Monash University
Thesis.pdf (3.86 MB)

Deep Sequence Models: Learning to Generate Data and Adversarial Attacks

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posted on 2021-07-05, 05:55 authored by MAHMOUD AHMED HOSSAMELDEEN MOHAMMAD AHMED IBRAHIM
Understanding sequential data like natural language sentences and learning to model it with generative models are fundamental research problems in artificial intelligence. Solving them helps to create machines that are imaginative and which can perform human-like reasoning and robust decision making. Advanced sequence models will have a significant impact on key areas including drug discovery, autonomous vehicles, and robotics. This thesis advances research in sequence models in two ways: by introducing controlling mechanisms into generative models, and by learning to efficiently generate attacks on natural language models.


Campus location


Principal supervisor

Dinh Phung

Additional supervisor 1

Trung Le

Additional supervisor 2

Viet Huynh

Additional supervisor 3

He Zhao

Year of Award


Department, School or Centre

Information Technology (Monash University Clayton)


Doctor of Philosophy

Degree Type



Faculty of Information Technology