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Interested in many aspects of artificial intelligence and deep neural networks, particularly the following:
- Generative adversarial networks and their applications to perception-related data synthesization such as music and images.
- Layer-by-layer analysis of trained deep neural networks to gather insight into how to modularise large models into a collection of smaller ones.
- Psychological parallels and applications relating our brain to deep neural networks and how both can be used to supplement each other.
Intrigued by computational theory including complexity theory and impossibility results.