Aaron Courville

PhD - Université de Montréal

Faruk Ahmed

PhD - Université de Montréal

Adrien Ali Taiga

PhD - Université de Montréal

Co-supervisor :

Anirudh Buvanesh

PhD - Université de Montréal

Principal supervisor :

Laurent Charlin

anirudb1102@gmail.com

Ching Lam Choi

Undergraduate - Université de Montréal

Master's Research - Université de Montréal

Timotheus Cooijmans

PhD - Université de Montréal

Master's Research - Université de Montréal

Juan Duque

PhD - Université de Montréal

Sarvjeet Ghotra

PhD - Université de Montréal

Arian Hosseini

PhD - Université de Montréal

Amr Khalifa

PhD - Université de Montréal

andrei.nicolicioiu@gmail.com

Samuel Lavoie

PhD - Université de Montréal

Zhixuan Lin

PhD - Université de Montréal

Research Intern - Ghent University

PhD - Université de Montréal

PhD - Université de Montréal

Co-supervisor :

Rishabh Agarwal

Andrei Nicolicioiu

PhD - Université de Montréal

Evgenii Nikishin

PhD - Université de Montréal

Principal supervisor :

PhD - Université de Montréal

Co-supervisor :

Johan Samir Obando Ceron

PhD - Université de Montréal

Co-supervisor :

Master's Research - Université de Montréal

pichedereck@gmail.com

Esra'a Saleh

PhD - Université de Montréal

Principal supervisor :

PhD - Université de Montréal

PhD - Université de Montréal

Co-supervisor :

Nithya Shikarpur

Master's Research - Université de Montréal

Principal supervisor :

Anna (Cheng-Zhi) Huang

Shawn Tan

PhD - Université de Montréal

PhD - Université de Montréal

Principal supervisor :

(Rex) Devon Hjelm

Ankit Vani

PhD - Université de Montréal

Yusong Wu

PhD - Université de Montréal

Principal supervisor :

Anna (Cheng-Zhi) Huang

Dinghuai Zhang

PhD - Université de Montréal

Co-supervisor :

Yoshua Bengio

Xiaofeng Zhang

PhD - Université de Montréal

Hattie Zhou

PhD - Université de Montréal

Principal supervisor :

Hugo Larochelle

Publications

Not All LLM Reasoners Are Created Equal

Arian Hosseini

Alessandro Sordoni

Daniel Toyama

Rishabh Agarwal

2024-10-09

NeurIPS.cc/2024/Workshop/Sys2-Reasoning (poster)

Don't flatten, tokenize! Unlocking the key to SoftMoE's efficacy in deep RL

Ghada Sokar

Johan Samir Obando Ceron

Hugo Larochelle

The use of deep neural networks in reinforcement learning (RL) often suffers from performance degradation as model size increases. While sof… (see more)t mixtures of experts (SoftMoEs) have recently shown promise in mitigating this issue for online RL, the reasons behind their effectiveness remain largely unknown. In this work we provide an in-depth analysis identifying the key factors driving this performance gain. We discover the surprising result that tokenizing the encoder output, rather than the use of multiple experts, is what is behind the efficacy of SoftMoEs. Indeed, we demonstrate that even with an appropriately scaled single expert, we are able to maintain the performance gains, largely thanks to tokenization.

2024-10-02

ArXiv (preprint)

VinePPO: Unlocking RL Potential For LLM Reasoning Through Refined Credit Assignment

Amirhossein Kazemnejad

Milad Aghajohari

Eva Portelance

2024-10-02

ArXiv (preprint)

Scattered Mixture-of-Experts Implementation

Shawn Tan

Yikang Shen

Rameswar Panda

ScatterMoE is an implementation of Sparse Mixture-of-Experts (SMoE) on GPUs. ScatterMoE builds upon techniques in existing implementations, … (see more)and overcoming some of the current limitations to improve batched inference, training speed, and memory footprint. This implementation achieves this by avoiding padding and making excessive copies of the input. We also fuse expert linear transforms and reordering operations with ParallelLinear, a module that can be used to extend the concept of SMoEs. 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.

2024-07-10

colmweb.org/COLM/2024/Conference (accepted)

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… (see more)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.

2024-07-10

colmweb.org/COLM/2024/Conference (accepted)

Adaptive Accompaniment with ReaLchords

Yusong Wu

Tim Cooijmans

Kyle Kastner

Adam Roberts

Ian Simon

Alexander Scarlatos

Chris Donahue

Cassie Tarakajian

Shayegan Omidshafiei

Natasha Jaques

Anna (Cheng-Zhi) Huang

Jamming requires coordination, anticipation, and collaborative creativity between musicians. Current generative models of music produce expr… (see more)essive output but are not able to generate in an online manner, meaning simultaneously with other musicians (human or otherwise). We propose ReaLchords, an online generative model for improvising chord accompaniment to user melody. We start with an online model pretrained by maximum likelihood, and use reinforcement learning to finetune the model for online use. The finetuning objective leverages both a novel reward model that provides feedback on both harmonic and temporal coherency between melody and chord, and a divergence term that implements a novel type of distillation from a teacher model that can see the future melody. Through quantitative experiments and listening tests, we demonstrate that the resulting model adapts well to unfamiliar input and produce fitting accompaniment. ReaLchords opens the door to live jamming, as well as simultaneous co-creation in other modalities.

