arXiv:2605. 19178v2 Announce Type: replace-cross Abstract: The great success of neural networks primarily arises from the presence of the large number of weight parameters combined with nonlinearities in the input-output relationship of single neurons.
By Giovanni di Sarra, Yasser Roudi
The study explores whether a Deep Belief Network (DBN) and a Bidirectional Gated Recurrent Unit (Bi‑GRU) can distinguish four distinct trajectory types in the three‑state majority‑vote model (MV3): approach from disorder, approach from order, departure to disorder, and departure to order. The DBN, trained unsupervised on static equilibrium snapshots, partially separates these trajectories in its 81‑dimensional latent space, while a two‑layer Bi‑GRU trained on sequences of DBN‑encoded snapshots achieves near‑perfect classification, as confirmed by t‑SNE visualizations on both training and test data. A sliding‑window application of the Bi‑GRU to continuous MV3 dynamics further demonstrates real‑time detection of the system’s current dynamical regime.
By Mauricio A. Valle, Gonzalo A. Ruz
arXiv:2607. 27077v1 Announce Type: new Abstract: Energy-Based Models (EBMs) provide an interpretable framework for generative modeling of scientific data, but poor Markov Chain Monte Carlo mixing often limits their reliability.
By Nicolas B\'ereux, Aur\'elien Decelle, Cyril Furtlehner, Beatriz Seoane
arXiv:2607. 00170v1 Announce Type: cross Abstract: Thermodynamic computing devices based on the Ising model show great promise for low-power AI inference and edge computing, but scalable methods for training large models for such hardware remain limited.
By Andrew G. Moore
Thermodynamic computing devices based on the Ising model show great promise for low-power AI inference and edge computing, but scalable methods for training large models for such hardware remain limited. Prior theory shows that the time-averaged behavior of high-temperature Gibbs-sampled Ising systems can implement feed-forward neural inference.
arXiv:2512. 11415v3 Announce Type: replace-cross Abstract: We show that nonequilibrium dynamics can play a constructive role in unsupervised machine learning by inducing the spontaneous emergence of latent-state cycles.
By Marco Baiesi, Alberto Rosso