Copertina

SETOL: A Semi-Empirical Theory of (Deep) Learning

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Martin, Charles H, Hinrichs, Christopher
Natura:	Preprint
Pubblicazione:	2025
Soggetti:	Machine Learning Statistical Mechanics
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

Review and Prospect of Algebraic Research in Equivalent Framework between Statistical Mechanics and Machine Learning Theory
di: Watanabe, Sumio
Pubblicazione: (2024)

Landscape Complexity for the Empirical Risk of Generalized Linear Models: Discrimination between Structured Data
di: Tsironis, Theodoros G., et al.
Pubblicazione: (2025)

Learning the Intrinsic Dimensionality of Fermi-Pasta-Ulam-Tsingou Trajectories: A Nonlinear Approach using a Deep Autoencoder Model
di: Marchetti, Gionni
Pubblicazione: (2026)

Deep generative modelling of canonical ensemble with differentiable thermal properties
di: Li, Shuo-Hui, et al.
Pubblicazione: (2024)

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

Susceptibilities and Patterning: A Primer on Linear Response in Bayesian Learning
di: Elliott, Chris, et al.
Pubblicazione: (2026)

Machine Learning H-theorem
di: Lier, Ruben
Pubblicazione: (2025)

Uncertainty relations and fluctuation theorems for Bayes nets
di: Wolpert, David H.
Pubblicazione: (2019)

Physics Informed Deep Learning for Strain Gradient Continuum Plasticity
di: Tyagi, Ankit, et al.
Pubblicazione: (2024)

Combining Reinforcement Learning and Tensor Networks, with an Application to Dynamical Large Deviations
di: Gillman, Edward, et al.
Pubblicazione: (2022)

$α$-divergence Improves the Entropy Production Estimation via Machine Learning
di: Kwon, Euijoon, et al.
Pubblicazione: (2023)

Learned Mappings for Targeted Free Energy Perturbation between Peptide Conformations
di: Willow, Soohaeng Yoo, et al.
Pubblicazione: (2023)

Learning from higher-order statistics, efficiently: hypothesis tests, random features, and neural networks
di: Székely, Eszter, et al.
Pubblicazione: (2023)

A differentiable programming framework for spin models
di: Farias, Tiago de Souza, et al.
Pubblicazione: (2023)

Beyond Explained Variance: A Cautionary Tale of PCA
di: Marchetti, Gionni
Pubblicazione: (2026)

Thermodynamic Isomorphism of Transformers: A Lagrangian Approach to Attention Dynamics
di: Kim, Gunn
Pubblicazione: (2026)

Ridge Regression from Poisson Resetting: A Renewal Perspective on Spectral Regularization
di: Jolakoski, Petar
Pubblicazione: (2026)

Efficient Identification of Critical Transitions via Flow Matching: A Scalable Generative Approach for Many-Body Systems
di: Lee, Qian-Rui, et al.
Pubblicazione: (2025)

Pointwise Generalization in Deep Neural Networks
di: Li, Shaojie, et al.
Pubblicazione: (2026)

Statistics of Min-max Normalized Eigenvalues in Random Matrices
di: Nakada, Hyakka, et al.
Pubblicazione: (2025)

Laws of thermodynamics for exponential families
di: Balsubramani, Akshay
Pubblicazione: (2025)

Scalable Boltzmann Generators for equilibrium sampling of large-scale materials
di: Schebek, Maximilian, et al.
Pubblicazione: (2025)

Emergence of Nonequilibrium Latent Cycles in Unsupervised Generative Modeling
di: Baiesi, Marco, et al.
Pubblicazione: (2025)

Dynamical symmetries in the fluctuation-driven regime: an application of Noether's theorem to noisy dynamical systems
di: Vastola, John J.
Pubblicazione: (2025)

Optimised Feature Subset Selection via Simulated Annealing
di: Martínez-García, Fernando, et al.
Pubblicazione: (2025)

Plastic tensor networks for interpretable generative modeling
di: Akamatsu, Katsuya O., et al.
Pubblicazione: (2025)

Building causation links in stochastic nonlinear systems from data
di: Chibbaro, Sergio, et al.
Pubblicazione: (2025)

Training thermodynamic computers by gradient descent
di: Whitelam, Stephen
Pubblicazione: (2025)

Information maximization for a broad variety of multi-armed bandit games
di: Barbier-Chebbah, Alex, et al.
Pubblicazione: (2025)

Identifying Ising and percolation phase transitions based on KAN method
di: Xu, Dian, et al.
Pubblicazione: (2025)

High-entropy Advantage in Neural Networks' Generalizability
di: Yang, Entao, et al.
Pubblicazione: (2025)

Optimal Computation from Fluctuation Responses
di: Lyu, Jinghao, et al.
Pubblicazione: (2025)

Thermodynamic Performance Limits for Score-Based Diffusion Models
di: Kodama, Nathan X., et al.
Pubblicazione: (2025)

Improving FMQA via Initial Training Data Design Considering Marginal Bit Coverage in One-Hot Encoding
di: Hayashi, Taiga, et al.
Pubblicazione: (2026)

Posterior Collapse as Automatic Spectral Pruning
di: Hirn, Johannes
Pubblicazione: (2026)

The impact of memory on learning sequence-to-sequence tasks
di: Seif, Alireza, et al.
Pubblicazione: (2022)

Discovering Causal Structure with Reproducing-Kernel Hilbert Space $ε$-Machines
di: Brodu, Nicolas, et al.
Pubblicazione: (2020)

Data driven modeling for self-similar dynamics
di: Tao, Ruyi, et al.
Pubblicazione: (2023)

Composing diffusion priors with explicit physical context via generative Gibbs sampling
di: Wang, Weizhou, et al.
Pubblicazione: (2026)

Dynamical Regimes of Diffusion Models
di: Biroli, Giulio, et al.
Pubblicazione: (2024)