'] | [ 4.79266495e-01 -2.83764690e-01 -3.14432919e-01 -3.53302807e-01
-6.61040068e-01 -5.75272739e-01 1.52545825e-01 -5.85194886e-01
-7.39549220e-01 2.97507234e-02 -1.64915640e-02 -2.55873561e-01
4.42679137e-01 -5.18929303e-01 -5.83353400e-01 -7.33140469e-01
1.67229176e-01 3.23244005e-01 6.42904401e-01 8.36330280... | [6.628302097320557, -0.5812715888023376] |
2924d1c7-4722-4899-b515-23d98699c98c | automatic-controllable-product-copywriting | 2206.10103 | null | https://arxiv.org/abs/2206.10103v1 | https://arxiv.org/pdf/2206.10103v1.pdf | Automatic Controllable Product Copywriting for E-Commerce | Automatic product description generation for e-commerce has witnessed significant advancement in the past decade. Product copywriting aims to attract users' interest and improve user experience by highlighting product characteristics with textual descriptions. As the services provided by e-commerce platforms become div... | ['Lingfei Wu', 'Bo Long', 'Yun Xiao', 'Meng Jiang', 'Qingkai Zeng', 'Xiaojie Guo'] | 2022-06-21 | null | null | null | null | ['product-recommendation', 'aspect-extraction'] | ['miscellaneous', 'natural-language-processing'] | [-3.49572524e-02 1.78435922e-01 -3.56983662e-01 -5.18066943e-01
-6.50402546e-01 -8.56886029e-01 6.10865593e-01 2.35376030e-01
1.46857455e-01 1.51174301e-02 3.45472723e-01 -2.23898560e-01
-6.71124235e-02 -9.21216846e-01 -2.56914258e-01 -3.53875346e-02
1.55009627e-01 8.02435100e-01 2.07592860e-01 -6.06357634... | [10.045573234558105, 6.079237461090088] |
bc267fc3-0083-4f2e-9e18-c9391a63a7ac | semi-detr-semi-supervised-object-detection | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Zhang_Semi-DETR_Semi-Supervised_Object_Detection_With_Detection_Transformers_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Zhang_Semi-DETR_Semi-Supervised_Object_Detection_With_Detection_Transformers_CVPR_2023_paper.pdf | Semi-DETR: Semi-Supervised Object Detection With Detection Transformers | We analyze the DETR-based framework on semi-supervised object detection (SSOD) and observe that (1) the one-to-one assignment strategy generates incorrect matching when the pseudo ground-truth bounding box is inaccurate, leading to training inefficiency; (2) DETR-based detectors lack deterministic correspondence be... | ['Guanbin Li', 'Jingdong Wang', 'Errui Ding', 'Junyu Han', 'Xiao Tan', 'Kuo Wang', 'Wei zhang', 'Xiangru Lin', 'Jiacheng Zhang'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['semi-supervised-object-detection', 'pseudo-label'] | ['computer-vision', 'miscellaneous'] | [ 9.28946808e-02 -1.51094526e-01 -3.64215046e-01 -6.18935466e-01
-1.00680268e+00 -4.60903913e-01 3.35638255e-01 -5.81689663e-02
-2.60603219e-01 1.89588457e-01 -3.78044546e-01 -9.05607454e-03
4.22619581e-02 -5.75097322e-01 -9.69741702e-01 -4.79631215e-01
5.32667220e-01 7.37280071e-01 9.15194750e-01 4.09307852... | [9.21371078491211, 1.2996522188186646] |
61f67ab0-a1f7-4d5c-91c9-7a5a61ff7a7e | end-to-end-resume-parsing-and-finding | 1910.03089 | null | https://arxiv.org/abs/1910.03089v2 | https://arxiv.org/pdf/1910.03089v2.pdf | End-to-End Resume Parsing and Finding Candidates for a Job Description using BERT | The ever-increasing number of applications to job positions presents a challenge for employers to find suitable candidates manually. We present an end-to-end solution for ranking candidates based on their suitability to a job description. We accomplish this in two stages. First, we build a resume parser which extracts ... | ['Ajit Kumar', 'Vedant Bhatia', 'Rajiv Ratn Shah', 'Prateek Rawat'] | 2019-09-30 | null | null | null | null | ['sentence-pair-classification'] | ['natural-language-processing'] | [ 2.33107582e-01 2.61076599e-01 -7.03730106e-01 -8.67006183e-01
-1.51590943e+00 -9.04353261e-01 6.06091261e-01 6.59278989e-01
-4.69032317e-01 9.23132300e-01 4.42838490e-01 -4.75235343e-01
-5.67759633e-01 -7.32916415e-01 -6.03218019e-01 3.92656744e-01
5.52620471e-01 9.02662158e-01 1.24426112e-01 -4.32440609... | [9.85311222076416, 9.217659950256348] |
27123412-0c7c-46c7-a4e0-cda3201c2e19 | a-generalized-framework-for-video-instance | 2211.08834 | null | https://arxiv.org/abs/2211.08834v2 | https://arxiv.org/pdf/2211.08834v2.pdf | A Generalized Framework for Video Instance Segmentation | The handling of long videos with complex and occluded sequences has recently emerged as a new challenge in the video instance segmentation (VIS) community. However, existing methods have limitations in addressing this challenge. We argue that the biggest bottleneck in current approaches is the discrepancy between train... | ['Seon Joo Kim', 'Joon-Young Lee', 'Seoung Wug Oh', 'Hanjung Kim', 'Jeongseok Hyun', 'Sukjun Hwang', 'Miran Heo'] | 2022-11-16 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Heo_A_Generalized_Framework_for_Video_Instance_Segmentation_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Heo_A_Generalized_Framework_for_Video_Instance_Segmentation_CVPR_2023_paper.pdf | cvpr-2023-1 | ['video-instance-segmentation'] | ['computer-vision'] | [ 6.87215338e-03 -2.22254664e-01 -5.24217844e-01 -1.85354829e-01
-9.33827162e-01 -5.94974399e-01 3.63877296e-01 -2.54653335e-01
-5.49734533e-01 4.95266855e-01 1.20449271e-02 -2.39199191e-01
1.95722416e-01 -5.47626495e-01 -9.71067548e-01 -3.61395150e-01
-3.28506939e-02 2.69148439e-01 7.65828133e-01 -7.73831606... | [9.18776798248291, 0.010488499887287617] |
4f05afb1-9883-4ba6-b7e7-91528d6dec21 | katakomba-tools-and-benchmarks-for-data | 2306.08772 | null | https://arxiv.org/abs/2306.08772v1 | https://arxiv.org/pdf/2306.08772v1.pdf | Katakomba: Tools and Benchmarks for Data-Driven NetHack | NetHack is known as the frontier of reinforcement learning research where learning-based methods still need to catch up to rule-based solutions. One of the promising directions for a breakthrough is using pre-collected datasets similar to recent developments in robotics, recommender systems, and more under the umbrella... | ['Sergey Kolesnikov', 'Denis Tarasov', 'Alexander Nikulin', 'Vladislav Kurenkov'] | 2023-06-14 | null | null | null | null | ['nethack', 'd4rl'] | ['playing-games', 'robots'] | [-1.52293622e-01 -9.34841931e-02 -2.97076672e-01 -4.36844677e-01
-5.27756631e-01 -7.51098156e-01 6.93064988e-01 2.40596429e-01
-6.09058797e-01 6.93599463e-01 2.12570474e-01 -3.28543603e-01
-4.95974123e-01 -5.83743930e-01 -6.13996983e-01 -3.37756723e-01
-2.04478994e-01 7.33114660e-01 3.46271247e-01 -5.98646522... | [4.076313018798828, 1.546043872833252] |
26fc6b82-128e-4bdc-9be7-cabf4a0c6e76 | multimodal-representation-learning-of | 2304.07675 | null | https://arxiv.org/abs/2304.07675v1 | https://arxiv.org/pdf/2304.07675v1.pdf | Multimodal Representation Learning of Cardiovascular Magnetic Resonance Imaging | Self-supervised learning is crucial for clinical imaging applications, given the lack of explicit labels in healthcare. However, conventional approaches that rely on precise vision-language alignment are not always feasible in complex clinical imaging modalities, such as cardiac magnetic resonance (CMR). CMR provides a... | ['David Chen', 'Ding Zhao', 'Douglas Weber', 'Debbie Kwon', 'Byung-Hak Kim', 'Christopher Nguyen', 'Pohao Chen', 'Wilson Tang', 'Jiacheng Zhu', 'Jaehyun Lee', 'Makiya Nakashima', 'Peide Huang', 'JieLin Qiu'] | 2023-04-16 | null | null | null | null | ['anatomy'] | ['miscellaneous'] | [ 2.70123720e-01 -2.12701201e-01 -3.04723233e-01 -3.95038068e-01
-1.18067229e+00 -6.76111758e-01 1.14693724e-01 4.70782131e-01
-1.40085161e-01 7.47873545e-01 3.38202178e-01 -4.45810229e-01
-2.61677742e-01 -3.24459821e-01 -2.99899340e-01 -7.12292910e-01
-2.00609177e-01 4.63341027e-01 -8.88495147e-02 3.53731543... | [14.885512351989746, -1.8815430402755737] |
39783990-e168-4aa4-b1d5-1dfa87069ebe | model-based-offline-reinforcement-learning | 2210.06692 | null | https://arxiv.org/abs/2210.06692v2 | https://arxiv.org/pdf/2210.06692v2.pdf | Model-Based Offline Reinforcement Learning with Pessimism-Modulated Dynamics Belief | Model-based offline reinforcement learning (RL) aims to find highly rewarding policy, by leveraging a previously collected static dataset and a dynamics model. While the dynamics model learned through reuse of the static dataset, its generalization ability hopefully promotes policy learning if properly utilized. To tha... | ['Yanhui Geng', 'Yunfeng Shao', 'Kaiyang Guo'] | 2022-10-13 | null | null | null | null | ['d4rl'] | ['robots'] | [-8.81704465e-02 2.14080229e-01 -7.99446225e-01 -4.71615382e-02
-5.98295093e-01 -5.09937823e-01 4.46194410e-01 6.92744926e-02
-6.44514978e-01 9.64796603e-01 2.66164280e-02 -3.81346345e-01
-3.72487605e-01 -7.98744321e-01 -7.77468085e-01 -8.78919482e-01
-4.60454911e-01 2.91450441e-01 -2.90275775e-02 -1.73088521... | [4.255938529968262, 2.439300298690796] |
63546088-d202-42e9-95b2-3759b0f9a1bc | use-of-speech-impairment-severity-for | 2305.10659 | null | https://arxiv.org/abs/2305.10659v1 | https://arxiv.org/pdf/2305.10659v1.pdf | Use of Speech Impairment Severity for Dysarthric Speech Recognition | A key challenge in dysarthric speech recognition is the speaker-level diversity attributed to both speaker-identity associated factors such as gender, and speech impairment severity. Most prior researches on addressing this issue focused on using speaker-identity only. To this end, this paper proposes a novel set of te... | ['Xunying Liu', 'Xurong Xie', 'Jianwei Yu', 'Guinan Li', 'Mingyu Cui', 'Jiajun Deng', 'Shujie Hu', 'Tianzi Wang', 'Zengrui Jin', 'Mengzhe Geng'] | 2023-05-18 | null | null | null | null | ['severity-prediction'] | ['computer-vision'] | [ 1.93772446e-02 1.30553365e-01 1.35336831e-01 -4.58399147e-01
-1.28095698e+00 -3.46859545e-01 4.66545999e-01 -4.44131464e-01
-5.13532221e-01 5.52468359e-01 1.02576661e+00 -1.90502480e-01
-4.52522226e-02 -1.22372307e-01 -9.17156935e-02 -6.36904359e-01
2.06468552e-01 3.74709487e-01 -1.00803494e-01 -4.91730094... | [14.53078842163086, 6.429051399230957] |
8fd396e1-2b60-4b7f-927a-f6c7c8b5d1a1 | tell-me-something-new-a-new-framework-for | 1805.07483 | null | http://arxiv.org/abs/1805.07483v2 | http://arxiv.org/pdf/1805.07483v2.pdf | Tell Me Something New: A New Framework for Asynchronous Parallel Learning | We present a novel approach for parallel computation in the context of
machine learning that we call "Tell Me Something New" (TMSN). This approach
involves a set of independent workers that use broadcast to update each other
when they observe "something new". TMSN does not require synchronization or a
head node and is ... | ['Yoav Freund', 'Julaiti Alafate'] | 2018-05-19 | null | null | null | null | ['splice-site-prediction'] | ['medical'] | [ 1.66807100e-01 1.53663278e-01 -3.08404654e-01 -7.62908518e-01
-1.14868212e+00 -2.27520093e-01 5.00089526e-01 4.65653419e-01
-6.60504878e-01 9.63434696e-01 -7.62379393e-02 -5.72412729e-01
-4.28719930e-02 -7.89984703e-01 -1.09143591e+00 -9.09137845e-01
-2.06970379e-01 1.11477184e+00 5.53749979e-01 -1.85804844... | [8.39270305633545, 4.300658702850342] |
68cd3cad-4eff-4da6-b596-1562a53869da | a-trio-method-for-retinal-vessel-segmentation | 2209.11230 | null | https://arxiv.org/abs/2209.11230v1 | https://arxiv.org/pdf/2209.11230v1.pdf | A Trio-Method for Retinal Vessel Segmentation using Image Processing | Inner Retinal neurons are a most essential part of the retina and they are supplied with blood via retinal vessels. This paper primarily focuses on the segmentation of retinal vessels using a triple preprocessing approach. DRIVE database was taken into consideration and preprocessed by Gabor Filtering, Gaussian Blur, a... | ['Manoj Sahni', 'Vinayak Singh', 'Mahendra Kumar Gourisaria'] | 2022-09-19 | null | null | null | null | ['edge-detection'] | ['computer-vision'] | [-1.26522973e-01 2.61056393e-01 4.08461303e-01 -3.16699088e-01
