Matthieu Wyart

49 papers A* 12Journal 37

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Deep networks learn to parse uniform-depth context-free languages from local statistics. CoRR Jack T. Parley, Francesco Cagnetta, Matthieu Wyart
2026	J	`jnl`	Deriving Neural Scaling Laws from the statistics of natural language. CoRR Francesco Cagnetta, Allan Raventós, Surya Ganguli, Matthieu Wyart
2026	J	`jnl`	Symmetry in language statistics shapes the geometry of model representations. CoRR Dhruva Karkada, Daniel J. Korchinski, Andres Nava, Matthieu Wyart, Yasaman Bahri
2025	J	`jnl`	Bigger Isn't Always Memorizing: Early Stopping Overparameterized Diffusion Models. CoRR Alessandro Favero, Antonio Sclocchi, Matthieu Wyart
2025	A*	`conf`	How Compositional Generalization and Creativity Improve as Diffusion Models are Trained. ICML Alessandro Favero, Antonio Sclocchi, Francesco Cagnetta, Pascal Frossard, Matthieu Wyart
2025	J	`jnl`	How compositional generalization and creativity improve as diffusion models are trained. CoRR Alessandro Favero, Antonio Sclocchi, Francesco Cagnetta, Pascal Frossard, Matthieu Wyart
2025	A*	`conf`	Learning curves theory for hierarchically compositional data with power-law distributed features. ICML Francesco Cagnetta, Hyunmo Kang, Matthieu Wyart
2025	J	`jnl`	Learning curves theory for hierarchically compositional data with power-law distributed features. CoRR Francesco Cagnetta, Hyunmo Kang, Matthieu Wyart
2025	J	`jnl`	On the Emergence of Linear Analogies in Word Embeddings. CoRR Daniel J. Korchinski, Dhruva Karkada, Yasaman Bahri, Matthieu Wyart
2025	A*	`conf`	Probing the Latent Hierarchical Structure of Data via Diffusion Models. ICLR Antonio Sclocchi, Alessandro Favero, Noam Itzhak Levi, Matthieu Wyart
2025	J	`jnl`	Scaling Laws and Representation Learning in Simple Hierarchical Languages: Transformers vs. Convolutional Architectures. CoRR Francesco Cagnetta, Alessandro Favero, Antonio Sclocchi, Matthieu Wyart
2024	J	`jnl`	A Phase Transition in Diffusion Models Reveals the Hierarchical Nature of Data. CoRR Antonio Sclocchi, Alessandro Favero, Matthieu Wyart
2024	J	`jnl`	Could ChatGPT get an Engineering Degree? Evaluating Higher Education Vulnerability to AI Assistants. CoRR Beatriz Borges, Negar Foroutan, Deniz Bayazit, Anna Sotnikova, Syrielle Montariol, Tanya Nazaretzky, Mohammadreza Banaei, Alireza Sakhaeirad, Philippe Servant, Seyed Parsa Neshaei, Jibril Frej, Angelika Romanou, Gail Weiss, Sepideh Mamooler, Zeming Chen, Simin Fan, Silin Gao, Mete Ismayilzada, Debjit Paul, Alexandre Schöpfer, Andrej Janchevski, Anja Tiede, Clarence Linden, Emanuele Troiani, Francesco Salvi, Freya Behrens, Giacomo Orsi, Giovanni Piccioli, Hadrien Sevel, Louis Coulon, Manuela Pineros-Rodriguez, Marin Bonnassies, Pierre Hellich, Puck van Gerwen, Sankalp Gambhir, Solal Pirelli, Thomas Blanchard, Timothée Callens, Toni Abi Aoun, Yannick Calvino Alonso, Yuri Cho, Alberto Silvio Chiappa, Antonio Sclocchi, Étienne Bruno, Florian Hofhammer, Gabriel Pescia, Geovani Rizk, Leello Dadi, Lucas Stoffl, Manoel Horta Ribeiro, Matthieu Bovel, Yueyang Pan, Aleksandra Radenovic, Alexandre Alahi, Alexander Mathis, Anne-Florence Bitbol, Boi Faltings, Cécile Hébert, Devis