Hung Bui

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adobe ∙ SRI International

Improving Bayesian Inference in Deep Neural Networks with Variational Structured Dropout

Approximate inference in deep Bayesian networks exhibits a dilemma of ho...
02/16/2021 ∙ by Son Nguyen, et al. ∙ 44 ∙ share

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BoMb-OT: On Batch of Mini-batches Optimal Transport

Mini-batch optimal transport (m-OT) has been successfully used in practi...
02/11/2021 ∙ by Khai Nguyen, et al. ∙ 12 ∙ share

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Learning Compositional Sparse Gaussian Processes with a Shrinkage Prior

Choosing a proper set of kernel functions is an important problem in lea...
12/21/2020 ∙ by Anh Tong, et al. ∙ 0 ∙ share

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Improving Relational Regularized Autoencoders with Spherical Sliced Fused Gromov Wasserstein

Relational regularized autoencoder (RAE) is a framework to learn the dis...
10/05/2020 ∙ by Khai Nguyen, et al. ∙ 3 ∙ share

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Vec2Face: Unveil Human Faces from their Blackbox Features in Face Recognition

Unveiling face images of a subject given his/her high-level representati...
03/16/2020 ∙ by Chi Nhan Duong, et al. ∙ 0 ∙ share

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Distributional Sliced-Wasserstein and Applications to Generative Modeling

Sliced-Wasserstein distance (SWD) and its variation, Max Sliced-Wasserst...
02/18/2020 ∙ by Khai Nguyen, et al. ∙ 4 ∙ share

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On Unbalanced Optimal Transport: An Analysis of Sinkhorn Algorithm

We provide a computational complexity analysis for the Sinkhorn algorith...
02/09/2020 ∙ by Khiem Pham, et al. ∙ 0 ∙ share

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On Efficient Multilevel Clustering via Wasserstein Distances

We propose a novel approach to the problem of multilevel clustering, whi...
09/19/2019 ∙ by Viet Huynh, et al. ∙ 0 ∙ share

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Prediction, Consistency, Curvature: Representation Learning for Locally-Linear Control

Many real-world sequential decision-making problems can be formulated as...
09/04/2019 ∙ by Nir Levine, et al. ∙ 25 ∙ share

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SpectralFPL: Online Spectral Learning for Single Topic Models

This paper studies how to efficiently learn an optimal latent variable m...
09/21/2017 ∙ by Tong Yu, et al. ∙ 0 ∙ share

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Graphical Model Sketch

Structured high-cardinality data arises in many domains, and poses a maj...
02/09/2016 ∙ by Branislav Kveton, et al. ∙ 0 ∙ share

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Boosted Markov Networks for Activity Recognition

We explore a framework called boosted Markov networks to combine the lea...
08/06/2014 ∙ by Truyen Tran, et al. ∙ 0 ∙ share

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Human Activity Learning and Segmentation using Partially Hidden Discriminative Models

Learning and understanding the typical patterns in the daily activities ...
08/06/2014 ∙ by Truyen Tran, et al. ∙ 0 ∙ share

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Automorphism Groups of Graphical Models and Lifted Variational Inference

Using the theory of group action, we first introduce the concept of the ...
09/26/2013 ∙ by Hung Bui, et al. ∙ 0 ∙ share

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Hung Bui

Featured Co-authors

Svetha Venkatesh

Sebastian Riedel

Ang Li

Truyen Tran

Kaushik Roy

Dinh Phung

Mohammad Ghavamzadeh

Branislav Kveton

Zheng Wen

Nhat Ho

XuanLong Nguyen

Improving Bayesian Inference in Deep Neural Networks with Variational Structured Dropout

BoMb-OT: On Batch of Mini-batches Optimal Transport

Learning Compositional Sparse Gaussian Processes with a Shrinkage Prior

Improving Relational Regularized Autoencoders with Spherical Sliced Fused Gromov Wasserstein

Vec2Face: Unveil Human Faces from their Blackbox Features in Face Recognition

Distributional Sliced-Wasserstein and Applications to Generative Modeling

On Unbalanced Optimal Transport: An Analysis of Sinkhorn Algorithm

On Efficient Multilevel Clustering via Wasserstein Distances

Prediction, Consistency, Curvature: Representation Learning for Locally-Linear Control

SpectralFPL: Online Spectral Learning for Single Topic Models

Graphical Model Sketch

Boosted Markov Networks for Activity Recognition

Human Activity Learning and Segmentation using Partially Hidden Discriminative Models

Automorphism Groups of Graphical Models and Lifted Variational Inference