2.81573921e-01 -5.47392011e-01 3.59521598e-01 4.72529233e-02
-8.71986806e-01 6.82335913e-01 -5.94266132e-02 -4.40001577e-01
-2.05439068e-02 -8.42793107e-01 -2.76286572e-01 -5.29648185e-01
-1.27213359e-01 -4.10648398e-02 7.82632470e-01 -5.07909097... | [15.813056945800781, -3.967346429824829] |
02ed0504-5a59-4b5d-a72a-50540bde0150 | metagraspnet-a-large-scale-benchmark-dataset | 2112.14663 | null | https://arxiv.org/abs/2112.14663v3 | https://arxiv.org/pdf/2112.14663v3.pdf | MetaGraspNet_v0: A Large-Scale Benchmark Dataset for Vision-driven Robotic Grasping via Physics-based Metaverse Synthesis | There has been increasing interest in smart factories powered by robotics systems to tackle repetitive, laborious tasks. One impactful yet challenging task in robotics-powered smart factory applications is robotic grasping: using robotic arms to grasp objects autonomously in different settings. Robotic grasping require... | ['Alexander Wong', 'Maximilian Gilles', 'E. Zhixuan Zeng', 'Yuhao Chen'] | 2021-12-29 | null | null | null | null | ['robotic-grasping'] | ['robots'] | [ 7.70489573e-02 -3.46640408e-01 3.87960603e-03 -1.71501160e-01
-2.84696341e-01 -7.52007425e-01 3.44352394e-01 -9.76574048e-02
5.92352357e-04 2.05807462e-01 -2.57350147e-01 -1.09798536e-01
-5.70875406e-01 -8.32553148e-01 -9.16430593e-01 -6.46720409e-01
-2.18688712e-01 8.87423396e-01 4.75619316e-01 -3.37515235... | [5.770267963409424, -0.852952241897583] |
7afe7440-4520-41cc-aa42-9248b0ca324b | self-supervised-anomaly-detection-a-survey | 2205.05173 | null | https://arxiv.org/abs/2205.05173v2 | https://arxiv.org/pdf/2205.05173v2.pdf | Self-Supervised Anomaly Detection: A Survey and Outlook | Over the past few years, anomaly detection, a subfield of machine learning that is mainly concerned with the detection of rare events, witnessed an immense improvement following the unprecedented growth of deep learning models. Recently, the emergence of self-supervised learning has sparked the development of new anoma... | ['Narges Armanfard', 'Thi Kieu Khanh Ho', 'Hadi Hojjati'] | 2022-05-10 | null | null | null | null | ['self-supervised-anomaly-detection', 'supervised-anomaly-detection'] | ['computer-vision', 'computer-vision'] | [-2.01732576e-01 -1.58726186e-01 1.54316396e-01 -5.41806817e-01
-2.84053832e-01 -1.54278249e-01 7.95974016e-01 7.78488100e-01
-3.73534054e-01 4.67263103e-01 -1.36986151e-01 -1.98085919e-01
-1.04144670e-01 -7.53119826e-01 -2.21856922e-01 -6.57534361e-01
-5.95707357e-01 4.16387200e-01 4.18154091e-01 -2.17627436... | [7.494948387145996, 2.5547075271606445] |
d522161b-e5a7-4c14-9f37-a1927df4785b | decomposition-based-generation-process-for | 2204.03845 | null | https://arxiv.org/abs/2204.03845v3 | https://arxiv.org/pdf/2204.03845v3.pdf | Decompositional Generation Process for Instance-Dependent Partial Label Learning | Partial label learning (PLL) is a typical weakly supervised learning problem, where each training example is associated with a set of candidate labels among which only one is true. Most existing PLL approaches assume that the incorrect labels in each training example are randomly picked as the candidate labels and mode... | ['Xin Geng', 'Ning Xu', 'Congyu Qiao'] | 2022-04-08 | null | null | null | null | ['partial-label-learning'] | ['methodology'] | [ 2.90394604e-01 3.73098910e-01 -3.24410588e-01 -6.45581841e-01
-9.84328866e-01 -6.36721253e-01 6.63522124e-01 5.03250718e-01
-2.64424622e-01 8.69748116e-01 -2.88387984e-01 4.41851504e-02
-1.34202570e-01 -7.21656382e-01 -8.77084732e-01 -9.84477103e-01
4.62272823e-01 8.84244442e-01 3.16799611e-01 4.21962172... | [9.433905601501465, 4.044144153594971] |
4c9a5741-1044-4025-878c-fd0e73671824 | hybrid-loss-for-learning-single-image-based | 1812.07134 | null | http://arxiv.org/abs/1812.07134v1 | http://arxiv.org/pdf/1812.07134v1.pdf | Hybrid Loss for Learning Single-Image-based HDR Reconstruction | This paper tackles high-dynamic-range (HDR) image reconstruction given only a
single low-dynamic-range (LDR) image as input. While the existing methods focus
on minimizing the mean-squared-error (MSE) between the target and reconstructed
images, we minimize a hybrid loss that consists of perceptual and adversarial
loss... | ['ShaoDi You', 'Rei Kawakami', 'Kenta Moriwaki', 'Takeshi Naemura', 'Ryota Yoshihashi'] | 2018-12-18 | null | null | null | null | ['single-image-based-hdr-reconstruction', 'hdr-reconstruction'] | ['computer-vision', 'computer-vision'] | [ 6.17564857e-01 -3.87200639e-02 1.32535025e-02 -2.80477524e-01
-8.57590318e-01 -1.94948897e-01 1.36843905e-01 -2.53157288e-01
-5.19982159e-01 5.98037958e-01 -1.81726396e-01 1.53397210e-02
2.81487219e-03 -8.68082464e-01 -8.18492591e-01 -9.03660297e-01
1.13396101e-01 -2.27221191e-01 4.39994663e-01 -1.59967259... | [10.98436164855957, -2.159470319747925] |
9951263d-cf14-40ab-a1cd-816f2dc67d8f | inverting-ransac-global-model-detection-via | null | null | http://openaccess.thecvf.com/content_cvpr_2015/html/Litman_Inverting_RANSAC_Global_2015_CVPR_paper.html | http://openaccess.thecvf.com/content_cvpr_2015/papers/Litman_Inverting_RANSAC_Global_2015_CVPR_paper.pdf | Inverting RANSAC: Global Model Detection via Inlier Rate Estimation | This work presents a novel approach for detecting inliers in a given set of correspondences (matches). It does so without explicitly identifying any consensus set, based on a method for inlier rate estimation (IRE). Given such an estimator for the inlier rate, we also present an algorithm that detects a globally optima... | ['Simon Korman', 'Shai Avidan', 'Roee Litman', 'Alexander Bronstein'] | 2015-06-01 | null | null | null | cvpr-2015-6 | ['homography-estimation'] | ['computer-vision'] | [ 1.16405822e-01 2.96906251e-02 -5.28799221e-02 1.34886548e-01
-1.05362678e+00 -7.13406801e-01 6.02794707e-01 -9.33000743e-02
3.99426147e-02 5.55394173e-01 1.57948554e-01 -1.21535279e-01
-3.21084410e-01 -5.42952180e-01 -8.09360266e-01 -8.23658645e-01
1.63946092e-01 8.14494193e-01 7.15626255e-02 -3.08091283... | [7.937934875488281, -2.3525514602661133] |
a10e046f-67dc-4fa4-835a-36924cb13e99 | deepnnk-explaining-deep-models-and-their | 2007.10505 | null | https://arxiv.org/abs/2007.10505v1 | https://arxiv.org/pdf/2007.10505v1.pdf | DeepNNK: Explaining deep models and their generalization using polytope interpolation | Modern machine learning systems based on neural networks have shown great success in learning complex data patterns while being able to make good predictions on unseen data points. However, the limited interpretability of these systems hinders further progress and application to several domains in the real world. This ... | ['Sarath Shekkizhar', 'Antonio Ortega'] | 2020-07-20 | null | null | null | null | ['interpretability-techniques-for-deep-learning'] | ['miscellaneous'] | [ 3.00864935e-01 5.26049256e-01 -1.03017651e-01 -3.88205945e-01
-5.91790199e-01 -5.38054049e-01 7.86681354e-01 -2.01308802e-01
1.05289882e-02 1.01024354e+00 -3.01627576e-01 -3.82107884e-01
-3.67248327e-01 -7.44277716e-01 -1.14844036e+00 -6.69797838e-01
-2.37658277e-01 7.33238876e-01 -2.67297834e-01 -2.98868120... | [8.593213081359863, 4.649456977844238] |
c8620b11-7e22-4c18-a815-3c7e62bf2fa1 | learning-enriched-illuminants-for-cross-and | 2203.11068 | null | https://arxiv.org/abs/2203.11068v1 | https://arxiv.org/pdf/2203.11068v1.pdf | Learning Enriched Illuminants for Cross and Single Sensor Color Constancy | Color constancy aims to restore the constant colors of a scene under different illuminants. However, due to the existence of camera spectral sensitivity, the network trained on a certain sensor, cannot work well on others. Also, since the training datasets are collected in certain environments, the diversity of illumin... | ['Houqiang Li', 'Xu Jia', 'Wengang Zhou', 'Jianzhuang Liu', 'Chi-Man Pun', 'Zhendong Wang', 'Xiaodong Cun'] | 2022-03-21 | null | null | null | null | ['color-constancy'] | ['computer-vision'] | [ 5.97310483e-01 -2.82009065e-01 -8.15309510e-02 -3.14125806e-01
-4.60195303e-01 -5.15629828e-01 3.84224981e-01 -3.83170784e-01
-3.77966672e-01 6.23899341e-01 4.86599877e-02 -9.26216394e-02
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3.92509490e-01 -1.57907959e-02 3.57269794e-01 -3.47688258... | [10.490192413330078, -2.6015408039093018] |
5220b8bd-a871-4019-9441-8c75ca81bb96 | modeling-a-hidden-dynamical-system-using | 1904.05172 | null | https://arxiv.org/abs/1904.05172v2 | https://arxiv.org/pdf/1904.05172v2.pdf | Modeling a Hidden Dynamical System Using Energy Minimization and Kernel Density Estimates | In this paper we develop a kernel density estimation (KDE) approach to modeling and forecasting recurrent trajectories on a compact manifold. For the purposes of this paper, a trajectory is a sequence of coordinates in a phase space defined by an underlying hidden dynamical system. Our work is inspired by earlier work ... | ['Trevor K. Karn', 'Steven Petrone', 'Christopher Griffin'] | 2019-04-08 | null | null | null | null | ['trajectory-modeling'] | ['time-series'] | [-3.84364545e-01 -4.63657305e-02 -8.19795728e-02 4.97811846e-02
-9.31394398e-01 -3.86826277e-01 6.22403979e-01 -5.19582219e-02
1.06551304e-01 6.60357594e-01 3.11282843e-01 -2.55465031e-01
-2.56471723e-01 -6.53859556e-01 -7.26499677e-01 -8.25011849e-01
-4.91867125e-01 4.23893273e-01 4.25254628e-02 1.87817901... | [6.758241653442383, 3.4801242351531982] |
3689ee43-4c43-438d-95ca-6b372731c140 | predicting-hurricane-evacuation-decisions | 2303.06557 | null | https://arxiv.org/abs/2303.06557v1 | https://arxiv.org/pdf/2303.06557v1.pdf | Predicting Hurricane Evacuation Decisions with Interpretable Machine Learning Models | The aggravating effects of climate change and the growing population in hurricane-prone areas escalate the challenges in large-scale hurricane evacuations. While hurricane preparedness and response strategies vastly rely on the accuracy and timeliness of the predicted households' evacuation decisions, current studies f... | ['Xilei Zhao', 'Shih-Kai Huang', 'Yuran Sun'] | 2023-03-12 | null | null | null | null | ['interpretable-machine-learning'] | ['methodology'] | [-2.90818304e-01 -2.14119732e-01 -4.67583388e-02 -2.92777449e-01
-1.73814252e-01 -9.37394425e-02 2.15131208e-01 4.36466962e-01
-5.46603262e-01 8.76557529e-01 6.14866674e-01 -7.77580738e-01
-5.69327831e-01 -1.04907000e+00 -7.14225844e-02 -8.70160341e-01
-3.25035602e-01 3.70705009e-01 -4.18121248e-01 -6.76805675... | [6.572482109069824, 2.0208423137664795] |
e894db83-da47-431a-8fa5-73d035887181 | cross-lingual-text-classification-of | 2108.13620 | null | https://arxiv.org/abs/2108.13620v1 | https://arxiv.org/pdf/2108.13620v1.pdf | Cross-Lingual Text Classification of Transliterated Hindi and Malayalam | Transliteration is very common on social media, but transliterated text is not adequately handled by modern neural models for various NLP tasks. In this work, we combine data augmentation approaches with a Teacher-Student training scheme to address this issue in a cross-lingual transfer setting for fine-tuning state-of... | ['Huzefa Rangwala', 'Hemant Purohit', 'Antonios Anastasopoulos', 'Jitin Krishnan'] | 2021-08-31 | null | null | null | null | ['transliteration'] | ['natural-language-processing'] | [ 2.27913707e-02 9.68458652e-02 -2.64595836e-01 -5.79522371e-01
-1.25370586e+00 -6.88674629e-01 9.40077186e-01 1.19214006e-01
-8.67366612e-01 8.65137398e-01 2.92461783e-01 -1.02145720e+00
6.09358788e-01 -5.70551157e-01 -9.39486921e-01 -2.79258251e-01
1.66259959e-01 9.79654074e-01 -4.39907700e-01 -7.60060906... | [11.063369750976562, 9.990811347961426] |
