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Poster
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Wed 14:30
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Caveats for information bottleneck in deterministic scenarios
Artemy Kolchinsky · Brendan D Tracey · Steven Van Kuyk
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Poster
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Wed 14:30
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Learning to Make Analogies by Contrasting Abstract Relational Structure
Felix Hill · Adam Santoro · David Barrett · Ari Morcos · Timothy Lillicrap
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Poster
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Wed 14:30
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Max-MIG: an Information Theoretic Approach for Joint Learning from Crowds
Peng Cao · Yilun Xu · Yuqing Kong · Yizhou Wang
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Poster
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Wed 14:30
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Stochastic Gradient/Mirror Descent: Minimax Optimality and Implicit Regularization
Navid Azizan · Babak Hassibi
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Poster
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Wed 14:30
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An analytic theory of generalization dynamics and transfer learning in deep linear networks
Andrew Lampinen · Surya Ganguli
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Poster
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Tue 14:30
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How Powerful are Graph Neural Networks?
Keyulu Xu · Weihua Hu · Jure Leskovec · Stefanie Jegelka
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Poster
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Wed 14:30
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Subgradient Descent Learns Orthogonal Dictionaries
Yu Bai · Qijia Jiang · Ju Sun
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Poster
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Wed 14:30
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Critical Learning Periods in Deep Networks
Alessandro Achille · Matteo Rovere · Stefano Soatto
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Poster
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Wed 14:30
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A Convergence Analysis of Gradient Descent for Deep Linear Neural Networks
Sanjeev Arora · Nadav Cohen · Noah Golowich · Wei Hu
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Poster
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Wed 14:30
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Adaptivity of deep ReLU network for learning in Besov and mixed smooth Besov spaces: optimal rate and curse of dimensionality
Taiji Suzuki
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Poster
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Wed 14:30
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Deterministic PAC-Bayesian generalization bounds for deep networks via generalizing noise-resilience
Vaishnavh Nagarajan · Zico Kolter
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Poster
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Thu 14:30
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Deep learning generalizes because the parameter-function map is biased towards simple functions
Guillermo Valle-Perez · Chico Q. Camargo · Ard Louis
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