44 citations · 83 across the 4 of their papers we have counts for
4 papers
CityLearn: Standardizing Research in Multi-Agent Reinforcement Learning for Demand Response and Urban Energy Management
Jose R Vazquez-Canteli, Sourav Dey, Gregor Henze +1
Rapid urbanization, increasing integration of distributed renewable energy resources, energy storage, and electric vehicles introduce new challenges for the power grid. In the US,…
Local Convolutions Cause an Implicit Bias towards High Frequency Adversarial Examples
Josue Ortega Caro, Yilong Ju, Ryan Pyle +4
Adversarial Attacks are still a significant challenge for neural networks. Recent work has shown that adversarial perturbations typically contain high-frequency features, but the r…
Deep Residual Autoencoders for Expectation Maximization-inspired Dictionary Learning
Bahareh Tolooshams, Sourav Dey, Demba Ba
We introduce a neural-network architecture, termed the constrained recurrent sparse autoencoder (CRsAE), that solves convolutional dictionary learning problems, thus establishing a…
Scalable Convolutional Dictionary Learning with Constrained Recurrent Sparse Auto-encoders
Bahareh Tolooshams, Sourav Dey, Demba Ba
Given a convolutional dictionary underlying a set of observed signals, can a carefully designed auto-encoder recover the dictionary in the presence of noise? We introduce an auto-e…