Energy-Based Dynamical Models for Neurocomputation, Learning, and Optimization
cs.LG, cond-mat.dis-nn, cs.SY, eess.SY, math.DS, q-bio.NC
Submitted: 2026-04-06
Updated: 2026-04-06
Journal ref: 2026 American Control Conference (ACC), May 27-29, New Orleans, LA
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Terminology
Sources
- Concrete Problems in AI Safety
- Hopfield Networks is All You Need
- Local Unsupervised Learning for Image Analysis
- Modern Methods in Associative Memory
- Memorization to Generalization: Emergence of Diffusion Models from Associative Memory
- A Biologically Plausible Dense Associative Memory with Exponential Capacity
- Hierarchical Associative Memory
- Dense Associative Memories with Analog Circuits
- Operator Learning for Reconstructing Flow Fields from Sparse Measurements: an Energy Transformer Approach
- Higher-Order Kuramoto Oscillator Network for Dense Associative Memory
- Analyzing Parametric Oscillator Ising Machines through the Kuramoto Lens
- Unsupervised and probabilistic learning with Contrastive Local Learning Networks: The Restricted Kirchhoff Machine
- At the Top of the Mountain, the World can Look Boltzmann-Like: Sampling Dynamics of Noisy Double-Well Systems
- Competition, stability, and functionality in excitatory-inhibitory neural circuits
- An Energy-Based Mechanism for Compositional Behavior
- Provably Safe Generative Sampling with Constricting Barrier Functions
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