27 citations · 122 across the 10 of their papers we have counts for
16 papers
Impact of Aliasing on Generalization in Deep Convolutional Networks
Cristina Vasconcelos, Hugo Larochelle, Vincent Dumoulin +3
We investigate the impact of aliasing on generalization in Deep Convolutional Networks and show that data augmentation schemes alone are unable to prevent it due to structural limi…
Bridging the Gap Between Adversarial Robustness and Optimization Bias
Fartash Faghri, Sven Gowal, Cristina Vasconcelos +3
We demonstrate that the choice of optimizer, neural network architecture, and regularizer significantly affect the adversarial robustness of linear neural networks, providing guara…
An Effective Anti-Aliasing Approach for Residual Networks
Cristina Vasconcelos, Hugo Larochelle, Vincent Dumoulin +2
Image pre-processing in the frequency domain has traditionally played a vital role in computer vision and was even part of the standard pipeline in the early days of deep learning.…
Beyond variance reduction: Understanding the true impact of baselines on policy optimization
Wesley Chung, Valentin Thomas, Marlos C. Machado +1
Bandit and reinforcement learning (RL) problems can often be framed as optimization problems where the goal is to maximize average performance while having access only to stochasti…
An operator view of policy gradient methods
Dibya Ghosh, Marlos C. Machado, Nicolas Le Roux
We cast policy gradient methods as the repeated application of two operators: a policy improvement operator , which maps any policy to a better one ,…
The Geometry of Sign Gradient Descent
Lukas Balles, Fabian Pedregosa, Nicolas Le Roux
Sign-based optimization methods have become popular in machine learning due to their favorable communication cost in distributed optimization and their surprisingly good performanc…