430 citations · 830 across the 11 of their papers we have counts for
3 papers · 1 filter
Latent Constraints: Learning to Generate Conditionally from Unconditional Generative Models
Jesse Engel, Matthew Hoffman, Adam Roberts
Deep generative neural networks have proven effective at both conditional and unconditional modeling of complex data distributions. Conditional generation enables interactive contr…
TensorFlow Distributions
Joshua V. Dillon, Ian Langmore, Dustin Tran +7
The TensorFlow Distributions library implements a vision of probability theory adapted to the modern deep-learning paradigm of end-to-end differentiable computation. Building on tw…
On the challenges of learning with inference networks on sparse, high-dimensional data
Rahul G. Krishnan, Dawen Liang, Matthew Hoffman
We study parameter estimation in Nonlinear Factor Analysis (NFA) where the generative model is parameterized by a deep neural network. Recent work has focused on learning such mode…