Statistical Mechanics and Artificial Neural Networks: Principles, Models, and Applications
Fuente:
arXiv
Saved in:
| Main Authors: | Böttcher, Lucas, Wheeler, Gregory |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Spectral Architecture Search for Neural Network Models
by: Peri, Gianluca, et al.
Published: (2025)
by: Peri, Gianluca, et al.
Published: (2025)
Entropic Confinement and Mode Connectivity in Overparameterized Neural Networks
by: Di Carlo, Luca, et al.
Published: (2025)
by: Di Carlo, Luca, et al.
Published: (2025)
Estimating Global Input Relevance and Enforcing Sparse Representations with a Scalable Spectral Neural Network Approach
by: Chicchi, Lorenzo, et al.
Published: (2024)
by: Chicchi, Lorenzo, et al.
Published: (2024)
Universal Scaling Laws of Absorbing Phase Transitions in Artificial Deep Neural Networks
by: Tamai, Keiichi, et al.
Published: (2023)
by: Tamai, Keiichi, et al.
Published: (2023)
Stable Attractors for Neural networks classification via Ordinary Differential Equations (SA-nODE)
by: Marino, Raffaele, et al.
Published: (2023)
by: Marino, Raffaele, et al.
Published: (2023)
A Minimal Model of Representation Collapse: Frustration, Stop-Gradient, and Dynamics
by: Yao, Louie Hong, et al.
Published: (2026)
by: Yao, Louie Hong, et al.
Published: (2026)
Dissecting the Interplay of Attention Paths in a Statistical Mechanics Theory of Transformers
by: Tiberi, Lorenzo, et al.
Published: (2024)
by: Tiberi, Lorenzo, et al.
Published: (2024)
Deep Neural Nets as Hamiltonians
by: Winer, Mike, et al.
Published: (2025)
by: Winer, Mike, et al.
Published: (2025)
An Analytical Characterization of Sloppiness in Neural Networks: Insights from Linear Models
by: Mao, Jialin, et al.
Published: (2025)
by: Mao, Jialin, et al.
Published: (2025)
A Generative Neural Annealer for Black-Box Combinatorial Optimization
by: Zhang, Yuan-Hang, et al.
Published: (2025)
by: Zhang, Yuan-Hang, et al.
Published: (2025)
Deterministic versus stochastic dynamical classifiers: opposing random adversarial attacks with noise
by: Chicchi, Lorenzo, et al.
Published: (2024)
by: Chicchi, Lorenzo, et al.
Published: (2024)
Phase transitions in the mini-batch size for sparse and dense two-layer neural networks
by: Marino, Raffaele, et al.
Published: (2023)
by: Marino, Raffaele, et al.
Published: (2023)
Inferring Higher-Order Couplings with Neural Networks
by: Decelle, Aurélien, et al.
Published: (2025)
by: Decelle, Aurélien, et al.
Published: (2025)
Dissecting a Small Artificial Neural Network
by: Yang, Xiguang, et al.
Published: (2025)
by: Yang, Xiguang, et al.
Published: (2025)
Gaussian Universality in Neural Network Dynamics with Generalized Structured Input Distributions
by: Bae, Jaeyong, et al.
Published: (2024)
by: Bae, Jaeyong, et al.
Published: (2024)
Optimal Protocols for Continual Learning via Statistical Physics and Control Theory
by: Mori, Francesco, et al.
Published: (2024)
by: Mori, Francesco, et al.
Published: (2024)
Message Passing Variational Autoregressive Network for Solving Intractable Ising Models
by: Ma, Qunlong, et al.
Published: (2024)
by: Ma, Qunlong, et al.
Published: (2024)
Interpreting the Synchronization Gap: The Hidden Mechanism Inside Diffusion Transformers
by: Albrychiewicz, Emil, et al.
Published: (2026)
by: Albrychiewicz, Emil, et al.
Published: (2026)
Statistical Mechanics of the Sub-Optimal Transport
by: Piombo, Riccardo, et al.
Published: (2026)
by: Piombo, Riccardo, et al.
Published: (2026)
Biased Generalization in Diffusion Models
by: Garnier-Brun, Jerome, et al.
Published: (2026)
by: Garnier-Brun, Jerome, et al.
Published: (2026)
How do Probabilistic Graphical Models and Graph Neural Networks Look at Network Data?
by: Lapenna, Michela, et al.
Published: (2025)
by: Lapenna, Michela, et al.
