11 citations · 11 across the 4 of their papers we have counts for
5 papers · 1 filter
Relational Neural Markov Random Fields
Yuqiao Chen, Sriraam Natarajan, Nicholas Ruozzi
Statistical Relational Learning (SRL) models have attracted significant attention due to their ability to model complex data while handling uncertainty. However, most of these mode…
Lifted Hybrid Variational Inference
Yuqiao Chen, Yibo Yang, Sriraam Natarajan +1
A variety of lifted inference algorithms, which exploit model symmetry to reduce computational cost, have been proposed to render inference tractable in probabilistic relational mo…
Learning Correlated Latent Representations with Adaptive Priors
Da Tang, Dawen Liang, Nicholas Ruozzi +1
Variational Auto-Encoders (VAEs) have been widely applied for learning compact, low-dimensional latent representations of high-dimensional data. When the correlation structure amon…
Correlated Variational Auto-Encoders
Da Tang, Dawen Liang, Tony Jebara +1
Variational Auto-Encoders (VAEs) are capable of learning latent representations for high dimensional data. However, due to the i.i.d. assumption, VAEs only optimize the singleton v…
Bethe Learning of Conditional Random Fields via MAP Decoding
Kui Tang, Nicholas Ruozzi, David Belanger +1
Many machine learning tasks can be formulated in terms of predicting structured outputs. In frameworks such as the structured support vector machine (SVM-Struct) and the structured…