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Modelling Data with Stochastic Generative Processes

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thesis
posted on 22.04.2020 by NHAN NGUYEN TRONG DAM
Learning and modelling complex data with generative models is a prominent research topic in modern artificial intelligence since generative models empower us to answer fundamental and important research questions into the generative processes of algorithmic intelligence. They also enable practical applications such as recognising patterns and anomaly, constructing high-level abstractions utilised in reasoning and decision making, and drawing deep insights from the data to facilitate data compression and augmentation. This dissertation aims to advance research in generative models by enriching their modelling capacity and enhancing their robustness to work more efficiently on a wider range of problems and data types.

History

Campus location

Australia

Principal supervisor

Dinh Phung

Additional supervisor 1

Trung Le

Additional supervisor 2

Viet Huynh

Year of Award

2020

Department, School or Centre

Clayton School of IT

Course

Doctor of Philosophy

Degree Type

DOCTORATE

Exports