Published: (2025)
Rare Event Analysis of Large Language Models
by: Dorman, Jake McAllister, et al.
Published: (2026)
by: Dorman, Jake McAllister, et al.
Published: (2026)
Explaining the effects of non-convergent sampling in the training of Energy-Based Models
by: Agoritsas, Elisabeth, et al.
Published: (2023)
by: Agoritsas, Elisabeth, et al.
Published: (2023)
Variational Evolutionary Network for Statistical Physics Systems
by: Ren, Yixiong, et al.
Published: (2024)
by: Ren, Yixiong, et al.
Published: (2024)
Intuition emerges in Maximum Caliber models at criticality
by: Arola-Fernández, Lluís
Published: (2025)
by: Arola-Fernández, Lluís
Published: (2025)
Performance of machine-learning-assisted Monte Carlo in sampling from simple statistical physics models
by: Del Bono, Luca Maria, et al.
Published: (2025)
by: Del Bono, Luca Maria, et al.
Published: (2025)
Demonstrating Real Advantage of Machine-Learning-Enhanced Monte Carlo for Combinatorial Optimization
by: Del Bono, Luca Maria, et al.
Published: (2025)
by: Del Bono, Luca Maria, et al.
Published: (2025)
Emergent Slow Thinking in LLMs as Inverse Tree Freezing
by: Hu, Sihan, et al.
Published: (2025)
by: Hu, Sihan, et al.
Published: (2025)
The critical slowing down in diffusion models
by: Del Bono, Luca Maria, et al.
Published: (2026)
by: Del Bono, Luca Maria, et al.
Published: (2026)
Inference in Spreading Processes with Neural-Network Priors
by: Ghio, Davide, et al.
Published: (2025)
by: Ghio, Davide, et al.
Published: (2025)
Overparametrization bends the landscape: BBP transitions at initialization in simple Neural Networks
by: Annesi, Brandon Livio, et al.
Published: (2025)
by: Annesi, Brandon Livio, et al.
Published: (2025)
Fundamental operating regimes, hyper-parameter fine-tuning and glassiness: towards an interpretable replica-theory for trained restricted Boltzmann machines
by: Fachechi, Alberto, et al.
Published: (2024)
by: Fachechi, Alberto, et al.
Published: (2024)
Discrete generative diffusion models without stochastic differential equations: a tensor network approach
by: Causer, Luke, et al.
Published: (2024)
by: Causer, Luke, et al.
Published: (2024)
Transfer Learning in $\ell_1$ Regularized Regression: Hyperparameter Selection Strategy based on Sharp Asymptotic Analysis
by: Okajima, Koki, et al.
Published: (2024)
by: Okajima, Koki, et al.
Published: (2024)
Dynamic neuron approach to deep neural networks: Decoupling neurons for renormalization group analysis
by: Lee, Donghee, et al.
Published: (2024)
by: Lee, Donghee, et al.
Published: (2024)
Generalization vs. Specialization under Concept Shift
by: Nguyen, Alex, et al.
Published: (2024)
by: Nguyen, Alex, et al.
Published: (2024)
Coding schemes in neural networks learning classification tasks
by: van Meegen, Alexander, et al.
Published: (2024)
by: van Meegen, Alexander, et al.
Published: (2024)
Nonequilbrium physics of generative diffusion models
by: Yu, Zhendong, et al.
Published: (2024)
by: Yu, Zhendong, et al.
Published: (2024)
A replica analysis of under-bagging
by: Takahashi, Takashi
Published: (2024)
by: Takahashi, Takashi
Published: (2024)
Spring-block theory of feature learning in deep neural networks
by: Shi, Cheng, et al.
Published: (2024)
by: Shi, Cheng, et al.
Published: (2024)
Similar Items
-
Spectral Architecture Search for Neural Network Models
by: Peri, Gianluca, et al.
Published: (2025) -
Entropic Confinement and Mode Connectivity in Overparameterized Neural Networks
by: Di Carlo, Luca, et al.
Published: (2025) -
Estimating Global Input Relevance and Enforcing Sparse Representations with a Scalable Spectral Neural Network Approach
by: Chicchi, Lorenzo, et al.
Published: (2024) -
Universal Scaling Laws of Absorbing Phase Transitions in Artificial Deep Neural Networks
by: Tamai, Keiichi, et al.
Published: (2023) -
Stable Attractors for Neural networks classification via Ordinary Differential Equations (SA-nODE)
by: Marino, Raffaele, et al.
Published: (2023)