Optimal Function Approximation with Deep Neural Networks: A Math Perspective (Giovanni Giorgis)
40:20
A Leisurely Introduction to Simplicial Sets (Aparna Jeyakumar)
37:21
Group Theory for Neural Network (Matthias Bonvin)
45:23
Progress, Machine Learning, and Why to Be Optimistic About the Future (Lukas Münzel)
47:27
Random Curves and Their Scaling Limits (Jonatan Wächter)
24:37
Ilya Sutskever: Sequence to Sequence Learning with Neural Networks at NeurIPS 2024
1:01:43
Dream Chemistry Award 2024 – special lecture: My Dream – Prof. Robert Hołyst
10:30
Why Neural Networks can learn (almost) anything
39:32