fa65d2a5-ac77-4f50-9eef-f3c73f8eab77 | diffdock-pp-rigid-protein-protein-docking | 2304.03889 | null | https://arxiv.org/abs/2304.03889v1 | https://arxiv.org/pdf/2304.03889v1.pdf | DiffDock-PP: Rigid Protein-Protein Docking with Diffusion Models | Understanding how proteins structurally interact is crucial to modern biology, with applications in drug discovery and protein design. Recent machine learning methods have formulated protein-small molecule docking as a generative problem with significant performance boosts over both traditional and deep learning baseli... | ['Tommi S. Jaakkola', 'Regina Barzilay', 'Céline Marquet', 'Gabriele Corso', 'Menghua Wu', 'Hannes Stärk', 'Ruslan Mammadov', 'Cedrik Laue', 'Mohamed Amine Ketata'] | 2023-04-08 | null | null | null | null | ['drug-discovery', 'protein-design'] | ['medical', 'medical'] | [-2.32464626e-01 2.08892092e-01 -1.20057143e-01 -3.47152114e-01
-1.03027809e+00 -8.86645257e-01 3.90940458e-01 1.00629732e-01
-1.70825347e-01 1.57605326e+00 1.81904420e-01 -6.25684679e-01
2.24137977e-01 -5.09427607e-01 -1.36372507e+00 -1.22657752e+00
-7.06950426e-02 9.69250739e-01 2.00775024e-02 -1.36201397... | [4.8602190017700195, 5.591073989868164] |
e8c3e7b7-e723-4ecd-907b-dd09922fa180 | bayesian-community-detection-for-networks | 2203.02090 | null | https://arxiv.org/abs/2203.02090v2 | https://arxiv.org/pdf/2203.02090v2.pdf | Bayesian community detection for networks with covariates | The increasing prevalence of network data in a vast variety of fields and the need to extract useful information out of them have spurred fast developments in related models and algorithms. Among the various learning tasks with network data, community detection, the discovery of node clusters or "communities," has argu... | ['Lizhen Lin', 'Nathaniel Josephs', 'Arash Amini', 'Luyi Shen'] | 2022-03-04 | null | null | null | null | ['stochastic-block-model'] | ['graphs'] | [ 2.67131507e-01 -1.90357417e-02 -3.29325289e-01 -3.38135272e-01
-3.16858530e-01 -4.92136687e-01 5.63703537e-01 3.51313591e-01
-1.45167291e-01 7.76245356e-01 2.06018433e-01 -3.66363674e-01
-5.47046006e-01 -1.00143194e+00 -4.09326255e-01 -9.23330545e-01
-6.07265890e-01 7.87229836e-01 1.54692635e-01 3.22732389... | [6.961796760559082, 5.1672043800354] |
f822da47-4d3d-46b3-a502-7ea24317644b | reconstructing-the-somatotopic-organization | 2306.05623 | null | https://arxiv.org/abs/2306.05623v2 | https://arxiv.org/pdf/2306.05623v2.pdf | Reconstructing the somatotopic organization of the corticospinal tract remains a challenge for modern tractography methods | The corticospinal tract (CST) is a critically important white matter fiber tract in the human brain that enables control of voluntary movements of the body. Diffusion MRI tractography is the only method that enables the study of the anatomy and variability of the CST pathway in human health. In this work, we explored t... | ["Lauren J. O'Donnell", 'Alexandra J. Golby', 'Ron Kikinis', 'Nikos Makris', 'Yogesh Rathi', 'Erickson Torio', 'Jarrett Rushmore', 'Yuanjing Feng', 'Yiang Pan', 'Fan Zhang', 'Jianzhong He'] | 2023-06-09 | null | null | null | null | ['anatomy'] | ['miscellaneous'] | [-3.66792530e-01 -1.85782805e-01 -1.63213283e-01 -1.40514210e-01
1.81647837e-01 -9.94080722e-01 4.16660041e-01 -3.92964482e-01
-6.55334651e-01 7.35416889e-01 8.87711346e-01 -6.53354049e-01
-1.91422880e-01 -2.49262288e-01 -1.38371840e-01 -4.99100477e-01
-7.30653524e-01 7.48059154e-01 2.32389405e-01 -7.99625274... | [14.011828422546387, -2.2482261657714844] |
9a2b6815-ef4e-413c-80bd-963213968ea4 | acrobat-a-multi-stain-breast-cancer | 2211.13621 | null | https://arxiv.org/abs/2211.13621v1 | https://arxiv.org/pdf/2211.13621v1.pdf | ACROBAT -- a multi-stain breast cancer histological whole-slide-image data set from routine diagnostics for computational pathology | The analysis of FFPE tissue sections stained with haematoxylin and eosin (H&E) or immunohistochemistry (IHC) is an essential part of the pathologic assessment of surgically resected breast cancer specimens. IHC staining has been broadly adopted into diagnostic guidelines and routine workflows to manually assess status ... | ['Mattias Rantalainen', 'Pekka Ruusuvuori', 'Johan Hartman', 'Anne-Vibeke Laenkholm', 'Leena Latonen', 'Kajsa Ledesma Eriksson', 'Abhinav Sharma', 'Sandra Kristiane Sinius Pouplier', 'Yanbo Feng', 'Dusan Rasic', 'Aino Kuusela', 'Sonja Koivukoski', 'Constance Boissin', 'Kimmo Kartasalo', 'Circe Carr', 'Leslie Solorzano'... | 2022-11-24 | null | null | null | null | ['unsupervised-pre-training'] | ['methodology'] | [ 2.91996479e-01 1.85167730e-01 -4.94066685e-01 -1.66673526e-01
-1.47441304e+00 -7.60262907e-01 1.31934196e-01 8.47533345e-01
-6.77432954e-01 4.93091375e-01 1.67754859e-01 -6.22036278e-01
-1.91461354e-01 -5.51440358e-01 -1.24979824e-01 -1.23079157e+00
-8.20879489e-02 8.75112474e-01 7.79528618e-02 -1.84948072... | [15.090856552124023, -3.064809799194336] |
bd5b3913-0fd7-4c5d-8682-1ffa673b08ad | adversarial-skill-networks-unsupervised-robot | 1910.09430 | null | https://arxiv.org/abs/1910.09430v2 | https://arxiv.org/pdf/1910.09430v2.pdf | Adversarial Skill Networks: Unsupervised Robot Skill Learning from Video | Key challenges for the deployment of reinforcement learning (RL) agents in the real world are the discovery, representation and reuse of skills in the absence of a reward function. To this end, we propose a novel approach to learn a task-agnostic skill embedding space from unlabeled multi-view videos. Our method learns... | ['Wolfram Burgard', 'Markus Merklinger', 'Oier Mees', 'Gabriel Kalweit'] | 2019-10-21 | null | null | null | null | ['video-alignment'] | ['computer-vision'] | [ 2.40627959e-01 1.01231970e-01 1.43792229e-02 -6.89952299e-02
-5.78259051e-01 -8.99935484e-01 5.52210987e-01 -5.25175154e-01
-6.21088386e-01 1.07840598e+00 1.96658954e-01 3.00449431e-01
-2.49320582e-01 -3.23130935e-01 -1.11761427e+00 -8.89313877e-01
-4.57804471e-01 3.60374600e-01 1.21336676e-01 -3.56471390... | [4.481469631195068, 0.8401564359664917] |
e49feab6-5447-4b91-a772-cfaa1440de1d | rethinking-optical-flow-from-geometric | 2303.08384 | null | https://arxiv.org/abs/2303.08384v1 | https://arxiv.org/pdf/2303.08384v1.pdf | Rethinking Optical Flow from Geometric Matching Consistent Perspective | Optical flow estimation is a challenging problem remaining unsolved. Recent deep learning based optical flow models have achieved considerable success. However, these models often train networks from the scratch on standard optical flow data, which restricts their ability to robustly and geometrically match image featu... | ['Yanwei Fu', 'Chenjie Cao', 'Qiaole Dong'] | 2023-03-15 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Dong_Rethinking_Optical_Flow_From_Geometric_Matching_Consistent_Perspective_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Dong_Rethinking_Optical_Flow_From_Geometric_Matching_Consistent_Perspective_CVPR_2023_paper.pdf | cvpr-2023-1 | ['geometric-matching'] | ['computer-vision'] | [-2.63700604e-01 -5.24162292e-01 -4.07311231e-01 -1.87807411e-01
-4.28911299e-01 -4.14662153e-01 3.84656876e-01 -4.33044881e-01
-3.02575082e-01 7.32187331e-01 3.13364923e-01 -1.14259496e-01
-3.34817581e-02 -7.76446164e-01 -6.47937655e-01 -4.10266370e-01
-2.10878342e-01 1.52956441e-01 1.98972404e-01 -7.04297200... | [8.76429557800293, -1.8457454442977905] |
c70a1e6c-8d26-4ccf-b7fc-98ae11cb3d98 | lightweight-estimation-of-hand-mesh-and | 2303.14838 | null | https://arxiv.org/abs/2303.14838v1 | https://arxiv.org/pdf/2303.14838v1.pdf | Lightweight Estimation of Hand Mesh and Biomechanically Feasible Kinematic Parameters | 3D hand pose estimation is a long-standing challenge in both robotics and computer vision communities due to its implicit depth ambiguity and often strong self-occlusion. Recently, in addition to the hand skeleton, jointly estimating hand pose and shape has gained more attraction. State-of-the-art methods adopt a model... | ['Yao Wang', 'Zhipeng Fan'] | 2023-03-26 | null | null | null | null | ['3d-hand-pose-estimation', '3d-hand-pose-estimation'] | ['computer-vision', 'graphs'] | [-2.72153243e-02 -2.73940898e-03 -3.42793763e-01 3.78743708e-02
-6.46086216e-01 -4.55991894e-01 2.48796046e-02 -2.42939353e-01
-5.40492952e-01 5.70805371e-01 -1.42786264e-01 2.52933707e-02
-5.29233851e-02 -4.32185948e-01 -9.50153768e-01 -3.49118531e-01
2.60824561e-01 9.27122831e-01 1.71994939e-01 -4.01613154... | [6.6884660720825195, -1.0028648376464844] |
d68104f6-901c-4d03-b8fd-48731d431ff6 | automated-audio-captioning-and-language-based | 2207.04156 | null | https://arxiv.org/abs/2207.04156v2 | https://arxiv.org/pdf/2207.04156v2.pdf | Automated Audio Captioning and Language-Based Audio Retrieval | This project involved participation in the DCASE 2022 Competition (Task 6) which had two subtasks: (1) Automated Audio Captioning and (2) Language-Based Audio Retrieval. The first subtask involved the generation of a textual description for audio samples, while the goal of the second was to find audio samples within a ... | ['Ankit Shah', 'Iffanice Houndayi', 'Yi Song', 'Patrick Kollman', 'Hyejin Park', 'Clive Gomes'] | 2022-07-08 | null | null | null | null | ['audio-captioning'] | ['audio'] | [ 1.53917909e-01 8.71105194e-02 6.04742169e-02 -2.30844766e-01
-2.10882354e+00 -9.58620548e-01 8.00952733e-01 3.16455930e-01
-2.42228299e-01 7.01216757e-01 8.17977130e-01 1.55326575e-01
1.46629969e-02 2.93515250e-03 -5.94078958e-01 -1.47331432e-01
-3.16161126e-01 6.64901495e-01 3.79294753e-01 -1.37738347... | [15.286962509155273, 4.893754482269287] |
2226dd92-5612-402d-ba8c-6681d33a3871 | a-comprehensive-survey-on-affective-computing | 2305.07665 | null | https://arxiv.org/abs/2305.07665v1 | https://arxiv.org/pdf/2305.07665v1.pdf | A Comprehensive Survey on Affective Computing; Challenges, Trends, Applications, and Future Directions | As the name suggests, affective computing aims to recognize human emotions, sentiments, and feelings. There is a wide range of fields that study affective computing, including languages, sociology, psychology, computer science, and physiology. However, no research has ever been done to determine how machine learning (M... | ['Jong Weon Lee', 'Md. Jalil Piran', 'Imran Ullah Khan', 'Haseeb Ali Khan', 'Sitara Afzal'] | 2023-05-08 | null | null | null | null | ['mixed-reality'] | ['computer-vision'] | [-1.80512667e-02 -1.43737584e-01 -2.07789734e-01 -6.15735471e-01
4.67989556e-02 -5.19464433e-01 3.97379667e-01 3.46228272e-01
-1.75512418e-01 5.88630259e-01 2.43858814e-01 2.84112155e-01
2.46895030e-01 -7.00838327e-01 3.07738125e-01 -5.83088875e-01
6.66442215e-02 -1.59586906e-01 -6.68217421e-01 -4.36498702... | [13.02918815612793, 5.675216197967529] |
97015013-d421-42ee-a70a-19189e488cf5 | scalable-dynamic-topic-modeling-with | 1610.07703 | null | https://arxiv.org/abs/1610.07703v3 | https://arxiv.org/pdf/1610.07703v3.pdf | Scalable Dynamic Topic Modeling with Clustered Latent Dirichlet Allocation (CLDA) | Topic modeling, a method for extracting the underlying themes from a collection of documents, is an increasingly important component of the design of intelligent systems enabling the sense-making of highly dynamic and diverse streams of text data. Traditional methods such as Dynamic Topic Modeling (DTM) do not lend the... | ['Amy W. Apon', 'Alexander Herzog', 'Ilya Safro', 'Paul W. Wilson', 'Chris Gropp'] | 2016-10-25 | null | null | null | null | ['dynamic-topic-modeling'] | ['natural-language-processing'] | [ 3.37357000e-02 -1.24095544e-01 -1.40672296e-01 -1.32122129e-01
-7.07565844e-01 -5.13124883e-01 8.63114774e-01 8.98614943e-01
-5.23331225e-01 6.08705223e-01 3.25438261e-01 -3.19196045e-01
-3.30831558e-01 -8.91569138e-01 -2.43873015e-01 -7.65001118e-01
