Talks

Interviews

Watch This Space for an Interview About Deep Dependent Typing Project

Machine Learning Street Talk: Math vs. AI: Who Decides What's True

Video: https://www.youtube.com/watch?v=TBjCvB_4mdo

March 2025

Machine Learning Street Talk: Categorical Deep Learning 1:2

Video: https://www.youtube.com/watch?v=rie-9AEhYdY&t=4319s

March 2024

Note: Nearly 100k Views!

Notes And/Or Videos for (some) Talks

From Deep Clustering and Grammar VAEs to Deep Dependent Typing

Abstract: For discrete modalities, like molecular structures or formal languages, the difficulty of training useful autoencoders lies not in achieving reconstruction, but in training the model so that the geometry of the latent space is aligned with the semantics of that discrete modality. In general, the inductive biases of optimiser and architecture in training of the autoencoder are not enough to guarantee that alignment. We are not however without techniques, consider grammar based VAEs and deep clustering.

But grammars—in this sense—are purely syntactic and clustering is too heuristic. How can we replace these techniques with more semantic and more principled approaches? Moreover how can we do so in such a way that the technique applies to the semantics of dependent type languages like L∃ ∀ N, whose essential role in the rise of reasoning models is matched only by LLMs themselves.

Our exposition of a proposed solution proceeds in three movements. In the first, we describe an application of the promised synthesis of deep clustering and grammar VAEs to the ListOps dataset. In the second, we identify that example as fitting into a more general categorical framework in the context of certain simplifying assumptions. In the third and final movement, we motivate dependent type theory and present our proposal for deep dependent typing—dropping that simplifying assumption and using functorial semantics to align the geometry of the latent space with the semantics of the dependent type system.

Slides: https://drive.google.com/file/d/1l3L4P7sZ6QB3rWSS9X1oEeLmb9m4-S-u/view?usp=sharing

Friday 20 March 2026

Australian Institute of Machine Learning

Adelaide University