892 citations · 2.3k across the 60 of their papers we have counts for
65 papers
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…
MED-TEX: Transferring and Explaining Knowledge with Less Data from Pretrained Medical Imaging Models
Thanh Nguyen-Duc, He Zhao, Jianfei Cai +1
Deep learning methods usually require a large amount of training data and lack interpretability. In this paper, we propose a novel knowledge distillation and model interpretation f…
Improving Adversarial Robustness by Enforcing Local and Global Compactness
Anh Bui, Trung Le, He Zhao +4
The fact that deep neural networks are susceptible to crafted perturbations severely impacts the use of deep learning in certain domains of application. Among many developed defens…
A Self-Attention Network based Node Embedding Model
Dai Quoc Nguyen, Tu Dinh Nguyen, Dinh Phung
Despite several signs of progress have been made recently, limited research has been conducted for an inductive setting where embeddings are required for newly unseen nodes -- a se…
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…