most citedText Generation with Deep Variational GAN

2 citations · 2 across the 5 of their papers we have counts for

collaborators

5 papers

cs.DC2021

High Performance Hyperspectral Image Classification using Graphics Processing Units

Mahmoud Hossam

Real-time remote sensing applications like search and rescue missions, military target detection, environmental monitoring, hazard prevention and other time-critical applications r…

cs.LG2021

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…

cs.LG2021★ 2 cited

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…

cs.LG2020

Explain2Attack: Text Adversarial Attacks via Cross-Domain Interpretability

Mahmoud Hossam, Trung Le, He Zhao +1

Training robust deep learning models for down-stream tasks is a critical challenge. Research has shown that down-stream models can be easily fooled with adversarial inputs that loo…

cs.LG2020

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…