Workshop on mathematical aspects of AI and Machine Learning. The core themes of this edition will be Diffusion Models, Associative Memories and Transformers.
Overview
Date:
Starts 8 June 2026, 09:00Ends 10 June 2026, 13:00Location:
Language:
EnglishLast sign up date:
15 May 2026
The workshop will explore the following topics:
Transformers: these architectures are at the core of Large Language Models and are rapidly revolutionising all fields of ML. Despite theoretical efforts, approaches remain scattered, and we lack a unifying framework. This theme aims to unify these perspectives on how to study transformers.
Diffusion Models/Flow Matching: these models are the state of the art for generative tasks. Theoretical progress in this field is proceeding at an unprecedented pace, making results from just one or two years ago obsolete. The goal is to assess the current state of research and describe the open questions that lie ahead.
Associative Memories: models of associative memory have recently returned as a core topic in AI research, given their analogies with aspects of learning and memorization in Transformers and Diffusion Models. In this theme, we aim to reconcile classical and recent results to repurpose them to understand these modern technologies.
See the full programme and speakers at the event page.
Theme: Mathematical Foundations of AI
The rapid pace of AI development has led to a critical gap: while applied research is thriving, foundational research remains under-represented compared to engineering applications. This theme aims to address the provocative question: "Is AI doomed to become an engineering-only field, or is it possible to build a science of AI?"
- Postdoc, Data Science and AI, Computer Science and Engineering