-3.17160010e-01 1.03128755e+00 5.64836621e-01 2.39741474... | [10.316554069519043, 7.1036176681518555] |
27b7ca41-421c-4a4f-a4ff-53954880d0f3 | a-new-semi-supervised-inductive-transfer | 2108.07930 | null | https://arxiv.org/abs/2108.07930v2 | https://arxiv.org/pdf/2108.07930v2.pdf | A new semi-supervised inductive transfer learning framework: Co-Transfer | In many practical data mining scenarios, such as network intrusion detection, Twitter spam detection, and computer-aided diagnosis, a source domain that is different from but related to a target domain is very common. In addition, a large amount of unlabeled data is available in both source and target domains, but labe... | ['Zhe Yuan', 'Yimin Wen'] | 2021-08-18 | null | null | null | null | ['spam-detection'] | ['natural-language-processing'] | [ 1.41018510e-01 4.04573046e-02 -3.42434853e-01 -5.29571533e-01
-6.10447943e-01 -3.19717944e-01 4.36018556e-01 1.57119602e-01
-3.51413906e-01 1.22452784e+00 -3.06932181e-01 -1.69151917e-01
-2.81046871e-02 -1.11117220e+00 -4.60159779e-01 -5.96173704e-01
2.55909473e-01 8.56610060e-01 6.17747545e-01 -9.29529816... | [10.353095054626465, 3.1385281085968018] |
bf79e989-06dd-40de-b17e-9ad10de02a14 | a-unified-image-preprocessing-framework-for | 2208.07110 | null | https://arxiv.org/abs/2208.07110v1 | https://arxiv.org/pdf/2208.07110v1.pdf | A Unified Image Preprocessing Framework For Image Compression | With the development of streaming media technology, increasing communication relies on sound and visual information, which puts a massive burden on online media. Data compression becomes increasingly important to reduce the volume of data transmission and storage. To further improve the efficiency of image compression,... | ['Xiaocheng Li', 'Weihui Deng', 'Moqi Zhang'] | 2022-08-15 | null | null | null | null | ['data-compression'] | ['time-series'] | [ 4.73752201e-01 -2.63864338e-01 -4.39960659e-01 -2.40901813e-01
-5.67978978e-01 4.93428446e-02 2.74065703e-01 3.78151238e-01
-5.47188878e-01 1.63106725e-01 2.87885278e-01 -3.35659862e-01
3.01158167e-02 -1.06166553e+00 -7.89024353e-01 -4.48664337e-01
-7.93371648e-02 1.90677688e-01 4.14832741e-01 -1.62488490... | [11.374841690063477, -1.5568381547927856] |
d1773c71-3b45-44e0-be46-1e8db0ece881 | rspnet-relative-speed-perception-for | 2011.07949 | null | https://arxiv.org/abs/2011.07949v2 | https://arxiv.org/pdf/2011.07949v2.pdf | RSPNet: Relative Speed Perception for Unsupervised Video Representation Learning | We study unsupervised video representation learning that seeks to learn both motion and appearance features from unlabeled video only, which can be reused for downstream tasks such as action recognition. This task, however, is extremely challenging due to 1) the highly complex spatial-temporal information in videos; an... | ['Chuang Gan', 'Mingkui Tan', 'Shilei Wen', 'Runhao Zeng', 'Xiang Long', 'Dongliang He', 'Deng Huang', 'Peihao Chen'] | 2020-10-27 | null | null | null | null | ['self-supervised-action-recognition'] | ['computer-vision'] | [ 2.04493999e-01 -4.19410974e-01 -6.00114048e-01 -5.02227724e-01
-5.89056492e-01 -5.23992181e-01 3.69502515e-01 -6.06260858e-02
-4.04989928e-01 2.84463078e-01 -1.72768049e-02 -9.44160968e-02
-9.77867050e-04 -4.82606322e-01 -8.15116465e-01 -8.66708815e-01
-1.06580064e-01 9.36982222e-03 2.01346099e-01 2.36444753... | [8.75222110748291, 0.7637948989868164] |
e7f4ab57-44bb-4bd5-9bc1-a0b7ef501287 | data-to-text-generation-with-variational | 2202.13756 | null | https://arxiv.org/abs/2202.13756v1 | https://arxiv.org/pdf/2202.13756v1.pdf | Data-to-text Generation with Variational Sequential Planning | We consider the task of data-to-text generation, which aims to create textual output from non-linguistic input. We focus on generating long-form text, i.e., documents with multiple paragraphs, and propose a neural model enhanced with a planning component responsible for organizing high-level information in a coherent a... | ['Mirella Lapata', 'Yao Fu', 'Ratish Puduppully'] | 2022-02-28 | null | null | null | null | ['data-to-text-generation'] | ['natural-language-processing'] | [ 6.27766430e-01 1.00679684e+00 -2.89260775e-01 -2.27204829e-01
-1.32853425e+00 -6.07304096e-01 1.40097511e+00 1.03358127e-01
-2.03293070e-01 1.26068342e+00 1.02640820e+00 -3.05803835e-01
3.46319139e-01 -9.28093970e-01 -9.69483018e-01 -3.66205812e-01
3.60303789e-01 1.20750713e+00 -2.09416747e-01 -6.35130927... | [11.69546890258789, 8.967421531677246] |
2f3bfb02-a5c9-43cb-bbb9-e5a85782f79c | towards-smart-city-security-violence-and | 2207.12850 | null | https://arxiv.org/abs/2207.12850v6 | https://arxiv.org/pdf/2207.12850v6.pdf | SSIVD-Net: A Novel Salient Super Image Classification & Detection Technique for Weaponized Violence | Detection of violence and weaponized violence in closed-circuit television (CCTV) footage requires a comprehensive approach. In this work, we introduce the \emph{Smart-City CCTV Violence Detection (SCVD)} dataset, specifically designed to facilitate the learning of weapon distribution in surveillance videos. To tackle ... | ['Abdulmotaleb El Saddik', 'Mustaqeem Khan', 'Reem Alameeri', 'Li Zhiyuan', 'Toluwani Aremu'] | 2022-07-26 | null | null | null | null | ['video-classification'] | ['computer-vision'] | [ 3.15015793e-01 -2.64438331e-01 -1.82701021e-01 2.94566788e-02
-7.43499994e-01 -5.39808095e-01 7.11663008e-01 -1.75671309e-01
-1.45109400e-01 2.93209523e-01 3.38161647e-01 -6.94752753e-01
-4.08454418e-01 -7.85620689e-01 -6.31180048e-01 -9.73143458e-01
1.66015714e-01 -9.40125585e-02 1.33221686e-01 -1.34698838... | [7.980055809020996, 0.7293256521224976] |
b50c63ce-70c9-4bef-a5c7-dab1efa0687c | motion-policy-networks | 2210.12209 | null | https://arxiv.org/abs/2210.12209v1 | https://arxiv.org/pdf/2210.12209v1.pdf | Motion Policy Networks | Collision-free motion generation in unknown environments is a core building block for robot manipulation. Generating such motions is challenging due to multiple objectives; not only should the solutions be optimal, the motion generator itself must be fast enough for real-time performance and reliable enough for practic... | ['Dieter Fox', 'Byron Boots', 'Bryan Peele', 'Clemens Eppner', 'Adithyavairan Murali', 'Adam Fishman'] | 2022-10-21 | null | null | null | null | ['robot-manipulation'] | ['robots'] | [-1.25200748e-01 3.01183820e-01 -1.56669319e-01 5.98535547e-03
-8.13591838e-01 -4.88269866e-01 4.82221812e-01 -3.29526722e-01
-4.92960751e-01 8.82255256e-01 7.99369663e-02 -2.74288982e-01
-2.01907486e-01 -7.73793161e-01 -1.03311670e+00 -6.08959079e-01
-5.26754200e-01 9.84166741e-01 3.13236505e-01 -4.75856274... | [4.728422164916992, 0.9833574891090393] |
ec61b8c8-8cf1-4cee-b068-aeff9aaec552 | scale-aware-super-resolution-network-with | 2305.19063 | null | https://arxiv.org/abs/2305.19063v1 | https://arxiv.org/pdf/2305.19063v1.pdf | Scale-aware Super-resolution Network with Dual Affinity Learning for Lesion Segmentation from Medical Images | Convolutional Neural Networks (CNNs) have shown remarkable progress in medical image segmentation. However, lesion segmentation remains a challenge to state-of-the-art CNN-based algorithms due to the variance in scales and shapes. On the one hand, tiny lesions are hard to be delineated precisely from the medical images... | ['Hao Chen', 'Pheng-Ann Heng', 'Huangjing Lin', 'Luyang Luo', 'Yanwen Li'] | 2023-05-30 | null | null | null | null | ['image-super-resolution', 'lesion-segmentation'] | ['computer-vision', 'medical'] | [ 4.94173378e-01 6.35505989e-02 -3.18257898e-01 -2.83967823e-01
-1.05610955e+00 -1.62906751e-01 -3.10556944e-02 -1.91928893e-01
-4.14870530e-01 4.60370183e-01 2.70962179e-01 -1.03799673e-02
-7.64556900e-02 -8.20683360e-01 -3.42311591e-01 -8.19967628e-01
3.41647655e-01 3.20861548e-01 1.00451589e+00 -2.10992768... | [14.633111000061035, -2.570448637008667] |
e0c9464d-1d37-4fe7-a06e-8d8a411c5134 | combining-explicit-and-implicit-1 | 2306.00342 | null | https://arxiv.org/abs/2306.00342v1 | https://arxiv.org/pdf/2306.00342v1.pdf | Combining Explicit and Implicit Regularization for Efficient Learning in Deep Networks | Works on implicit regularization have studied gradient trajectories during the optimization process to explain why deep networks favor certain kinds of solutions over others. In deep linear networks, it has been shown that gradient descent implicitly regularizes toward low-rank solutions on matrix completion/factorizat... | ['Dan Zhao'] | 2023-06-01 | combining-explicit-and-implicit | https://papers.nips.cc/paper_files/paper/2022/hash/1419d8554191a65ea4f2d8e1057973e4-Abstract-Conference.html | https://papers.nips.cc/paper_files/paper/2022/file/1419d8554191a65ea4f2d8e1057973e4-Paper-Conference.pdf | neurips-2022-11 | ['matrix-completion'] | ['methodology'] | [-7.45893046e-02 3.03111494e-01 -3.80571038e-01 -5.19809961e-01
-3.79891574e-01 -3.22717726e-01 6.37639463e-01 1.24445245e-01
-6.82890415e-01 5.54396451e-01 6.80349469e-01 -4.01687354e-01
-2.60708153e-01 -5.70302904e-01 -8.64554763e-01 -6.61770105e-01
-9.17680487e-02 2.92166799e-01 -2.93244153e-01 -3.83841962... | [8.389641761779785, 3.629164457321167] |
1ffc946c-47d0-4f8b-92c8-c8a27c9784a3 | flexible-android-malware-detection-model | 2210.14225 | null | https://arxiv.org/abs/2210.14225v1 | https://arxiv.org/pdf/2210.14225v1.pdf | Flexible Android Malware Detection Model based on Generative Adversarial Networks with Code Tensor | The behavior of malware threats is gradually increasing, heightened the need for malware detection. However, existing malware detection methods only target at the existing malicious samples, the detection of fresh malicious code and variants of malicious code is limited. In this paper, we propose a novel scheme that de... | ['Linxi Han', 'Fengyang Deng', 'Zhao Yang'] | 2022-10-25 | null | null | null | null | ['android-malware-detection'] | ['miscellaneous'] | [ 2.31526002e-01 -5.57972848e-01 -2.01973483e-01 1.49707600e-01
-2.86701739e-01 -5.87526739e-01 4.60648775e-01 -7.60703444e-01
1.09238520e-01 1.79785788e-01 -8.25351253e-02 -3.59965295e-01
3.55941862e-01 -9.33979094e-01 -4.18423951e-01 -9.90119338e-01
-4.46593165e-02 2.24682257e-01 4.18034405e-01 -2.79368639... | [14.40152645111084, 9.645242691040039] |
cff96c2d-337b-4242-abf9-59b6e82e44bb | an-mrc-framework-for-semantic-role-labeling-1 | null | null | https://openreview.net/forum?id=PgcGLPyh8f | https://openreview.net/pdf?id=PgcGLPyh8f | An MRC Framework for Semantic Role Labeling | Semantic Role Labeling (SRL) aims at recognizing the predicate-argument structure of a sentence and can be decomposed into two subtasks: predicate disambiguation and argument labeling. Prior work deals with these two tasks independently, which ignores the semantic connection between the two tasks. In this paper, we pro... | ['Anonymous'] | 2022-01-16 | null | null | null | acl-arr-january-2022-1 | ['semantic-role-labeling'] | ['natural-language-processing'] | [ 7.78192043e-01 5.18806577e-01 -4.05226469e-01 -5.48019767e-01
-7.70892382e-01 -8.87928784e-01 6.31948292e-01 8.65012288e-01
-4.85788882e-01 6.82273209e-01 6.44325018e-01 -4.74529386e-01
-2.37406775e-01 -9.96930778e-01 -4.84141022e-01 -5.07980585e-01
3.33106428e-01 5.18651128e-01 6.59337044e-01 -4.16446507... | [10.23563289642334, 9.240662574768066] |
