Online local learning for generative thermodynamic computing
Read the original on arXiv Machine Learning →The Flow has not summarised this story yet — read it at arXiv Machine Learning.
The Flow has not summarised this story yet — read it at arXiv Machine Learning.
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. We introduce a training algorithm based on Parallel Trajectory Tempering (PTT), which exploits the continuity of the optimization path to maintain equilibrium sampling throughout learning.
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.
The paper introduces the Shift‑Invariant Variance Estimator (SIVE) to remove same‑state noise from thermodynamic variance calculations in neural‑network training. SIVE provides an unbiased estimate of path variance without requiring Markov chain Monte Carlo stationarity, and it reveals a reproducible mid‑training trough and rebound in MNIST MLP trajectories that raw variance masks. Experiments across multiple localization scales confirm that SIVE captures a large portion of observation noise and satisfies an early‑drop/late‑rise criterion in most trajectory‑scale pairs.
arXiv:2610.27641v1 Announce Type: cross Abstract: Temperature scaling is typically applied as a post-hoc transformation of model probabilities. In posterior-induced flow language models, this creates...
arXiv:2605.16929v2 Announce Type: replace Abstract: Global climate models are essential tools to simulate past and potential future pathways of climate change, as well as associated climate impacts....
arXiv:2506. 19136v4 Announce Type: replace Abstract: We show that the out-of-equilibrium driving protocol of score-based generative models (SGMs) can be learned via local learning rules.