Search Results for author: Viet Huynh

Found 10 papers, 3 papers with code

Improved and Efficient Text Adversarial Attacks using Target Information

no code implementations27 Apr 2021 Mahmoud Hossam, Trung Le, He Zhao, Viet Huynh, Dinh Phung

There has been recently a growing interest in studying adversarial examples on natural language models in the black-box setting.

Text Generation with Deep Variational GAN

no code implementations27 Apr 2021 Mahmoud Hossam, Trung Le, Michael Papasimeon, Viet Huynh, Dinh Phung

Generating realistic sequences is a central task in many machine learning applications.

Text Generation

Neural Topic Model via Optimal Transport

no code implementations ICLR 2021 He Zhao, Dinh Phung, Viet Huynh, Trung Le, Wray Buntine

Recently, Neural Topic Models (NTMs) inspired by variational autoencoders have obtained increasingly research interest due to their promising results on text analysis.

Topic Models

OptiGAN: Generative Adversarial Networks for Goal Optimized Sequence Generation

1 code implementation16 Apr 2020 Mahmoud Hossam, Trung Le, Viet Huynh, Michael Papasimeon, Dinh Phung

One of the challenging problems in sequence generation tasks is the optimized generation of sequences with specific desired goals.

Tree-Wasserstein Barycenter for Large-Scale Multilevel Clustering and Scalable Bayes

no code implementations10 Oct 2019 Tam Le, Viet Huynh, Nhat Ho, Dinh Phung, Makoto Yamada

We study in this paper a variant of Wasserstein barycenter problem, which we refer to as tree-Wasserstein barycenter, by leveraging a specific class of ground metrics, namely tree metrics, for Wasserstein distance.

On Efficient Multilevel Clustering via Wasserstein Distances

1 code implementation19 Sep 2019 Viet Huynh, Nhat Ho, Nhan Dam, XuanLong Nguyen, Mikhail Yurochkin, Hung Bui, and Dinh Phung

We propose a novel approach to the problem of multilevel clustering, which aims to simultaneously partition data in each group and discover grouping patterns among groups in a potentially large hierarchically structured corpus of data.

Probabilistic Multilevel Clustering via Composite Transportation Distance

no code implementations29 Oct 2018 Nhat Ho, Viet Huynh, Dinh Phung, Michael. I. Jordan

We propose a novel probabilistic approach to multilevel clustering problems based on composite transportation distance, which is a variant of transportation distance where the underlying metric is Kullback-Leibler divergence.

Multilevel Clustering via Wasserstein Means

1 code implementation ICML 2017 Nhat Ho, XuanLong Nguyen, Mikhail Yurochkin, Hung Hai Bui, Viet Huynh, Dinh Phung

We propose a novel approach to the problem of multilevel clustering, which aims to simultaneously partition data in each group and discover grouping patterns among groups in a potentially large hierarchically structured corpus of data.

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