Cover Image

Quantitative CLTs in Deep Neural Networks

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Favaro, Stefano, Hanin, Boris, Marinucci, Domenico, Nourdin, Ivan, Peccati, Giovanni
Format:	Preprint
Published:	2023
Subjects:	Machine Learning Artificial Intelligence Probability
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

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

Deep Neural Nets as Hamiltonians
by: Winer, Mike, et al.
Published: (2025)

Entropic bounds for conditionally Gaussian vectors and applications to neural networks
by: Celli, Lucia, et al.
Published: (2025)

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

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

Optimal local central limit theorems on Wiener chaos
by: Ebina, Masahisa, et al.
Published: (2025)

Neural Network Parameter-optimization of Gaussian pmDAGs
by: Saremi, Mehrzad
Published: (2023)

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

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

Fractal and Regular Geometry of Deep Neural Networks
by: Di Lillo, Simmaco, et al.
Published: (2025)

Neural Expectation Operators
by: Qi, Qian
Published: (2025)

Deep Conditional Measure Quantization
by: Turinici, Gabriel
Published: (2023)

Learning Rate Transfer in Normalized Transformers
by: Shigida, Boris, et al.
Published: (2026)

Phase Transitions in the Fluctuations of Functionals of Random Neural Networks
by: Di Lillo, Simmaco, et al.
Published: (2026)

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

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

Advancing Deep Learning through Probability Engineering: A Pragmatic Paradigm for Modern AI
by: Zhang, Jianyi
Published: (2025)

Explicit Density Approximation for Neural Implicit Samplers Using a Bernstein-Based Convex Divergence
by: de Frutos, José Manuel, et al.
Published: (2025)

Implicit Bias of the JKO Scheme
by: Halmos, Peter, et al.
Published: (2025)

Neural Brownian Motion
by: Qi, Qian
Published: (2025)

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

Large-width functional asymptotics for deep Gaussian neural networks
by: Bracale, Daniele, et al.
Published: (2021)

Toward Dynamic Stability Assessment of Power Grid Topologies using Graph Neural Networks
by: Nauck, Christian, et al.
Published: (2022)

A Systematic Literature Review of Spatio-Temporal Graph Neural Network Models for Time Series Forecasting and Classification
by: Corradini, Flavio, et al.
Published: (2024)

Multi-hop Upstream Anticipatory Traffic Signal Control with Deep Reinforcement Learning
by: Li, Xiaocan, et al.
Published: (2024)

Learning Chaotic Systems and Long-Term Predictions with Neural Jump ODEs
by: Krach, Florian, et al.
Published: (2024)

$α$-TCAV: A Unified Framework for Testing with Concept Activation Vectors
by: Schnoor, Ekkehard, et al.
Published: (2026)

Causal Effect Identification in Heterogeneous Environments from Higher-Order Moments
by: Kivva, Yaroslav, et al.
Published: (2025)

A Unified Theory of $θ$-Expectations
by: Qi, Qian
Published: (2025)

Nonparametric Identification of Latent Concepts
by: Zheng, Yujia, et al.
Published: (2025)

Representative Arm Identification: A fixed confidence approach to identify cluster representatives
by: Gharat, Sarvesh, et al.
Published: (2024)

Greedy Selection under Independent Increments: A Toy Model Analysis
by: Yang, Huitao
Published: (2025)

A Mean-Field Theory of $Θ$-Expectations
by: Qi, Qian
Published: (2025)

Note on Martingale Theory and Applications
by: Zou, Xiandong
Published: (2026)

Beyond Propagation of Chaos: A Stochastic Algorithm for Mean Field Optimization
by: Tankala, Chandan, et al.
Published: (2025)

Soft-to-Hard Routing in Sparse Mixture-of-Experts Models
by: Rastegar, Reza
Published: (2026)

Generalisation of Total Uncertainty in AI: A Theoretical Study
by: Shariatmadar, Keivan
Published: (2024)

Generalized Gaussian Temporal Difference Error for Uncertainty-aware Reinforcement Learning
by: Kim, Seyeon, et al.
Published: (2024)

Neural Thermodynamic Laws for Large Language Model Training
by: Liu, Ziming, et al.
Published: (2025)

A Neural Scaling Law from Lottery Ticket Ensembling
by: Liu, Ziming, et al.
Published: (2023)