Search Results for author: Dawen Liang

Found 9 papers, 5 papers with code

Learning Correlated Latent Representations with Adaptive Priors

no code implementations14 Jun 2019 Da Tang, Dawen Liang, Nicholas Ruozzi, Tony Jebara

Variational Auto-Encoders (VAEs) have been widely applied for learning compact, low-dimensional latent representations of high-dimensional data.

Link Prediction

Correlated Variational Auto-Encoders

2 code implementations ICLR Workshop DeepGenStruct 2019 Da Tang, Dawen Liang, Tony Jebara, Nicholas Ruozzi

Variational Auto-Encoders (VAEs) are capable of learning latent representations for high dimensional data.

Link Prediction

The Deconfounded Recommender: A Causal Inference Approach to Recommendation

no code implementations20 Aug 2018 Yixin Wang, Dawen Liang, Laurent Charlin, David M. Blei

To this end, we develop a causal approach to recommendation, one where watching a movie is a "treatment" and a user's rating is an "outcome."

Causal Inference Recommendation Systems

Variational Autoencoders for Collaborative Filtering

14 code implementations16 Feb 2018 Dawen Liang, Rahul G. Krishnan, Matthew D. Hoffman, Tony Jebara

This non-linear probabilistic model enables us to go beyond the limited modeling capacity of linear factor models which still largely dominate collaborative filtering research. We introduce a generative model with multinomial likelihood and use Bayesian inference for parameter estimation.

Bayesian Inference Collaborative Filtering +2

On the challenges of learning with inference networks on sparse, high-dimensional data

1 code implementation17 Oct 2017 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.

Variational Inference

A Generative Product-of-Filters Model of Audio

1 code implementation20 Dec 2013 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.

Speaker Identification

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