Imagen de Portada

Analytic theory of dropout regularization

Fuente: arXiv

Guardado en:

Detalles Bibliográficos
Autores principales:	Mori, Francesco, Mignacco, Francesca
Formato:	Preprint
Publicado:	2025
Materias:	Machine Learning Disordered Systems and Neural Networks Statistical Mechanics
Acceso en línea:	Acceder al recurso
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

A statistical physics framework for optimal learning
por: Mignacco, Francesca, et al.
Publicado: (2025)

Optimal Protocols for Continual Learning via Statistical Physics and Control Theory
por: Mori, Francesco, et al.
Publicado: (2024)

Dissecting the Interplay of Attention Paths in a Statistical Mechanics Theory of Transformers
por: Tiberi, Lorenzo, et al.
Publicado: (2024)

Nonlinear classification of neural manifolds with contextual information
por: Mignacco, Francesca, et al.
Publicado: (2024)

An Analytical Characterization of Sloppiness in Neural Networks: Insights from Linear Models
por: Mao, Jialin, et al.
Publicado: (2025)

Spring-block theory of feature learning in deep neural networks
por: Shi, Cheng, et al.
Publicado: (2024)

Two failure modes of deep transformers and how to avoid them: a unified theory of signal propagation at initialisation
por: Giorlandino, Alessio, et al.
Publicado: (2025)

Fundamental operating regimes, hyper-parameter fine-tuning and glassiness: towards an interpretable replica-theory for trained restricted Boltzmann machines
por: Fachechi, Alberto, et al.
Publicado: (2024)

Pseudo-likelihood produces associative memories able to generalize, even for asymmetric couplings
por: D'Amico, Francesco, et al.
Publicado: (2025)

Uncovering statistical structure in large-scale neural activity with Restricted Boltzmann Machines
por: Béreux, Nicolas, et al.
Publicado: (2026)

Inferring Higher-Order Couplings with Neural Networks
por: Decelle, Aurélien, et al.
Publicado: (2025)

The Physics of Data and Tasks: Theories of Locality and Compositionality in Deep Learning
por: Favero, Alessandro
Publicado: (2025)

A theoretical framework for overfitting in energy-based modeling
por: Catania, Giovanni, et al.
Publicado: (2025)

Uncertainty in AI-driven Monte Carlo simulations
por: Tzivrailis, Dimitrios, et al.
Publicado: (2025)

On the role of non-linear latent features in bipartite generative neural networks
por: Bonnaire, Tony, et al.
Publicado: (2025)

PCA recovery thresholds in low-rank matrix inference with sparse noise
por: Adomaityte, Urte, et al.
Publicado: (2025)

The Copycat Perceptron: Smashing Barriers Through Collective Learning
por: Catania, Giovanni, et al.
Publicado: (2023)

The autoregressive neural network architecture of the Boltzmann distribution of pairwise interacting spins systems
por: Biazzo, Indaco
Publicado: (2023)

Distinct mechanisms underlying in-context learning in transformers
por: Gibson, Cole, et al.
Publicado: (2026)

Interpreting the Synchronization Gap: The Hidden Mechanism Inside Diffusion Transformers
por: Albrychiewicz, Emil, et al.
Publicado: (2026)

Universal Scaling Laws of Absorbing Phase Transitions in Artificial Deep Neural Networks
por: Tamai, Keiichi, et al.
Publicado: (2023)

Explaining the effects of non-convergent sampling in the training of Energy-Based Models
por: Agoritsas, Elisabeth, et al.
Publicado: (2023)

Discrete generative diffusion models without stochastic differential equations: a tensor network approach
por: Causer, Luke, et al.
Publicado: (2024)

Factual recall in linear associative memories: sharp asymptotics and mechanistic insights
por: Giorlandino, Alessio, et al.
Publicado: (2026)

Sampling with flows, diffusion and autoregressive neural networks: A spin-glass perspective
por: Ghio, Davide, et al.
Publicado: (2023)

Stochastic Gradient Descent-like relaxation is equivalent to Metropolis dynamics in discrete optimization and inference problems
por: Angelini, Maria Chiara, et al.
Publicado: (2023)

Escape dynamics and implicit bias of one-pass SGD in overparameterized quadratic networks
por: Bocchi, Dario, et al.
Publicado: (2026)

Inferring effective couplings with Restricted Boltzmann Machines
por: Decelle, Aurélien, et al.
Publicado: (2023)

From Classical to Quantum: Extending Prometheus for Unsupervised Discovery of Phase Transitions in Three Dimensions and Quantum Systems
por: Yee, Brandon, et al.
Publicado: (2026)

Transfer Learning in $\ell_1$ Regularized Regression: Hyperparameter Selection Strategy based on Sharp Asymptotic Analysis
por: Okajima, Koki, et al.
Publicado: (2024)

Dynamic neuron approach to deep neural networks: Decoupling neurons for renormalization group analysis
por: Lee, Donghee, et al.
Publicado: (2024)

Generalization vs. Specialization under Concept Shift
por: Nguyen, Alex, et al.
Publicado: (2024)

Gaussian Universality in Neural Network Dynamics with Generalized Structured Input Distributions
por: Bae, Jaeyong, et al.
Publicado: (2024)

Coding schemes in neural networks learning classification tasks
por: van Meegen, Alexander, et al.
Publicado: (2024)

Nonequilbrium physics of generative diffusion models
por: Yu, Zhendong, et al.
Publicado: (2024)

The Symmetric Perceptron: a Teacher-Student Scenario
por: Catania, Giovanni, et al.
Publicado: (2026)

A replica analysis of under-bagging
por: Takahashi, Takashi
Publicado: (2024)

Weight fluctuations in (deep) linear neural networks and a derivation of the inverse-variance flatness relation
por: Gross, Markus, et al.
Publicado: (2023)

Fast training and sampling of Restricted Boltzmann Machines
por: Béreux, Nicolas, et al.
Publicado: (2024)

Machine learning the Ising transition: A comparison between discriminative and generative approaches
por: Zhang, Difei, et al.
Publicado: (2024)