5c861189-d0fb-4ef2-8f69-7bc3da003ea7 | multi-conditional-latent-variable-model-for | null | null | http://openaccess.thecvf.com/content_iccv_2015/html/Eleftheriadis_Multi-Conditional_Latent_Variable_ICCV_2015_paper.html | http://openaccess.thecvf.com/content_iccv_2015/papers/Eleftheriadis_Multi-Conditional_Latent_Variable_ICCV_2015_paper.pdf | Multi-Conditional Latent Variable Model for Joint Facial Action Unit Detection | We propose a novel multi-conditional latent variable model for simultaneous facial feature fusion and detection of facial action units. In our approach we exploit the structure-discovery capabilities of generative models such as Gaussian processes, and the discriminative power of classifiers such as logistic function. ... | ['Ognjen Rudovic', 'Stefanos Eleftheriadis', 'Maja Pantic'] | 2015-12-01 | null | null | null | iccv-2015-12 | ['action-unit-detection', 'facial-action-unit-detection'] | ['computer-vision', 'computer-vision'] | [ 3.23782772e-01 -4.81154546e-02 -2.54382104e-01 -2.77971983e-01
-1.10579562e+00 -1.99836269e-01 9.80077028e-01 -2.78117150e-01
-3.17831576e-01 6.89505696e-01 -5.18840216e-02 3.75294477e-01
-2.01121002e-01 -3.25103819e-01 -4.36882496e-01 -1.28123891e+00
-8.83647725e-02 3.05756658e-01 1.03377968e-01 2.88568705... | [13.59305477142334, 1.6501468420028687] |
e57c3f42-26b1-41e6-82fc-99d0eecbc9a4 | multiview-hessian-discriminative-sparse | 1307.3811 | null | http://arxiv.org/abs/1307.3811v1 | http://arxiv.org/pdf/1307.3811v1.pdf | Multiview Hessian Discriminative Sparse Coding for Image Annotation | Sparse coding represents a signal sparsely by using an overcomplete
dictionary, and obtains promising performance in practical computer vision
applications, especially for signal restoration tasks such as image denoising
and image inpainting. In recent years, many discriminative sparse coding
algorithms have been devel... | ['DaCheng Tao', 'Yuanyan Tang', 'Weifeng Liu', 'Jun Cheng'] | 2013-07-15 | null | null | null | null | ['multiview-learning'] | ['computer-vision'] | [-1.06088175e-02 -1.73708200e-01 -3.60216588e-01 -6.05132341e-01
-8.03887725e-01 -3.54976624e-01 2.20761955e-01 -2.37875104e-01
7.92248622e-02 3.99008155e-01 5.69788337e-01 2.20417947e-01
8.75324756e-02 -2.59160548e-01 -6.85434282e-01 -8.30355644e-01
2.28342071e-01 7.62844831e-02 -1.10599175e-01 -6.14339188... | [11.436014175415039, -1.6095242500305176] |
89b94d37-47ea-4b2b-901d-3c893c0d02b7 | u-net-with-hierarchical-bottleneck-attention | 2107.04721 | null | https://arxiv.org/abs/2107.04721v1 | https://arxiv.org/pdf/2107.04721v1.pdf | U-Net with Hierarchical Bottleneck Attention for Landmark Detection in Fundus Images of the Degenerated Retina | Fundus photography has routinely been used to document the presence and severity of retinal degenerative diseases such as age-related macular degeneration (AMD), glaucoma, and diabetic retinopathy (DR) in clinical practice, for which the fovea and optic disc (OD) are important retinal landmarks. However, the occurrence... | ['Michael Beyeler', 'Jacob Granley', 'Ziming Qi', 'Shuyun Tang'] | 2021-07-09 | null | null | null | null | ['optic-disc-detection', 'fovea-detection'] | ['medical', 'medical'] | [-8.40207338e-02 1.71008017e-02 4.66999114e-02 3.59653868e-02
-4.14566219e-01 -2.71254241e-01 1.13601692e-01 7.47486725e-02
-5.20816565e-01 6.21808350e-01 3.02217126e-01 -5.04696429e-01
-1.73803605e-02 -5.20136416e-01 -4.00059491e-01 -5.58248639e-01
-2.41697982e-01 -1.37808830e-01 4.76735801e-01 2.85659790... | [15.819815635681152, -3.9959566593170166] |
fc480cd3-80d4-4288-acac-22fc1c23b49c | borderdet-border-feature-for-dense-object | 2007.11056 | null | https://arxiv.org/abs/2007.11056v3 | https://arxiv.org/pdf/2007.11056v3.pdf | BorderDet: Border Feature for Dense Object Detection | Dense object detectors rely on the sliding-window paradigm that predicts the object over a regular grid of image. Meanwhile, the feature maps on the point of the grid are adopted to generate the bounding box predictions. The point feature is convenient to use but may lack the explicit border information for accurate lo... | ['Jian Sun', 'Songtao Liu', 'Zeming Li', 'Yuchen Ma', 'Han Qiu'] | 2020-07-21 | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/2211_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123460528.pdf | eccv-2020-8 | ['dense-object-detection'] | ['computer-vision'] | [-1.89069480e-01 -1.50340036e-01 -4.29588586e-01 -2.63299257e-01
-9.27967727e-01 -4.51672435e-01 4.93286341e-01 -4.62593101e-02
-4.50492144e-01 3.27735037e-01 -5.68680689e-02 -1.17142119e-01
2.85245597e-01 -6.99501693e-01 -7.15891421e-01 -5.11864960e-01
3.33690457e-02 2.33190414e-02 8.81868780e-01 -3.29619348... | [8.640538215637207, -0.429386705160141] |
24d65348-bb65-4ba7-99c0-8129795d4f82 | open-world-detr-transformer-based-open-world | 2212.02969 | null | https://arxiv.org/abs/2212.02969v1 | https://arxiv.org/pdf/2212.02969v1.pdf | Open World DETR: Transformer based Open World Object Detection | Open world object detection aims at detecting objects that are absent in the object classes of the training data as unknown objects without explicit supervision. Furthermore, the exact classes of the unknown objects must be identified without catastrophic forgetting of the previous known classes when the corresponding ... | ['Gim Hee Lee', 'Mingli Ding', 'Yongqiang Zhang', 'Na Dong'] | 2022-12-06 | null | null | null | null | ['open-world-object-detection'] | ['computer-vision'] | [ 9.52057391e-02 2.67376661e-01 -1.27755195e-01 -2.90123165e-01
-6.01951301e-01 -5.62041223e-01 2.71358520e-01 1.73278838e-01
-3.91440570e-01 9.42328334e-01 -3.68758112e-01 1.97884515e-01
3.15104306e-01 -8.19669783e-01 -9.68355656e-01 -8.21481824e-01
1.07476331e-01 9.03867900e-01 1.03698611e+00 1.75847441... | [9.366954803466797, 1.4811415672302246] |
678d8818-d0fa-4da0-891b-5af3f80db6c8 | sequence-guided-protein-structure | 2007.06847 | null | https://arxiv.org/abs/2007.06847v3 | https://arxiv.org/pdf/2007.06847v3.pdf | Sequence-guided protein structure determination using graph convolutional and recurrent networks | Single particle, cryogenic electron microscopy (cryo-EM) experiments now routinely produce high-resolution data for large proteins and their complexes. Building an atomic model into a cryo-EM density map is challenging, particularly when no structure for the target protein is known a priori. Existing protocols for this... | ['Saulo H. P. de Oliveira', 'Po-Nan Li', 'Soichi Wakatsuki', 'Henry van den Bedem'] | 2020-07-14 | null | null | null | null | ['cryogenic-electron-microscopy-cryo-em'] | ['computer-vision'] | [ 3.34366918e-01 2.76169982e-02 4.22984302e-01 -5.51421344e-01
-7.79631913e-01 -5.20024240e-01 3.49353194e-01 5.21706641e-01
-7.41690338e-01 1.08717716e+00 -2.56405920e-01 -7.98758149e-01
1.98856056e-01 -6.32848978e-01 -1.00417113e+00 -7.51430988e-01
-9.69665870e-02 1.01857901e+00 1.20892107e-01 -4.52196822... | [13.347379684448242, -3.0915119647979736] |
8dfd1560-4e3e-4060-aee2-d67a71002e3f | video-text-tracking-for-dense-and-small-text | 2304.00018 | null | https://arxiv.org/abs/2304.00018v1 | https://arxiv.org/pdf/2304.00018v1.pdf | Video text tracking for dense and small text based on pp-yoloe-r and sort algorithm | Although end-to-end video text spotting methods based on Transformer can model long-range dependencies and simplify the train process, it will lead to large computation cost with the increase of the frame size in the input video. Therefore, considering the resolution of ICDAR 2023 DSText is 1080 * 1920 and slicing the ... | ['Hongen Liu'] | 2023-03-31 | null | null | null | null | ['text-spotting', 'small-object-detection'] | ['computer-vision', 'computer-vision'] | [ 2.25758359e-01 -6.97720647e-01 -1.62353247e-01 -7.03874230e-02
-6.22167230e-01 -2.99412787e-01 4.10006195e-01 -2.28184149e-01
-6.64394259e-01 3.93889666e-01 1.24921583e-01 -5.16672790e-01
2.98614442e-01 -5.52826762e-01 -6.91306710e-01 -7.13934541e-01
4.52370644e-01 5.41032195e-01 7.93613374e-01 4.24050748... | [12.005133628845215, 2.191074848175049] |
9242d0d9-9920-412d-84a0-6bebb572232b | trickvos-a-bag-of-tricks-for-video-object | 2306.15377 | null | https://arxiv.org/abs/2306.15377v2 | https://arxiv.org/pdf/2306.15377v2.pdf | TrickVOS: A Bag of Tricks for Video Object Segmentation | Space-time memory (STM) network methods have been dominant in semi-supervised video object segmentation (SVOS) due to their remarkable performance. In this work, we identify three key aspects where we can improve such methods; i) supervisory signal, ii) pretraining and iii) spatial awareness. We then propose TrickVOS; ... | ['Albert Saa-Garriga', 'Bruno Manganelli', 'Anastasios Drosou', 'Armando Domi', 'Koskinas Ioannis', 'Mehmet Kerim Yucel', 'Konstantinos Georgiadis', 'Evangelos Skartados'] | 2023-06-27 | null | null | null | null | ['semi-supervised-video-object-segmentation', 'video-object-segmentation', 'video-semantic-segmentation'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 3.58568817e-01 1.06947534e-02 -7.09938526e-01 -1.91892162e-01
-8.07037771e-01 -5.71528196e-01 6.70002043e-01 -3.79568070e-01
-5.77510834e-01 4.89703089e-01 -5.24104163e-02 -5.33598483e-01
2.03255087e-01 -2.31933638e-01 -1.18265343e+00 -4.56711501e-01
8.91118348e-02 7.33130872e-01 9.44556892e-01 1.46485910... | [9.063101768493652, -0.13000692427158356] |
bd2400de-9465-4950-b980-cc50a36dd0cb | exploiting-semantic-epsilon-greedy | 2201.10803 | null | https://arxiv.org/abs/2201.10803v2 | https://arxiv.org/pdf/2201.10803v2.pdf | Exploiting Semantic Epsilon Greedy Exploration Strategy in Multi-Agent Reinforcement Learning | Multi-agent reinforcement learning (MARL) can model many real world applications. However, many MARL approaches rely on epsilon greedy for exploration, which may discourage visiting advantageous states in hard scenarios. In this paper, we propose a new approach QMIX(SEG) for tackling MARL. It makes use of the value fun... | ['Ho-fung Leung', 'Hon Tik Tse'] | 2022-01-26 | null | null | null | null | ['smac-1', 'smac'] | ['playing-games', 'playing-games'] | [-2.70937741e-01 8.29959586e-02 -4.72973913e-01 4.25044522e-02
-8.19518507e-01 -4.55999970e-01 7.40006804e-01 2.31284693e-01
-7.28936374e-01 1.08697009e+00 4.15853947e-01 -3.73445630e-01
-5.56369364e-01 -7.65453100e-01 -8.48240852e-01 -8.63666832e-01
-5.37011445e-01 8.94492328e-01 4.10733104e-01 -6.97514892... | [3.8789241313934326, 1.7781462669372559] |
4843730c-1e28-4191-8665-553353f40f35 | towards-cross-modality-medical-image | 2010.01532 | null | https://arxiv.org/abs/2010.01532v1 | https://arxiv.org/pdf/2010.01532v1.pdf | Towards Cross-modality Medical Image Segmentation with Online Mutual Knowledge Distillation | The success of deep convolutional neural networks is partially attributed to the massive amount of annotated training data. However, in practice, medical data annotations are usually expensive and time-consuming to be obtained. Considering multi-modality data with the same anatomic structures are widely available in cl... | ['Pheng-Ann Heng', 'Shujun Wang', 'Lequan Yu', 'Kang Li'] | 2020-10-04 | null | null | null | null | ['cardiac-segmentation'] | ['medical'] | [ 5.24044394e-01 4.26928550e-01 -4.15511072e-01 -3.89198929e-01
-1.19963765e+00 -5.05582571e-01 2.33005822e-01 1.16881415e-01
-4.06723440e-01 8.46640527e-01 1.31448656e-01 -2.98556089e-01
-1.08967930e-01 -5.64339578e-01 -6.92458570e-01 -9.62125778e-01
3.75521302e-01 5.65638006e-01 3.13048571e-01 7.37991109... | [14.63249397277832, -2.1840310096740723] |
cdd62ae4-9d48-417d-b178-6f0e55b50493 | a-pooling-based-scene-text-proposal-technique | 1811.10003 | null | http://arxiv.org/abs/1811.10003v1 | http://arxiv.org/pdf/1811.10003v1.pdf | A pooling based scene text proposal technique for scene text reading in the wild | Automatic reading texts in scenes has attracted increasing interest in recent
years as texts often carry rich semantic information that is useful for scene
understanding. In this paper, we propose a novel scene text proposal technique
aiming for accurate reading texts in scenes. Inspired by the pooling layer in
