jensandClaude Sonnet 4.6
b5104ebc97
- Remove cd_jens: dead code never called, contained multiple bugs. - cd_gaussian_binary: dbv formula simplified — b_v terms cancelled between positive and negative phases, making the subtraction redundant. - cd_binary_gaussian: Gibbs loop (CD-k > 1) now properly samples both visible (sample(prob(...))) and hidden (+ sample_gaussian) states when do_rao_blackwell=False. Previously used means throughout, giving biased gradient estimates for k > 1. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
GRBM
Paper
https://medium.com/@rtdcunha/gaussian-bernoulli-restricted-boltzmann-machines-4a68b8765485 https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0171015&type=printable
Code
https://github.com/DSL-Lab/GRBM/tree/main
CRBM
Paper
https://www.ee.nthu.edu.tw/hchen/pubs/iee2003.pdf
Regularization
https://benihime91.github.io/blog/machinelearning/deeplearning/python3.x/tensorflow2.x/2020/10/08/adamW.html https://medium.com/analytics-vidhya/l1-vs-l2-regularization-which-is-better-d01068e6658c https://jamesmccaffreyblog.com/2019/05/09/the-difference-between-neural-network-l2-regularization-and-weight-decay