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ORCIDMatthieu Wyart
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2020 – today
- 2024
[c8]Umberto M. Tomasini, Matthieu Wyart:
How Deep Networks Learn Sparse and Hierarchical Data: the Sparse Random Hierarchy Model. ICML 2024- [i23]Antonio Sclocchi, Alessandro Favero, Matthieu Wyart:
A Phase Transition in Diffusion Models Reveals the Hierarchical Nature of Data. CoRR abs/2402.16991 (2024) - [i22]Umberto M. Tomasini, Matthieu Wyart:
How Deep Networks Learn Sparse and Hierarchical Data: the Sparse Random Hierarchy Model. CoRR abs/2404.10727 (2024) - [i21]Francesco Cagnetta, Matthieu Wyart:
Towards a theory of how the structure of language is acquired by deep neural networks. CoRR abs/2406.00048 (2024) - [i20]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:
Could ChatGPT get an Engineering Degree? Evaluating Higher Education Vulnerability to AI Assistants. CoRR abs/2408.11841 (2024) - 2023
- [j5]Umberto M. Tomasini, Leonardo Petrini, Francesco Cagnetta, Matthieu Wyart:
How deep convolutional neural networks lose spatial information with training. Mach. Learn. Sci. Technol. 4(4): 45026 (2023) - [c7]Francesco Cagnetta, Alessandro Favero, Matthieu Wyart:
What Can Be Learnt With Wide Convolutional Neural Networks? ICML 2023: 3347-3379 - [c6]Antonio Sclocchi, Mario Geiger, Matthieu Wyart:
Dissecting the Effects of SGD Noise in Distinct Regimes of Deep Learning. ICML 2023: 30381-30405 - [i19]Antonio Sclocchi, Mario Geiger, Matthieu Wyart:
Dissecting the Effects of SGD Noise in Distinct Regimes of Deep Learning. CoRR abs/2301.13703 (2023) - [i18]Leonardo Petrini, Francesco Cagnetta, Umberto M. Tomasini, Alessandro Favero, Matthieu Wyart:
How Deep Neural Networks Learn Compositional Data: The Random Hierarchy Model. CoRR abs/2307.02129 (2023) - [i17]Antonio Sclocchi, Matthieu Wyart:
On the different regimes of Stochastic Gradient Descent. CoRR abs/2309.10688 (2023) - 2022
- [c5]Umberto M. Tomasini, Antonio Sclocchi, Matthieu Wyart:
Failure and success of the spectral bias prediction for Laplace Kernel Ridge Regression: the case of low-dimensional data. ICML 2022: 21548-21583 - [c4]Leonardo Petrini, Francesco Cagnetta, Eric Vanden-Eijnden, Matthieu Wyart:
Learning sparse features can lead to overfitting in neural networks. NeurIPS 2022 - [i16]Umberto M. Tomasini, Antonio Sclocchi, Matthieu Wyart:
Failure and success of the spectral bias prediction for Kernel Ridge Regression: the case of low-dimensional data. CoRR abs/2202.03348 (2022) - [i15]Leonardo Petrini, Francesco Cagnetta, Eric Vanden-Eijnden, Matthieu Wyart:
Learning sparse features can lead to overfitting in neural networks. CoRR abs/2206.12314 (2022) - [i14]Francesco Cagnetta, Alessandro Favero, Matthieu Wyart:
How Wide Convolutional Neural Networks Learn Hierarchical Tasks. CoRR abs/2208.01003 (2022) - [i13]Umberto M. Tomasini, Leonardo Petrini, Francesco Cagnetta, Matthieu Wyart:
How deep convolutional neural networks lose spatial information with training. CoRR abs/2210.01506 (2022) - 2021
- [j4]Jonas Paccolat, Stefano Spigler, Matthieu Wyart:
How isotropic kernels perform on simple invariants. Mach. Learn. Sci. Technol. 2(2): 25020 (2021) - [c3]Leonardo Petrini, Alessandro Favero, Mario Geiger, Matthieu Wyart:
