Max Schwarzer

Learning and Controlling Silicon Dopant Transitions in Graphene using Scanning Transmission Electron Microscopy

Max Schwarzer

Jesse Farebrother

Joshua Greaves

Ekin Dogus Cubuk

Rishabh Agarwal

Aaron Courville

Marc Gendron-Bellemare

Sergei Kalinin

Igor Mordatch

Pablo Samuel Castro

Kevin M Roccapriore

We introduce a machine learning approach to determine the transition dynamics of silicon atoms on a single layer of carbon atoms, when stimu… (voir plus)lated by the electron beam of a scanning transmission electron microscope (STEM). Our method is data-centric, leveraging data collected on a STEM. The data samples are processed and filtered to produce symbolic representations, which we use to train a neural network to predict transition probabilities. These learned transition dynamics are then leveraged to guide a single silicon atom throughout the lattice to pre-determined target destinations. We present empirical analyses that demonstrate the efficacy and generality of our approach.

2025-05-20

Advanced Materials Interfaces (publié)

doi.org

arxiv.org

The Position Dependence of Electron Beam Induced Effects in 2D Materials with Deep Neural Networks

Kevin M Roccapriore

Max Schwarzer

Joshua Greaves

Jesse Farebrother

Riccardo Torsi

Rishabh Agarwal

Colton Bishop

Igor Mordatch

Ekin Dogus Cubuk

Aaron Courville

Marc Gendron-Bellemare

Joshua Robinson

Pablo Samuel Castro

Sergei V Kalinin

2024-07-01

Microscopy and Microanalysis (publié)

doi.org

Large Language Models as Generalizable Policies for Embodied Tasks

Andrew Szot

Max Schwarzer

Harsh Agrawal

Bogdan Mazoure

Walter Talbott

Rin Metcalf

Natalie Mackraz

(Rex) Devon Hjelm

Alexander T Toshev

2024-01-16

ICLR.cc/2024/Conference (poster)

doi.org

openreview.net

Learning Silicon Dopant Transitions in Graphene using Scanning Transmission Electron Microscopy

Max Schwarzer

Jesse Farebrother

Joshua Greaves

Kevin Roccapriore

Ekin Dogus Cubuk

Rishabh Agarwal

Aaron Courville

Marc Gendron-Bellemare

Sergei Kalinin

Igor Mordatch

Pablo Samuel Castro

We introduce a machine learning approach to determine the transition rates of silicon atoms on a single layer of carbon atoms, when stimulat… (voir plus)ed by the electron beam of a scanning transmission electron microscope (STEM). Our method is data-centric, leveraging data collected on a STEM. The data samples are processed and filtered to produce symbolic representations, which we use to train a neural network to predict transition rates. These rates are then applied to guide a single silicon atom throughout the lattice to pre-determined target destinations. We present empirical analyses that demonstrate the efficacy and generality of our approach.

2023-10-27

NeurIPS.cc/2023/Workshop/AI4Mat (spotlight)

openreview.net

Learning Silicon Dopant Transitions in Graphene using Scanning Transmission Electron Microscopy

Max Schwarzer

Jesse Farebrother

Joshua Greaves

Kevin Roccapriore

Ekin Dogus Cubuk

Rishabh Agarwal

Aaron Courville

Marc Gendron-Bellemare

Sergei Kalinin

Igor Mordatch

Pablo Samuel Castro

We introduce a machine learning approach to determine the transition rates of silicon atoms on a single layer of carbon atoms, when stimulat… (voir plus)ed by the electron beam of a scanning transmission electron microscope (STEM). Our method is data-centric, leveraging data collected on a STEM. The data samples are processed and filtered to produce symbolic representations, which we use to train a neural network to predict transition rates. These rates are then applied to guide a single silicon atom throughout the lattice to pre-determined target destinations. We present empirical analyses that demonstrate the efficacy and generality of our approach.

