Prépublications

  • Did I Faithfully Say What I Thought? Bridging the Gap Between Neural Activity and Self-Explanations in Large Language Models
    , Jean-Noel Vittaut, Nicolas Chesneau, et Marie-Jeanne Lesot
    In ArXiv, 2025.
    #NLP
    [arXiv]

  • V-JEPA 2: Self-Supervised Video Models Enable Understanding, Prediction and Planning
    Mido Assran*, Adrien Bardes*, David Fan*, Quentin Garrido*, Russell Howes*, Mojtaba Komeili*, Matthew Muckley*, Ammar Rizvi*, Claire Roberts*, Koustuv Sinha*, , Sergio Arnaud*, Abha Gejji*, Ada Martin*, Francois Robert Hogan*, Daniel Dugas*, Piotr Bojanowski, Vasil Khalidov, Patrick Labatut, Francisco Massa, Marc Szafraniec, Kapil Krishnakumar, Yong Li, Xiaodong Ma, , Franziska Meier*, Yann LeCun*, Michael Rabbat* et Nicolas Ballas*
    Technical Report, 2025.
    #DL
    [website], [arXiv], [code], [huggingface], [blogpost]

  • Monitoring morphometric drift in lifelong learning segmentation of the spinal cord
    Enamundram Naga Karthik, Sandrine Bédard, Jan Valošek, Christoph S. Aigner, Elise Bannier, Josef Bednařík, Virginie Callot, Anna Combes, Armin Curt, Gergely David, Falk Eippert, Lynn Farner, Michael G Fehlings, Patrick Freund, Tobias Granberg, Cristina Granziera, RHSCIR Network Imaging Group, Ulrike Horn, Tomáš Horák, Suzanne Humphreys, Markus Hupp, Anne Kerbrat, Nawal Kinany, Shannon Kolind, Petr Kudlička, Anna Lebret, Lisa Eunyoung Lee, Caterina Mainero, Allan R. Martin, Megan McGrath, Govind Nair, Kristin P. O'Grady, Jiwon Oh, Russell Ouellette, Nikolai Pfender, Dario Pfyffer, Pierre-François Pradat, Alexandre Prat, Emanuele Pravatà, Daniel S. Reich, Ilaria Ricchi, Naama Rotem-Kohavi, Simon Schading-Sassenhausen, Maryam Seif, Andrew Smith, Seth A Smith, Grace Sweeney, Roger Tam, Anthony Traboulsee, Constantina Andrada Treaba, Charidimos Tsagkas, Zachary Vavasour, Dimitri Van De Ville, Kenneth Arnold Weber II, et Julien Cohen-Adad
    In ArXiv, 2025.
    #NLP
    [arXiv]

  • Torque-Aware Momentum
    , , Aristide Baratin, Reza Babanezhad Harikandeh, Gintare Karolina Dziugaite, Razvan Pascanu et
    In ArXiv, 2024.
    #DL
    [arXiv]

  • TRecViT: A Recurrent Video Transformer
    Viorica Pătrăucean, Xu Owen He, Joseph Heyward, Chuhan Zhang, Mehdi S. M. Sajjadi, George-Cristian Muraru, , Mahdi Karami, Ross Goroshin, Yutian Chen, Simon Osindero, João Carreira et Razvan Pascanu
    In ArXiv, 2024.
    #DL
    [arXiv], [code]

  • Too Big to Fool: Resisting Deception in Language Models
    , Mats Leon Richter, Juan Rodriguez, , et Maxime Gasse
    In ArXiv, 2024.
    #NLP
    [arXiv]

  • Unraveling the Complexity of Memory in RL Agents: An Approach for Classification and Evaluation
    Egor Cherepanov, Nikita Kachaev, , Alexey K. Kovalev et Aleksandr I. Panov
    In ArXiv, 2024.
    #RL
    [arXiv]

  • Interpretability Needs a New Paradigm
    , Himabindu Lakkaraju, Siva Reddy et
    In ArXiv, 2024.
    #NLP, #DL
    [arXiv]

  • Protein Language Models: Is Scaling Necessary?
    , Robert M. Vernon, Almer van der Sloot, Benjamin Schulz, et Christopher James Langmead
    In bioRxiv, 2024.
    #DL, #Other
    [bioRxiv], [code]

  • Interpretability in Action: Exploratory Analysis of VPT, a Minecraft Agent
    Karolis Jucys, George Adamopoulos, Mehrab Hamidi, Stephanie Milani, , , Sonia Joseph, Blake Richards, Irina Rish et Özgür Şimşek
    Workshop on Mechanistic Interpretability @ ICML, 2024.
    #Other
    [arXiv]

  • Segmentation of Multiple Sclerosis Lesions across Hospitals: Learn Continually or Train from Scratch?
    , Anne Kerbrat, Pierre Labauge, Tobias Granberg, Jason Talbott, Daniel S. Reich, Massimo Filippi, Rohit Bakshi, Virginie Callot, et Julien Cohen-Adad
    In ArXiv, 2022.
    [Medical Imaging meets NeurIPS, 2022]
    #DL, #Other
    [arXiv], [code]

  • Feature diversity in self-supervised learning
    et
    Conference on Lifelong Learning Agents (CoLLAs) Workshop Track, 2022.
    #DL
    [arXiv]

  • An Introduction to Lifelong Supervised Learning
    Shagun Sodhani, Mojtaba Faramazi, Sanket Vaibhav Mehta, , , et
    In ArXiv, 2022.
    #DL
    [arXiv]

  • Maximum Reward Formulation In Reinforcement Learning
    Sai Krishna Gottipati, Yashaswi Pathak, Rohan Nuttall, Raviteja Chunduru, Ahmed Touati, Sriram Ganapathi Subramanian, Matthew E Taylor et
    In arXiv, 2020.
    #RL
    [arXiv]