Prépublications

Articles de conférence et de revue

2024

  1. Exploring Quantization for Efficient Pre-Training of Transformer Language Models
    , , et
    Findings of the Association for Computational Linguistics (EMNLP), 2024.
    #NLP, #DL
    [arXiv]

  2. Lookbehind-SAM: k steps back, 1 step forward
    , , Aristide Baratin et
    International Conference on Machine Learning (ICML), 2024.
    #DL
    [arXiv], [code], [YouTube]

  3. Promoting Exploration in Memory-Augmented Adam using Critical Momenta
    , , Aristide Baratin, Reza Babanezhad Harikandeh, , Simon Lacoste-Julien, Razvan Pascanu et
    Transactions on Machine Learning Research (TMLR), 2024.
    #DL
    [arXiv]

  4. Mastering Memory Tasks with World Models
    , , et
    International Conference on Learning Representations (ICLR), 2024. [Oral presentation.]
    #RL, #DL
    [openreview]

  5. On the Costs and Benefits of Adopting Lifelong Learning for Software Analytics - Empirical Study on Brown Build and Risk Prediction
    Doriane Olewicki, Sarra Habchi, Mathieu Nayrolles, , et Bram Adams
    International Conference on Software Engineering (ICSE) - Software Engineering in Practice Track, 2024. [ICSE24 SEIP Distinguished Paper Award.]
    #DL
    [arXiv]

  6. Fast and Accurate Output Error Estimation for Memristor-Based Deep Neural Networks
    Jonathan Kern, Sébastien Henwood, , Elsa Dupraz, Abdeldjalil Aïssa-El-Bey, Yvon Savaria et François Leduc-Primeau
    IEEE Transactions on Signal Processing, 2024.
    #DL
    [paper]

2023

  1. Training DNNs Resilient to Adversarial and Random Bit-Flips by Learning Quantization Ranges
    , , Jean Pierre David et François Leduc-Primeau
    Transactions on Machine Learning Research (TMLR), 2023.
    #DL
    [openreview], [code]

  2. An Empirical Investigation of the Role of Pre-training in Lifelong Learning
    Sanket Vaibhav Mehta, , et Emma Strubell
    Journal of Machine Learning Research, 2023.
    #DL
    [arXiv]

  3. DEUP: Direct Epistemic Uncertainty Prediction
    Moksh Jain, Salem Lahlou, , Victor Butoi, Paul Bertin, , Maksym Korablyov et Yoshua Bengio
    Transactions on Machine Learning Research (TMLR), 2023.
    #DL
    [arXiv], [code]

  4. Label fusion and training methods for reliable representation of inter-rater uncertainty
    Andreanne Lemay, Charley Gros, et Julien Cohen-Adad
    The Journal of Machine Learning for Biomedical Imaging (MELBA), 2023.
    #DL
    [paper]

2022

  1. TAG: Task-based Accumulated Gradients for Lifelong Learning
    , Balaraman Ravindran et
    Conference on Lifelong Learning Agents (CoLLAs), 2022.
    [Workshop on Theory and Foundation of Continual Learning, ICML, 2021]
    #DL
    [arXiv], [code]

  2. Improving Meta-Learning Generalization with Activation-Based Early-Stopping
    , Christopher Pal, et
    Conference on Lifelong Learning Agents (CoLLAs), 2022.
    #DL
    [arXiv], [code], [YouTube]

  3. Biological Sequence Design with GFlowNets
    Moksh Jain, Emmanuel Bengio, Alex-Hernandez Garcia, , Bonaventure F. P. Dossou, Chanakya Ekbote, Jie Fu, Tianyu Zhang, Micheal Kilgour, Dinghuai Zhang, Lena Simine, Payel Das et Yoshua Bengio
    International Conference on Machine Learning (ICML), 2022.
    #DL
    [arXiv], [code]

  4. Memory Augmented Optimizers for Deep Learning
    , Prasanna Parthasarathi, Mido Assran et
    International Conference on Learning Representations (ICLR), 2022.
    #DL
    [openreview], [code]

  5. PatchUp: A Feature-Space Block-Level Regularization Technique for Convolutional Neural Networks
    , Mohammad Amini, , Vikas Verma et
    AAAI Conference on Artificial Intelligence (AAAI), 2022.
    #DL
    [arXiv], [code]

2021

  1. IIRC: Incremental Implicitly-Refined Classification
    , , Shagun Sodhani et
    Conference on Computer Vision and Pattern Recognition (CVPR), 2021.
    #DL
    [arXiv], [code], [website], [PyPI], [docs]