42 citations · 73 across the 10 of their papers we have counts for
10 papers
Improved and Efficient Text Adversarial Attacks using Target Information
Mahmoud Hossam, Trung Le, He Zhao +2
There has been recently a growing interest in studying adversarial examples on natural language models in the black-box setting. These methods attack natural language classifiers b…
Text Generation with Deep Variational GAN
Mahmoud Hossam, Trung Le, Michael Papasimeon +2
Generating realistic sequences is a central task in many machine learning applications. There has been considerable recent progress on building deep generative models for sequence…
Topic Modelling Meets Deep Neural Networks: A Survey
He Zhao, Dinh Phung, Viet Huynh +3
Topic modelling has been a successful technique for text analysis for almost twenty years. When topic modelling met deep neural networks, there emerged a new and increasingly popul…
Artificial intelligence supported anemia control system (AISACS) to prevent anemia in maintenance hemodialysis patients
Toshiaki Ohara, Hiroshi Ikeda, Yoshiki Sugitani +5
Anemia, for which erythropoiesis-stimulating agents (ESAs) and iron supplements (ISs) are used as preventive measures, presents important difficulties for hemodialysis patients. Ne…
Neural Topic Model via Optimal Transport
He Zhao, Dinh Phung, Viet Huynh +2
Recently, Neural Topic Models (NTMs) inspired by variational autoencoders have obtained increasingly research interest due to their promising results on text analysis. However, it…
OptiGAN: Generative Adversarial Networks for Goal Optimized Sequence Generation
Mahmoud Hossam, Trung Le, Viet Huynh +2
One of the challenging problems in sequence generation tasks is the optimized generation of sequences with specific desired goals. Current sequential generative models mainly gener…
Tree-Wasserstein Barycenter for Large-Scale Multilevel Clustering and Scalable Bayes
Tam Le, Viet Huynh, Nhat Ho +2
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…
On Efficient Multilevel Clustering via Wasserstein Distances
Viet Huynh, Nhat Ho, Nhan Dam +4
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 pote…
Probabilistic Multilevel Clustering via Composite Transportation Distance
Nhat Ho, Viet Huynh, Dinh Phung +1
We propose a novel probabilistic approach to multilevel clustering problems based on composite transportation distance, which is a variant of transportation distance where the unde…
Multilevel Clustering via Wasserstein Means
Nhat Ho, XuanLong Nguyen, Mikhail Yurochkin +3
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 pote…