most citedPlaying Doom with SLAM-Augmented Deep Reinforcement Learning

45 citations · 53 across the 6 of their papers we have counts for

collaborators

6 papers

math.NA2021

Fast and Green Computing with Graphics Processing Units for solving Sparse Linear Systems

Abal-Kassim Cheik Ahamed, Alban Desmaison, Frederic Magoules

In this paper, we aim to introduce a new perspective when comparing highly parallelized algorithms on GPU: the energy consumption of the GPU. We give an analysis of the performance…

cs.LG20161 cited

Learning to superoptimize programs - Workshop Version

Rudy Bunel, Alban Desmaison, M. Pawan Kumar +2

Superoptimization requires the estimation of the best program for a given computational task. In order to deal with large programs, superoptimization techniques perform a stochasti…

cs.AI201645 cited

Playing Doom with SLAM-Augmented Deep Reinforcement Learning

Shehroze Bhatti, Alban Desmaison, Ondrej Miksik +3

A number of recent approaches to policy learning in 2D game domains have been successful going directly from raw input images to actions. However when employed in complex 3D enviro…

stat.ML20167 cited

Inducing Interpretable Representations with Variational Autoencoders

N. Siddharth, Brooks Paige, Alban Desmaison +5

We develop a framework for incorporating structured graphical models in the \emph{encoders} of variational autoencoders (VAEs) that allows us to induce interpretable representation…

cs.CV2016

Efficient Linear Programming for Dense CRFs

Thalaiyasingam Ajanthan, Alban Desmaison, Rudy Bunel +3

The fully connected conditional random field (CRF) with Gaussian pairwise potentials has proven popular and effective for multi-class semantic segmentation. While the energy of a d…

cs.CV2016

Efficient Continuous Relaxations for Dense CRF

Alban Desmaison, Rudy Bunel, Pushmeet Kohli +2

Dense conditional random fields (CRF) with Gaussian pairwise potentials have emerged as a popular framework for several computer vision applications such as stereo correspondence a…