the dee... | ['Shangxuan Tian', 'Mounir Mokhtari', 'Shijian Lu', 'Nizar Ouarti', 'Dinh NguyenVan'] | 2018-11-25 | null | null | null | null | ['text-spotting'] | ['computer-vision'] | [ 5.33622205e-01 -2.87840217e-01 2.08636209e-01 -7.03556240e-01
-6.81281567e-01 -1.35317177e-01 1.15625715e+00 5.65986991e-01
-8.33258152e-01 3.02962691e-01 4.97498333e-01 -5.88372350e-02
4.81639318e-02 -9.65308666e-01 -5.92168927e-01 -4.77090091e-01
7.34734535e-01 3.18590194e-01 6.40140533e-01 -1.28344417... | [12.028962135314941, 2.2874255180358887] |
7c8a72c3-6fa3-490c-b4fb-78c879f8cc32 | flamingo-a-visual-language-model-for-few-shot-1 | 2204.14198 | null | https://arxiv.org/abs/2204.14198v2 | https://arxiv.org/pdf/2204.14198v2.pdf | Flamingo: a Visual Language Model for Few-Shot Learning | Building models that can be rapidly adapted to novel tasks using only a handful of annotated examples is an open challenge for multimodal machine learning research. We introduce Flamingo, a family of Visual Language Models (VLM) with this ability. We propose key architectural innovations to: (i) bridge powerful pretrai... | ['Karen Simonyan', 'Andrew Zisserman', 'Oriol Vinyals', 'Ricardo Barreira', 'Mikolaj Binkowski', 'Sahand Sharifzadeh', 'Aida Nematzadeh', 'Andrew Brock', 'Sebastian Borgeaud', 'Jacob Menick', 'Marianne Monteiro', 'Sina Samangooei', 'Zhitao Gong', 'Tengda Han', 'Serkan Cabi', 'Eliza Rutherford', 'Roman Ring', 'Malcolm R... | 2022-04-29 | flamingo-a-visual-language-model-for-few-shot | https://storage.googleapis.com/deepmind-media/DeepMind.com/Blog/tackling-multiple-tasks-with-a-single-visual-language-model/flamingo.pdf | https://storage.googleapis.com/deepmind-media/DeepMind.com/Blog/tackling-multiple-tasks-with-a-single-visual-language-model/flamingo.pdf | deepmind-2022-4 | ['generative-visual-question-answering', 'video-question-answering', 'zero-shot-cross-modal-retrieval'] | ['computer-vision', 'computer-vision', 'miscellaneous'] | [ 1.96652368e-01 -2.35556170e-01 -3.18357408e-01 -2.24551693e-01
-9.66002166e-01 -7.68734097e-01 9.96869981e-01 6.83937967e-02
-7.23022699e-01 4.50936764e-01 3.63296390e-01 -3.45739663e-01
2.30330944e-01 -3.63334328e-01 -7.75788367e-01 -1.57366738e-01
-1.28989309e-01 5.45466721e-01 4.95432585e-01 -4.29521114... | [10.692691802978516, 1.726918339729309] |
75fda6c0-e8b7-4880-b90f-f2403673f906 | robust-reference-based-super-resolution-with | null | null | http://openaccess.thecvf.com/content_CVPR_2020/html/Shim_Robust_Reference-Based_Super-Resolution_With_Similarity-Aware_Deformable_Convolution_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Shim_Robust_Reference-Based_Super-Resolution_With_Similarity-Aware_Deformable_Convolution_CVPR_2020_paper.pdf | Robust Reference-Based Super-Resolution With Similarity-Aware Deformable Convolution | In this paper, we propose a novel and efficient reference feature extraction module referred to as the Similarity Search and Extraction Network (SSEN) for reference-based super-resolution (RefSR) tasks. The proposed module extracts aligned relevant features from a reference image to increase the performance over single... | [' In So Kweon', ' Jinsun Park', 'Gyumin Shim'] | 2020-06-01 | null | null | null | cvpr-2020-6 | ['reference-based-super-resolution'] | ['computer-vision'] | [ 5.93021989e-01 -1.47570744e-01 -1.93085566e-01 -1.19352333e-01
-7.30726242e-01 -1.50249466e-01 4.44890797e-01 -1.72907308e-01
-5.92086434e-01 7.78263390e-01 1.89724445e-01 2.36318380e-01
-3.05386811e-01 -5.84790409e-01 -5.03384769e-01 -5.50831497e-01
2.48767540e-01 -1.30076278e-02 5.38706005e-01 -3.17291111... | [10.94304084777832, -1.956843376159668] |
05d79aff-1bda-418a-b5e7-9c22bfe32486 | residue-based-label-protection-mechanisms-in | 2205.04166 | null | https://arxiv.org/abs/2205.04166v1 | https://arxiv.org/pdf/2205.04166v1.pdf | Residue-based Label Protection Mechanisms in Vertical Logistic Regression | Federated learning (FL) enables distributed participants to collaboratively learn a global model without revealing their private data to each other. Recently, vertical FL, where the participants hold the same set of samples but with different features, has received increased attention. This paper first presents one lab... | ['Ye Wu', 'Anran Li', 'Yang Liu', 'Lan Zhang', 'Juntao Tan'] | 2022-05-09 | null | null | null | null | ['inference-attack'] | ['adversarial'] | [ 9.64499563e-02 -2.29010701e-01 -2.15983927e-01 -4.83077049e-01
-8.97231460e-01 -1.21882796e+00 1.88960657e-01 1.57276914e-01
-3.37629735e-01 7.58875966e-01 -4.64649387e-02 -3.41553003e-01
1.74674224e-02 -9.10509944e-01 -6.03953779e-01 -1.25663424e+00
8.23472813e-03 -4.34040159e-01 -1.51477098e-01 3.55803907... | [5.837385177612305, 6.760833263397217] |
6401b7a3-1ee9-4b5c-90c6-7ccf932575b2 | rstt-real-time-spatial-temporal-transformer | 2203.14186 | null | https://arxiv.org/abs/2203.14186v1 | https://arxiv.org/pdf/2203.14186v1.pdf | RSTT: Real-time Spatial Temporal Transformer for Space-Time Video Super-Resolution | Space-time video super-resolution (STVSR) is the task of interpolating videos with both Low Frame Rate (LFR) and Low Resolution (LR) to produce High-Frame-Rate (HFR) and also High-Resolution (HR) counterparts. The existing methods based on Convolutional Neural Network~(CNN) succeed in achieving visually satisfied resul... | ['Ilya Zharkov', 'Tianyu Ding', 'Luming Liang', 'Zhicheng Geng'] | 2022-03-27 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Geng_RSTT_Real-Time_Spatial_Temporal_Transformer_for_Space-Time_Video_Super-Resolution_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Geng_RSTT_Real-Time_Spatial_Temporal_Transformer_for_Space-Time_Video_Super-Resolution_CVPR_2022_paper.pdf | cvpr-2022-1 | ['space-time-video-super-resolution', 'video-super-resolution'] | ['computer-vision', 'computer-vision'] | [ 1.21934824e-01 -1.16945788e-01 -8.92494544e-02 -2.64934868e-01
-8.78366888e-01 -3.97491530e-02 3.01618218e-01 -5.70614457e-01
-3.86020452e-01 9.36322987e-01 1.68985110e-02 -2.97482222e-01
2.09690362e-01 -8.41972589e-01 -8.80388677e-01 -4.03637409e-01
4.53130119e-02 -2.45247573e-01 5.10865331e-01 -2.47693852... | [11.015734672546387, -1.8536139726638794] |
ed4d9d9f-1cec-4fc3-9dc8-82bbc5f5bd87 | selecting-robust-features-for-machine | 2304.05294 | null | https://arxiv.org/abs/2304.05294v5 | https://arxiv.org/pdf/2304.05294v5.pdf | Selecting Robust Features for Machine Learning Applications using Multidata Causal Discovery | Robust feature selection is vital for creating reliable and interpretable Machine Learning (ML) models. When designing statistical prediction models in cases where domain knowledge is limited and underlying interactions are unknown, choosing the optimal set of features is often difficult. To mitigate this issue, we int... | ['Andreas Gerhardus', 'Jakob Runge', 'Milton S. Gomez', 'Frederick Iat-Hin Tam', 'Tom Beucler', 'Saranya Ganesh S.'] | 2023-04-11 | null | null | null | null | ['causal-discovery', 'interpretable-machine-learning'] | ['knowledge-base', 'methodology'] | [ 2.82519311e-01 -3.83247137e-01 -4.67706114e-01 -3.86764914e-01
-1.49869740e-01 -5.93591094e-01 9.61737692e-01 2.66005874e-01
2.76507456e-02 1.02206028e+00 4.98848259e-01 -8.14812541e-01
-9.51892376e-01 -1.04914856e+00 -5.53470910e-01 -7.78005719e-01
-7.75951385e-01 3.08385789e-01 7.20850304e-02 -8.01888853... | [7.698473930358887, 5.106581211090088] |
35a0cc49-02da-45b9-9e34-1864c7c76c63 | quick-and-not-so-dirty-unsupervised-selection-1 | 1911.07176 | null | https://arxiv.org/abs/1911.07176v2 | https://arxiv.org/pdf/1911.07176v2.pdf | Quick and (not so) Dirty: Unsupervised Selection of Justification Sentences for Multi-hop Question Answering | We propose an unsupervised strategy for the selection of justification sentences for multi-hop question answering (QA) that (a) maximizes the relevance of the selected sentences, (b) minimizes the overlap between the selected facts, and (c) maximizes the coverage of both question and answer. This unsupervised sentence ... | ['Mihai Surdeanu', 'Vikas Yadav', 'Steven Bethard'] | 2019-11-17 | quick-and-not-so-dirty-unsupervised-selection | https://aclanthology.org/D19-1260 | https://aclanthology.org/D19-1260.pdf | ijcnlp-2019-11 | ['multi-hop-question-answering'] | ['knowledge-base'] | [ 3.81750852e-01 8.55830908e-01 1.16667688e-01 -7.42480278e-01
-2.01644373e+00 -7.11914062e-01 5.66356719e-01 6.49396896e-01
-4.89042521e-01 9.50402439e-01 5.76281428e-01 -4.58433837e-01
-4.36308891e-01 -8.95045877e-01 -8.35487783e-01 -1.38674229e-01
5.12971461e-01 1.03603375e+00 6.71585023e-01 -8.12214553... | [11.260625839233398, 7.961385726928711] |
431b4f2c-b8b7-440b-aff7-a115837fcd60 | safer-data-efficient-and-safe-reinforcement | null | null | https://openreview.net/forum?id=xwAw8QZkpWZ | https://openreview.net/pdf?id=xwAw8QZkpWZ | SAFER: Data-Efficient and Safe Reinforcement Learning Through Skill Acquisition | Though many reinforcement learning (RL) problems involve learning policies in settings that are difficult to specify safety constraints and sparse rewards, current methods struggle to rapidly and safely acquire successful policies. Behavioral priors, which extract useful policy primitives for learning from offline data... | ['Nevan Wichers', 'Bo Dai', 'Yinlam Chow', 'Dylan Z Slack'] | 2021-09-29 | null | null | null | null | ['robotic-grasping'] | ['robots'] | [ 2.97410935e-01 1.97013125e-01 -6.59187734e-01 -2.30016783e-01
-7.46857524e-01 -6.90574348e-01 6.12870038e-01 2.09711000e-01
-7.24148750e-01 1.00453150e+00 3.22132617e-01 -3.72082084e-01
-3.85653734e-01 -6.16455674e-01 -1.01384342e+00 -6.19054079e-01
-3.55948031e-01 5.54544568e-01 1.39847383e-01 -3.16228509... | [4.207458019256592, 1.6722207069396973] |
4c3b8795-12f9-4f95-81eb-18d5ca42d339 | pooled-motion-features-for-first-person | 1412.6505 | null | http://arxiv.org/abs/1412.6505v2 | http://arxiv.org/pdf/1412.6505v2.pdf | Pooled Motion Features for First-Person Videos | In this paper, we present a new feature representation for first-person
videos. In first-person video understanding (e.g., activity recognition), it is
very important to capture both entire scene dynamics (i.e., egomotion) and
salient local motion observed in videos. We describe a representation framework
based on time... | ['Brandon Rothrock', 'M. S. Ryoo', 'Larry Matthies'] | 2014-12-19 | pooled-motion-features-for-first-person-1 | http://openaccess.thecvf.com/content_cvpr_2015/html/Ryoo_Pooled_Motion_Features_2015_CVPR_paper.html | http://openaccess.thecvf.com/content_cvpr_2015/papers/Ryoo_Pooled_Motion_Features_2015_CVPR_paper.pdf | cvpr-2015-6 | ['activity-recognition-in-videos'] | ['computer-vision'] | [-7.52354935e-02 -8.03905249e-01 -3.01438749e-01 -2.48957440e-01
-2.64614135e-01 -6.19595885e-01 9.91657197e-01 1.32781804e-01
-4.83742326e-01 4.49218124e-01 7.33004391e-01 5.33963323e-01
-2.46125117e-01 -5.40487409e-01 -5.56993902e-01 -6.87010348e-01
-7.70173550e-01 -4.22526717e-01 3.41643929e-01 -1.33761898... | [8.132308006286621, 0.37899357080459595] |
c13bc75d-9636-43fa-9605-2026b49f4466 | gesture-to-gesture-translation-in-the-wild | 1907.05916 | null | https://arxiv.org/abs/1907.05916v3 | https://arxiv.org/pdf/1907.05916v3.pdf | Gesture-to-Gesture Translation in the Wild via Category-Independent Conditional Maps | Recent works have shown Generative Adversarial Networks (GANs) to be particularly effective in image-to-image translations. However, in tasks such as body pose and hand gesture translation, existing methods usually require precise annotations, e.g. key-points or skeletons, which are time-consuming to draw. In this work... | ['Yahui Liu', 'Bruno Lepri', 'Marco De Nadai', 'Gloria Zen', 'Nicu Sebe'] | 2019-07-12 | null | null | null | null | ['gesture-to-gesture-translation'] | ['computer-vision'] | [ 4.37518239e-01 2.14714855e-01 6.83401302e-02 -4.01603699e-01
