Evaluating Gene Expression Dynamics Using Pairwise RNA FISH Data. - Matthieu Wyart; David Botstein; Ned S. Wingreen (PLoS Comput. Biol., 2010)

Simulations of driven overdamped frictionless hard spheres. - Edan Lerner; Gustavo Düring; Matthieu Wyart (Comput. Phys. Commun., 2013)

Comparing Dynamics: Deep Neural Networks versus Glassy Systems. - Marco Baity-Jesi; Levent Sagun; Mario Geiger; Stefano Spigler; Gérard Ben Arous; Chiara Cammarota; Yann LeCun; Matthieu Wyart; Giulio Biroli (ICML, 2018)

The jamming transition as a paradigm to understand the loss landscape of deep neural networks. - Mario Geiger; Stefano Spigler; Stéphane d'Ascoli; Levent Sagun; Marco Baity-Jesi; Giulio Biroli; Matthieu Wyart (CoRR, 2018)

A jamming transition from under- to over-parametrization affects loss landscape and generalization. - Stefano Spigler; Mario Geiger; Stéphane d'Ascoli; Levent Sagun; Giulio Biroli; Matthieu Wyart (CoRR, 2018)

Scaling description of generalization with number of parameters in deep learning. - Mario Geiger; Arthur Jacot; Stefano Spigler; Franck Gabriel; Levent Sagun; Stéphane d'Ascoli; Giulio Biroli; Clément Hongler; Matthieu Wyart (CoRR, 2019)

Asymptotic learning curves of kernel methods: empirical data v.s. Teacher-Student paradigm. - Stefano Spigler; Mario Geiger; Matthieu Wyart (CoRR, 2019)

Disentangling feature and lazy learning in deep neural networks: an empirical study. - Mario Geiger; Stefano Spigler; Arthur Jacot; Matthieu Wyart (CoRR, 2019)

How isotropic kernels learn simple invariants. - Jonas Paccolat; Stefano Spigler; Matthieu Wyart (CoRR, 2020)

Compressing invariant manifolds in neural nets. - Jonas Paccolat; Leonardo Petrini; Mario Geiger; Kevin Tyloo; Matthieu Wyart (CoRR, 2020)

Perspective: A Phase Diagram for Deep Learning unifying Jamming, Feature Learning and Lazy Training. - Mario Geiger; Leonardo Petrini; Matthieu Wyart (CoRR, 2020)

Direct coupling analysis of epistasis in allosteric materials. - Barbara Bravi; Riccardo Ravasio; Carolina Brito; Matthieu Wyart (PLoS Comput. Biol., 2020)

Locality defeats the curse of dimensionality in convolutional teacher-student scenarios. - Alessandro Favero; Francesco Cagnetta; Matthieu Wyart (NeurIPS, 2021)

Relative stability toward diffeomorphisms indicates performance in deep nets. - Leonardo Petrini; Alessandro Favero; Mario Geiger; Matthieu Wyart (NeurIPS, 2021)

Relative stability toward diffeomorphisms in deep nets indicates performance. - Leonardo Petrini; Alessandro Favero; Mario Geiger; Matthieu Wyart (CoRR, 2021)

How memory architecture affects performance and learning in simple POMDPs. - Mario Geiger; Christophe Eloy; Matthieu Wyart (CoRR, 2021)

How isotropic kernels perform on simple invariants. - Jonas Paccolat; Stefano Spigler; Matthieu Wyart (Mach. Learn. Sci. Technol., 2021)

Failure and success of the spectral bias prediction for Laplace Kernel Ridge Regression: the case of low-dimensional data. - Umberto M. Tomasini; Antonio Sclocchi; Matthieu Wyart (ICML, 2022)

Learning sparse features can lead to overfitting in neural networks. - Leonardo Petrini; Francesco Cagnetta; Eric Vanden-Eijnden; Matthieu Wyart (NeurIPS, 2022)

Failure and success of the spectral bias prediction for Kernel Ridge Regression: the case of low-dimensional data. - Umberto M. Tomasini; Antonio Sclocchi; Matthieu Wyart (CoRR, 2022)

How Wide Convolutional Neural Networks Learn Hierarchical Tasks. - Francesco Cagnetta; Alessandro Favero; Matthieu Wyart (CoRR, 2022)

What Can Be Learnt With Wide Convolutional Neural Networks? - Francesco Cagnetta; Alessandro Favero; Matthieu Wyart (ICML, 2023)

Dissecting the Effects of SGD Noise in Distinct Regimes of Deep Learning. - Antonio Sclocchi; Mario Geiger; Matthieu Wyart (ICML, 2023)

How Deep Neural Networks Learn Compositional Data: The Random Hierarchy Model. - Leonardo Petrini; Francesco Cagnetta; Umberto M. Tomasini; Alessandro Favero; Matthieu Wyart (CoRR, 2023)

On the different regimes of Stochastic Gradient Descent. - Antonio Sclocchi; Matthieu Wyart (CoRR, 2023)

How deep convolutional neural networks lose spatial information with training. - Umberto M. Tomasini; Leonardo Petrini; Francesco Cagnetta; Matthieu Wyart (Mach. Learn. Sci. Technol., 2023)

How Deep Networks Learn Sparse and Hierarchical Data: the Sparse Random Hierarchy Model. - Umberto M. Tomasini; Matthieu Wyart (ICML, 2024)

Towards a theory of how the structure of language is acquired by deep neural networks. - Francesco Cagnetta; Matthieu Wyart (NeurIPS, 2024)

A Phase Transition in Diffusion Models Reveals the Hierarchical Nature of Data. - Antonio Sclocchi; Alessandro Favero; Matthieu Wyart (CoRR, 2024)

Could ChatGPT get an Engineering Degree? Evaluating Higher Education Vulnerability to AI Assistants. - 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; et al. (CoRR, 2024)

Probing the Latent Hierarchical Structure of Data via Diffusion Models. - Antonio Sclocchi; Alessandro Favero; Noam Itzhak Levi; Matthieu Wyart (ICLR, 2025)

How compositional generalization and creativity improve as diffusion models are trained. - Alessandro Favero; Antonio Sclocchi; Francesco Cagnetta; Pascal Frossard; Matthieu Wyart (CoRR, 2025)

Learning curves theory for hierarchically compositional data with power-law distributed features. - Francesco Cagnetta; Hyunmo Kang; Matthieu Wyart (CoRR, 2025)

Scaling Laws and Representation Learning in Simple Hierarchical Languages: Transformers vs. Convolutional Architectures. - Francesco Cagnetta; Alessandro Favero; Antonio Sclocchi; Matthieu Wyart (CoRR, 2025)

Bigger Isn't Always Memorizing: Early Stopping Overparameterized Diffusion Models. - Alessandro Favero; Antonio Sclocchi; Matthieu Wyart (CoRR, 2025)

On the Emergence of Linear Analogies in Word Embeddings. - Daniel J. Korchinski; Dhruva Karkada; Yasaman Bahri; Matthieu Wyart (CoRR, 2025)