Copertina

Statistical Mechanics of Min-Max Problems

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Ichikawa, Yuma, Hukushima, Koji
Natura:	Preprint
Pubblicazione:	2024
Soggetti:	Machine Learning Disordered Systems and Neural Networks Statistics Theory
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

Ratio Divergence Learning Using Target Energy in Restricted Boltzmann Machines: Beyond Kullback--Leibler Divergence Learning
di: Ishida, Yuichi, et al.
Pubblicazione: (2024)

Tensor-Network Population Annealing
di: Oshima, Takumi, et al.
Pubblicazione: (2026)

Thermal Min-Max Games: Unifying Bounded Rationality and Typical-Case Equilibrium
di: Ichikawa, Yuma
Pubblicazione: (2026)

High-Dimensional Learning Dynamics of Quantized Models with Straight-Through Estimator
di: Ichikawa, Yuma, et al.
Pubblicazione: (2025)

Statistical properties of probabilistic context-sensitive grammars
di: Nakaishi, Kai, et al.
Pubblicazione: (2024)

Damage spreading and coupling in spin glasses and hard spheres
di: Hukushima, Koji, et al.
Pubblicazione: (2024)

Critical Phase Transition in Large Language Models
di: Nakaishi, Kai, et al.
Pubblicazione: (2024)

Proof of avoidability of the quantum first-order transition in transverse magnetization in quantum annealing of finite-dimensional spin glasses
di: Yamaguchi, Mizuki, et al.
Pubblicazione: (2023)

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

Kernel Density Estimators in Large Dimensions
di: Biroli, Giulio, et al.
Pubblicazione: (2024)

On the Geometry of Regularization in Adversarial Training: High-Dimensional Asymptotics and Generalization Bounds
di: Vilucchio, Matteo, et al.
Pubblicazione: (2024)

Asymptotics of Learning with Deep Structured (Random) Features
di: Schröder, Dominik, et al.
Pubblicazione: (2024)

High-dimensional robust regression under heavy-tailed data: Asymptotics and Universality
di: Adomaityte, Urte, et al.
Pubblicazione: (2023)

Asymmetric Scaling Laws from Sparse Features
di: Sous, John, et al.
Pubblicazione: (2026)

Classification of Heavy-tailed Features in High Dimensions: a Superstatistical Approach
di: Adomaityte, Urte, et al.
Pubblicazione: (2023)

Variational Gaussian Approximation in Replica Analysis of Parametric Models
di: Takahashi, Takashi
Pubblicazione: (2025)

Spectral Thresholds in Correlated Spiked Models and Fundamental Limits of Partial Least Squares
di: Mergny, Pierre, et al.
Pubblicazione: (2025)

Generalization Dynamics of Linear Diffusion Models
di: Merger, Claudia, et al.
Pubblicazione: (2025)

Effect of global shrinkage parameter of horseshoe prior in compressed sensing
di: Nagano, Yasushi, et al.
Pubblicazione: (2023)

Statistical mechanics of extensive-width Bayesian neural networks near interpolation
di: Barbier, Jean, et al.
Pubblicazione: (2025)

Statistical physics of deep learning: Optimal learning of a multi-layer perceptron near interpolation
di: Barbier, Jean, et al.
Pubblicazione: (2025)

A theoretical perspective on mode collapse in variational inference
di: Soletskyi, Roman, et al.
Pubblicazione: (2024)

The Role of Pseudo-labels in Self-training Linear Classifiers on High-dimensional Gaussian Mixture Data
di: Takahashi, Takashi
Pubblicazione: (2022)

Spectral Phase Transition and Optimal PCA in Block-Structured Spiked models
di: Mergny, Pierre, et al.
Pubblicazione: (2024)

The twin peaks of learning neural networks
di: Demyanenko, Elizaveta, et al.
Pubblicazione: (2024)

Precise asymptotic analysis of Sobolev training for random feature models
di: Fisher, Katharine E, et al.
Pubblicazione: (2025)

Generalization performance of narrow one-hidden layer networks in the teacher-student setting
di: Ortiz, Rodrigo Pérez, et al.
Pubblicazione: (2025)

Gaussian Universality of Perceptrons with Random Labels
di: Gerace, Federica, et al.
Pubblicazione: (2022)

High-dimensional manifold of solutions in neural networks: insights from statistical physics
di: Malatesta, Enrico M.
Pubblicazione: (2023)

Optimal Protocols for Continual Learning via Statistical Physics and Control Theory
di: Mori, Francesco, et al.
Pubblicazione: (2024)

Phase Transitions in a Modified Ising Spin Glass Model: A Tensor-Network-based Sampling Approach
di: Oshima, Takumi, et al.
Pubblicazione: (2026)

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

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

Gaussian mixtures and non-parametric likelihoods through the lens of statistical mechanics
di: Ghosh, Subhroshekhar, et al.
Pubblicazione: (2026)

Sharp feature-learning transitions and Bayes-optimal neural scaling laws in extensive-width networks
di: Nguyen, Minh-Toan, et al.
Pubblicazione: (2026)

Graphical model for factorization and completion of relatively high rank tensors by sparse sampling
di: Cavaliere, Angelo Giorgio, et al.
Pubblicazione: (2025)

Limits of message passing for node classification: How class-bottlenecks restrict signal-to-noise ratio
di: Rubin, Jonathan, et al.
Pubblicazione: (2025)

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

Optimal generalisation and learning transition in extensive-width shallow neural networks near interpolation
di: Barbier, Jean, et al.
Pubblicazione: (2025)

Analysis of Diffusion Models for Manifold Data
di: George, Anand Jerry, et al.
Pubblicazione: (2025)