Aaron Courville

sarvjeet-singh.ghotra@mila.quebec

Juan Duque

PhD - Université de Montréal

juan.duque@mila.quebec

PhD - Université de Montréal

udaykaran.kapur@mila.quebec

Arian Hosseini

PhD - Université de Montréal

Uday Kapur

PhD - Université de Montréal

Amr Khalifa

PhD - Université de Montréal

amr-khalifa.alshaykh@mila.quebec

samuel.lavoie@mila.quebec

Samuel Lavoie

PhD - Université de Montréal

Zhixuan Lin

PhD - Université de Montréal

PhD - Université de Montréal

Principal supervisor :

Simon Lacoste-Julien

alan.milligan@mila.quebec

PhD - Université de Montréal

Principal supervisor :

Gauthier Gidel

andjela.mladenovic@mila.quebec

mohammed.muqeeth@mila.quebec

Muqeeth Mohammed

PhD - Université de Montréal

PhD - Université de Montréal

Co-supervisor :

andrei.nicolicioiu@gmail.com

morgane.moss@mila.quebec

Andrei Nicolicioiu

PhD - Université de Montréal

Michael Noukhovitch

PhD - Université de Montréal

Johan Samir Obando Ceron

PhD - Université de Montréal

Co-supervisor :

michell.payanoperez@mila.quebec

johan.ceron@mila.quebec

PhD - Université de Montréal

Dereck Piché

PhD - Université de Montréal

pichedereck@gmail.com

khaled.rouissi@mila.quebec

Khaled Rouissi

Master's Research - Université de Montréal

Esra'a Saleh

PhD - Université de Montréal

Principal supervisor :

Glen Berseth

esraa.saleh@mila.quebec

Vedant Shah

PhD - Université de Montréal

vedant.shah@mila.quebec

PhD - Université de Montréal

tsirigoc@mila.quebec

colin.yiyang-lu@mila.quebec

Yusong Wu

PhD - Université de Montréal

Principal supervisor :

Anna (Cheng-Zhi) Huang

wu.yusong@mila.quebec

Colin Yiyang Lu

PhD - Université de Montréal

Sujin yun

PhD - Université de Montréal

sujin.yun@mila.quebec

xiaofeng.zhang@mila.quebec

Xiaofeng Zhang

PhD - Université de Montréal

Publications

Learning Silicon Dopant Transitions in Graphene using Scanning Transmission Electron Microscopy

Max Schwarzer

Jesse Farebrother

Joshua Greaves

Kevin Roccapriore

Ekin Dogus Cubuk

Bellemare Marc-Emmanuel

Sergei Kalinin

Igor Mordatch

We introduce a machine learning approach to determine the transition rates of silicon atoms on a single layer of carbon atoms, when stimulat… (see more)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.

2023-10-26

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

Sparse Universal Transformer

Shawn Tan

Yikang Shen

Zhenfang Chen

Chuang Gan

The Universal Transformer (UT) is a variant of the Transformer that shares parameters across its layers and is Turing-complete under certain… (see more) assumptions. Empirical evidence also shows that UTs have better compositional generalization than Vanilla Transformers (VTs) in formal language tasks. The parameter-sharing also affords it better parameter efficiency than VTs. Despite its many advantages, most state-of-the-art NLP systems use VTs as their backbone model instead of UTs. This is mainly because scaling UT parameters is more compute and memory intensive than scaling up a VT. This paper proposes the Sparse Universal Transformer (SUT), which leverages Sparse Mixture of Experts (SMoE) to reduce UT's computation complexity while retaining its parameter efficiency and generalization ability. Experiments show that SUT combines the best of both worlds, achieving strong generalization results on formal language tasks (Logical inference and CFQ) and impressive parameter and computation efficiency on standard natural language benchmarks like WMT'14.

2023-10-06

EMNLP/2023/Conference (accepted)

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

Max Schwarzer

Johan Obando-Ceron

Bellemare Marc-Emmanuel

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… (see more)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.