Tuia, François Maréchal, George Candea, Giuseppe Carleo, Jean-Cédric Chappelier, Nicolas Flammarion, Jean-Marie Fürbringer, Jean-Philippe Pellet, Karl Aberer, Lenka Zdeborová, Marcel Salathé, Martin Jaggi, Martin Rajman, Mathias Payer, Matthieu Wyart, Michael Gastpar, Michele Ceriotti, Ola Svensson, Olivier Lévêque, Paolo Ienne, Rachid Guerraoui, Robert West, Sanidhya Kashyap, Valerio Piazza, Viesturs Simanis, Viktor Kuncak, Volkan Cevher, Philippe Schwaller, Sacha Friedli, Patrick Jermann, Tanja Käser, Antoine Bosselut
2024	A*	`conf`	How Deep Networks Learn Sparse and Hierarchical Data: the Sparse Random Hierarchy Model. ICML Umberto M. Tomasini, Matthieu Wyart
2024	J	`jnl`	How Deep Networks Learn Sparse and Hierarchical Data: the Sparse Random Hierarchy Model. CoRR Umberto M. Tomasini, Matthieu Wyart
2024	J	`jnl`	Probing the Latent Hierarchical Structure of Data via Diffusion Models. CoRR Antonio Sclocchi, Alessandro Favero, Noam Itzhak Levi, Matthieu Wyart
2024	A*	`conf`	Towards a theory of how the structure of language is acquired by deep neural networks. NeurIPS Francesco Cagnetta, Matthieu Wyart
2024	J	`jnl`	Towards a theory of how the structure of language is acquired by deep neural networks. CoRR Francesco Cagnetta, Matthieu Wyart
2023	A*	`conf`	Dissecting the Effects of SGD Noise in Distinct Regimes of Deep Learning. ICML Antonio Sclocchi, Mario Geiger, Matthieu Wyart
2023	J	`jnl`	Dissecting the Effects of SGD Noise in Distinct Regimes of Deep Learning. CoRR Antonio Sclocchi, Mario Geiger, Matthieu Wyart
2023	J	`jnl`	How Deep Neural Networks Learn Compositional Data: The Random Hierarchy Model. CoRR Leonardo Petrini, Francesco Cagnetta, Umberto M. Tomasini, Alessandro Favero, Matthieu Wyart
2023	J	`jnl`	How deep convolutional neural networks lose spatial information with training. Mach. Learn. Sci. Technol. Umberto M. Tomasini, Leonardo Petrini, Francesco Cagnetta, Matthieu Wyart
2023	J	`jnl`	On the different regimes of Stochastic Gradient Descent. CoRR Antonio Sclocchi, Matthieu Wyart
2023	A*	`conf`	What Can Be Learnt With Wide Convolutional Neural Networks? ICML Francesco Cagnetta, Alessandro Favero, Matthieu Wyart
2022	J	`jnl`	Failure and success of the spectral bias prediction for Kernel Ridge Regression: the case of low-dimensional data. CoRR Umberto M. Tomasini, Antonio Sclocchi, Matthieu Wyart
2022	A*	`conf`	Failure and success of the spectral bias prediction for Laplace Kernel Ridge Regression: the case of low-dimensional data. ICML Umberto M. Tomasini, Antonio Sclocchi, Matthieu Wyart
2022	J	`jnl`	How Wide Convolutional Neural Networks Learn Hierarchical Tasks. CoRR Francesco Cagnetta, Alessandro Favero, Matthieu Wyart
2022	J	`jnl`	How deep convolutional neural networks lose spatial information with training. CoRR Umberto M. Tomasini, Leonardo Petrini, Francesco Cagnetta, Matthieu Wyart
2022	A*	`conf`	Learning sparse features can lead to overfitting in neural networks. NeurIPS Leonardo Petrini, Francesco Cagnetta, Eric Vanden-Eijnden, Matthieu Wyart
2022	J	`jnl`	Learning sparse features can lead to overfitting in neural networks. CoRR Leonardo Petrini, Francesco Cagnetta, Eric Vanden-Eijnden, Matthieu Wyart
