Deep Unsupervised Learning using Nonequilibrium Thermodynamics

Jascha Sohl-Dickstein, Eric Weiss, Niru Maheswaranathan, Surya Ganguli
; Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:2256-2265, 2015.

Abstract

A central problem in machine learning involves modeling complex data-sets using highly flexible families of probability distributions in which learning, sampling, inference, and evaluation are still analytically or computationally tractable. Here, we develop an approach that simultaneously achieves both flexibility and tractability. The essential idea, inspired by non-equilibrium statistical physics, is to systematically and slowly destroy structure in a data distribution through an iterative forward diffusion process. We then learn a reverse diffusion process that restores structure in data, yielding a highly flexible and tractable generative model of the data. This approach allows us to rapidly learn, sample from, and evaluate probabilities in deep generative models with thousands of layers or time steps, as well as to compute conditional and posterior probabilities under the learned model. We additionally release an open source reference implementation of the algorithm.

Cite this Paper


BibTeX
@InProceedings{pmlr-v37-sohl-dickstein15, title = {Deep Unsupervised Learning using Nonequilibrium Thermodynamics}, author = {Jascha Sohl-Dickstein and Eric Weiss and Niru Maheswaranathan and Surya Ganguli}, booktitle = {Proceedings of the 32nd International Conference on Machine Learning}, pages = {2256--2265}, year = {2015}, editor = {Francis Bach and David Blei}, volume = {37}, series = {Proceedings of Machine Learning Research}, address = {Lille, France}, month = {07--09 Jul}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v37/sohl-dickstein15.pdf}, url = {http://proceedings.mlr.press/v37/sohl-dickstein15.html}, abstract = {A central problem in machine learning involves modeling complex data-sets using highly flexible families of probability distributions in which learning, sampling, inference, and evaluation are still analytically or computationally tractable. Here, we develop an approach that simultaneously achieves both flexibility and tractability. The essential idea, inspired by non-equilibrium statistical physics, is to systematically and slowly destroy structure in a data distribution through an iterative forward diffusion process. We then learn a reverse diffusion process that restores structure in data, yielding a highly flexible and tractable generative model of the data. This approach allows us to rapidly learn, sample from, and evaluate probabilities in deep generative models with thousands of layers or time steps, as well as to compute conditional and posterior probabilities under the learned model. We additionally release an open source reference implementation of the algorithm.} }
Endnote
%0 Conference Paper %T Deep Unsupervised Learning using Nonequilibrium Thermodynamics %A Jascha Sohl-Dickstein %A Eric Weiss %A Niru Maheswaranathan %A Surya Ganguli %B Proceedings of the 32nd International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2015 %E Francis Bach %E David Blei %F pmlr-v37-sohl-dickstein15 %I PMLR %J Proceedings of Machine Learning Research %P 2256--2265 %U http://proceedings.mlr.press %V 37 %W PMLR %X A central problem in machine learning involves modeling complex data-sets using highly flexible families of probability distributions in which learning, sampling, inference, and evaluation are still analytically or computationally tractable. Here, we develop an approach that simultaneously achieves both flexibility and tractability. The essential idea, inspired by non-equilibrium statistical physics, is to systematically and slowly destroy structure in a data distribution through an iterative forward diffusion process. We then learn a reverse diffusion process that restores structure in data, yielding a highly flexible and tractable generative model of the data. This approach allows us to rapidly learn, sample from, and evaluate probabilities in deep generative models with thousands of layers or time steps, as well as to compute conditional and posterior probabilities under the learned model. We additionally release an open source reference implementation of the algorithm.
RIS
TY - CPAPER TI - Deep Unsupervised Learning using Nonequilibrium Thermodynamics AU - Jascha Sohl-Dickstein AU - Eric Weiss AU - Niru Maheswaranathan AU - Surya Ganguli BT - Proceedings of the 32nd International Conference on Machine Learning PY - 2015/06/01 DA - 2015/06/01 ED - Francis Bach ED - David Blei ID - pmlr-v37-sohl-dickstein15 PB - PMLR SP - 2256 DP - PMLR EP - 2265 L1 - http://proceedings.mlr.press/v37/sohl-dickstein15.pdf UR - http://proceedings.mlr.press/v37/sohl-dickstein15.html AB - A central problem in machine learning involves modeling complex data-sets using highly flexible families of probability distributions in which learning, sampling, inference, and evaluation are still analytically or computationally tractable. Here, we develop an approach that simultaneously achieves both flexibility and tractability. The essential idea, inspired by non-equilibrium statistical physics, is to systematically and slowly destroy structure in a data distribution through an iterative forward diffusion process. We then learn a reverse diffusion process that restores structure in data, yielding a highly flexible and tractable generative model of the data. This approach allows us to rapidly learn, sample from, and evaluate probabilities in deep generative models with thousands of layers or time steps, as well as to compute conditional and posterior probabilities under the learned model. We additionally release an open source reference implementation of the algorithm. ER -
APA
Sohl-Dickstein, J., Weiss, E., Maheswaranathan, N. & Ganguli, S.. (2015). Deep Unsupervised Learning using Nonequilibrium Thermodynamics. Proceedings of the 32nd International Conference on Machine Learning, in PMLR 37:2256-2265

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