Relative stability toward diffeomorphisms indicates performance in deep nets. NeurIPS 2021: 8727-8739 - [c2]Alessandro Favero, Francesco Cagnetta, Matthieu Wyart:
Locality defeats the curse of dimensionality in convolutional teacher-student scenarios. NeurIPS 2021: 9456-9467 - [i12]Leonardo Petrini, Alessandro Favero, Mario Geiger, Matthieu Wyart:
Relative stability toward diffeomorphisms in deep nets indicates performance. CoRR abs/2105.02468 (2021) - [i11]Alessandro Favero, Francesco Cagnetta, Matthieu Wyart:
Locality defeats the curse of dimensionality in convolutional teacher-student scenarios. CoRR abs/2106.08619 (2021) - [i10]Mario Geiger, Christophe Eloy, Matthieu Wyart:
How memory architecture affects performance and learning in simple POMDPs. CoRR abs/2106.08849 (2021) - 2020
- [j3]Barbara Bravi, Riccardo Ravasio, Carolina Brito, Matthieu Wyart:
Direct coupling analysis of epistasis in allosteric materials. PLoS Comput. Biol. 16(3) (2020) - [i9]Jonas Paccolat, Stefano Spigler, Matthieu Wyart:
How isotropic kernels learn simple invariants. CoRR abs/2006.09754 (2020) - [i8]Jonas Paccolat, Leonardo Petrini, Mario Geiger, Kevin Tyloo, Matthieu Wyart:
Compressing invariant manifolds in neural nets. CoRR abs/2007.11471 (2020) - [i7]Mario Geiger, Leonardo Petrini, Matthieu Wyart:
Perspective: A Phase Diagram for Deep Learning unifying Jamming, Feature Learning and Lazy Training. CoRR abs/2012.15110 (2020)
2010 – 2019
- 2019
- [i6]Mario Geiger, Arthur Jacot, Stefano Spigler, Franck Gabriel, Levent Sagun, Stéphane d'Ascoli, Giulio Biroli, Clément Hongler, Matthieu Wyart:
Scaling description of generalization with number of parameters in deep learning. CoRR abs/1901.01608 (2019) - [i5]Stefano Spigler, Mario Geiger, Matthieu Wyart:
Asymptotic learning curves of kernel methods: empirical data v.s. Teacher-Student paradigm. CoRR abs/1905.10843 (2019) - [i4]Mario Geiger, Stefano Spigler, Arthur Jacot, Matthieu Wyart:
Disentangling feature and lazy learning in deep neural networks: an empirical study. CoRR abs/1906.08034 (2019) - 2018
- [c1]Marco Baity-Jesi, Levent Sagun, Mario Geiger, Stefano Spigler, Gérard Ben Arous, Chiara Cammarota, Yann LeCun, Matthieu Wyart, Giulio Biroli:
Comparing Dynamics: Deep Neural Networks versus Glassy Systems. ICML 2018: 324-333 - [i3]Marco Baity-Jesi, Levent Sagun, Mario Geiger, Stefano Spigler, Gérard Ben Arous, Chiara Cammarota, Yann LeCun, Matthieu Wyart, Giulio Biroli:
Comparing Dynamics: Deep Neural Networks versus Glassy Systems. CoRR abs/1803.06969 (2018) - [i2]Mario Geiger, Stefano Spigler, Stéphane d'Ascoli, Levent Sagun, Marco Baity-Jesi, Giulio Biroli, Matthieu Wyart:
The jamming transition as a paradigm to understand the loss landscape of deep neural networks. CoRR abs/1809.09349 (2018) - [i1]Stefano Spigler, Mario Geiger, Stéphane d'Ascoli, Levent Sagun, Giulio Biroli, Matthieu Wyart:
A jamming transition from under- to over-parametrization affects loss landscape and generalization. CoRR abs/1810.09665 (2018) - 2013
- [j2]Edan Lerner, Gustavo Düring, Matthieu Wyart:
Simulations of driven overdamped frictionless hard spheres. Comput. Phys. Commun. 184(3): 628-637 (2013) - 2010
- [j1]Matthieu Wyart, David Botstein, Ned S. Wingreen:
Evaluating Gene Expression Dynamics Using Pairwise RNA FISH Data. PLoS Comput. Biol. 6(11) (2010)
Coauthor Index
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