Student-t processes as infinite-width limits of posterior Bayesian neural networks
Fuente:
arXiv
Saved in:
| Main Authors: | Caporali, Francesco, Favaro, Stefano, Trevisan, Dario |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Quantitative convergence of trained single layer neural networks to Gaussian processes
by: Mosig, Eloy, et al.
Published: (2025)
by: Mosig, Eloy, et al.
Published: (2025)
Large-width functional asymptotics for deep Gaussian neural networks
by: Bracale, Daniele, et al.
Published: (2021)
by: Bracale, Daniele, et al.
Published: (2021)
Large deviation principles for convolutional Bayesian neural networks
by: Bassetti, Federico, et al.
Published: (2026)
by: Bassetti, Federico, et al.
Published: (2026)
Exact full-RSB SAT/UNSAT transition in infinitely wide two-layer neural networks
by: Annesi, Brandon L., et al.
Published: (2024)
by: Annesi, Brandon L., et al.
Published: (2024)
Proportional infinite-width infinite-depth limit for deep linear neural networks
by: Bassetti, Federico, et al.
Published: (2024)
by: Bassetti, Federico, et al.
Published: (2024)
A Gaussian process limit for the self-normalized Ewens-Pitman process
by: Bercu, Bernard, et al.
Published: (2026)
by: Bercu, Bernard, et al.
Published: (2026)
Trained quantum neural networks are Gaussian processes
by: Girardi, Filippo, et al.
Published: (2024)
by: Girardi, Filippo, et al.
Published: (2024)
Holographic functions and neural networks
by: Szegedy, Balazs
Published: (2026)
by: Szegedy, Balazs
Published: (2026)
Accuracy estimation of neural networks by extreme value theory
by: Junike, Gero, et al.
Published: (2025)
by: Junike, Gero, et al.
Published: (2025)
Stably unactivated neurons in ReLU neural networks
by: Brownlowe, Natalie, et al.
Published: (2024)
by: Brownlowe, Natalie, et al.
Published: (2024)
Quantitative CLTs in Deep Neural Networks
by: Favaro, Stefano, et al.
Published: (2023)
by: Favaro, Stefano, et al.
Published: (2023)
A simple algorithm for output range analysis for deep neural networks
by: Rojas, Helder, et al.
Published: (2024)
by: Rojas, Helder, et al.
Published: (2024)
Approximation and interpolation of deep neural networks
by: Constantinescu, Vlad-Raul, et al.
Published: (2023)
by: Constantinescu, Vlad-Raul, et al.
Published: (2023)
Partially Stochastic Infinitely Deep Bayesian Neural Networks
by: Calvo-Ordonez, Sergio, et al.
Published: (2024)
by: Calvo-Ordonez, Sergio, et al.
Published: (2024)
Are Bayesian networks typically faithful?
by: Boeken, Philip, et al.
Published: (2024)
by: Boeken, Philip, et al.
Published: (2024)
Distributionally robust approximation property of neural networks
by: Ceylan, Mihriban, et al.
Published: (2025)
by: Ceylan, Mihriban, et al.
Published: (2025)
Asymptotic convexity of wide and shallow neural networks
by: Borkar, Vivek, et al.
Published: (2025)
by: Borkar, Vivek, et al.
Published: (2025)
Analyzing homogenous and heterogeneous multi-server queues via neural networks
by: Sherzer, Eliran
Published: (2025)
by: Sherzer, Eliran
Published: (2025)
Universal approximation property of Banach space-valued random feature models including random neural networks
by: Neufeld, Ariel, et al.
Published: (2023)
by: Neufeld, Ariel, et al.
Published: (2023)
Residual connections provably mitigate oversmoothing in graph neural networks
by: Chen, Ziang, et al.
Published: (2025)
by: Chen, Ziang, et al.
Published: (2025)
Fisher information flow in artificial neural networks
by: Weimar, Maximilian, et al.
Published: (2025)
by: Weimar, Maximilian, et al.
Published: (2025)
Infinite-channel deep stable convolutional neural networks
by: Bracale, Daniele, et al.
Published: (2021)
by: Bracale, Daniele, et al.
