energy-based-model

This paper introduces a physics-inspired framework for structural attribution in cyber-physical IoT systems, using an undirected energy-based representation to provide dependency-aware explanations without requiring a directed causal graph. Experiments on an industrial IoT testbed demonstrate higher attribution accuracy, robustness, and scalability compared to existing graph-based methods.

The Controlled Dynamics Attractor Transformer (CDAT) combines a mixture von Mises-Fisher attention energy with a Hopfield refinement energy and CANN-inspired excitation-inhibition modulation, providing topology-constrained dynamical systems for stable inference. It achieves state-of-the-art performance on graph anomaly detection and classification benchmarks.

This paper introduces a new energy-based model for linear inverse problems that learns normalized posterior densities, overcoming limitations of diffusion models. It enables unbiased sampling, adaptive sampling, and blind degradation estimation, with competitive performance on ImageNet, CelebA, and AFHQ.