430 citations · 830 across the 11 of their papers we have counts for
6 papers · 1 filter
Automatic Reparameterisation of Probabilistic Programs
Maria I. Gorinova, Dave Moore, Matthew D. Hoffman
Probabilistic programming has emerged as a powerful paradigm in statistics, applied science, and machine learning: by decoupling modelling from inference, it promises to allow mode…
Simple, Distributed, and Accelerated Probabilistic Programming
Dustin Tran, Matthew Hoffman, Dave Moore +5
We describe a simple, low-level approach for embedding probabilistic programming in a deep learning ecosystem. In particular, we distill probabilistic programming down to a single…
Variational Autoencoders for Collaborative Filtering
Dawen Liang, Rahul G. Krishnan, Matthew D. Hoffman +1
We extend variational autoencoders (VAEs) to collaborative filtering for implicit feedback. This non-linear probabilistic model enables us to go beyond the limited modeling capacit…
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…
The Segmented iHMM: A Simple, Efficient Hierarchical Infinite HMM
Ardavan Saeedi, Matthew Hoffman, Matthew Johnson +1
We propose the segmented iHMM (siHMM), a hierarchical infinite hidden Markov model (iHMM) that supports a simple, efficient inference scheme. The siHMM is well suited to segmentati…
A trust-region method for stochastic variational inference with applications to streaming data
Lucas Theis, Matthew D. Hoffman
Stochastic variational inference allows for fast posterior inference in complex Bayesian models. However, the algorithm is prone to local optima which can make the quality of the p…