2 citations · 5 across the 27 of their papers we have counts for
7 papers · 1 filter
A Random Matrix Theory Perspective on the Spectrum of Learned Features and Asymptotic Generalization Capabilities
Yatin Dandi, Luca Pesce, Hugo Cui +3
A key property of neural networks is their capacity of adapting to data during training. Yet, our current mathematical understanding of feature learning and its relationship to gen…
Streamlined optical training of large-scale modern deep learning architectures with direct feedback alignment
Ziao Wang, Kilian Müller, Matthew Filipovich +12
Modern deep learning relies nearly exclusively on dedicated electronic hardware accelerators. Photonic approaches, with low consumption and high operation speed, are increasingly c…
The phase diagram of compressed sensing with -norm regularization
Damien Barbier, Carlo Lucibello, Luca Saglietti +2
Noiseless compressive sensing is a two-steps setting that allows for undersampling a sparse signal and then reconstructing it without loss of information. The LASSO algorithm, base…
Bayes-optimal learning of an extensive-width neural network from quadratically many samples
Antoine Maillard, Emanuele Troiani, Simon Martin +2
We consider the problem of learning a target function corresponding to a single hidden layer neural network, with a quadratic activation function after the first layer, and random…
Online Learning and Information Exponents: On The Importance of Batch size, and Time/Complexity Tradeoffs
Luca Arnaboldi, Yatin Dandi, Florent Krzakala +3
We study the impact of the batch size on the iteration time of training two-layer neural networks with one-pass stochastic gradient descent (SGD) on multi-index target fu…
Fundamental computational limits of weak learnability in high-dimensional multi-index models
Emanuele Troiani, Yatin Dandi, Leonardo Defilippis +3
Multi-index models - functions which only depend on the covariates through a non-linear transformation of their projection on a subspace - are a useful benchmark for investigating…
Repetita Iuvant: Data Repetition Allows SGD to Learn High-Dimensional Multi-Index Functions
Luca Arnaboldi, Yatin Dandi, Florent Krzakala +2
Neural networks can identify low-dimensional relevant structures within high-dimensional noisy data, yet our mathematical understanding of how they do so remains scarce. Here, we i…