Publications

Advancing Cultural Inclusivity: Optimizing Embedding Spaces for Balanced Music Recommendations

Armin Moradi

Nicola Neophytou

Golnoosh Farnadi

2024-05-27

ArXiv (prépublication)

doi.org

arxiv.org

Estimating Expectations without Sampling: Neural Stein Estimation

Mohsin Hasan

Dinghuai Zhang

Cheikh Ahmed

Awa Khouna

Yoshua Bengio

We propose a method for estimating the expected value of a given function …

2024-05-27

approximateinference.org/AABI/2024/Symposium (accepté)

openreview.net

Implicitly Bayesian Prediction Rules in Deep Learning

Bruno Mlodozeniec

Richard E. Turner

David Scott Krueger

The Bayesian approach leads to coherent updates of predictions under new data, which makes adhering to Bayesian principles appealing in deci… (voir plus)sion-making contexts. Traditionally, integrating Bayesian principles into models like deep neural networks involves setting priors on parameters and approximating posteriors. This is done despite the fact that, typically, priors on parameters reflect any prior beliefs only insofar as they dictate function space behaviour. In this paper, we rethink this approach and consider what properties characterise a prediction rule as being Bayesian. Algorithms meeting such criteria can be deemed implicitly Bayesian — they make the same predictions as some Bayesian model, without explicitly manifesting priors and posteriors. We argue this might be a more fruitful approach towards integrating Bayesian principles into deep learning. In this paper, we propose how to measure how close a general prediction rule is to being implicitly Bayesian, and empirically evaluate multiple prediction strategies using our approach. We also show theoretically that agents relying on non-implicitly Bayesian prediction rules can be easily exploited in adversarial betting settings.

2024-05-27

approximateinference.org/AABI/2024/Symposium_Archival_Track (accepté)

proceedings.mlr.press

openreview.net

An Introduction to Vision-Language Modeling

Florian Bordes

Richard Yuanzhe Pang

Anurag Ajay

Alexander C. Li

Adrien Bardes

Suzanne Petryk

Oscar Mañas

Zhiqiu Lin

Anas Mahmoud

Bargav Jayaraman

Mark Ibrahim

Melissa Hall

Yunyang Xiong

Jonathan Lebensold

Candace Ross

Srihari Jayakumar

Chuan Guo

Diane Bouchacourt

Haider Al-Tahan

Karthik Padthe … (voir 21 de plus)

Vasu Sharma

Huijuan Xu 0001

Xiaoqing Ellen Tan

Megan Richards

Samuel Lavoie

Pietro Astolfi

Reyhane Askari Hemmat

Jun Chen

Kushal Tirumala

Rim Assouel

Mazda Moayeri

Arjang Talattof

Kamalika Chaudhuri

Zechun Liu

Xilun Chen

Quentin Garrido

Karen Ullrich

Aishwarya Agrawal

Kate Saenko

Asli Celikyilmaz

Vikas Chandra

2024-05-27

ArXiv (prépublication)

doi.org

arxiv.org

Listenable Maps for Zero-Shot Audio Classifiers

Francesco Paissan

Luca Della Libera

Mirco Ravanelli

Cem (Yusuf) Subakan

Interpreting the decisions of deep learning models, including audio classifiers, is crucial for ensuring the transparency and trustworthines… (voir plus)s of this technology. In this paper, we introduce LMAC-ZS (Listenable Maps for Audio Classifiers in the Zero-Shot context), which, to the best of our knowledge, is the first decoder-based post-hoc interpretation method for explaining the decisions of zero-shot audio classifiers. The proposed method utilizes a novel loss function that maximizes the faithfulness to the original similarity between a given text-and-audio pair. We provide an extensive evaluation using the Contrastive Language-Audio Pretraining (CLAP) model to showcase that our interpreter remains faithful to the decisions in a zero-shot classification context. Moreover, we qualitatively show that our method produces meaningful explanations that correlate well with different text prompts.

2024-05-27

ArXiv (prépublication)

doi.org

arxiv.org

Neuro-GPT: Towards A Foundation Model for EEG

Wenhui Cui

Woojae Jeong

Philipp Thölke

Takfarinas Medani

Karim Jerbi

Anand A. Joshi

Richard M. Leahy

To handle the scarcity and heterogeneity of electroencephalography (EEG) data for Brain-Computer Interface (BCI) tasks, and to harness the p… (voir plus)ower of large publicly available data sets, we propose Neuro-GPT, a foundation model consisting of an EEG encoder and a GPT model. The foundation model is pre-trained on a large-scale data set using a self-supervised task that learns how to reconstruct masked EEG segments. We then fine-tune the model on a Motor Imagery Classification task to validate its performance in a low-data regime (9 subjects). Our experiments demonstrate that applying a foundation model can significantly improve classification performance compared to a model trained from scratch, which provides evidence for the generalizability of the foundation model and its ability to address challenges of data scarcity and heterogeneity in EEG. The code is publicly available at github.com/wenhui0206/NeuroGPT.

2024-05-27

2024 IEEE International Symposium on Biomedical Imaging (ISBI) (publié)

doi.org

arxiv.org

Partial Models for Building Adaptive Model-Based Reinforcement Learning Agents

Safa Alver

Ali Rahimi-Kalahroudi

Doina Precup

In neuroscience, one of the key behavioral tests for determining whether a subject of study exhibits model-based behavior is to study its ad… (voir plus)aptiveness to local changes in the environment. In reinforcement learning, however, recent studies have shown that modern model-based agents display poor adaptivity to such changes. The main reason for this is that modern agents are typically designed to improve sample efficiency in single task settings and thus do not take into account the challenges that can arise in other settings. In local adaptation settings, one particularly important challenge is in quickly building and maintaining a sufficiently accurate model after a local change. This is challenging for deep model-based agents as their models and replay buffers are monolithic structures lacking distribution shift handling capabilities. In this study, we show that the conceptually simple idea of partial models can allow deep model-based agents to overcome this challenge and thus allow for building locally adaptive model-based agents. By modeling the different parts of the state space through different models, the agent can not only maintain a model that is accurate across the state space, but it can also quickly adapt it in the presence of a local change in the environment. We demonstrate this by showing that the use of partial models in agents such as deep Dyna-Q, PlaNet and Dreamer can allow for them to effectively adapt to the local changes in their environments.

2024-05-27

ArXiv (prépublication)

doi.org

arxiv.org

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