Cover Image

Dissecting a Small Artificial Neural Network

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Yang, Xiguang, Arora, Krish, Bachmann, Michael
Format:	Preprint
Published:	2025
Subjects:	Disordered Systems and Neural Networks Statistical Mechanics Machine Learning Computational Physics
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Universal Scaling Laws of Absorbing Phase Transitions in Artificial Deep Neural Networks
by: Tamai, Keiichi, et al.
Published: (2023)

Thermodynamics-inspired Explanations of Artificial Intelligence
by: Mehdi, Shams, et al.
Published: (2022)

Dissecting the Interplay of Attention Paths in a Statistical Mechanics Theory of Transformers
by: Tiberi, Lorenzo, et al.
Published: (2024)

Inferring Higher-Order Couplings with Neural Networks
by: Decelle, Aurélien, et al.
Published: (2025)

Inference in Spreading Processes with Neural-Network Priors
by: Ghio, Davide, et al.
Published: (2025)

An Analytical Characterization of Sloppiness in Neural Networks: Insights from Linear Models
by: Mao, Jialin, et al.
Published: (2025)

Gaussian Universality in Neural Network Dynamics with Generalized Structured Input Distributions
by: Bae, Jaeyong, et al.
Published: (2024)

How do Probabilistic Graphical Models and Graph Neural Networks Look at Network Data?
by: Lapenna, Michela, et al.
Published: (2025)

Statistical Mechanics and Artificial Neural Networks: Principles, Models, and Applications
by: Böttcher, Lucas, et al.
Published: (2024)

The committee machine: Computational to statistical gaps in learning a two-layers neural network
by: Aubin, Benjamin, et al.
Published: (2018)

Universal Approximation of Mean-Field Models via Transformers
by: Biswal, Shiba, et al.
Published: (2024)

Variational Neural Annealing
by: Hibat-Allah, Mohamed, et al.
Published: (2021)

Nearest-Neighbours Neural Network architecture for efficient sampling of statistical physics models
by: Del Bono, Luca Maria, et al.
Published: (2024)

Message Passing Variational Autoregressive Network for Solving Intractable Ising Models
by: Ma, Qunlong, et al.
Published: (2024)

Dataset-learning duality and emergent criticality
by: Kukleva, Ekaterina, et al.
Published: (2024)

Thermodynamics of bidirectional associative memories
by: Barra, Adriano, et al.
Published: (2022)

Sparse Autoregressive Neural Networks for Classical Spin Systems
by: Biazzo, Indaco, et al.
Published: (2024)

Neural Network Matrix Product Operator: A Multi-Dimensionally Integrable Machine Learning Potential
by: Hino, Kentaro, et al.
Published: (2024)

The Symmetric Perceptron: a Teacher-Student Scenario
by: Catania, Giovanni, et al.
Published: (2026)

Spectral Architecture Search for Neural Network Models
by: Peri, Gianluca, et al.
Published: (2025)

Discrete generative diffusion models without stochastic differential equations: a tensor network approach
by: Causer, Luke, et al.
Published: (2024)

Weight fluctuations in (deep) linear neural networks and a derivation of the inverse-variance flatness relation
by: Gross, Markus, et al.
Published: (2023)

Two failure modes of deep transformers and how to avoid them: a unified theory of signal propagation at initialisation
by: Giorlandino, Alessio, et al.
Published: (2025)

Entropic Confinement and Mode Connectivity in Overparameterized Neural Networks
by: Di Carlo, Luca, et al.
Published: (2025)

Generative artificial intelligence for computational chemistry: a roadmap to predicting emergent phenomena
by: Tiwary, Pratyush, et al.
Published: (2024)

Branch-and-Bound Tensor Networks for Exact Ground-State Characterization
by: Wang, Yijia, et al.
Published: (2026)

Amorphous Solid Model of Vectorial Hopfield Neural Networks
by: Gallavotti, F., et al.
Published: (2025)

Overparametrization bends the landscape: BBP transitions at initialization in simple Neural Networks
by: Annesi, Brandon Livio, et al.
Published: (2025)

Analytic theory of dropout regularization
by: Mori, Francesco, et al.
Published: (2025)

The Physics of Data and Tasks: Theories of Locality and Compositionality in Deep Learning
by: Favero, Alessandro
Published: (2025)

A theoretical framework for overfitting in energy-based modeling
by: Catania, Giovanni, et al.
Published: (2025)

Uncertainty in AI-driven Monte Carlo simulations
by: Tzivrailis, Dimitrios, et al.
Published: (2025)

Pseudo-likelihood produces associative memories able to generalize, even for asymmetric couplings
by: D'Amico, Francesco, et al.
Published: (2025)

On the role of non-linear latent features in bipartite generative neural networks
by: Bonnaire, Tony, et al.
Published: (2025)

PCA recovery thresholds in low-rank matrix inference with sparse noise
by: Adomaityte, Urte, et al.
Published: (2025)

The Copycat Perceptron: Smashing Barriers Through Collective Learning
by: Catania, Giovanni, et al.
Published: (2023)

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)

The autoregressive neural network architecture of the Boltzmann distribution of pairwise interacting spins systems
by: Biazzo, Indaco
Published: (2023)

Distinct mechanisms underlying in-context learning in transformers
by: Gibson, Cole, et al.
Published: (2026)

Interpreting the Synchronization Gap: The Hidden Mechanism Inside Diffusion Transformers
by: Albrychiewicz, Emil, et al.
Published: (2026)