Beyond NTK: A Mean-Field Analysis of Neural Networks with Polynomial Width, Samples, and Time
33:11
Theoretical and Practical Insights from Linear Transformers
30:47
On the Connection between Neural Networks and Kernels: a Modern Perspective -Simon Du
25:40
Mean Field Approach for Variational Inference | Intuition & General Derivation
1:04:28
Structured State Space Models for Deep Sequence Modeling (Albert Gu, CMU)
49:56
Mean-Field Theory of Two-Layers Neural Networks: Dimension Free Bounds and Example
34:32
Physics Informed Neural Networks (PINNs) [Physics Informed Machine Learning]
1:05:39
Andrea Montanari (Stanford) -- Mean Field Descriptions of Two Layers Neural Network
1:02:34