223 citations · 513 across the 11 of their papers we have counts for
5 papers · 1 filter
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…
Modeling User Exposure in Recommendation
Dawen Liang, Laurent Charlin, James McInerney +1
Collaborative filtering analyzes user preferences for items (e.g., books, movies, restaurants, academic papers) by exploiting the similarity patterns across users. In implicit feed…
Beta Process Non-negative Matrix Factorization with Stochastic Structured Mean-Field Variational Inference
Dawen Liang, Matthew D. Hoffman
Beta process is the standard nonparametric Bayesian prior for latent factor model. In this paper, we derive a structured mean-field variational inference algorithm for a beta proce…
A Generative Product-of-Filters Model of Audio
Dawen Liang, Matthew D. Hoffman, Gautham J. Mysore
We propose the product-of-filters (PoF) model, a generative model that decomposes audio spectra as sparse linear combinations of "filters" in the log-spectral domain. PoF makes sim…