2023-07-24

International Conference on Machine Learning (Accept (Poster))

proceedings.mlr.press

Mastering the Unsupervised Reinforcement Learning Benchmark from Pixels

Sai Rajeswar

Pietro Mazzaglia

Tim Verbelen

Alexandre Piché

Bart Dhoedt

Alexandre Lacoste

Controlling artificial agents from visual sensory data is an arduous task. Reinforcement learning (RL) algorithms can succeed but require la… (see more)rge amounts of interactions between the agent and the environment. To alleviate the issue, unsupervised RL proposes to employ self-supervised interaction and learning, for adapting faster to future tasks. Yet, as shown in the Unsupervised RL Benchmark (URLB; Laskin et al. 2021), whether current unsupervised strategies can improve generalization capabilities is still unclear, especially in visual control settings. In this work, we study the URLB and propose a new method to solve it, using unsupervised model-based RL, for pre-training the agent, and a task-aware fine-tuning strategy combined with a new proposed hybrid planner, Dyna-MPC, to adapt the agent for downstream tasks. On URLB, our method obtains 93.59% overall normalized performance, surpassing previous baselines by a staggering margin. The approach is empirically evaluated through a large-scale empirical study, which we use to validate our design choices and analyze our models. We also show robust performance on the Real-Word RL benchmark, hinting at resiliency to environment perturbations during adaptation. Project website: https://masteringurlb.github.io/

2023-07-24

International Conference on Machine Learning (Accept (Oral & Poster))

proceedings.mlr.press

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

Colton Bishop

Maxim Ziatdinov

Igor Mordatch

Ekin D Cubuk

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… (see more) 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 (published)

Meta-Value Learning: a General Framework for Learning with Learning Awareness

Tim Cooijmans

Milad Aghajohari

2023-07-16

ArXiv (preprint)

Generative Adversarial Nets

Moez Krichen

Ian J. Goodfellow

Mehdi Mirza

Generative Adversarial Networks (GANs) are very popular frameworks for generating high-quality data, and are immensely used in both the acad… (see more)emia and industry in many domains. Arguably, their most substantial impact has been in the area of computer vision, where they achieve state-of-the-art image generation. This chapter gives an introduction to GANs, by discussing their principle mechanism and presenting some of their inherent problems during training and evaluation. We focus on these three issues: (1) mode collapse, (2) vanishing gradients, and (3) generation of low-quality images. We then list some architecture-variant and loss-variant GANs that remedy the above challenges. Lastly, we present two utilization examples of GANs for real-world applications: Data augmentation and face images generation.

2023-07-05

2023 14th International Conference on Computing Communication and Networking Technologies (ICCCNT) (published)

arxiv.org

Learning with Learning Awareness using Meta-Values

Tim Cooijmans

Milad Aghajohari

2023-06-18

ICML.cc/2023/Workshop/Frontiers4LCD (published)

Investigating Multi-Task Pretraining and Generalization in Reinforcement Learning

Bellemare Marc-Emmanuel

Google Brain

2023-04-30

ICLR (Accept: poster)

Latent State Marginalization as a Low-Cost Approach for Improving Exploration

Qinqing Zheng

Ricky T. Q. Chen

While the maximum entropy (MaxEnt) reinforcement learning (RL) framework -- often touted for its exploration and robustness capabilities -- … (see more)is usually motivated from a probabilistic perspective, the use of deep probabilistic models has not gained much traction in practice due to their inherent complexity. In this work, we propose the adoption of latent variable policies within the MaxEnt framework, which we show can provably approximate any policy distribution, and additionally, naturally emerges under the use of world models with a latent belief state. We discuss why latent variable policies are difficult to train, how naive approaches can fail, then subsequently introduce a series of improvements centered around low-cost marginalization of the latent state, allowing us to make full use of the latent state at minimal additional cost. We instantiate our method under the actor-critic framework, marginalizing both the actor and critic. The resulting algorithm, referred to as Stochastic Marginal Actor-Critic (SMAC), is simple yet effective. We experimentally validate our method on continuous control tasks, showing that effective marginalization can lead to better exploration and more robust training. Our implementation is open sourced at https://github.com/zdhNarsil/Stochastic-Marginal-Actor-Critic.

2023-04-30

International Conference on Learning Representations (Accept: poster)

Sample-Efficient Reinforcement Learning by Breaking the Replay Ratio Barrier

Bellemare Marc-Emmanuel