42 citations · 52 across the 3 of their papers we have counts for
8 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…
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…