00:00:00 / 00:00:00

Appears in collection : On Future Synergies for Stochastic and Learning Algorithms / Sur les synergies futures autour des algorithmes d'apprentissage et stochastiques

RBMs are generative models capable of fitting complex dataset's probability distributions. Thanks to their simple structure, they are particularly well suited for interpretability and pattern extraction, a feature particularly appealing for scientific use. In this talk, we show that RBMs operate in two distinct regimes, depending on the procedure followed to estimate the log-likelihood gradient during the training. Short sampling times fit machines that are trained to reproduce exactly the dynamics followed to train them, long samplings (as compared to the MCMC mixing time) are need to learn a good model for the data. The non-equilibrium regime should be used to generate high quality samples in short learning and sampling times, but cannot be used to extract the unnormalized data probability of the data necessary for interpretability. In practice, it is hard to extract good equilibrium models for structured datasets (which is the typical case in biological applications) due to a divergence of the Monte Carlo mixing times. In this work, we show this barrier can be surmounted using biased Monte Carlo methods.

Information about the video

Citation data

  • DOI 10.24350/CIRM.V.19817903
  • Cite this video Seoane Beatriz (9/30/21). Biased Monte Carlo sampling in RBMs. CIRM. Audiovisual resource. DOI: 10.24350/CIRM.V.19817903
  • URL https://dx.doi.org/10.24350/CIRM.V.19817903


  • DECELLE, Aurélien, FURTLEHNER, Cyril, et SEOANE, Beatriz. Equilibrium and non-Equilibrium regimes in the learning of Restricted Boltzmann Machines. arXiv preprint arXiv:2105.13889, 2021. - https://arxiv.org/abs/2105.13889
  • DECELLE, Aurélien, FISSORE, Giancarlo, et FURTLEHNER, Cyril. Thermodynamics of restricted Boltzmann machines and related learning dynamics. Journal of Statistical Physics, 2018, vol. 172, no 6, p. 1576-1608. - http://dx.doi.org/10.1007/s10955-018-2105-y
  • DECELLE, Aurélien et FURTLEHNER, Cyril. Exact training of Restricted Boltzmann machines on intrinsically low dimensional data. arXiv preprint arXiv:2103.10755, 2021. - https://arxiv.org/abs/2103.10755
  • MARTIN-MAYOR, V., SEOANE, B., et YLLANES, D. Tethered Monte Carlo: Managing rugged free-energy landscapes with a Helmholtz-potential formalism. Journal of Statistical Physics, 2011, vol. 144, no 3, p. 554-596. - http://dx.doi.org/10.1007/s10955-011-0261-4
  • FERNÁNDEZ, L. A., MARTÍN-MAYOR, Víctor, SEOANE, B., et al. Equilibrium fluid-solid coexistence of hard spheres. Physical review letters, 2012, vol. 108, no 16, p. 165701. - http://dx.doi.org/10.1103/PhysRevLett.108.165701

Last related questions on MathOverflow

You have to connect your Carmin.tv account with mathoverflow to add question

Ask a question on MathOverflow


  • Bookmark videos
  • Add videos to see later &
    keep your browsing history
  • Comment with the scientific
  • Get notification updates
    for your favorite subjects
Give feedback