2024-07-08

Proceedings of the 41st International Conference on Machine Learning (published)

proceedings.mlr.press

Modeling Caption Diversity in Contrastive Vision-Language Pretraining

Samuel Lavoie

Polina Kirichenko

Mark Ibrahim

Mahmoud Assran

Andrew Gordon Wilson

Nicolas Ballas

There are a thousand ways to caption an image. Contrastive Language Pretraining (CLIP) on the other hand, works by mapping an image and its … (see more)caption to a single vector -- limiting how well CLIP-like models can represent the diverse ways to describe an image. In this work, we introduce Llip, Latent Language Image Pretraining, which models the diversity of captions that could match an image. Llip's vision encoder outputs a set of visual features that are mixed into a final representation by conditioning on information derived from the text. We show that Llip outperforms non-contextualized baselines like CLIP and SigLIP on a variety of tasks even with large-scale encoders. Llip improves zero-shot classification by an average of 2.9\% zero-shot classification benchmarks with a ViT-G/14 encoder. Specifically, Llip attains a zero-shot top-1 accuracy of 83.5\% on ImageNet outperforming a similarly sized CLIP by 1.4\%. We also demonstrate improvement on zero-shot retrieval on MS-COCO by 6.0\%. We provide a comprehensive analysis of the components introduced by the method and demonstrate that Llip leads to richer visual representations.

2024-07-08

Proceedings of the 41st International Conference on Machine Learning (published)

In value-based 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… (see more)or insights into the advantages of sparse training techniques and demonstrate that gradual magnitude pruning enables {value-based} agents to maximize parameter effectiveness. This results in networks that yield dramatic performance improvements over traditional networks, using only a small fraction of the full network parameters. Our code is publicly available, see Appendix A for details.

2024-07-08

Proceedings of the 41st International Conference on Machine Learning (published)

proceedings.mlr.press

SAFT: Towards Out-of-Distribution Generalization in Fine-Tuning

Bac Nguyen

Stefan Uhlich

Fabien Cardinaux

Lukas Mauch

Marzieh Edraki

Handling distribution shifts from training data, known as out-of-distribution (OOD) generalization, poses a significant challenge in the fie… (see more)ld of machine learning. While a pre-trained vision-language model like CLIP has demonstrated remarkable zero-shot performance, further adaptation of the model to downstream tasks leads to undesirable degradation for OOD data. In this work, we introduce Sparse Adaptation for Fine-Tuning (SAFT), a method that prevents fine-tuning from forgetting the general knowledge in the pre-trained model. SAFT only updates a small subset of important parameters whose gradient magnitude is large, while keeping the other parameters frozen. SAFT is straightforward to implement and conceptually simple. Extensive experiments show that with only 0.1% of the model parameters, SAFT can significantly improve the performance of CLIP. It consistently outperforms baseline methods across several benchmarks. On the few-shot learning benchmark of ImageNet and its variants, SAFT gives a gain of 5.15% on average over the conventional fine-tuning method in OOD settings.

2024-07-03

ArXiv (preprint)

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

Joshua Robinson

Sergei V Kalinin

2024-07-01

Microscopy and Microanalysis (published)

On the consistency of hyper-parameter selection in value-based deep reinforcement learning

Johan Samir Obando Ceron

J. G. Ara'ujo

Deep reinforcement learning (deep RL) has achieved tremendous success on various domains through a combination of algorithmic design and car… (see more)eful selection of hyper-parameters. Algorithmic improvements are often the result of iterative enhancements built upon prior approaches, while hyper-parameter choices are typically inherited from previous methods or fine-tuned specifically for the proposed technique. Despite their crucial impact on performance, hyper-parameter choices are frequently overshadowed by algorithmic advancements. This paper conducts an extensive empirical study focusing on the reliability of hyper-parameter selection for value-based deep reinforcement learning agents, including the introduction of a new score to quantify the consistency and reliability of various hyper-parameters. Our findings not only help establish which hyper-parameters are most critical to tune, but also help clarify which tunings remain consistent across different training regimes.

2024-06-25

ArXiv (preprint)

Advantage Alignment Algorithms

Juan Agustin Duque

Milad Aghajohari

Tim Cooijmans

Tianyu Zhang

2024-06-19

ICML.cc/2024/Workshop/ARLET (poster)