Enregistré dans:
| Auteurs principaux: | Vilucchio, Matteo, Dandi, Yatin, Rossignol, Matéo Pirio, Gerbelot, Cedric, Krzakala, Florent |
|---|---|
| Format: | Preprint |
| Publié: |
2025
|
| Sujets: | |
| Accès en ligne: | https://arxiv.org/abs/2502.20003 |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
Documents similaires
Deep Learning as Neural Low-Degree Filtering: A Spectral Theory of Hierarchical Feature Learning
par: Dandi, Yatin, et autres
Publié: (2026)
par: Dandi, Yatin, et autres
Publié: (2026)
The Computational Advantage of Depth: Learning High-Dimensional Hierarchical Functions with Gradient Descent
par: Dandi, Yatin, et autres
Publié: (2025)
par: Dandi, Yatin, et autres
Publié: (2025)
Asymptotic Characterisation of Robust Empirical Risk Minimisation Performance in the Presence of Outliers
par: Vilucchio, Matteo, et autres
Publié: (2023)
par: Vilucchio, Matteo, et autres
Publié: (2023)
Deep Learning of Compositional Targets with Hierarchical Spectral Methods
par: Tabanelli, Hugo, et autres
Publié: (2026)
par: Tabanelli, Hugo, et autres
Publié: (2026)
Provable Learning of Random Hierarchy Models and Hierarchical Shallow-to-Deep Chaining
par: Ren, Yunwei, et autres
Publié: (2026)
par: Ren, Yunwei, et autres
Publié: (2026)
Optimal Spectral Transitions in High-Dimensional Multi-Index Models
par: Defilippis, Leonardo, et autres
Publié: (2025)
par: Defilippis, Leonardo, et autres
Publié: (2025)
Sampling with flows, diffusion and autoregressive neural networks: A spin-glass perspective
par: Ghio, Davide, et autres
Publié: (2023)
par: Ghio, Davide, et autres
Publié: (2023)
A Random Matrix Theory Perspective on the Spectrum of Learned Features and Asymptotic Generalization Capabilities
par: Dandi, Yatin, et autres
Publié: (2024)
par: Dandi, Yatin, et autres
Publié: (2024)
Repetita Iuvant: Data Repetition Allows SGD to Learn High-Dimensional Multi-Index Functions
par: Arnaboldi, Luca, et autres
Publié: (2024)
par: Arnaboldi, Luca, et autres
Publié: (2024)
A High Dimensional Statistical Model for Adversarial Training: Geometry and Trade-Offs
par: Tanner, Kasimir, et autres
Publié: (2024)
par: Tanner, Kasimir, et autres
Publié: (2024)
Fundamental limits of learning in sequence multi-index models and deep attention networks: High-dimensional asymptotics and sharp thresholds
par: Troiani, Emanuele, et autres
Publié: (2025)
par: Troiani, Emanuele, et autres
Publié: (2025)
How Two-Layer Neural Networks Learn, One (Giant) Step at a Time
par: Dandi, Yatin, et autres
Publié: (2023)
par: Dandi, Yatin, et autres
Publié: (2023)
Scaling Laws from Sequential Feature Recovery: A Solvable Hierarchical Model
par: Wortsman-Zurich, Arie, et autres
Publié: (2026)
par: Wortsman-Zurich, Arie, et autres
Publié: (2026)
Universality laws for Gaussian mixtures in generalized linear models
par: Dandi, Yatin, et autres
Publié: (2023)
par: Dandi, Yatin, et autres
Publié: (2023)
Online Learning and Information Exponents: On The Importance of Batch size, and Time/Complexity Tradeoffs
par: Arnaboldi, Luca, et autres
Publié: (2024)
par: Arnaboldi, Luca, et autres
Publié: (2024)
The Benefits of Reusing Batches for Gradient Descent in Two-Layer Networks: Breaking the Curse of Information and Leap Exponents
par: Dandi, Yatin, et autres
Publié: (2024)
par: Dandi, Yatin, et autres
Publié: (2024)
Asymptotics of feature learning in two-layer networks after one gradient-step
par: Cui, Hugo, et autres
Publié: (2024)
par: Cui, Hugo, et autres
Publié: (2024)
Rigorous dynamical mean field theory for stochastic gradient descent methods
par: Gerbelot, Cedric, et autres
Publié: (2022)
par: Gerbelot, Cedric, et autres
Publié: (2022)
Fundamental computational limits of weak learnability in high-dimensional multi-index models
par: Troiani, Emanuele, et autres
Publié: (2024)
par: Troiani, Emanuele, et autres
Publié: (2024)
Learning with Restricted Boltzmann Machines: Asymptotics of AMP and GD in High Dimensions
par: Xu, Yizhou, et autres
Publié: (2025)
par: Xu, Yizhou, et autres
Publié: (2025)
Rigorous Asymptotics for First-Order Algorithms Through the Dynamical Cavity Method
par: Dandi, Yatin, et autres
Publié: (2026)
par: Dandi, Yatin, et autres
