1.3k citations · 2.5k across the 29 of their papers we have counts for
6 papers · 1 filter
Bayesian Policy Gradients via Alpha Divergence Dropout Inference
Peter Henderson, Thang Doan, Riashat Islam +1
Policy gradient methods have had great success in solving continuous control tasks, yet the stochastic nature of such problems makes deterministic value estimation difficult. We pr…
Alpha-Divergences in Variational Dropout
Bogdan Mazoure, Riashat Islam
We investigate the use of alternative divergences to Kullback-Leibler (KL) in variational inference(VI), based on the Variational Dropout \cite{kingma2015}. Stochastic gradient var…
Bayesian Hypernetworks
David Krueger, Chin-Wei Huang, Riashat Islam +3
We study Bayesian hypernetworks: a framework for approximate Bayesian inference in neural networks. A Bayesian hypernetwork $\h$ is a neural network which learns to transform a sim…
Deep Reinforcement Learning that Matters
Peter Henderson, Riashat Islam, Philip Bachman +3
In recent years, significant progress has been made in solving challenging problems across various domains using deep reinforcement learning (RL). Reproducing existing work and acc…
Reproducibility of Benchmarked Deep Reinforcement Learning Tasks for Continuous Control
Riashat Islam, Peter Henderson, Maziar Gomrokchi +1
Policy gradient methods in reinforcement learning have become increasingly prevalent for state-of-the-art performance in continuous control tasks. Novel methods typically benchmark…
Deep Bayesian Active Learning with Image Data
Yarin Gal, Riashat Islam, Zoubin Ghahramani
Even though active learning forms an important pillar of machine learning, deep learning tools are not prevalent within it. Deep learning poses several difficulties when used in an…