Aaron Courville

alexandre.ganito@mila.quebec

adrien.taiga@mila.quebec

Alexandre Diz Ganito

Maîtrise recherche - Université de Montréal

Amr Khalifa

Doctorat - Université de Montréal

amr-khalifa.alshaykh@mila.quebec

andrei.nicolicioiu@mila.quebec

Andrei Nicolicioiu

Doctorat - Université de Montréal

ching-lam.choi@mila.quebec

Ankit Vani

Doctorat - Université de Montréal

Baccalauréat - Université de Montréal

Doctorat - Université de Montréal

Superviseur⋅e principal⋅e :

Doctorat - Université de Montréal

Co-superviseur⋅e :

Yoshua Bengio

dinghuai.zhang@mila.quebec

Esra'a Saleh

Doctorat - Université de Montréal

Superviseur⋅e principal⋅e :

Glen Berseth

esraa.saleh@mila.quebec

evgenii.nikishin@mila.quebec

Evgenii Nikishin

Doctorat - Université de Montréal

Superviseur⋅e principal⋅e :

Pierre-Luc Bacon

Faruk Ahmed

Doctorat - Université de Montréal

faruk.ahmed@mila.quebec

Johan Samir Obando Ceron

Doctorat - Université de Montréal

Co-superviseur⋅e :

johan.ceron@mila.quebec

Juan Duque

Doctorat - Université de Montréal

juan.duque@mila.quebec

manoosh.samiei@mila.quebec

Manoosh Samiei

Doctorat - Université de Montréal

Doctorat - Université de Montréal

Co-superviseur⋅e :

mohammed.muqeeth@mila.quebec

schwarzm@mila.quebec

Hattie Zhou

Doctorat - Université de Montréal

Superviseur⋅e principal⋅e :

Hugo Larochelle

mengfei.zhou@mila.quebec

Michael Noukhovitch

Doctorat - Université de Montréal

Collaborateur·rice de recherche

morgane.moss@mila.quebec

Muqeeth Mohammed

Collaborateur·rice de recherche - Université de Montréal

Stagiaire de recherche - Ghent University

pietro.mazzaglia@mila.quebec

Stagiaire de recherche - Université de Montréal

razvan.ciuca@mila.quebec

Rishabh Agarwal

Doctorat - Université de Montréal

Co-superviseur⋅e :

samuel.lavoie@mila.quebec

Samuel Lavoie Marchildon

Doctorat - Université de Montréal

Sarvjeet Ghotra

Maîtrise recherche - Université de Montréal

Superviseur⋅e principal⋅e :

Aishwarya Agrawal

sarvjeet-singh.ghotra@mila.quebec

xiaofeng.zhang@mila.quebec

Arian Hosseini

Doctorat - Université de Montréal

Shawn Tan

Doctorat - Université de Montréal

Doctorat - Université de Montréal

Doctorat - Université de Montréal

Yusong Wu

Doctorat - Université de Montréal

Superviseur⋅e principal⋅e :

Anna (Cheng-Zhi) Huang

wu.yusong@mila.quebec

Zhixuan Lin

Doctorat - Université de Montréal

zhixuan.lin@mila.quebec

Publications

SPARO: Selective Attention for Robust and Compositional Transformer Encodings for Vision

Ankit Vani

Bac Nguyen

Samuel Lavoie

Ranjay Krishna

Selective attention helps us focus on task-relevant aspects in the constant flood of our sensory input. This constraint in our perception al… (voir plus)lows us to robustly generalize under distractions and to new compositions of perceivable concepts. Transformers employ a similar notion of attention in their architecture, but representation learning models with transformer backbones like CLIP and DINO often fail to demonstrate robustness and compositionality. We highlight a missing architectural prior: unlike human perception, transformer encodings do not separately attend over individual concepts. In response, we propose SPARO, a read-out mechanism that partitions encodings into separately-attended slots, each produced by a single attention head. Using SPARO with CLIP imparts an inductive bias that the vision and text modalities are different views of a shared compositional world with the same corresponding concepts. Using SPARO, we demonstrate improvements on downstream recognition, robustness, retrieval, and compositionality benchmarks with CLIP (up to +14% for ImageNet, +4% for SugarCrepe), and on nearest neighbors and linear probe for ImageNet with DINO (+3% each). We also showcase a powerful ability to intervene and select individual SPARO concepts to further improve downstream task performance (up from +4% to +9% for SugarCrepe) and use this ability to study the robustness of SPARO's representation structure. Finally, we provide insights through ablation experiments and visualization of learned concepts.

2024-04-24

ArXiv (prépublication)

Scattered Mixture-of-Experts Implementation

Shawn Tan

Yikang Shen

Rameswar Panda

We present ScatterMoE, an implementation of Sparse Mixture-of-Experts (SMoE) on GPUs. ScatterMoE builds upon existing implementations, and o… (voir plus)vercoming some of the limitations to improve inference and training speed, and memory footprint. This implementation achieves this by avoiding padding and making excessive copies of the input. We introduce ParallelLinear, the main component we use to build our implementation and the various kernels used to speed up the operation. We benchmark our implementation against Megablocks, and show that it enables a higher throughput and lower memory footprint. We also show how ParallelLinear enables extension of the Mixture-of-Experts concept by demonstrating with an implementation of Mixture of Attention.

2024-03-13

ArXiv (prépublication)

In deep reinforcement learning, a pruned network is a good network

Johan Samir Obando Ceron

Recent work has shown that deep reinforcement learning agents have difficulty in effectively using their network parameters. We leverage pri… (voir plus)or insights into the advantages of sparse training techniques and demonstrate that gradual magnitude pruning enables agents to maximize parameter effectiveness. This results in networks that yield dramatic performance improvements over traditional networks and exhibit a type of"scaling law", using only a small fraction of the full network parameters.

