Image de couverture de livre

Large-width functional asymptotics for deep Gaussian neural networks

Fuente: arXiv

Enregistré dans:

Détails bibliographiques
Auteurs principaux:	Bracale, Daniele, Favaro, Stefano, Fortini, Sandra, Peluchetti, Stefano
Format:	Preprint
Publié:	2021
Sujets:	Probability Machine Learning
Accès en ligne:	Acceder al recurso
Tags:	Ajouter un tag Pas de tags, Soyez le premier à ajouter un tag!

Documents similaires

Infinite-channel deep stable convolutional neural networks
par: Bracale, Daniele, et autres
Publié: (2021)

Non-asymptotic approximations of Gaussian neural networks via second-order Poincaré inequalities
par: Bordino, Alberto, et autres
Publié: (2023)

Student-t processes as infinite-width limits of posterior Bayesian neural networks
par: Caporali, Francesco, et autres
Publié: (2025)

Quasi-Bayesian sequential deconvolution
par: Favaro, Stefano, et autres
Publié: (2024)

Quasi-Bayes empirical Bayes: a sequential approach to the Poisson compound decision problem
par: Favaro, Stefano, et autres
Publié: (2024)

Function-Space MCMC for Bayesian Wide Neural Networks
par: Pezzetti, Lucia, et autres
Publié: (2024)

BM$^2$: Coupled Schrödinger Bridge Matching
par: Peluchetti, Stefano
Publié: (2024)

Revenue Maximization Under Sequential Price Competition Via The Estimation Of s-Concave Demand Functions
par: Bracale, Daniele, et autres
Publié: (2025)

Spectral complexity of deep neural networks
par: Di Lillo, Simmaco, et autres
Publié: (2024)

Approximation and interpolation of deep neural networks
par: Constantinescu, Vlad-Raul, et autres
Publié: (2023)

Holographic functions and neural networks
par: Szegedy, Balazs
Publié: (2026)

A simple algorithm for output range analysis for deep neural networks
par: Rojas, Helder, et autres
Publié: (2024)

Quantitative convergence of trained single layer neural networks to Gaussian processes
par: Mosig, Eloy, et autres
Publié: (2025)

Large deviation principles for convolutional Bayesian neural networks
par: Bassetti, Federico, et autres
Publié: (2026)

Quantitative CLTs in Deep Neural Networks
par: Favaro, Stefano, et autres
Publié: (2023)

Nonlinear spiked covariance matrices and signal propagation in deep neural networks
par: Wang, Zhichao, et autres
Publié: (2024)

A Gaussian process limit for the self-normalized Ewens-Pitman process
par: Bercu, Bernard, et autres
Publié: (2026)

Phase transitions reveal hierarchical structure in deep neural networks
par: Ersoy, Ibrahim Talha, et autres
Publié: (2025)

A martingale approach to Gaussian fluctuations and laws of iterated logarithm for Ewens-Pitman model
par: Bercu, Bernard, et autres
Publié: (2024)

Trained quantum neural networks are Gaussian processes
par: Girardi, Filippo, et autres
Publié: (2024)

KamonBench: A Grammar-Based Dataset for Evaluating Compositional Factor Recovery in Vision-Language Models
par: Sproat, Richard, et autres
Publié: (2026)

Fisher information flow in artificial neural networks
par: Weimar, Maximilian, et autres
Publié: (2025)

Accuracy estimation of neural networks by extreme value theory
par: Junike, Gero, et autres
Publié: (2025)

Stably unactivated neurons in ReLU neural networks
par: Brownlowe, Natalie, et autres
Publié: (2024)

Gaussian random field approximation via Stein's method with applications to wide random neural networks
par: Balasubramanian, Krishnakumar, et autres
Publié: (2023)

Space-time deep neural network approximations for high-dimensional partial differential equations
par: Hornung, Fabian, et autres
Publié: (2020)

A mathematical theory for understanding when abstract representations emerge in neural networks
par: Wang, Bin, et autres
Publié: (2025)

Binomial flows: Denoising and flow matching for discrete ordinal data
par: Shenfeld, Yair, et autres
Publié: (2026)

Distributionally robust approximation property of neural networks
par: Ceylan, Mihriban, et autres
Publié: (2025)

Asymptotic convexity of wide and shallow neural networks
par: Borkar, Vivek, et autres
Publié: (2025)

Analyzing homogenous and heterogeneous multi-server queues via neural networks
par: Sherzer, Eliran
Publié: (2025)

Universal approximation property of Banach space-valued random feature models including random neural networks
par: Neufeld, Ariel, et autres
Publié: (2023)

A Malliavin calculus approach to score functions in diffusion generative models
par: Mirafzali, Ehsan, et autres
Publié: (2025)

Wasserstein Convergence of Score-based Generative Models under Semiconvexity and Discontinuous Gradients
par: Bruno, Stefano, et autres
Publié: (2025)

A new look on large deviations and concentration inequalities for the Ewens-Pitman model
par: Bercu, Bernard, et autres
Publié: (2025)

Residual connections provably mitigate oversmoothing in graph neural networks
par: Chen, Ziang, et autres
Publié: (2025)

Low-power multi-mode fiber projector overcomes shallow neural networks classifiers
par: Ancora, Daniele, et autres
Publié: (2022)

Random-Bridges as Stochastic Transports for Generative Models
par: Goria, Stefano, et autres
Publié: (2025)

Non-asymptotic convergence analysis of the stochastic gradient Hamiltonian Monte Carlo algorithm with discontinuous stochastic gradient with applications to training of ReLU neural networks
par: Liang, Luxu, et autres
Publié: (2024)

Long-time asymptotics of noisy SVGD outside the population limit
par: Priser, Victor, et autres
Publié: (2024)