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Mon 16:30 On the Information Bottleneck Theory of Deep Learning
Andrew Saxe · Yamini Bansal · Joel Dapello · Madhu Advani · Artemy Kolchinsky · Brendan D Tracey · David D Cox
Wed 14:30 Adaptivity of deep ReLU network for learning in Besov and mixed smooth Besov spaces: optimal rate and curse of dimensionality
Taiji Suzuki
Wed 14:30 Deterministic PAC-Bayesian generalization bounds for deep networks via generalizing noise-resilience
Vaishnavh Nagarajan · Zico Kolter
Wed 14:30 Critical Learning Periods in Deep Networks
Alessandro Achille · Matteo Rovere · Stefano Soatto
Wed 14:30 An analytic theory of generalization dynamics and transfer learning in deep linear networks
Andrew Lampinen · Surya Ganguli
Wed 14:30 On the Relation Between the Sharpest Directions of DNN Loss and the SGD Step Length
Stanislaw Jastrzebski · Zachary Kenton · Nicolas Ballas · Asja Fischer · Yoshua Bengio · Amos Storkey
Wed 14:30 A Convergence Analysis of Gradient Descent for Deep Linear Neural Networks
Sanjeev Arora · Nadav Cohen · Noah Golowich · Wei Hu
Wed 14:30 Stochastic Gradient/Mirror Descent: Minimax Optimality and Implicit Regularization
Navid Azizan · Babak Hassibi
Thu 14:30 Deep learning generalizes because the parameter-function map is biased towards simple functions
Guillermo Valle-Perez · Chico Q. Camargo · Ard Louis
Mon 12:15 On the Theory of Implicit Deep Learning: Global Convergence with Implicit Layers
Kenji Kawaguchi
Mon 17:00 One Network Fits All? Modular versus Monolithic Task Formulations in Neural Networks
Atish Agarwala · Abhimanyu Das · Brendan Juba · Rina Panigrahy · Vatsal Sharan · Xin Wang · Qiuyi Zhang
Mon 17:00 On the geometry of generalization and memorization in deep neural networks
Cory Stephenson · Suchismita Padhy · Abhinav Ganesh · Yue Hui · Hanlin Tang · SueYeon Chung