Categories for AI talk: Causal Model Abstraction & Grounding via Category Theory - by Taco Cohen
Categories for AI talk: Category Theory Inspired by LLMs - by Tai-Danae Bradley
Categories for AI 1: Why Category Theory? By Bruno Gavranović
NSSS 2023 - Day 2: Casual abstraction for faithful, interpretable mdl explanation(Christopher Potts)
Categories for AI talk: Neural network layers as parametric spans - by Pietro Vertechi
Categories for AI 3: Categorical Dataflow: Optics and Lenses as data structures for backpropagation
Uncovering and Inducing Interpretable Causal Structure in Deep Learning Models | Atticus Geiger
Taco Cohen: "Categorical Causality & Systems Theory"
Atticus Geiger - Theories and Tools for Mechanistic Interpretability via Causal Abstraction
Neural Software Abstractions: Michael Chang Dissertation Talk
Sarah Catanzaro - Against Machine Learning; For Causal Inference
Causal Models in Machine Learning
What is causal inference, and why should data scientists know? by Ludvig Hult
HVL Data Science Seminar |Fabio Massimo Zennaro | Learning Abstractions between Causal Models
Causality 101 with Robert Ness - #342
Yoshua Bengio Guest Talk - Towards Causal Representation Learning
Categories for AI 5: Monoids, Monads, Mappings, and lstMs - by Andrew Dudzik
The Debate Over “Understanding” in AI’s Large Language Models
Deep Learning for AI, Yoshua Bengio
AI Trends 2023: Causality and the Impact on Large Language Models with Robert Osazuwa Ness - 616