-7.83784568e-01 -6.77334070e-01 7.06462741e-01 -6.98508978e-01
-3.20900321e-01 5.50333321e-01 8.75462294e-02 -1.16614841e-01
5.31391799e-01 -7.48191655e-01 -1.04623461e+00 -7.96622038e-01
5.61584532e-01 6.75498009e-01 2.86993206e-01 -1.65292040... | [11.741859436035156, -0.6981605887413025] |
b94a5630-af83-432e-8b71-7e7370692aff | domain-adaptation-for-time-series-under | 2302.03133 | null | https://arxiv.org/abs/2302.03133v2 | https://arxiv.org/pdf/2302.03133v2.pdf | Domain Adaptation for Time Series Under Feature and Label Shifts | Unsupervised domain adaptation (UDA) enables the transfer of models trained on source domains to unlabeled target domains. However, transferring complex time series models presents challenges due to the dynamic temporal structure variations across domains. This leads to feature shifts in the time and frequency represen... | ['Marinka Zitnik', 'Theodoros Tsiligkaridis', 'Consuelo Cuevas', 'Teddy Koker', 'Owen Queen', 'Huan He'] | 2023-02-06 | null | null | null | null | ['universal-domain-adaptation'] | ['computer-vision'] | [ 3.98824275e-01 -5.12234628e-01 -2.73736626e-01 -5.34114003e-01
-8.79637539e-01 -1.24474907e+00 4.41719323e-01 1.32566705e-01
-1.90478176e-01 7.16993809e-01 -1.03263870e-01 -1.80770665e-01
2.79388460e-03 -6.10919237e-01 -6.05374813e-01 -6.63809359e-01
-2.63562232e-01 5.43722332e-01 1.94716856e-01 -5.67971878... | [10.301176071166992, 3.0330746173858643] |
30de02c5-7c1e-4f38-b1c8-e7e60d3999e3 | duck-rumour-detection-on-social-media-by | null | null | https://openreview.net/forum?id=VxlfmC-73ww | https://openreview.net/pdf?id=VxlfmC-73ww | DUCK: Rumour Detection on Social Media by Modelling User and Comment Propagation Networks | Social media rumours, a form of misinformation, can mislead the public and cause significant economic and social disruption. Motivated by the observation that the user network --- which captures $\textit{who}$ engage with a story --- and the comment network --- which captures $\textit{how}$ they react to it --- provid... | ['Anonymous'] | 2022-01-16 | null | null | null | acl-arr-january-2022-1 | ['rumour-detection'] | ['natural-language-processing'] | [-3.05108905e-01 6.07562244e-01 -3.76064956e-01 -8.41822103e-02
-3.55607450e-01 -4.45247054e-01 6.24676824e-01 4.07244414e-01
1.93538725e-01 8.09472978e-01 6.20885015e-01 -3.68870676e-01
6.13218360e-03 -9.06351686e-01 -4.49186862e-01 -1.90245122e-01
-6.51561916e-01 3.35116267e-01 3.14861746e-03 -7.33276725... | [8.185026168823242, 10.142399787902832] |
dc452556-70d1-4916-a4b6-a88a140b623c | colo-scrl-self-supervised-contrastive | 2303.15671 | null | https://arxiv.org/abs/2303.15671v1 | https://arxiv.org/pdf/2303.15671v1.pdf | Colo-SCRL: Self-Supervised Contrastive Representation Learning for Colonoscopic Video Retrieval | Colonoscopic video retrieval, which is a critical part of polyp treatment, has great clinical significance for the prevention and treatment of colorectal cancer. However, retrieval models trained on action recognition datasets usually produce unsatisfactory retrieval results on colonoscopic datasets due to the large do... | ['Suncheng Xiang', 'Dahong Qian', 'Zefang Yu', 'Crystal Cai', 'Shilun Cai', 'Qingzhong Chen'] | 2023-03-28 | null | null | null | null | ['video-retrieval', 'general-knowledge'] | ['computer-vision', 'miscellaneous'] | [ 2.67814845e-01 -2.24706203e-01 -6.90339327e-01 4.61402647e-02
-9.69847620e-01 -2.08755657e-01 4.55297828e-01 3.97050619e-01
-4.80937809e-01 3.48797649e-01 3.08325559e-01 -3.78679037e-01
-3.62564832e-01 -6.05214059e-01 -6.84251726e-01 -5.30236185e-01
-1.14007063e-01 1.89732835e-01 -2.97089084e-03 -1.29860133... | [14.302314758300781, -3.066619873046875] |
8600b667-fa57-43e3-9fe8-d4ab48099301 | combating-the-elsagate-phenomenon-deep | 1904.08910 | null | http://arxiv.org/abs/1904.08910v1 | http://arxiv.org/pdf/1904.08910v1.pdf | Combating the Elsagate phenomenon: Deep learning architectures for disturbing cartoons | Watching cartoons can be useful for children's intellectual, social and
emotional development. However, the most popular video sharing platform today
provides many videos with Elsagate content. Elsagate is a phenomenon that
depicts childhood characters in disturbing circumstances (e.g., gore, toilet
humor, drinking uri... | ['Sandra Avila', 'Edson Bollis', 'Akari Ishikawa'] | 2019-04-18 | null | null | null | null | ['pornography-detection'] | ['computer-vision'] | [-2.17096344e-01 1.05093792e-01 -2.22912833e-01 2.20337719e-01
8.94914716e-02 -6.27405822e-01 2.74090201e-01 3.10218811e-01
-5.98636977e-02 4.66119111e-01 4.84820634e-01 1.07654044e-02
3.36725026e-01 -8.74230027e-01 -6.60511553e-01 -3.60351533e-01
-1.48391277e-01 -4.09290165e-01 3.84448349e-01 -2.68574834... | [12.453344345092773, 1.1874504089355469] |
51939322-c0bc-4c76-948c-cc1f933825a8 | automated-stance-detection-in-complex-topics | 2305.13047 | null | https://arxiv.org/abs/2305.13047v1 | https://arxiv.org/pdf/2305.13047v1.pdf | Automated stance detection in complex topics and small languages: the challenging case of immigration in polarizing news media | Automated stance detection and related machine learning methods can provide useful insights for media monitoring and academic research. Many of these approaches require annotated training datasets, which limits their applicability for languages where these may not be readily available. This paper explores the applicabi... | ['Maximilian Schich', 'Indrek Ibrus', 'Andres Karjus', 'Mark Mets'] | 2023-05-22 | null | null | null | null | ['stance-detection'] | ['natural-language-processing'] | [-1.41812205e-01 2.42711473e-02 -6.78474069e-01 -1.27665430e-01
-1.24800098e+00 -6.91693485e-01 1.12184489e+00 8.87380183e-01
-8.73344421e-01 9.26334441e-01 7.68977106e-01 -5.06800711e-01
1.55771464e-01 -7.73892105e-01 -3.27212512e-01 -4.75801319e-01
1.35442078e-01 8.87842953e-01 3.68673086e-01 -6.35047436... | [9.048691749572754, 10.001863479614258] |
c607c910-7812-4a23-8d48-0312c8cb948c | bridging-the-gap-between-decision-and-logits | 2306.08909 | null | https://arxiv.org/abs/2306.08909v1 | https://arxiv.org/pdf/2306.08909v1.pdf | Bridging the Gap between Decision and Logits in Decision-based Knowledge Distillation for Pre-trained Language Models | Conventional knowledge distillation (KD) methods require access to the internal information of teachers, e.g., logits. However, such information may not always be accessible for large pre-trained language models (PLMs). In this work, we focus on decision-based KD for PLMs, where only teacher decisions (i.e., top-1 labe... | ['Yang Liu', 'Peng Li', 'Zonghan Yang', 'Qinhong Zhou'] | 2023-06-15 | null | null | null | null | ['reading-comprehension', 'machine-reading-comprehension'] | ['natural-language-processing', 'natural-language-processing'] | [ 8.44160616e-02 5.08383393e-01 -4.08458263e-01 -6.48091555e-01
-8.30161154e-01 -8.57523918e-01 3.08790267e-01 5.68951607e-01
-6.25747859e-01 6.96447492e-01 5.50618395e-02 -9.38306153e-01
4.82474305e-02 -7.52756417e-01 -1.06661522e+00 -3.64390880e-01
5.17991722e-01 6.29559457e-01 2.00803906e-01 3.07530705... | [10.873868942260742, 8.237993240356445] |
6eb25317-6805-417c-9377-376e660fb469 | exploration-via-epistemic-value-estimation | 2303.04012 | null | https://arxiv.org/abs/2303.04012v1 | https://arxiv.org/pdf/2303.04012v1.pdf | Exploration via Epistemic Value Estimation | How to efficiently explore in reinforcement learning is an open problem. Many exploration algorithms employ the epistemic uncertainty of their own value predictions -- for instance to compute an exploration bonus or upper confidence bound. Unfortunately the required uncertainty is difficult to estimate in general with ... | ['Hado van Hasselt', 'John Shawe-Taylor', 'Simon Schmitt'] | 2023-03-07 | null | null | null | null | ['efficient-exploration'] | ['methodology'] | [-2.23153636e-01 7.25342095e-01 -4.86054212e-01 -2.37278253e-01
-1.22794020e+00 -7.08422720e-01 4.92280453e-01 1.92313865e-02
-7.64905989e-01 1.56232572e+00 -2.78249718e-02 -6.05041802e-01
-5.66684306e-01 -9.23793077e-01 -1.08698034e+00 -8.63527536e-01
-4.65531051e-01 7.82049060e-01 1.23155542e-01 -1.00030333... | [4.134439945220947, 2.407958507537842] |
5898814e-c555-41e5-a917-3d95e7c3a44d | mlseg-image-and-video-segmentation-as-multi | 2203.04187 | null | https://arxiv.org/abs/2203.04187v2 | https://arxiv.org/pdf/2203.04187v2.pdf | RankSeg: Adaptive Pixel Classification with Image Category Ranking for Segmentation | The segmentation task has traditionally been formulated as a complete-label pixel classification task to predict a class for each pixel from a fixed number of predefined semantic categories shared by all images or videos. Yet, following this formulation, standard architectures will inevitably encounter various challeng... | ['Han Hu', 'Xiangyu Yue', 'Yuhui Yuan', 'Haodi He'] | 2022-03-08 | null | null | null | null | ['video-instance-segmentation'] | ['computer-vision'] | [ 8.71001065e-01 -2.08708003e-01 -6.02087438e-01 -6.16032600e-01
-1.09853625e+00 -6.65752828e-01 9.71421525e-02 -2.16253512e-02
-5.33312023e-01 4.17497426e-01 -5.87107658e-01 -1.90304175e-01
-7.98262060e-02 -5.68345249e-01 -8.36776257e-01 -9.95368659e-01
2.40646377e-01 3.28557521e-01 6.29865527e-01 2.81546891... | [9.42280101776123, 0.34918877482414246] |
f564bc06-e997-4e71-b798-1dc21f75c004 | human-evaluation-of-conversations-is-an-open | 2201.04723 | null | https://arxiv.org/abs/2201.04723v1 | https://arxiv.org/pdf/2201.04723v1.pdf | Human Evaluation of Conversations is an Open Problem: comparing the sensitivity of various methods for evaluating dialogue agents | At the heart of improving conversational AI is the open problem of how to evaluate conversations. Issues with automatic metrics are well known (Liu et al., 2016, arXiv:1603.08023), with human evaluations still considered the gold standard. Unfortunately, how to perform human evaluations is also an open problem: differi... | ['Jason Weston', 'Y-Lan Boureau', 'Stephen Roller', 'Rebecca Qian', 'Orion Hsu', 'Eric Michael Smith'] | 2022-01-12 | null | https://aclanthology.org/2022.nlp4convai-1.8 | https://aclanthology.org/2022.nlp4convai-1.8.pdf | nlp4convai-acl-2022-5 | ['dialogue-evaluation'] | ['natural-language-processing'] | [ 3.24583962e-03 2.67750740e-01 -9.34607983e-02 -4.01205897e-01
-7.33256340e-01 -1.02062821e+00 8.12711298e-01 3.24956387e-01
-6.41379714e-01 9.40129161e-01 6.20598972e-01 -2.90239692e-01
-2.35964909e-01 -3.83026332e-01 -1.44406229e-01 -5.50929308e-01
3.39932382e-01 7.73746252e-01 2.52327681e-01 -4.31290895... | [12.493019104003906, 8.181510925292969] |
ad7faf7e-df65-4c53-9b53-b95a9f1a93a2 | superpoint-transformer-for-3d-scene-instance | 2211.15766 | null | https://arxiv.org/abs/2211.15766v1 | https://arxiv.org/pdf/2211.15766v1.pdf | Superpoint Transformer for 3D Scene Instance Segmentation | Most existing methods realize 3D instance segmentation by extending those models used for 3D object detection or 3D semantic segmentation. However, these non-straightforward methods suffer from two drawbacks: 1) Imprecise bounding boxes or unsatisfactory semantic predictions limit the performance of the overall 3D inst... | ['Xiangmin Xu', 'Junpeng Tan', 'Chunmei Qing', 'Jiahao Sun'] | 2022-11-28 | null | null | null | null | ['3d-instance-segmentation-1'] | ['computer-vision'] | [ 9.13549587e-02 3.12595695e-01 -3.32325757e-01 -5.24362266e-01
-9.67421830e-01 -4.11407828e-01 2.89026856e-01 -1.61489993e-01
-2.83382744e-01 1.80249155e-01 -4.34275120e-01 -4.03277308e-01
8.32540840e-02 -9.49926138e-01 -1.04876482e+00 -4.77457404e-01
