Image de couverture de livre

A method for quantifying the generalization capabilities of generative models for solving Ising models

Fuente: arXiv

Enregistré dans:

Détails bibliographiques
Auteurs principaux:	Ma, Qunlong, Ma, Zhi, Gao, Ming
Format:	Preprint
Publié:	2024
Sujets:	Disordered Systems and Neural Networks Artificial Intelligence Machine Learning
Accès en ligne:	Acceder al recurso
Tags:	Ajouter un tag Pas de tags, Soyez le premier à ajouter un tag!

Documents similaires

Message Passing Variational Autoregressive Network for Solving Intractable Ising Models
par: Ma, Qunlong, et autres
Publié: (2024)

The Persian Rug: solving toy models of superposition using large-scale symmetries
par: Cowsik, Aditya, et autres
Publié: (2024)

Generalization through variance: how noise shapes inductive biases in diffusion models
par: Vastola, John J.
Publié: (2025)

Autoregressive model path dependence near Ising criticality
par: Teoh, Yi Hong, et autres
Publié: (2024)

A solvable model of learning generative diffusion: theory and insights
par: Cui, Hugo, et autres
Publié: (2025)

A Spin Glass Characterization of Neural Networks
par: Li, Jun
Publié: (2025)

A Geometric Perspective on the Difficulties of Learning GNN-based SAT Solvers
par: Skenderi, Geri
Publié: (2025)

A Scalable Measure of Loss Landscape Curvature for Analyzing the Training Dynamics of LLMs
par: Kalra, Dayal Singh, et autres
Publié: (2026)

Connecting NTK and NNGP: A Unified Theoretical Framework for Wide Neural Network Learning Dynamics
par: Avidan, Yehonatan, et autres
Publié: (2023)

Non-equilibrium active noise enhances generative memory in diffusion models
par: Behera, Agnish Kumar, et autres
Publié: (2024)

KAN: Kolmogorov-Arnold Networks
par: Liu, Ziming, et autres
Publié: (2024)

How Do Transformers "Do" Physics? Investigating the Simple Harmonic Oscillator
par: Kantamneni, Subhash, et autres
Publié: (2024)

Smooth Kolmogorov Arnold networks enabling structural knowledge representation
par: Samadi, Moein E., et autres
Publié: (2024)

Scaling Laws and Spectra of Shallow Neural Networks in the Feature Learning Regime
par: Defilippis, Leonardo, et autres
Publié: (2025)

Identifying internal patterns in (1+1)-dimensional directed percolation using neural networks
par: Parkhomenko, Danil, et autres
Publié: (2025)

Grokking vs. Learning: Same Features, Different Encodings
par: Manning-Coe, Dmitry, et autres
Publié: (2025)

Quantifying Hyperparameter Transfer and the Importance of Embedding Layer Learning Rate
par: Kalra, Dayal Singh, et autres
Publié: (2026)

Applications of Statistical Field Theory in Deep Learning
par: Ringel, Zohar, et autres
Publié: (2025)

Spectral Dynamics in Deep Networks: Feature Learning, Outlier Escape, and Learning Rate Transfer
par: Lauditi, Clarissa, et autres
Publié: (2026)

On the origin of neural scaling laws: from random graphs to natural language
par: Barkeshli, Maissam, et autres
Publié: (2026)

Learning Shrinks the Hard Tail: Training-Dependent Inference Scaling in a Solvable Linear Model
par: Levi, Noam
Publié: (2026)

More Bang for the Buck: Improving the Inference of Large Language Models at a Fixed Budget using Reset and Discard (ReD)
par: Meir, Sagi, et autres
Publié: (2026)

Enhancing Noise-Robust Losses for Large-Scale Noisy Data Learning
par: Staats, Max, et autres
Publié: (2023)

Representation Learning on a Random Lattice
par: Brill, Aryeh
Publié: (2025)

Towards Distributed Neural Architectures
par: Cowsik, Aditya, et autres
Publié: (2025)

Parameter Symmetry Potentially Unifies Deep Learning Theory
par: Ziyin, Liu, et autres
Publié: (2025)

Preisach Attention: A Hysteretic Model of Sequential Memory
par: Frydrych, Piotr
Publié: (2026)

A solvable high-dimensional model where nonlinear autoencoders learn structure invisible to PCA while test loss misaligns with generalization
par: Mendes, Vicente Conde, et autres
Publié: (2026)

Solving excited states for long-range interacting trapped ions with neural networks
par: Ma, Yixuan, et autres
Publié: (2025)

Nature-Inspired Local Propagation
par: Betti, Alessandro, et autres
Publié: (2024)

Predictive Coding Networks and Inference Learning: Tutorial and Survey
par: van Zwol, Björn, et autres
Publié: (2024)

Predictive Coding Graphs are a Superset of Feedforward Neural Networks
par: van Zwol, Björn
Publié: (2026)

Approximation Theory for Neural Networks: Old and New
par: Mukherjee, Soumendu Sundar, et autres
Publié: (2026)

Machine learning the Ising transition: A comparison between discriminative and generative approaches
par: Zhang, Difei, et autres
Publié: (2024)

Differential learning kinetics govern the transition from memorization to generalization during in-context learning
par: Nguyen, Alex, et autres
Publié: (2024)

$L_0$ Regularization of Field-Aware Factorization Machine through Ising Model
par: Okamoto, Yasuharu
Publié: (2024)

Lattice Protein Folding with Variational Annealing
par: Khandoker, Shoummo Ahsan, et autres
Publié: (2025)

Nonequilbrium physics of generative diffusion models
par: Yu, Zhendong, et autres
Publié: (2024)

Memorisation, convergence and generalisation in generative models
par: Maillard, Antoine, et autres
Publié: (2026)

Graph Neural Network Approach to Predicting Magnetization in Quasi-One-Dimensional Ising Systems
par: Slavin, V., et autres
Publié: (2025)