4 citations · 10 across the 5 of their papers we have counts for
15 papers
Low-Pass Filtering SGD for Recovering Flat Optima in the Deep Learning Optimization Landscape
Devansh Bisla, Jing Wang, Anna Choromanska
In this paper, we study the sharpness of a deep learning (DL) loss landscape around local minima in order to reveal systematic mechanisms underlying the generalization abilities of…
A Theoretical-Empirical Approach to Estimating Sample Complexity of DNNs
Devansh Bisla, Apoorva Nandini Saridena, Anna Choromanska
This paper focuses on understanding how the generalization error scales with the amount of the training data for deep neural networks (DNNs). Existing techniques in statistical lea…
Approximating Ground State Energies and Wave Functions of Physical Systems with Neural Networks
Cesar Lema, Anna Choromanska
Quantum theory has been remarkably successful in providing an understanding of physical systems at foundational scales. Solving the Schrödinger equation provides full knowledge of…
SGB: Stochastic Gradient Bound Method for Optimizing Partition Functions
Jing Wang, Anna Choromanska
This paper addresses the problem of optimizing partition functions in a stochastic learning setting. We propose a stochastic variant of the bound majorization algorithm that relies…
Multi-modal Experts Network for Autonomous Driving
Shihong Fang, Anna Choromanska
End-to-end learning from sensory data has shown promising results in autonomous driving. While employing many sensors enhances world perception and should lead to more robust and r…
Learning to Score Behaviors for Guided Policy Optimization
Aldo Pacchiano, Jack Parker-Holder, Yunhao Tang +3
We introduce a new approach for comparing reinforcement learning policies, using Wasserstein distances (WDs) in a newly defined latent behavioral space. We show that by utilizing t…