1.3k citations · 1.7k across the 25 of their papers we have counts for
25 papers
Feature Learning in -regularized DNNs: Attraction/Repulsion and Sparsity
Arthur Jacot, Eugene Golikov, Clément Hongler +1
We study the loss surface of DNNs with regularization. We show that the loss in terms of the parameters can be reformulated into a loss in terms of the layerwise activation…
Saddle-to-Saddle Dynamics in Deep Linear Networks: Small Initialization Training, Symmetry, and Sparsity
Arthur Jacot, François Ged, Berfin Şimşek +2
The dynamics of Deep Linear Networks (DLNs) is dramatically affected by the variance of the parameters at initialization . For DLNs of width , we show a phase transit…
Geometry of the Loss Landscape in Overparameterized Neural Networks: Symmetries and Invariances
Berfin Şimşek, François Ged, Arthur Jacot +4
We study how permutation symmetries in overparameterized multi-layer neural networks generate `symmetry-induced' critical points. Assuming a network with layers of minimal wi…
Correlations of primary fields in the critical Ising model
Dmitry Chelkak, Clément Hongler, Konstantin Izyurov
We prove convergence of renormalized correlations of primary fields, i. e., spins, disorders, fermions and energy densities, in the scaling limit of the critical Ising model in arb…
Smart Proofs via Smart Contracts: Succinct and Informative Mathematical Derivations via Decentralized Markets
Sylvain Carré, Franck Gabriel, Clément Hongler +2
Modern mathematics is built on the idea that proofs should be translatable into formal proofs, whose validity is an objective question, decidable by a computer. Yet, in practice, p…
Kernel Alignment Risk Estimator: Risk Prediction from Training Data
Arthur Jacot, Berfin Şimşek, Francesco Spadaro +2
We study the risk (i.e. generalization error) of Kernel Ridge Regression (KRR) for a kernel with ridge and i.i.d. observations. For this, we introduce two objects: the Si…