2021	J	`jnl`	How isotropic kernels perform on simple invariants. Mach. Learn. Sci. Technol. Jonas Paccolat, Stefano Spigler, Matthieu Wyart
2021	J	`jnl`	How memory architecture affects performance and learning in simple POMDPs. CoRR Mario Geiger, Christophe Eloy, Matthieu Wyart
2021	A*	`conf`	Locality defeats the curse of dimensionality in convolutional teacher-student scenarios. NeurIPS Alessandro Favero, Francesco Cagnetta, Matthieu Wyart
2021	J	`jnl`	Locality defeats the curse of dimensionality in convolutional teacher-student scenarios. CoRR Alessandro Favero, Francesco Cagnetta, Matthieu Wyart
2021	J	`jnl`	Relative stability toward diffeomorphisms in deep nets indicates performance. CoRR Leonardo Petrini, Alessandro Favero, Mario Geiger, Matthieu Wyart
2021	A*	`conf`	Relative stability toward diffeomorphisms indicates performance in deep nets. NeurIPS Leonardo Petrini, Alessandro Favero, Mario Geiger, Matthieu Wyart
2020	J	`jnl`	Compressing invariant manifolds in neural nets. CoRR Jonas Paccolat, Leonardo Petrini, Mario Geiger, Kevin Tyloo, Matthieu Wyart
2020	J	`jnl`	Direct coupling analysis of epistasis in allosteric materials. PLoS Comput. Biol. Barbara Bravi, Riccardo Ravasio, Carolina Brito, Matthieu Wyart
2020	J	`jnl`	How isotropic kernels learn simple invariants. CoRR Jonas Paccolat, Stefano Spigler, Matthieu Wyart
2020	J	`jnl`	Perspective: A Phase Diagram for Deep Learning unifying Jamming, Feature Learning and Lazy Training. CoRR Mario Geiger, Leonardo Petrini, Matthieu Wyart
2019	J	`jnl`	Asymptotic learning curves of kernel methods: empirical data v.s. Teacher-Student paradigm. CoRR Stefano Spigler, Mario Geiger, Matthieu Wyart
2019	J	`jnl`	Disentangling feature and lazy learning in deep neural networks: an empirical study. CoRR Mario Geiger, Stefano Spigler, Arthur Jacot, Matthieu Wyart
2019	J	`jnl`	Scaling description of generalization with number of parameters in deep learning. CoRR Mario Geiger, Arthur Jacot, Stefano Spigler, Franck Gabriel, Levent Sagun, Stéphane d'Ascoli, Giulio Biroli, Clément Hongler, Matthieu Wyart
2018	J	`jnl`	A jamming transition from under- to over-parametrization affects loss landscape and generalization. CoRR Stefano Spigler, Mario Geiger, Stéphane d'Ascoli, Levent Sagun, Giulio Biroli, Matthieu Wyart
2018	A*	`conf`	Comparing Dynamics: Deep Neural Networks versus Glassy Systems. ICML Marco Baity-Jesi, Levent Sagun, Mario Geiger, Stefano Spigler, Gérard Ben Arous, Chiara Cammarota, Yann LeCun, Matthieu Wyart, Giulio Biroli
2018	J	`jnl`	Comparing Dynamics: Deep Neural Networks versus Glassy Systems. CoRR Marco Baity-Jesi, Levent Sagun, Mario Geiger, Stefano Spigler, Gérard Ben Arous, Chiara Cammarota, Yann LeCun, Matthieu Wyart, Giulio Biroli
2018	J	`jnl`	The jamming transition as a paradigm to understand the loss landscape of deep neural networks. CoRR Mario Geiger, Stefano Spigler, Stéphane d'Ascoli, Levent Sagun, Marco Baity-Jesi, Giulio Biroli, Matthieu Wyart
2013	J	`jnl`	Simulations of driven overdamped frictionless hard spheres. Comput. Phys. Commun. Edan Lerner, Gustavo Düring, Matthieu Wyart
2010	J	`jnl`	Evaluating Gene Expression Dynamics Using Pairwise RNA FISH Data. PLoS Comput. Biol. Matthieu Wyart, David Botstein, Ned S. Wingreen