Link/DOI string | Publication Date timestamp[ns] | Title string | Authors string | Abstract string | Categories string | label int64 | source string | Classification_embedding list | Proximity_embedding list | top_10_similar string | max_similarity float64 | avg_similarity float64 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
http://arxiv.org/abs/1902.05605v4 | 2019-02-14T00:00:00 | CrossQ: Batch Normalization in Deep Reinforcement Learning for Greater Sample Efficiency and Simplicity | Aditya Bhatt; Daniel Palenicek; Boris Belousov; Max Argus; Artemij Amiranashvili; Thomas Brox; Jan Peters | Sample efficiency is a crucial problem in deep reinforcement learning. Recent algorithms, such as REDQ and DroQ, found a way to improve the sample efficiency by increasing the update-to-data (UTD) ratio to 20 gradient update steps on the critic per environment sample. However, this comes at the expense of a greatly inc... | cs.LG; stat.ML | 1 | ICLR-2024 | [
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http://arxiv.org/abs/2003.13898v3 | 2020-03-31T00:00:00 | Edge Guided GANs with Contrastive Learning for Semantic Image Synthesis | Hao Tang; Xiaojuan Qi; Guolei Sun; Dan Xu; Nicu Sebe; Radu Timofte; Luc Van Gool | We propose a novel ECGAN for the challenging semantic image synthesis task. Although considerable improvement has been achieved, the quality of synthesized images is far from satisfactory due to three largely unresolved challenges. 1) The semantic labels do not provide detailed structural information, making it difficu... | cs.CV; cs.LG; eess.IV | 1 | ICLR-2023 | [
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http://arxiv.org/abs/2006.07796v4 | 2020-06-14T00:00:00 | Structure by Architecture: Structured Representations without Regularization | Felix Leeb; Guilia Lanzillotta; Yashas Annadani; Michel Besserve; Stefan Bauer; Bernhard Schölkopf | We study the problem of self-supervised structured representation learning using autoencoders for downstream tasks such as generative modeling. Unlike most methods which rely on matching an arbitrary, relatively unstructured, prior distribution for sampling, we propose a sampling technique that relies solely on the ind... | cs.LG; cs.CV; stat.ML | 1 | ICLR-2023 | [
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http://arxiv.org/abs/2007.09890v3 | 2020-07-20T00:00:00 | Learning the Positions in CountSketch | Simin Liu; Tianrui Liu; Ali Vakilian; Yulin Wan; David P. Woodruff | We consider sketching algorithms which first quickly compress data by multiplication with a random sketch matrix, and then apply the sketch to quickly solve an optimization problem, e.g., low rank approximation. In the learning-based sketching paradigm proposed by Indyk et al. [2019], the sketch matrix is found by choo... | cs.LG; cs.DS; cs.NA; math.NA; stat.ML | 1 | ICLR-2023 | [
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http://arxiv.org/abs/2008.03738v2 | 2020-08-09T00:00:00 | Treatment Effects Estimation by Uniform Transformer | Ruoqi Yu; Shulei Wang | In observational studies, balancing covariates in different treatment groups is essential to estimate treatment effects. One of the most commonly used methods for such purposes is weighting. The performance of this class of methods usually depends on strong regularity conditions for the underlying model, which might no... | stat.ME; math.ST; stat.TH | 1 | ICLR-2024 | [
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... | {"http://arxiv.org/abs/2306.06263v1": 0.922035813331604, "http://arxiv.org/abs/2208.08544v3": 0.921151340007782, "http://arxiv.org/abs/2210.00079v3": 0.9103657603263855, "http://arxiv.org/abs/2307.11503v1": 0.8976566791534424, "http://arxiv.org/abs/2206.02792v1": 0.896964430809021, "http://arxiv.org/abs/2201.12293v4": ... | 0.922036 | 0.898741 |
http://arxiv.org/abs/2102.09407v5 | 2021-02-18T00:00:00 | Adaptive Rational Activations to Boost Deep Reinforcement Learning | Quentin Delfosse; Patrick Schramowski; Martin Mundt; Alejandro Molina; Kristian Kersting | Latest insights from biology show that intelligence not only emerges from the connections between neurons but that individual neurons shoulder more computational responsibility than previously anticipated. This perspective should be critical in the context of constantly changing distinct reinforcement learning environm... | cs.LG | 1 | ICLR-2024 | [
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http://arxiv.org/abs/2102.10882v3 | 2021-02-22T00:00:00 | Conditional Positional Encodings for Vision Transformers | Xiangxiang Chu; Zhi Tian; Bo Zhang; Xinlong Wang; Chunhua Shen | "We propose a conditional positional encoding (CPE) scheme for vision Transformers. Unlike previous (...TRUNCATED) | cs.CV; cs.AI; cs.LG | 1 | ICLR-2023 | [-0.0836898684501648,-0.6185870170593262,0.19620980322360992,-0.5301298499107361,-0.0449054241180419(...TRUNCATED) | [0.6201993227005005,0.22138410806655884,-0.05666289106011391,0.013244099915027618,-0.195098504424095(...TRUNCATED) | {} | null | null |
http://arxiv.org/abs/2103.01403v3 | 2021-03-02T00:00:00 | A Minimalist Dataset for Systematic Generalization of Perception, Syntax, and Semantics | Qing Li; Siyuan Huang; Yining Hong; Yixin Zhu; Ying Nian Wu; Song-Chun Zhu | "Inspired by humans' exceptional ability to master arithmetic and generalize to new problems, we pre(...TRUNCATED) | cs.LG; cs.AI; cs.CV | 1 | ICLR-2023 | [-0.4402918815612793,-0.46931028366088867,0.8327910900115967,-0.6764956712722778,0.33663028478622437(...TRUNCATED) | [0.1770109236240387,0.49474531412124634,-0.012065744958817959,-0.6595368981361389,0.2432122379541397(...TRUNCATED) | {} | null | null |
http://arxiv.org/abs/2105.03692v4 | 2021-05-08T00:00:00 | Incompatibility Clustering as a Defense Against Backdoor Poisoning Attacks | Charles Jin; Melinda Sun; Martin Rinard | "We propose a novel clustering mechanism based on an incompatibility property between subsets of dat(...TRUNCATED) | cs.LG; cs.CR; stat.ML | 1 | ICLR-2023 | [-0.37018436193466187,-1.4452083110809326,-0.31904152035713196,-0.45182371139526367,-0.1214764565229(...TRUNCATED) | [-0.516154944896698,-0.31915998458862305,-0.5338889956474304,0.39147329330444336,-0.2601101398468017(...TRUNCATED) | {} | null | null |
http://arxiv.org/abs/2105.14559v3 | 2021-05-30T00:00:00 | Active Learning in Bayesian Neural Networks with Balanced Entropy Learning Principle | Jae Oh Woo | "Acquiring labeled data is challenging in many machine learning applications with limited budgets. A(...TRUNCATED) | cs.LG; stat.ML | 1 | ICLR-2023 | [-0.34924548864364624,-0.8688428997993469,0.09013675153255463,-0.8125857710838318,-0.209998384118080(...TRUNCATED) | [0.19341912865638733,1.2231199741363525,-1.0490398406982422,-0.12455292791128159,-0.4252555370330810(...TRUNCATED) | {} | null | null |
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