23 citations · 39 across the 4 of their papers we have counts for
4 papers
Efficient Methods for Unsupervised Learning of Probabilistic Models
Jascha Sohl-Dickstein
In this thesis I develop a variety of techniques to train, evaluate, and sample from intractable and high dimensional probabilistic models. Abstract exceeds arXiv space limitations…
Hamiltonian Monte Carlo with Reduced Momentum Flips
Jascha Sohl-Dickstein
Hamiltonian Monte Carlo (or hybrid Monte Carlo) with partial momentum refreshment explores the state space more slowly than it otherwise would due to the momentum reversals which o…
Hamiltonian Annealed Importance Sampling for partition function estimation
Jascha Sohl-Dickstein, Benjamin J. Culpepper
We introduce an extension to annealed importance sampling that uses Hamiltonian dynamics to rapidly estimate normalization constants. We demonstrate this method by computing log li…
The Natural Gradient by Analogy to Signal Whitening, and Recipes and Tricks for its Use
Jascha Sohl-Dickstein
The natural gradient allows for more efficient gradient descent by removing dependencies and biases inherent in a function's parameterization. Several papers present the topic thor…