10 citations · 27 across the 10 of their papers we have counts for
6 papers · 1 filter
The Computational Advantage of Depth: Learning High-Dimensional Hierarchical Functions with Gradient Descent
Yatin Dandi, Luca Pesce, Lenka Zdeborová +1
Understanding the advantages of deep neural networks trained by gradient descent (GD) compared to shallow models remains an open theoretical challenge. In this paper, we introduce…
Fundamental limits of Non-Linear Low-Rank Matrix Estimation
Pierre Mergny, Justin Ko, Florent Krzakala +1
We consider the task of estimating a low-rank matrix from non-linear and noisy observations. We prove a strong universality result showing that Bayes-optimal performances are chara…
Analysis of Bootstrap and Subsampling in High-dimensional Regularized Regression
Lucas Clarté, Adrien Vandenbroucque, Guillaume Dalle +3
We investigate popular resampling methods for estimating the uncertainty of statistical models, such as subsampling, bootstrap and the jackknife, and their performance in high-dime…
Asymptotics of feature learning in two-layer networks after one gradient-step
Hugo Cui, Luca Pesce, Yatin Dandi +4
In this manuscript, we investigate the problem of how two-layer neural networks learn features from data, and improve over the kernel regime, after being trained with a single grad…
The Benefits of Reusing Batches for Gradient Descent in Two-Layer Networks: Breaking the Curse of Information and Leap Exponents
Yatin Dandi, Emanuele Troiani, Luca Arnaboldi +3
We investigate the training dynamics of two-layer neural networks when learning multi-index target functions. We focus on multi-pass gradient descent (GD) that reuses the batches m…
Analysis of learning a flow-based generative model from limited sample complexity
Hugo Cui, Florent Krzakala, Eric Vanden-Eijnden +1
We study the problem of training a flow-based generative model, parametrized by a two-layer autoencoder, to sample from a high-dimensional Gaussian mixture. We provide a sharp end-…