Cover Image

Phase transitions in the mini-batch size for sparse and dense two-layer neural networks

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Marino, Raffaele, Ricci-Tersenghi, Federico
Format:	Preprint
Published:	2023
Subjects:	Disordered Systems and Neural Networks Statistical Mechanics Artificial Intelligence Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Analytical solution to Heisenberg spin glass models on sparse random graphs and their de Almeida-Thouless line
by: Del Bono, Luca Maria, et al.
Published: (2024)

Discontinuous BBP transitions
by: Bocchi, Dario, et al.
Published: (2026)

Strong ergodicity breaking in dynamical mean-field equations for mixed p-spin glasses
by: Citro, Vincenzo, et al.
Published: (2025)

Biased thermodynamics can explain the behaviour of smart optimization algorithms that work above the dynamical threshold
by: Cavaliere, Angelo Giorgio, et al.
Published: (2023)

Stochastic Gradient Descent-like relaxation is equivalent to Metropolis dynamics in discrete optimization and inference problems
by: Angelini, Maria Chiara, et al.
Published: (2023)

Transition path sampling in Ising models on heterogeneous graphs
by: Cipolloni, Riccardo, et al.
Published: (2026)

Networks of neural networks: more is different
by: Agliari, Elena, et al.
Published: (2025)

Interacting Copies of Random Constraint Satisfaction Problems
by: Angelini, Maria Chiara, et al.
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)

Demonstrating Real Advantage of Machine-Learning-Enhanced Monte Carlo for Combinatorial Optimization
by: Del Bono, Luca Maria, et al.
Published: (2025)

Stable Attractors for Neural networks classification via Ordinary Differential Equations (SA-nODE)
by: Marino, Raffaele, et al.
Published: (2023)

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

The Garden of Forking Paths: Observing Dynamic Parameters Distribution in Large Language Models
by: Nicolini, Carlo, et al.
Published: (2024)

Multiscaling in the 3D critical site-diluted Ising ferromagnet
by: Marinari, E., et al.
Published: (2023)

Estimating Global Input Relevance and Enforcing Sparse Representations with a Scalable Spectral Neural Network Approach
by: Chicchi, Lorenzo, et al.
Published: (2024)

Predictive power of a Bayesian effective action for fully-connected one hidden layer neural networks in the proportional limit
by: Baglioni, P., et al.
Published: (2024)

Application of deep neural networks for computing the renormalization group flow of the two-dimensional phi^4 field theory
by: Zhao, Yueqi, et al.
Published: (2025)

Microscopic and collective signatures of feature learning in neural networks
by: Corti, Andrea, et al.
Published: (2025)

Low energy excitations in a long prism geometry: computing the lower critical dimension of the Ising spin glass
by: Bernaschi, Massimo, et al.
Published: (2026)

Train Stochastic Non Linear Coupled ODEs to Classify and Generate
by: Gagliani, Stefano, et al.
Published: (2025)

Learning in Wilson-Cowan model for metapopulation
by: Marino, Raffaele, et al.
Published: (2024)

Banded non-Hermitian random matrices, neural networks, and eigenvalue degeneracies
by: Huang, Richard, et al.
Published: (2026)

Phase probabilities in first-order transitions using machine learning
by: Sukhoverkhova, Diana, et al.
Published: (2024)

Competing nonlinearities, criticality, and order-to-chaos transition in deep networks
by: Lesser, Omri, et al.
Published: (2026)

Spectral properties, localization transition and multifractal eigenvectors of the Laplacian on heterogeneous networks
by: da Silva, Jeferson D., et al.
Published: (2024)

Synaptic plasticity alters the nature of chaos transition in neural networks
by: Du, Wenkang, et al.
Published: (2024)

Information content in continuous attractor neural networks is preserved in the presence of moderate disordered background connectivity
by: Kühn, Tobias, et al.
Published: (2023)

Sampling the space of solutions of an artificial neural network
by: Zambon, Alessandro, et al.
Published: (2025)

A Fourier perspective on the learning dynamics of neural networks: from sample complexities to mechanistic insights
by: Ricci, Fabiola, et al.
Published: (2026)

Non-linear classification capability of quantum neural networks due to emergent quantum metastability
by: Boneberg, Mario, et al.
Published: (2024)

Electrical conductivity of a random nanowire network: comparison of two-dimensional and quasi-three-dimensional models
by: Tarasevich, Yuri Yu., et al.
Published: (2026)

Deterministic versus stochastic dynamical classifiers: opposing random adversarial attacks with noise
by: Chicchi, Lorenzo, et al.
Published: (2024)

On the supra-linear storage in dense networks of grid and place cells
by: Barra, Adriano, et al.
Published: (2025)

Dissipative quantum many-body dynamics in (1+1)D quantum cellular automata and quantum neural networks
by: Boneberg, Mario, et al.
Published: (2023)

Magnetization-resolved density of states and quasi-first order transition in the two-dimensional random bond Ising model: an entropic sampling study
by: Liu, Yi, et al.
Published: (2025)

Evidence of Replica Symmetry Breaking under the Nishimori conditions in epidemic inference on graphs
by: Braunstein, Alfredo, et al.
Published: (2025)

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

Bethe $M$-layer construction for the percolation problem
by: Angelini, Maria Chiara, et al.
Published: (2024)

The dynamics of neural codes in biological and artificial neural networks
by: Morales, Guillermo B.
Published: (2024)

Dynamical stability for dense patterns in discrete attractor neural networks
by: Cohen, Uri, et al.
Published: (2025)