default search action
Matthieu Wyart
Person information
Refine list
refinements active!
zoomed in on ?? of ?? records
view refined list in
export refined list as
2020 – today
- 2024
- [c8]Umberto M. Tomasini, Matthieu Wyart:
How Deep Networks Learn Sparse and Hierarchical Data: the Sparse Random Hierarchy Model. ICML 2024 - [i24]Antonio Sclocchi, Alessandro Favero, Matthieu Wyart:
A Phase Transition in Diffusion Models Reveals the Hierarchical Nature of Data. CoRR abs/2402.16991 (2024) - [i23]Umberto M. Tomasini, Matthieu Wyart:
How Deep Networks Learn Sparse and Hierarchical Data: the Sparse Random Hierarchy Model. CoRR abs/2404.10727 (2024) - [i22]Francesco Cagnetta, Matthieu Wyart:
Towards a theory of how the structure of language is acquired by deep neural networks. CoRR abs/2406.00048 (2024) - [i21]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) - [i20]Antonio Sclocchi, Alessandro Favero, Noam Itzhak Levi, Matthieu Wyart:
Probing the Latent Hierarchical Structure of Data via Diffusion Models. CoRR abs/2410.13770 (2024) - 2023
- [j5]Umberto M. Tomasini
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
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
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
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
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
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
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
A jamming transition from under- to over-parametrization affects loss landscape and generalization. CoRR abs/1810.09665 (2018) - 2013
- [j2]Edan Lerner
Simulations of driven overdamped frictionless hard spheres. Comput. Phys. Commun. 184(3): 628-637 (2013) - 2010
- [j1]Matthieu Wyart
Evaluating Gene Expression Dynamics Using Pairwise RNA FISH Data. PLoS Comput. Biol. 6(11) (2010)
Coauthor Index
manage site settings
To protect your privacy, all features that rely on external API calls from your browser are turned off by default. You need to opt-in for them to become active. All settings here will be stored as cookies with your web browser. For more information see our F.A.Q.
Unpaywalled article links
Add open access links from to the list of external document links (if available).
Privacy notice: By enabling the option above, your browser will contact the API of unpaywall.org to load hyperlinks to open access articles. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Unpaywall privacy policy.
Archived links via Wayback Machine
For web page which are no longer available, try to retrieve content from the of the Internet Archive (if available).
Privacy notice: By enabling the option above, your browser will contact the API of archive.org to check for archived content of web pages that are no longer available. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Internet Archive privacy policy.
Reference lists
Add a list of references from ,
, and
to record detail pages.
load references from crossref.org and opencitations.net
Privacy notice: By enabling the option above, your browser will contact the APIs of crossref.org, opencitations.net, and semanticscholar.org to load article reference information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Crossref privacy policy and the OpenCitations privacy policy, as well as the AI2 Privacy Policy covering Semantic Scholar.
Citation data
Add a list of citing articles from and
to record detail pages.
load citations from opencitations.net
Privacy notice: By enabling the option above, your browser will contact the API of opencitations.net and semanticscholar.org to load citation information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the OpenCitations privacy policy as well as the AI2 Privacy Policy covering Semantic Scholar.
OpenAlex data
Load additional information about publications from .
Privacy notice: By enabling the option above, your browser will contact the API of openalex.org to load additional information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the information given by OpenAlex.
last updated on 2024-12-01 00:11 CET by the dblp team
all metadata released as open data under CC0 1.0 license
see also: Terms of Use | Privacy Policy | Imprint
dblp was originally created in 1993 at:
since 2018, dblp has been operated and maintained by:
the dblp computer science bibliography is funded and supported by: