Search Results for author: Hung H. Bui

Found 10 papers, 3 papers with code

Predictive Coding for Locally-Linear Control

1 code implementation ICML 2020 Rui Shu, Tung Nguyen, Yin-Lam Chow, Tuan Pham, Khoat Than, Mohammad Ghavamzadeh, Stefano Ermon, Hung H. Bui

High-dimensional observations and unknown dynamics are major challenges when applying optimal control to many real-world decision making tasks.

Decision Making

Training Variational Autoencoders with Buffered Stochastic Variational Inference

no code implementations27 Feb 2019 Rui Shu, Hung H. Bui, Jay Whang, Stefano Ermon

The recognition network in deep latent variable models such as variational autoencoders (VAEs) relies on amortized inference for efficient posterior approximation that can scale up to large datasets.

Latent Variable Models Variational Inference

Amortized Inference Regularization

no code implementations NeurIPS 2018 Rui Shu, Hung H. Bui, Shengjia Zhao, Mykel J. Kochenderfer, Stefano Ermon

In this paper, we leverage the fact that VAEs rely on amortized inference and propose techniques for amortized inference regularization (AIR) that control the smoothness of the inference model.

Density Estimation Representation Learning

A DIRT-T Approach to Unsupervised Domain Adaptation

2 code implementations ICLR 2018 Rui Shu, Hung H. Bui, Hirokazu Narui, Stefano Ermon

Domain adaptation refers to the problem of leveraging labeled data in a source domain to learn an accurate model in a target domain where labels are scarce or unavailable.

Unsupervised Domain Adaptation

Bottleneck Conditional Density Estimation

1 code implementation ICML 2017 Rui Shu, Hung H. Bui, Mohammad Ghavamzadeh

We introduce a new framework for training deep generative models for high-dimensional conditional density estimation.

Density Estimation

Robust Dialog State Tracking for Large Ontologies

no code implementations7 May 2016 Franck Dernoncourt, Ji Young Lee, Trung H. Bui, Hung H. Bui

The Dialog State Tracking Challenge 4 (DSTC 4) differentiates itself from the previous three editions as follows: the number of slot-value pairs present in the ontology is much larger, no spoken language understanding output is given, and utterances are labeled at the subdialog level.

Coreference Resolution Spoken Language Understanding

MCMC for Hierarchical Semi-Markov Conditional Random Fields

no code implementations6 Aug 2014 Truyen Tran, Dinh Phung, Svetha Venkatesh, Hung H. Bui

In this contribution, we propose a new approximation technique that may have the potential to achieve sub-cubic time complexity in length and linear time depth, at the cost of some loss of quality.

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