Publications

WASH: Train your Ensemble with Communication-Efficient Weight Shuffling, then Average
Louis Fournier
Adel Nabli
Masih Aminbeidokhti
Marco Pedersoli
Eugene Belilovsky
Edouard Oyallon
The performance of deep neural networks is enhanced by ensemble methods, which average the output of several models. However, this comes at … (voir plus)an increased cost at inference. Weight averaging methods aim at balancing the generalization of ensembling and the inference speed of a single model by averaging the parameters of an ensemble of models. Yet, naive averaging results in poor performance as models converge to different loss basins, and aligning the models to improve the performance of the average is challenging. Alternatively, inspired by distributed training, methods like DART and PAPA have been proposed to train several models in parallel such that they will end up in the same basin, resulting in good averaging accuracy. However, these methods either compromise ensembling accuracy or demand significant communication between models during training. In this paper, we introduce WASH, a novel distributed method for training model ensembles for weight averaging that achieves state-of-the-art image classification accuracy. WASH maintains models within the same basin by randomly shuffling a small percentage of weights during training, resulting in diverse models and lower communication costs compared to standard parameter averaging methods.
2024-05-27
ArXiv (prépublication)
doi.org
arxiv.org
AdaFisher: Adaptive Second Order Optimization via Fisher Information
Damien Martins Gomes
Yanlei Zhang
Eugene Belilovsky
Guy Wolf
Mahdi S. Hosseini
First-order optimization methods are currently the mainstream in training deep neural networks (DNNs). Optimizers like Adam incorporate limi… (voir plus)ted curvature information by employing the diagonal matrix preconditioning of the stochastic gradient during the training. Despite their widespread, second-order optimization algorithms exhibit superior convergence properties compared to their first-order counterparts e.g. Adam and SGD. However, their practicality in training DNNs are still limited due to increased per-iteration computations and suboptimal accuracy compared to the first order methods. We present AdaFisher--an adaptive second-order optimizer that leverages a block-diagonal approximation to the Fisher information matrix for adaptive gradient preconditioning. AdaFisher aims to bridge the gap between enhanced convergence capabilities and computational efficiency in second-order optimization framework for training DNNs. Despite the slow pace of second-order optimizers, we showcase that AdaFisher can be reliably adopted for image classification, language modelling and stand out for its stability and robustness in hyperparameter tuning. We demonstrate that AdaFisher outperforms the SOTA optimizers in terms of both accuracy and convergence speed. Code available from \href{https://github.com/AtlasAnalyticsLab/AdaFisher}{https://github.com/AtlasAnalyticsLab/AdaFisher}
2024-05-26
ArXiv (prépublication)
doi.org
arxiv.org
Reproducibility Study on Adversarial Attacks Against Robust Transformer Trackers
Fatemeh Nourilenjan Nokabadi
Jean-Francois Lalonde
Christian Gagné
2024-05-26
TMLR (accepté)
doi.org
openreview.net
Stimulus information guides the emergence of behavior-related signals in primary somatosensory cortex during learning.
Mariangela Panniello
Colleen J Gillon
Roberto Maffulli
Marco Celotto
Stefano Panzeri
Blake Richards
Michael M Kohl
2024-05-25
Cell Reports (publié)
doi.org
Towards a Generic Representation of Combinatorial Problems for Learning-Based Approaches
Léo Boisvert
Hélène Verhaeghe
Quentin Cappart
2024-05-25
Integration of Constraint Programming, Artificial Intelligence, and Operations Research (publié)
doi.org
arxiv.org
Efficient Adversarial Training in LLMs with Continuous Attacks
Sophie Xhonneux
Alessandro Sordoni
Stephan Günnemann
Gauthier Gidel
Leo Schwinn
Large language models (LLMs) are vulnerable to adversarial attacks that can bypass their safety guardrails. In many domains, adversarial tra… (voir plus)ining has proven to be one of the most promising methods to reliably improve robustness against such attacks. Yet, in the context of LLMs, current methods for adversarial training are hindered by the high computational costs required to perform discrete adversarial attacks at each training iteration. We address this problem by instead calculating adversarial attacks in the continuous embedding space of the LLM, which is orders of magnitudes more efficient. We propose a fast adversarial training algorithm (C-AdvUL) composed of two losses: the first makes the model robust on continuous embedding attacks computed on an adversarial behaviour dataset; the second ensures the usefulness of the final model by fine-tuning on utility data. Moreover, we introduce C-AdvIPO, an adversarial variant of IPO that does not require utility data for adversarially robust alignment. Our empirical evaluation on four models from different families (Gemma, Phi3, Mistral, Zephyr) and at different scales (2B, 3.8B, 7B) shows that both algorithms substantially enhance LLM robustness against discrete attacks (GCG, AutoDAN, PAIR), while maintaining utility. Our results demonstrate that robustness to continuous perturbations can extrapolate to discrete threat models. Thereby, we present a path toward scalable adversarial training algorithms for robustly aligning LLMs.