2023-10-27

NeurIPS.cc/2023/Workshop/AI4Mat (spotlight)

openreview.net

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 Dogus Cubuk

Aaron Courville

Pablo Samuel Castro

Marc Gendron-Bellemare

Sergei V Kalinin

2023-07-22

Microscopy and Microanalysis (publié)

doi.org

Bigger, Better, Faster: Human-level Atari with human-level efficiency

Max Schwarzer

Johan Samir Obando Ceron

Aaron Courville

Marc Gendron-Bellemare

Rishabh Agarwal

Pablo Samuel Castro

We introduce a value-based RL agent, which we call BBF, that achieves super-human performance in the Atari 100K benchmark. BBF relies on sca… (voir plus)ling the neural networks used for value estimation, as well as a number of other design choices that enable this scaling in a sample-efficient manner. We conduct extensive analyses of these design choices and provide insights for future work. We end with a discussion about updating the goalposts for sample-efficient RL research on the ALE. We make our code and data publicly available at https://github.com/google-research/google-research/tree/master/bigger_better_faster.

2023-07-03

Proceedings of the 40th International Conference on Machine Learning (publié)

doi.org

openreview.net

Bigger, Better, Faster: Human-level Atari with human-level efficiency

Max Schwarzer

Johan Samir Obando Ceron

Aaron Courville

Marc Gendron-Bellemare

Rishabh Agarwal

Pablo Samuel Castro

We introduce a value-based RL agent, which we call BBF, that achieves super-human performance in the Atari 100K benchmark. BBF relies on sca… (voir plus)ling the neural networks used for value estimation, as well as a number of other design choices that enable this scaling in a sample-efficient manner. We conduct extensive analyses of these design choices and provide insights for future work. We end with a discussion about updating the goalposts for sample-efficient RL research on the ALE. We make our code and data publicly available at https://github.com/google-research/google-research/tree/master/bigger_better_faster.

2023-05-30

ArXiv (prépublication)

doi.org

arxiv.org

Sample-Efficient Reinforcement Learning by Breaking the Replay Ratio Barrier

Marc Gendron-Bellemare

Aaron Courville

Increasing the replay ratio, the number of updates of an agent's parameters per environment interaction, is an appealing strategy for improv… (voir plus)ing the sample efficiency of deep reinforcement learning algorithms. In this work, we show that fully or partially resetting the parameters of deep reinforcement learning agents causes better replay ratio scaling capabilities to emerge. We push the limits of the sample efficiency of carefully-modified algorithms by training them using an order of magnitude more updates than usual, significantly improving their performance in the Atari 100k and DeepMind Control Suite benchmarks. We then provide an analysis of the design choices required for favorable replay ratio scaling to be possible and discuss inherent limits and tradeoffs.

2023-02-01

ICLR.cc/2023/Conference (notable)

openreview.net

Simplicial Embeddings in Self-Supervised Learning and Downstream Classification

Samuel Lavoie

Simplicial Embeddings (SEM) are representations learned through self-supervised learning (SSL), wherein a representation is projected into …

2023-02-01

ICLR.cc/2023/Conference (notable)

doi.org

openreview.net

Bigger, Better, Faster: Human-level Atari with human-level efficiency

Max Schwarzer

Johan Samir Obando Ceron

Aaron Courville

Marc Gendron-Bellemare

Rishabh Agarwal

Pablo Samuel Castro

We introduce a value-based RL agent, which we call BBF, that achieves super-human performance in the Atari 100K benchmark. BBF relies on sca… (voir plus)ling the neural networks used for value estimation, as well as a number of other design choices that enable this scaling in a sample-efficient manner. We conduct extensive analyses of these design choices and provide insights for future work. We end with a discussion about updating the goalposts for sample-efficient RL research on the ALE. We make our code and data publicly available at https://github.com/google-research/google-research/tree/master/bigger_better_faster.

2023-01-01

ICML (publié)

doi.org

openreview.net

The Primacy Bias in Deep Reinforcement Learning

2022-06-28

Proceedings of the 39th International Conference on Machine Learning (publié)