Search Results for author: Da Tang

Found 8 papers, 2 papers with code

Active Multitask Learning with Committees

no code implementations24 Mar 2021 Jingxi Xu, Da Tang, Tony Jebara

The cost of annotating training data has traditionally been a bottleneck for supervised learning approaches.

Transfer Learning

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 Variational Predictive Natural Gradient

1 code implementation7 Mar 2019 Da Tang, Rajesh Ranganath

Unlike traditional natural gradients for variational inference, this natural gradient accounts for the relationship between model parameters and variational parameters.

General Classification Latent Variable Models +1

Item Recommendation with Variational Autoencoders and Heterogenous Priors

no code implementations17 Jul 2018 Giannis Karamanolakis, Kevin Raji Cherian, Ananth Ravi Narayan, Jie Yuan, Da Tang, Tony Jebara

In recent years, Variational Autoencoders (VAEs) have been shown to be highly effective in both standard collaborative filtering applications and extensions such as incorporation of implicit feedback.

Collaborative Filtering

Subgoal Discovery for Hierarchical Dialogue Policy Learning

no code implementations EMNLP 2018 Da Tang, Xiujun Li, Jianfeng Gao, Chong Wang, Lihong Li, Tony Jebara

Experiments with simulated and real users show that our approach performs competitively against a state-of-the-art method that requires human-defined subgoals.

Hierarchical Reinforcement Learning

Initialization and Coordinate Optimization for Multi-way Matching

no code implementations2 Nov 2016 Da Tang, Tony Jebara

We consider the problem of consistently matching multiple sets of elements to each other, which is a common task in fields such as computer vision.

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