MCMC Should Mix: Learning Energy-Based Model with Flow-Based Backbone

no code implementations • ICLR 2022 • Erik Nijkamp, Ruiqi Gao, Pavel Sountsov, Srinivas Vasudevan, Bo Pang, Song-Chun Zhu, Ying Nian Wu

However, MCMC sampling of EBMs in high-dimensional data space is generally not mixing, because the energy function, which is usually parametrized by deep network, is highly multi-modal in the data space.

MCMC Should Mix: Learning Energy-Based Model with Neural Transport Latent Space MCMC

no code implementations • 12 Jun 2020 • Erik Nijkamp, Ruiqi Gao, Pavel Sountsov, Srinivas Vasudevan, Bo Pang, Song-Chun Zhu, Ying Nian Wu

Learning energy-based model (EBM) requires MCMC sampling of the learned model as an inner loop of the learning algorithm.

NeuTra-lizing Bad Geometry in Hamiltonian Monte Carlo Using Neural Transport

1 code implementation • 9 Mar 2019 • Matthew Hoffman, Pavel Sountsov, Joshua V. Dillon, Ian Langmore, Dustin Tran, Srinivas Vasudevan

Hamiltonian Monte Carlo is a powerful algorithm for sampling from difficult-to-normalize posterior distributions.

Variational Inference

Simple, Distributed, and Accelerated Probabilistic Programming

1 code implementation • NeurIPS 2018 • Dustin Tran, Matthew Hoffman, Dave Moore, Christopher Suter, Srinivas Vasudevan, Alexey Radul, Matthew Johnson, Rif A. Saurous

For both a state-of-the-art VAE on 64x64 ImageNet and Image Transformer on 256x256 CelebA-HQ, our approach achieves an optimal linear speedup from 1 to 256 TPUv2 chips.

Probabilistic Programming

TensorFlow Distributions

9 code implementations • 28 Nov 2017 • Joshua V. Dillon, Ian Langmore, Dustin Tran, Eugene Brevdo, Srinivas Vasudevan, Dave Moore, Brian Patton, Alex Alemi, Matt Hoffman, Rif A. Saurous

The TensorFlow Distributions library implements a vision of probability theory adapted to the modern deep-learning paradigm of end-to-end differentiable computation.

Probabilistic Programming