Jesse Farebrother

Discovering the Electron Beam Induced Transition Rates for Silicon Dopants in Graphene with Deep Neural Networks in the STEM

Kevin M Roccapriore

Max Schwarzer

Joshua Greaves

Jesse Farebrother

Rishabh Agarwal

Colton Bishop

Maxim Ziatdinov

Igor Mordatch

Ekin D Cubuk

Aaron Courville

Pablo Samuel Castro

Bellemare Marc-Emmanuel

Sergei V Kalinin

Journal Article Discovering the Electron Beam Induced Transition Rates for Silicon Dopants in Graphene with Deep Neural Networks in the STEM… (voir plus) Get access Kevin M Roccapriore, Kevin M Roccapriore Center for Nanophase Materials Sciences, Oak Ridge National Laboratory, Oak Ridge, TN, United States Search for other works by this author on: Oxford Academic Google Scholar Max Schwarzer, Max Schwarzer Mila - Québec AI Institute, Montréal, QC, CanadaDepartment of Computer Science and Operations Research, Université de Montréal, Montréal, QC, CanadaGoogle Research, Brain Team Search for other works by this author on: Oxford Academic Google Scholar Joshua Greaves, Joshua Greaves Google Research, Brain Team Search for other works by this author on: Oxford Academic Google Scholar Jesse Farebrother, Jesse Farebrother Mila - Québec AI Institute, Montréal, QC, CanadaGoogle Research, Brain TeamSchool of Computer Science, McGill University, Montréal, QC, Canada Search for other works by this author on: Oxford Academic Google Scholar Rishabh Agarwal, Rishabh Agarwal Mila - Québec AI Institute, Montréal, QC, CanadaDepartment of Computer Science and Operations Research, Université de Montréal, Montréal, QC, CanadaGoogle Research, Brain Team Search for other works by this author on: Oxford Academic Google Scholar Colton Bishop, Colton Bishop Google Research, Brain Team Search for other works by this author on: Oxford Academic Google Scholar Maxim Ziatdinov, Maxim Ziatdinov Center for Nanophase Materials Sciences, Oak Ridge National Laboratory, Oak Ridge, TN, United StatesComputational Sciences and Engineering Division, Oak Ridge National Laboratory, Oak Ridge, TN, United States Search for other works by this author on: Oxford Academic Google Scholar Igor Mordatch, Igor Mordatch Google Research, Brain Team Search for other works by this author on: Oxford Academic Google Scholar Ekin D Cubuk, Ekin D Cubuk Google Research, Brain Team Search for other works by this author on: Oxford Academic Google Scholar Aaron Courville, Aaron Courville Mila - Québec AI Institute, Montréal, QC, CanadaDepartment of Computer Science and Operations Research, Université de Montréal, Montréal, QC, Canada Search for other works by this author on: Oxford Academic Google Scholar ... Show more Pablo Samuel Castro, Pablo Samuel Castro Google Research, Brain Team Search for other works by this author on: Oxford Academic Google Scholar Marc G Bellemare, Marc G Bellemare Mila - Québec AI Institute, Montréal, QC, CanadaGoogle Research, Brain TeamSchool of Computer Science, McGill University, Montréal, QC, Canada Search for other works by this author on: Oxford Academic Google Scholar Sergei V Kalinin Sergei V Kalinin Department of Materials Science and Engineering, University of Tennessee, Knoxville TN, United States Corresponding author: sergei2@utk.edu Search for other works by this author on: Oxford Academic Google Scholar Microscopy and Microanalysis, Volume 29, Issue Supplement_1, 1 August 2023, Pages 1932–1933, https://doi.org/10.1093/micmic/ozad067.1000 Published: 22 July 2023

2023-07-21

Microscopy and Microanalysis (publié)

doi.org

Investigating Multi-Task Pretraining and Generalization in Reinforcement Learning

Bellemare Marc-Emmanuel

Google Brain

2023-04-30

ICLR (Accept: poster)

openreview.net

Proto-Value Networks: Scaling Representation Learning with Auxiliary Tasks

Joshua Greaves

Bellemare Marc-Emmanuel

Auxiliary tasks improve the representations learned by deep reinforcement learning agents. Analytically, their effect is reasonably well und… (voir plus)erstood; in practice, however, their primary use remains in support of a main learning objective, rather than as a method for learning representations. This is perhaps surprising given that many auxiliary tasks are defined procedurally, and hence can be treated as an essentially infinite source of information about the environment. Based on this observation, we study the effectiveness of auxiliary tasks for learning rich representations, focusing on the setting where the number of tasks and the size of the agent's network are simultaneously increased. For this purpose, we derive a new family of auxiliary tasks based on the successor measure. These tasks are easy to implement and have appealing theoretical properties. Combined with a suitable off-policy learning rule, the result is a representation learning algorithm that can be understood as extending Mahadevan & Maggioni (2007)'s proto-value functions to deep reinforcement learning -- accordingly, we call the resulting object proto-value networks. Through a series of experiments on the Arcade Learning Environment, we demonstrate that proto-value networks produce rich features that may be used to obtain performance comparable to established algorithms, using only linear approximation and a small number (~4M) of interactions with the environment's reward function.

2023-04-30

International Conference on Learning Representations (Accept: poster)

doi.org

openreview.net

A Novel Stochastic Gradient Descent Algorithm for LearningPrincipal Subspaces

Charline Le Lan

Joshua Greaves

Jesse Farebrother

Mark Rowland

Fabian Pedregosa

Rishabh Agarwal

Bellemare Marc-Emmanuel

In this paper, we derive an algorithm that learns a principal subspace from sample entries, can be applied when the approximate subspace i… (voir plus)s represented by a neural network, and hence can bescaled to datasets with an effectively infinite number of rows and columns. Our method consistsin defining a loss function whose minimizer is the desired principal subspace, and constructing agradient estimate of this loss whose bias can be controlled.

2023-04-10

Proceedings of The 26th International Conference on Artificial Intelligence and Statistics (publié)

doi.org

proceedings.mlr.press

Revisiting Successor Features for Inverse Reinforcement Learning

Sanjiban Choudhury

2019-12-31