2.28297189e-01 7.86087334e-01 9.41956818e-01 8.23041424... | [8.02025318145752, -3.1766180992126465] |
1c346adb-f137-40e6-b035-827403fa768b | security-aware-virtual-network-embedding | 2202.02452 | null | https://arxiv.org/abs/2202.02452v1 | https://arxiv.org/pdf/2202.02452v1.pdf | Security-Aware Virtual Network Embedding Algorithm based on Reinforcement Learning | Virtual network embedding (VNE) algorithm is always the key problem in network virtualization (NV) technology. At present, the research in this field still has the following problems. The traditional way to solve VNE problem is to use heuristic algorithm. However, this method relies on manual embedding rules, which doe... | ['Abderrahim Benslimane', 'Chunxiao Jiang', 'Chao Wang', 'Peiying Zhang'] | 2022-02-03 | null | null | null | null | ['network-embedding'] | ['methodology'] | [-2.07639366e-01 -1.76052287e-01 -4.55019116e-01 1.32133111e-01
4.92339641e-01 -4.44768310e-01 1.28732160e-01 -1.00401700e-01
-5.53361118e-01 8.33943129e-01 -5.50516903e-01 -6.67328179e-01
-3.97669464e-01 -1.31634736e+00 -2.51496136e-01 -4.58837986e-01
-2.63742227e-02 4.93017763e-01 5.20782053e-01 -3.29144239... | [5.891761302947998, 1.7314261198043823] |
716c24b1-7c85-4d2e-816f-53e596c64e61 | moderately-balanced-representation-learning | 2209.01956 | null | https://arxiv.org/abs/2209.01956v1 | https://arxiv.org/pdf/2209.01956v1.pdf | Moderately-Balanced Representation Learning for Treatment Effects with Orthogonality Information | Estimating the average treatment effect (ATE) from observational data is challenging due to selection bias. Existing works mainly tackle this challenge in two ways. Some researchers propose constructing a score function that satisfies the orthogonal condition, which guarantees that the established ATE estimator is "ort... | ['Zhixiang Huang', 'Dongdong Wang', 'Qi Wu', 'Shumin Ma', 'Cheuk Hang Leung', 'Yiyan Huang'] | 2022-09-05 | null | null | null | null | ['selection-bias'] | ['natural-language-processing'] | [ 2.87040859e-01 -2.73018211e-01 -9.19133365e-01 -3.43472540e-01
-1.05026150e+00 -6.68700859e-02 2.89444625e-01 5.95787428e-02
4.30793501e-02 6.43680990e-01 7.34480619e-01 -2.48527363e-01
-5.10383308e-01 -6.10444248e-01 -5.25434673e-01 -1.03440440e+00
1.72685534e-01 -3.96340638e-02 -4.51380461e-01 1.67863101... | [8.099390029907227, 5.394636154174805] |
260abffd-54c1-4e07-a990-5bd7aa33a1ab | semantic-motion-segmentation-using-dense-crf | 1504.06587 | null | http://arxiv.org/abs/1504.06587v1 | http://arxiv.org/pdf/1504.06587v1.pdf | Semantic Motion Segmentation Using Dense CRF Formulation | While the literature has been fairly dense in the areas of scene
understanding and semantic labeling there have been few works that make use of
motion cues to embellish semantic performance and vice versa. In this paper, we
address the problem of semantic motion segmentation, and show how semantic and
motion priors aug... | ['Prateek Singhal', 'N. Dinesh Reddy', 'K. Madhava Krishna'] | 2015-04-24 | null | null | null | null | ['motion-detection'] | ['computer-vision'] | [ 2.34781399e-01 9.37682167e-02 -2.37751067e-01 -6.40846133e-01
-5.58617830e-01 -7.62824476e-01 7.49996841e-01 -1.17456866e-02
-7.81457961e-01 6.48884654e-01 -5.21024615e-02 -2.46795088e-01
1.79561704e-01 -6.49879575e-01 -6.15823328e-01 -6.31865203e-01
1.36597157e-01 1.02011013e+00 8.76563609e-01 8.71467292... | [8.546489715576172, -1.59078848361969] |
5b5f962d-0723-46ba-95b5-ecd3e26fa7a7 | learning-to-transfer-transferring-latent-task | null | null | http://openaccess.thecvf.com/content_iccv_2015/html/Almaev_Learning_to_Transfer_ICCV_2015_paper.html | http://openaccess.thecvf.com/content_iccv_2015/papers/Almaev_Learning_to_Transfer_ICCV_2015_paper.pdf | Learning to Transfer: Transferring Latent Task Structures and Its Application to Person-Specific Facial Action Unit Detection | In this article we explore the problem of constructing person-specific models for the detection of facial Action Units (AUs), addressing the problem from the point of view of Transfer Learning and Multi-Task Learning. Our starting point is the fact that some expressions, such as smiles, are very easily elicited, annota... | ['Brais Martinez', 'Michel Valstar', 'Timur Almaev'] | 2015-12-01 | null | null | null | iccv-2015-12 | ['action-unit-detection', 'facial-action-unit-detection'] | ['computer-vision', 'computer-vision'] | [ 3.36331010e-01 3.69869024e-01 1.19204573e-01 -3.32618386e-01
-8.35698545e-01 -4.69434589e-01 6.27330005e-01 -1.30750522e-01
-3.80731463e-01 5.97156703e-01 2.14691222e-01 4.83601987e-01
7.93376490e-02 -4.60545540e-01 -7.13142574e-01 -6.95936322e-01
-1.14441449e-02 6.22439265e-01 -2.99147759e-02 -3.30017805... | [13.586904525756836, 1.6718381643295288] |
368f3b12-fe54-494a-85cc-60f4ec234852 | learning-symbolic-rules-over-abstract-meaning | 2307.02689 | null | https://arxiv.org/abs/2307.02689v1 | https://arxiv.org/pdf/2307.02689v1.pdf | Learning Symbolic Rules over Abstract Meaning Representations for Textual Reinforcement Learning | Text-based reinforcement learning agents have predominantly been neural network-based models with embeddings-based representation, learning uninterpretable policies that often do not generalize well to unseen games. On the other hand, neuro-symbolic methods, specifically those that leverage an intermediate formal repre... | ['Alexander Gray', 'Asim Munawar', 'Pavan Kapanipathi', 'Achille Fokoue', 'Michiaki Tatsubori', 'Rosario Uceda-Sosa', 'Keerthiram Murugesan', 'Prithviraj Sen', 'Daiki Kimura', 'Sarathkrishna Swaminathan', 'Subhajit Chaudhury'] | 2023-07-05 | null | null | null | null | ['representation-learning'] | ['methodology'] | [ 1.16184622e-01 4.70309347e-01 -2.31516585e-01 -2.65275657e-01
1.12750614e-02 -5.92145145e-01 7.82283962e-01 3.02957505e-01
-6.45528316e-01 9.70057547e-01 -8.30389187e-02 -5.74058414e-01
-4.39723879e-01 -1.24200010e+00 -7.40796864e-01 -3.30369502e-01
-7.34900683e-02 8.49991262e-01 2.76801169e-01 -6.95194483... | [3.9194955825805664, 1.3945703506469727] |
15b02f2b-4a31-49ab-8b93-1618e6805f93 | global-and-local-feature-learning-for-ego | 2002.06685 | null | https://arxiv.org/abs/2002.06685v1 | https://arxiv.org/pdf/2002.06685v1.pdf | Global and Local Feature Learning for Ego-Network Analysis | In an ego-network, an individual (ego) organizes its friends (alters) in different groups (social circles). This social network can be efficiently analyzed after learning representations of the ego and its alters in a low-dimensional, real vector space. These representations are then easily exploited via statistical mo... | ['Konstantin Ziegler', 'Fatemeh Salehi Rizi', 'Michael Granitzer'] | 2020-02-16 | null | null | null | null | ['learning-network-representations'] | ['methodology'] | [-8.20883960e-02 5.03663659e-01 -4.60855842e-01 -5.01507878e-01
4.03289974e-01 -4.91833806e-01 1.00956547e+00 5.51966786e-01
9.41627622e-02 2.22975984e-01 7.94085622e-01 4.30865027e-02
-2.87957430e-01 -1.52500677e+00 -5.52218974e-01 -3.46480846e-01
-5.64501762e-01 5.34780085e-01 -1.50285259e-01 -4.45897505... | [7.167958736419678, 6.186869144439697] |
238e16dd-2545-4dcc-814a-f6fe87d8eacc | safe-reinforcement-learning-of-dynamic-high | 2209.13308 | null | https://arxiv.org/abs/2209.13308v2 | https://arxiv.org/pdf/2209.13308v2.pdf | Safe Reinforcement Learning of Dynamic High-Dimensional Robotic Tasks: Navigation, Manipulation, Interaction | Safety is a crucial property of every robotic platform: any control policy should always comply with actuator limits and avoid collisions with the environment and humans. In reinforcement learning, safety is even more fundamental for exploring an environment without causing any damage. While there are many proposed sol... | ['Georgia Chalvatzaki', 'Jan Peters', 'Zhiyuan Hu', 'Snehal Jauhri', 'Davide Tateo', 'Kuo Zhang', 'Puze Liu'] | 2022-09-27 | null | null | null | null | ['safe-exploration'] | ['robots'] | [-1.21281862e-01 4.32158262e-01 -4.67019230e-01 1.80882961e-01
-1.68724701e-01 -5.71264505e-01 3.65030229e-01 -9.18063521e-02
-7.28537619e-01 1.05576098e+00 -4.63340789e-01 -2.45383844e-01
-6.73138797e-01 -3.79904598e-01 -8.17107320e-01 -7.66447604e-01
-7.15169728e-01 5.69985926e-01 4.53964472e-01 -6.56419337... | [4.699005126953125, 1.5864927768707275] |
3a778069-d27e-4bab-ac11-b71f74dbd7b3 | probabilistic-radiomics-ambiguous-diagnosis | 1910.08878 | null | https://arxiv.org/abs/1910.08878v1 | https://arxiv.org/pdf/1910.08878v1.pdf | Probabilistic Radiomics: Ambiguous Diagnosis with Controllable Shape Analysis | Radiomics analysis has achieved great success in recent years. However, conventional Radiomics analysis suffers from insufficiently expressive hand-crafted features. Recently, emerging deep learning techniques, e.g., convolutional neural networks (CNNs), dominate recent research in Computer-Aided Diagnosis (CADx). Unfo... | ['Rongyao Fang', 'Jiancheng Yang', 'Bingbing Ni', 'Yi Xu', 'Linguo Li', 'Yamin Li'] | 2019-10-20 | null | null | null | null | ['probabilistic-deep-learning'] | ['computer-vision'] | [ 2.73307174e-01 5.50393581e-01 -3.66036087e-01 -2.53433794e-01
-1.12108636e+00 -8.91942754e-02 2.64481068e-01 -1.49989530e-01
-2.65779272e-02 6.51269257e-01 3.27515513e-01 -2.17889979e-01
-3.08829010e-01 -5.88321447e-01 -7.30666697e-01 -9.34658349e-01
5.06733596e-01 5.14033735e-01 1.51467070e-01 2.44655952... | [14.892059326171875, -2.247610569000244] |
80900f28-dd2c-4f01-88f7-a9027e58a855 | multi-scale-self-contrastive-learning-with | 2203.03838 | null | https://arxiv.org/abs/2203.03838v1 | https://arxiv.org/pdf/2203.03838v1.pdf | Multi-Scale Self-Contrastive Learning with Hard Negative Mining for Weakly-Supervised Query-based Video Grounding | Query-based video grounding is an important yet challenging task in video understanding, which aims to localize the target segment in an untrimmed video according to a sentence query. Most previous works achieve significant progress by addressing this task in a fully-supervised manner with segment-level labels, which r... | ['Wei Hu', 'Daizong Liu', 'Shentong Mo'] | 2022-03-08 | null | null | null | null | ['video-grounding'] | ['computer-vision'] | [ 4.22073424e-01 -7.93412700e-02 -6.94104493e-01 -4.12395954e-01
-1.05188584e+00 -4.57202405e-01 2.61953175e-01 1.43041490e-02
-2.75461167e-01 5.58328390e-01 -1.49714937e-02 1.17369860e-01
1.99244544e-01 -7.39926398e-01 -1.11084116e+00 -6.89349711e-01
1.50385737e-01 1.98201835e-01 8.53497267e-01 9.50826611... | [9.29771900177002, 0.29803866147994995] |
83c45802-a6ef-48be-bacd-2d7e949c22b5 | mnemosyne-learning-to-train-transformers-with | 2302.01128 | null | https://arxiv.org/abs/2302.01128v3 | https://arxiv.org/pdf/2302.01128v3.pdf | Mnemosyne: Learning to Train Transformers with Transformers | In this work, we propose a new class of learnable optimizers, called \textit{Mnemosyne}. It is based on the novel spatio-temporal low-rank implicit attention Transformers that can learn to train entire neural network architectures, including other Transformers, without any task-specific optimizer tuning. We show that M... | ['Avinava Dubey', 'Jie Tan', 'Tingnan Zhang', 'Vikas Sindhwani', 'Sumeet Singh', 'Krzysztof Marcin Choromanski', 'Deepali Jain'] | 2023-02-02 | null | null | null | null | ['feature-engineering'] | ['methodology'] | [-1.25441132e-02 1.72829881e-01 2.38811374e-01 -1.58189908e-01
-7.19424725e-01 -3.50593656e-01 4.70097780e-01 -2.41369054e-01
-7.33059227e-01 5.99444091e-01 2.27828711e-01 -3.77670586e-01
-4.19562221e-01 -4.63735372e-01 -9.70766306e-01 -7.09682703e-01
5.34795551e-03 9.92961347e-01 5.23514807e-01 -3.77061099... | [8.926549911499023, 2.850337266921997] |
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