Publié: (2026)
On the Geometry of Regularization in Adversarial Training: High-Dimensional Asymptotics and Generalization Bounds
par: Vilucchio, Matteo, et autres
Publié: (2024)
par: Vilucchio, Matteo, et autres
Publié: (2024)
A Gentle Introduction to Gradient-Based Optimization and Variational Inequalities for Machine Learning
par: Wadia, Neha S., et autres
Publié: (2023)
par: Wadia, Neha S., et autres
Publié: (2023)
Fundamental limits of Non-Linear Low-Rank Matrix Estimation
par: Mergny, Pierre, et autres
Publié: (2024)
par: Mergny, Pierre, et autres
Publié: (2024)
Multi-layer State Evolution Under Random Convolutional Design
par: Daniels, Mara, et autres
Publié: (2022)
par: Daniels, Mara, et autres
Publié: (2022)
Single-Head Attention in High Dimensions: A Theory of Generalization, Weights Spectra, and Scaling Laws
par: Boncoraglio, Fabrizio, et autres
Publié: (2025)
par: Boncoraglio, Fabrizio, et autres
Publié: (2025)
The Nuclear Route: Sharp Asymptotics of ERM in Overparameterized Quadratic Networks
par: Erba, Vittorio, et autres
Publié: (2025)
par: Erba, Vittorio, et autres
Publié: (2025)
Asymptotics of SGD in Sequence-Single Index Models and Single-Layer Attention Networks
par: Arnaboldi, Luca, et autres
Publié: (2025)
par: Arnaboldi, Luca, et autres
Publié: (2025)
Fundamental Limits of Matrix Sensing: Exact Asymptotics, Universality, and Applications
par: Xu, Yizhou, et autres
Publié: (2025)
par: Xu, Yizhou, et autres
Publié: (2025)
A Noise Sensitivity Exponent Controls Large Statistical-to-Computational Gaps in Single- and Multi-Index Models
par: Defilippis, Leonardo, et autres
Publié: (2026)
par: Defilippis, Leonardo, et autres
Publié: (2026)
On the existence of consistent adversarial attacks in high-dimensional linear classification
par: Vilucchio, Matteo, et autres
Publié: (2025)
par: Vilucchio, Matteo, et autres
Publié: (2025)
Permutation recovery of spikes in noisy high-dimensional tensor estimation
par: Arous, Gérard Ben, et autres
Publié: (2024)
par: Arous, Gérard Ben, et autres
Publié: (2024)
A phase transition between positional and semantic learning in a solvable model of dot-product attention
par: Cui, Hugo, et autres
Publié: (2024)
par: Cui, Hugo, et autres
Publié: (2024)
Stochastic gradient descent in high dimensions for multi-spiked tensor PCA
par: Arous, Gérard Ben, et autres
Publié: (2024)
par: Arous, Gérard Ben, et autres
Publié: (2024)
Escaping mediocrity: how two-layer networks learn hard generalized linear models with SGD
par: Arnaboldi, Luca, et autres
Publié: (2023)
par: Arnaboldi, Luca, et autres
Publié: (2023)
Optimal scaling laws in learning hierarchical multi-index models
par: Defilippis, Leonardo, et autres
Publié: (2026)
par: Defilippis, Leonardo, et autres
Publié: (2026)
Computational Thresholds in Multi-Modal Learning via the Spiked Matrix-Tensor Model
par: Tabanelli, Hugo, et autres
Publié: (2025)
par: Tabanelli, Hugo, et autres
Publié: (2025)
Langevin dynamics for high-dimensional optimization: the case of multi-spiked tensor PCA
par: Arous, Gérard Ben, et autres
Publié: (2024)
par: Arous, Gérard Ben, et autres
Publié: (2024)
Spectral Phase Transition and Optimal PCA in Block-Structured Spiked models
par: Mergny, Pierre, et autres
Publié: (2024)
par: Mergny, Pierre, et autres
Publié: (2024)
Analysis of learning a flow-based generative model from limited sample complexity
par: Cui, Hugo, et autres
Publié: (2023)
par: Cui, Hugo, et autres
Publié: (2023)
Documents similaires
-
Deep Learning as Neural Low-Degree Filtering: A Spectral Theory of Hierarchical Feature Learning
par: Dandi, Yatin, et autres
Publié: (2026) -
The Computational Advantage of Depth: Learning High-Dimensional Hierarchical Functions with Gradient Descent
par: Dandi, Yatin, et autres
Publié: (2025) -
Asymptotic Characterisation of Robust Empirical Risk Minimisation Performance in the Presence of Outliers
par: Vilucchio, Matteo, et autres
Publié: (2023) -
Deep Learning of Compositional Targets with Hierarchical Spectral Methods
par: Tabanelli, Hugo, et autres
Publié: (2026) -
Provable Learning of Random Hierarchy Models and Hierarchical Shallow-to-Deep Chaining
par: Ren, Yunwei, et autres
Publié: (2026)