44 citations · 80 across the 6 of their papers we have counts for
3 papers
Neural Pharmacodynamic State Space Modeling
Zeshan Hussain, Rahul G. Krishnan, David Sontag
Modeling the time-series of high-dimensional, longitudinal data is important for predicting patient disease progression. However, existing neural network based approaches that lear…
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…