2024-05-24
ArXiv (prépublication)
doi.org
arxiv.org
Neural computations in prosopagnosia
Simon Faghel-Soubeyrand
Anne-Raphaelle Richoz
Delphine Waeber
Jessica Woodhams
Roberto Caldara
Frédéric Gosselin
Ian Charest
We aimed to identify neural computations underlying the loss of face identification ability by modelling the brain activity of brain-lesione… (voir plus)d patient PS, a well-documented case of acquired pure prosopagnosia. We collected a large dataset of high-density electrophysiological (EEG) recordings from PS and neurotypicals while they completed a one-back task on a stream of face, object, animal and scene images. We found reduced neural decoding of face identity around the N170 window in PS, and conjointly revealed normal non-face identification in this patient. We used Representational Similarity Analysis (RSA) to correlate human EEG representations with those of deep neural network (DNN) models of vision and caption-level semantics, offering a window into the neural computations at play in patient PS’s deficits. Brain representational dissimilarity matrices (RDMs) were computed for each participant at 4 ms steps using cross-validated classifiers. PS’s brain RDMs showed significant reliability across sessions, indicating meaningful measurements of brain representations with RSA even in the presence of significant lesions. Crucially, computational analyses were able to reveal PS’s representational deficits in high-level visual and semantic brain computations. Such multi-modal data-driven characterisations of prosopagnosia highlight the complex nature of processes contributing to face recognition in the human brain. Highlights We assess the neural computations in the prosopagnosic patient PS using EEG, RSA, and deep neural networks Neural dynamics of brain-lesioned PS are reliably captured using RSA Neural decoding shows normal evidence for non-face individuation in PS Neural decoding shows abnormal neural evidence for face individuation in PS PS shows impaired high-level visual and semantic neural computations
2024-05-24
Cerebral Cortex (New York, NY) (publié)
doi.org
Predicting the Impact of Model Expansion through the Minima Manifold: A Loss Landscape Perspective
Pranshu Malviya
Jerry Huang
Quentin Fournier
Sarath Chandar
The optimal model for a given task is often challenging to determine, requiring training multiple models from scratch which becomes prohibit… (voir plus)ive as dataset and model sizes grow. A more efficient alternative is to reuse smaller pre-trained models by expanding them, however, this is not widely adopted as how this impacts training dynamics remains poorly understood. While prior works have introduced statistics to measure these effects, they remain flawed. To rectify this, we offer a new approach for understanding and quantifying the impact of expansion through the lens of the loss landscape, which has been shown to contain a manifold of linearly connected minima. Building on this new perspective, we propose a metric to study the impact of expansion by estimating the size of the manifold. Experimental results show a clear relationship between gains in performance and manifold size, enabling the comparison of candidate models and presenting a first step towards expanding models more reliably based on geometric properties of the loss landscape.
2024-05-24
ArXiv (prépublication)
doi.org
arxiv.org
Assortment Optimization with Visibility Constraints
Théo Barré
Omar El Housni
Andrea Lodi
2024-05-22
Integer Programming and Combinatorial Optimization (publié)
doi.org
arxiv.org
Attention as an RNN
Leo Feng
Frederick Tung
Hossein Hajimirsadeghi
Mohamed Osama Ahmed
Yoshua Bengio
Greg Mori
2024-05-22
ArXiv (prépublication)
doi.org
arxiv.org
Mining Action Rules for Defect Reduction Planning
Khouloud Oueslati
gabriel laberge
Maxime Lamothe
Foutse Khomh
Defect reduction planning plays a vital role in enhancing software quality and minimizing software maintenance costs. By training a black bo… (voir plus)x machine learning model and"explaining"its predictions, explainable AI for software engineering aims to identify the code characteristics that impact maintenance risks. However, post-hoc explanations do not always faithfully reflect what the original model computes. In this paper, we introduce CounterACT, a Counterfactual ACTion rule mining approach that can generate defect reduction plans without black-box models. By leveraging action rules, CounterACT provides a course of action that can be considered as a counterfactual explanation for the class (e.g., buggy or not buggy) assigned to a piece of code. We compare the effectiveness of CounterACT with the original action rule mining algorithm and six established defect reduction approaches on 9 software projects. Our evaluation is based on (a) overlap scores between proposed code changes and actual developer modifications; (b) improvement scores in future releases; and (c) the precision, recall, and F1-score of the plans. Our results show that, compared to competing approaches, CounterACT's explainable plans achieve higher overlap scores at the release level (median 95%) and commit level (median 85.97%), and they offer better trade-off between precision and recall (median F1-score 88.12%). Finally, we venture beyond planning and explore leveraging Large Language models (LLM) for generating code edits from our generated plans. Our results show that suggested LLM code edits supported by our plans are actionable and are more likely to pass relevant test cases than vanilla LLM code recommendations.
2024-05-22
ArXiv (prépublication)
doi.org
arxiv.org
Static graph approximations of dynamic contact networks for epidemic forecasting
Razieh Shirzadkhani
Shenyang Huang
Abby Leung
Reihaneh Rabbany
2024-05-22
Scientific Reports (publié)
doi.org