energy-based-models

This paper presents a general neurosymbolic reasoning and learning methodology that modularly integrates Answer Set Programming with an Energy Based Model substrate, supporting joint optimization in continuous latent space and end-to-end training. It demonstrates applications on MNIST, CLEVR, and MOT benchmarks.

This paper frames model merging as probabilistic inference under a product-of-experts scenario, showing that existing methods are special cases and proposing a heavy-tailed Cauchy expert design that better captures real residual behavior, achieving significant improvements over state-of-the-art baselines.

This paper presents a five-arm ablation methodology for diagnosing which component of retrieval-warmed energy-based reasoning (RW-EBR) drives performance gains, applied to structured reasoning tasks like graph reachability and Sudoku. The method separates effects of class-prior bias, stochastic warm-starting, and graph-aligned value reuse.

This paper extends Equilibrium Propagation to skew-gradient systems and demonstrates an equivalence between deep Energy-Based Models and Hamiltonian neural networks, focusing on diffusively coupled Fitzhugh-Nagumo neurons. It derives a layer-wise Hamiltonian recurrence relation for inference in such networks.

Aleph, a new formal reasoning AI system, leads major benchmarks, validating Yann LeCun's emphasis on Energy-Based Models for AI reasoning.

The article introduces SentinelMesh, an autonomous security system using Energy-Based Models (EBMs) and TAME governance to handle incident response at scale, arguing that physics-based approaches outperform LLMs in threat modeling.

This paper introduces FedeKD, a reliability-aware framework for federated knowledge distillation that uses an energy-based gating mechanism to mitigate negative transfer in heterogeneous settings. The authors demonstrate that weighting knowledge transfer based on sample-wise trust improves robustness and predictive performance without requiring public datasets.

OpenAI presents implicit generation and generalization methods for energy-based models (EBMs) that use Langevin dynamics for iterative refinement to generate samples without explicit generator networks. The approach offers advantages including adaptive computation time, flexibility in learning disconnected data modes, and built-in compositionality through product of experts.

This paper establishes mathematical equivalences between generative adversarial networks (GANs), inverse reinforcement learning (IRL), and energy-based models (EBMs), demonstrating that certain IRL methods are equivalent to GANs with evaluable generator density. The work bridges three research communities to enable knowledge transfer for developing more stable and scalable algorithms.