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LogME Practical Assessment of Pre-trained Models for Transfer Learning
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Local Correlation Clustering with Asymmetric Classification Errors
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Locally Adaptive Label Smoothing Improves Predictive Churn
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Locally Persistent Exploration in Continuous Control Tasks with Sparse Rewards
LocallyPersistentExplorationinContinuousControlTaskswithSparseRewardsSusanAmin12MaziarGomrokchi12HosseinAboutalebi34HarshSajita12DoinaPrecup12Abstractcallforacleverexplorationstrategythatexposesthe...
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Lipschitz normalization for self-attention layers with application to graph neural networks
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Local Algorithms for Finding Densely Connected Clusters
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Link Prediction with Persistent Homology An Interactive View
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Leveraging Sparse Linear Layers for Debuggable Deep Networks
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LIME Learning Inductive Bias for Primitives of Mathematical Reasoning
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Linear Transformers Are Secretly Fast Weight Programmers
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LieTransformer Equivariant Self-Attention for Lie Groups
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Leveraging Public Data for Practical Private Query Release
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Leveraging Non-uniformity in First-order Non-convex Optimization
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Leveraging Language to Learn Program Abstractions and Search Heuristics
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Leveraging Good Representations in Linear Contextual Bandits
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Leveraged Weighted Loss for Partial Label Learning
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Let’s Agree to Degree Comparing Graph Convolutional Networks in the Message-Passing Framework
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Lenient Regret and Good-Action Identification in Gaussian Process Bandits
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Learn-to-Share A Hardware-friendly Transfer Learning Framework Exploiting Computation and Parameter Sharing
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Learning While Playing in Mean-Field Games Convergence and Optimality
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