Copertina

Optimizing p-spin models through hypergraph neural networks and deep reinforcement learning

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Zeng, Li, Shen, Mutian, Pu, Tianle, Nussinov, Zohar, Feng, Qing, Chen, Chao, Liu, Zhong, Fan, Changjun
Natura:	Preprint
Pubblicazione:	2026
Soggetti:	Disordered Systems and Neural Networks Computational Physics
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

Pattern Expansion of Spin Glasses
di: Shen, Mutian, et al.
Pubblicazione: (2026)

Universal fragility of spin-glass ground-states under single bond changes
di: Shen, Mutian, et al.
Pubblicazione: (2023)

The Physics of Local Optimization in Complex Disordered Systems
di: Shen, Mutian, et al.
Pubblicazione: (2025)

Critical feature learning in deep neural networks
di: Fischer, Kirsten, et al.
Pubblicazione: (2024)

Renormalization group for deep neural networks: Universality of learning and scaling laws
di: Coppola, Gorka Peraza, et al.
Pubblicazione: (2025)

Improving deep neural network performance through sampling
di: Ghantasala, Lakshmi A., et al.
Pubblicazione: (2025)

Topological mechanical neural networks as classifiers through in situ backpropagation learning
di: Li, Shuaifeng, et al.
Pubblicazione: (2025)

Emergence of a Boundary-Sensitive Phase in Hyperbolic Ising Models
di: Wang, Xingzhi, et al.
Pubblicazione: (2025)

Arbitrage equilibrium and the emergence of universal microstructure in deep neural networks
di: Venkatasubramanian, Venkat, et al.
Pubblicazione: (2024)

Task learning through stimulation-induced plasticity in neural networks
di: Borra, Francesco, et al.
Pubblicazione: (2024)

Emergent weight morphologies in deep neural networks
di: de Jong, Pascal, et al.
Pubblicazione: (2025)

Spring-block theory of feature learning in deep neural networks
di: Shi, Cheng, et al.
Pubblicazione: (2024)

Implicit bias produces neural scaling laws in learning curves, from perceptrons to deep networks
di: D'Amico, Francesco, et al.
Pubblicazione: (2025)

Lecture notes: From Gaussian processes to feature learning
di: Helias, Moritz, et al.
Pubblicazione: (2026)

A statistical mechanics framework for Bayesian deep neural networks beyond the infinite-width limit
di: Pacelli, R., et al.
Pubblicazione: (2022)

Computing frustration and near-monotonicity in deep neural networks
di: Wendin, Joel, et al.
Pubblicazione: (2025)

Finite-time Lyapunov exponents of deep neural networks
di: Storm, L., et al.
Pubblicazione: (2023)

High-dimensional learning of narrow neural networks
di: Cui, Hugo
Pubblicazione: (2024)

Tensor networks for $p$-spin models
di: Lanthier, Benjamin, et al.
Pubblicazione: (2024)

Deep neural networks from the perspective of ergodic theory
di: Zhang, Fan
Pubblicazione: (2023)

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

Optimized control protocols for stable skyrmion creation using deep reinforcement learning
di: Song, Ji Seok, et al.
Pubblicazione: (2026)

Generalized hetero-associative neural networks
di: Agliari, Elena, et al.
Pubblicazione: (2024)

Statistical physics of complex systems: glasses, spin glasses, continuous constraint satisfaction problems, high-dimensional inference and neural networks
di: Urbani, Pierfrancesco
Pubblicazione: (2024)

Efficiency of neural-network state representations of one-dimensional quantum spin systems
di: Pan, Ruizhi, et al.
Pubblicazione: (2023)

A self-learning magnetic Hopfield neural network with intrinsic gradient descent adaption
di: Niu, Chang, et al.
Pubblicazione: (2025)

Partial annealing and pattern decorrelation in associative neural networks
di: Albanese, Linda, et al.
Pubblicazione: (2026)

Empowering deep neural quantum states through efficient optimization
di: Chen, Ao, et al.
Pubblicazione: (2023)

Instant prediction of relaxation in moiré superlattices using neural networks
di: Belonovskii, Aleksei V., et al.
Pubblicazione: (2025)

Glassy dynamics near the interpolation transition in deep recurrent networks
di: Hertz, John, et al.
Pubblicazione: (2024)

Analyzing partially-polarized light with a photonic deep random neural network
di: Petrini, Alessandro, et al.
Pubblicazione: (2025)

Adaptive kernel predictors from feature-learning infinite limits of neural networks
di: Lauditi, Clarissa, et al.
Pubblicazione: (2025)

Online unsupervised Hebbian learning in deep photonic neuromorphic networks
di: Li, Xi, et al.
Pubblicazione: (2026)

Glassy dynamics in deep neural networks: A structural comparison
di: Winter, Max Kerr, et al.
Pubblicazione: (2024)

Transform then Explore: a Simple and Effective Technique for Exploratory Combinatorial Optimization with Reinforcement Learning
di: Pu, Tianle, et al.
Pubblicazione: (2024)

Liquid and solid layers in a thermal deep learning machine
di: Huang, Gang, et al.
Pubblicazione: (2025)

Programmable polyaniline nano neural network. A simple physical model
di: Langer, Jerzy J.
Pubblicazione: (2024)

Phase transitions from linear to nonlinear information processing in neural networks
di: Matsumura, Masaya, et al.
Pubblicazione: (2025)

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

Structure of activity in multiregion recurrent neural networks
di: Clark, David G., et al.
Pubblicazione: (2024)