2024-02-19

ArXiv (prépublication)

Distributional GFlowNets with Quantile Flows

Dinghuai Zhang

Ling Pan

Ricky T. Q. Chen

Yoshua Bengio

Generative Flow Networks (GFlowNets) are a new family of probabilistic samplers where an agent learns a stochastic policy for generating com… (voir plus)plex combinatorial structure through a series of decision-making steps. Despite being inspired from reinforcement learning, the current GFlowNet framework is relatively limited in its applicability and cannot handle stochasticity in the reward function. In this work, we adopt a distributional paradigm for GFlowNets, turning each flow function into a distribution, thus providing more informative learning signals during training. By parameterizing each edge flow through their quantile functions, our proposed \textit{quantile matching} GFlowNet learning algorithm is able to learn a risk-sensitive policy, an essential component for handling scenarios with risk uncertainty. Moreover, we find that the distributional approach can achieve substantial improvement on existing benchmarks compared to prior methods due to our enhanced training algorithm, even in settings with deterministic rewards.

2024-02-16

TMLR (accepté)

V-STaR: Training Verifiers for Self-Taught Reasoners

Arian Hosseini

Xingdi Yuan

Nikolay Malkin

Alessandro Sordoni

Rishabh Agarwal

Common self-improvement approaches for large language models (LLMs), such as STaR (Zelikman et al., 2022), iteratively fine-tune LLMs on sel… (voir plus)f-generated solutions to improve their problem-solving ability. However, these approaches discard the large amounts of incorrect solutions generated during this process, potentially neglecting valuable information in such solutions. To address this shortcoming, we propose V-STaR that utilizes both the correct and incorrect solutions generated during the self-improvement process to train a verifier using DPO that judges correctness of model-generated solutions. This verifier is used at inference time to select one solution among many candidate solutions. Running V-STaR for multiple iterations results in progressively better reasoners and verifiers, delivering a 4% to 17% test accuracy improvement over existing self-improvement and verification approaches on common code generation and math reasoning benchmarks with LLaMA2 models.

2024-02-09

ArXiv (prépublication)

Diffusion Generative Flow Samplers: Improving learning signals through partial trajectory optimization

Dinghuai Zhang

Ricky T. Q. Chen

Cheng-Hao Liu

Yoshua Bengio

2024-01-16

ICLR.cc/2024/Conference (poster)

LOQA: Learning with Opponent Q-Learning Awareness

Milad Aghajohari

Juan Agustin Duque

Tim Cooijmans

In various real-world scenarios, interactions among agents often resemble the dynamics of general-sum games, where each agent strives to opt… (voir plus)imize its own utility. Despite the ubiquitous relevance of such settings, decentralized machine learning algorithms have struggled to find equilibria that maximize individual utility while preserving social welfare. In this paper we introduce Learning with Opponent Q-Learning Awareness (LOQA) , a novel reinforcement learning algorithm tailored to optimizing an agent's individual utility while fostering cooperation among adversaries in partially competitive environments. LOQA assumes that each agent samples actions proportionally to their action-value function Q. Experimental results demonstrate the effectiveness of LOQA at achieving state-of-the-art performance in benchmark scenarios such as the Iterated Prisoner's Dilemma and the Coin Game. LOQA achieves these outcomes with a significantly reduced computational footprint compared to previous works, making it a promising approach for practical multi-agent applications.

2024-01-16

ICLR.cc/2024/Conference (poster)

The Curse of Diversity in Ensemble-Based Exploration

Zhixuan Lin

Pierluca D'Oro

Evgenii Nikishin

We uncover a surprising phenomenon in deep reinforcement learning: training a diverse ensemble of data-sharing agents -- a well-established … (voir plus)exploration strategy -- can significantly impair the performance of the individual ensemble members when compared to standard single-agent training. Through careful analysis, we attribute the degradation in performance to the low proportion of self-generated data in the shared training data for each ensemble member, as well as the inefficiency of the individual ensemble members to learn from such highly off-policy data. We thus name this phenomenon *the curse of diversity*. We find that several intuitive solutions -- such as a larger replay buffer or a smaller ensemble size -- either fail to consistently mitigate the performance loss or undermine the advantages of ensembling. Finally, we demonstrate the potential of representation learning to counteract the curse of diversity with a novel method named Cross-Ensemble Representation Learning (CERL) in both discrete and continuous control domains. Our work offers valuable insights into an unexpected pitfall in ensemble-based exploration and raises important caveats for future applications of similar approaches.

2024-01-16

ICLR.cc/2024/Conference (poster)

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

Max Schwarzer

Jesse Farebrother

Joshua Greaves

Ekin Dogus Cubuk

Rishabh Agarwal

Sergei V. Kalinin

Igor Mordatch

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.

2023-11-21

ArXiv (prépublication)

Using Representation Expressiveness and Learnability to Evaluate Self-Supervised Learning Methods

Yuchen Lu

Zhen Liu

Aristide Baratin

Romain Laroche

Alessandro Sordoni

2023-11-14

TMLR (accepté)

Learning Silicon Dopant Transitions in Graphene using Scanning Transmission Electron Microscopy

Max Schwarzer

Jesse Farebrother

Joshua Greaves

Kevin Roccapriore

Ekin Dogus Cubuk

Rishabh Agarwal

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… (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)

Learning Silicon Dopant Transitions in Graphene using Scanning Transmission Electron Microscopy

Max Schwarzer

Jesse Farebrother

Joshua Greaves

Kevin Roccapriore

Ekin Dogus Cubuk

Rishabh Agarwal

Sergei Kalinin

Igor Mordatch

2023-10-27

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