Published: (2021)
Nonlinear spiked covariance matrices and signal propagation in deep neural networks
by: Wang, Zhichao, et al.
Published: (2024)
by: Wang, Zhichao, et al.
Published: (2024)
Solving stochastic partial differential equations using neural networks in the Wiener chaos expansion
by: Neufeld, Ariel, et al.
Published: (2024)
by: Neufeld, Ariel, et al.
Published: (2024)
Polygonal Unadjusted Langevin Algorithms: Creating stable and efficient adaptive algorithms for neural networks
by: Lim, Dong-Young, et al.
Published: (2021)
by: Lim, Dong-Young, et al.
Published: (2021)
Phase transitions reveal hierarchical structure in deep neural networks
by: Ersoy, Ibrahim Talha, et al.
Published: (2025)
by: Ersoy, Ibrahim Talha, et al.
Published: (2025)
Similarity Learning with neural networks
by: Sanfins, Gabriel, et al.
Published: (2024)
by: Sanfins, Gabriel, et al.
Published: (2024)
Convergence rates for gradient descent in the training of overparameterized artificial neural networks with piecewise affine activation
by: Jentzen, Arnulf, et al.
Published: (2021)
by: Jentzen, Arnulf, et al.
Published: (2021)
Gaussian random field approximation via Stein's method with applications to wide random neural networks
by: Balasubramanian, Krishnakumar, et al.
Published: (2023)
by: Balasubramanian, Krishnakumar, et al.
Published: (2023)
Non-asymptotic approximations of Gaussian neural networks via second-order Poincaré inequalities
by: Bordino, Alberto, et al.
Published: (2023)
by: Bordino, Alberto, et al.
Published: (2023)
Fixed width treelike neural networks capacity analysis -- generic activations
by: Stojnic, Mihailo
Published: (2024)
by: Stojnic, Mihailo
Published: (2024)
From Betting to Empirical Bernstein LIL
by: Orabona, Francesco
Published: (2026)
by: Orabona, Francesco
Published: (2026)
BN-Pool: Bayesian Nonparametric Pooling for Graphs
by: Castellana, Daniele, et al.
Published: (2025)
by: Castellana, Daniele, et al.
Published: (2025)
Bayesian inference of planted matchings: Local posterior approximation and infinite-volume limit
by: Fan, Zhou, et al.
Published: (2026)
by: Fan, Zhou, et al.
Published: (2026)
Uncertainty quantification and posterior sampling for network reconstruction
by: Peixoto, Tiago P.
Published: (2025)
by: Peixoto, Tiago P.
Published: (2025)
Spectral complexity of deep neural networks
by: Di Lillo, Simmaco, et al.
Published: (2024)
by: Di Lillo, Simmaco, et al.
Published: (2024)
Variational Bayesian inference for CP tensor completion with side information
by: Budzinskiy, Stanislav, et al.
Published: (2022)
by: Budzinskiy, Stanislav, et al.
Published: (2022)
Laws of large numbers and central limit theorem for Ewens-Pitman model
by: Contardi, Claudia, et al.
Published: (2024)
by: Contardi, Claudia, et al.
Published: (2024)
Singular-limit analysis of gradient descent with noise injection
by: Shalova, Anna, et al.
Published: (2024)
by: Shalova, Anna, et al.
Published: (2024)
The twin peaks of learning neural networks
by: Demyanenko, Elizaveta, et al.
Published: (2024)
by: Demyanenko, Elizaveta, et al.
Published: (2024)
Similar Items
-
Quantitative convergence of trained single layer neural networks to Gaussian processes
by: Mosig, Eloy, et al.
Published: (2025) -
Large-width functional asymptotics for deep Gaussian neural networks
by: Bracale, Daniele, et al.
Published: (2021) -
Large deviation principles for convolutional Bayesian neural networks
by: Bassetti, Federico, et al.
Published: (2026) -
Exact full-RSB SAT/UNSAT transition in infinitely wide two-layer neural networks
by: Annesi, Brandon L., et al.
Published: (2024) -
Proportional infinite-width infinite-depth limit for deep linear neural networks
by: Bassetti, Federico, et al.
Published: (2024)