:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Authors:	Calvo-Ordonez, Sergio, Meunier, Matthieu, Piatti, Francesco, Shi, Yuantao
Format:	Preprint
Published:	2024
Subjects:	Machine Learning Probability
Online Access:	https://arxiv.org/abs/2402.03495
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Limit Theorems for Stochastic Gradient Descent with Infinite Variance
by: Blanchet, Jose, et al.
Published: (2024)

Feature Learning Dynamics in Infinite-Depth Neural Networks
by: Yao, Zihan, et al.
Published: (2025)

Diffusion-Based Stochastic Operator Networks for Uncertainty Quantification in Stochastic Partial Differential Equations
by: Huynh, Phuoc-Toan, et al.
Published: (2026)

Optimization Trade-offs in Asynchronous Federated Learning: A Stochastic Networks Approach
by: Alahyane, Abdelkrim, et al.
Published: (2026)

Efficiency of Parallel and Restart Exploration Strategies in Model Free Stochastic Simulations
by: Garcia, Ernesto, et al.
Published: (2025)

Approximation to Deep Q-Network by Stochastic Delay Differential Equations
by: Lu, Jianya, et al.
Published: (2025)

Bayesian Inference with Deep Weakly Nonlinear Networks
by: Hanin, Boris, et al.
Published: (2024)

Exact Gradients for Stochastic Spiking Neural Networks Driven by Rough Signals
by: Holberg, Christian, et al.
Published: (2024)

Stochastic Port-Hamiltonian Neural Networks: Universal Approximation with Passivity Guarantees
by: Di Persio, Luca, et al.
Published: (2026)

Unified Stochastic Framework for Neural Network Quantization and Pruning
by: Zhang, Haoyu, et al.
Published: (2024)

Universality in Deep Neural Networks: An approach via the Lindeberg exchange principle
by: Giovagnini, Filippo, et al.
Published: (2026)

Deep Neural Networks as Iterated Function Systems and a Generalization Bound
by: Vacher, Jonathan
Published: (2026)

On the Equivalence of Random Network Distillation, Deep Ensembles, and Bayesian Inference
by: Zanger, Moritz A., et al.
Published: (2026)

Quantitative CLTs in Deep Neural Networks
by: Favaro, Stefano, et al.
Published: (2023)

Student-t processes as infinite-width limits of posterior Bayesian neural networks
by: Caporali, Francesco, et al.
Published: (2025)

Stochastic Scaling Limits and Synchronization by Noise in Deep Transformer Models
by: Agazzi, Andrea, et al.
Published: (2026)

Central Limit Theorem for Bayesian Neural Network trained with Variational Inference
by: Descours, Arnaud, et al.
Published: (2024)

Stochastic Operator Network: A Stochastic Maximum Principle Based Approach to Operator Learning
by: Bausback, Ryan, et al.
Published: (2025)

Partial recovery and weak consistency in the non-uniform hypergraph Stochastic Block Model
by: Dumitriu, Ioana, et al.
Published: (2021)

Polynomially Over-Parameterized Convolutional Neural Networks Contain Structured Strong Winning Lottery Tickets
by: da Cunha, Arthur, et al.
Published: (2023)

Active Learning with Fully Bayesian Neural Networks for Discontinuous and Nonstationary Data
by: Ziatdinov, Maxim
Published: (2024)

Infinite-dimensional Mahalanobis Distance with Applications to Kernelized Novelty Detection
by: Zozoulenko, Nikita, et al.
Published: (2024)

Neural Laplace for learning Stochastic Differential Equations
by: Carrel, Adrien
Published: (2024)

Finite-Dimensional Gaussian Approximation for Deep Neural Networks: Universality in Random Weights
by: Balasubramanian, Krishnakumar, et al.
Published: (2025)

Bayesian Inference with Shaped Deep Non-linear MLPs
by: Hanin, Boris, et al.
Published: (2026)

Dichotomy of Feature Learning and Unlearning: Fast-Slow Analysis on Neural Networks with Stochastic Gradient Descent
by: Imai, Shota, et al.
Published: (2026)

Efficient Training of Neural SDEs Using Stochastic Optimal Control
by: Daems, Rembert, et al.
Published: (2025)

Weighted Conditional Flow Matching
by: Calvo-Ordonez, Sergio, et al.
Published: (2025)

Efficient Training of Deep Neural Operator Networks via Randomized Sampling
by: Karumuri, Sharmila, et al.
Published: (2024)

Stochastic Differential Equations models for Least-Squares Stochastic Gradient Descent
by: Schertzer, Adrien, et al.
Published: (2024)

The Loss Surfaces of Neural Networks with General Activation Functions
by: Baskerville, Nicholas P., et al.
Published: (2020)

Sampling from Bayesian Neural Network Posteriors with Symmetric Minibatch Splitting Langevin Dynamics
by: Paulin, Daniel, et al.
Published: (2024)

Depth Degeneracy in Neural Networks: Vanishing Angles in Fully Connected ReLU Networks on Initialization
by: Jakub, Cameron, et al.
Published: (2023)

Symmetries in Overparametrized Neural Networks: A Mean-Field View
by: Maass, Javier, et al.
Published: (2024)

Random ReLU Neural Networks as Non-Gaussian Processes
by: Parhi, Rahul, et al.
Published: (2024)

Convergence Analysis of Newton's Method for Neural Networks in the Overparameterized Limit
by: Riedl, Konstantin, et al.
Published: (2026)

Uncertainty Modeling in Graph Neural Networks via Stochastic Differential Equations
by: Bergna, Richard, et al.
Published: (2024)

Les Houches Lectures on Deep Learning at Large & Infinite Width
by: Bahri, Yasaman, et al.
Published: (2023)

Pointwise Generalization in Deep Neural Networks
by: Li, Shaojie, et al.
Published: (2026)

Structured Partial Stochasticity in Bayesian Neural Networks
by: Rochussen, Tommy
Published: (2024)