106 citations · 197 across the 33 of their papers we have counts for
5 papers · 1 filter
Neural Networks as Kernel Learners: The Silent Alignment Effect
Alexander Atanasov, Blake Bordelon, Cengiz Pehlevan
Neural networks in the lazy training regime converge to kernel machines. Can neural networks in the rich feature learning regime learn a kernel machine with a data-dependent kernel…
Capacity of Group-invariant Linear Readouts from Equivariant Representations: How Many Objects can be Linearly Classified Under All Possible Views?
Matthew Farrell, Blake Bordelon, Shubhendu Trivedi +1
Equivariance has emerged as a desirable property of representations of objects subject to identity-preserving transformations that constitute a group, such as translations and rota…
Learning Curves for SGD on Structured Features
Blake Bordelon, Cengiz Pehlevan
The generalization performance of a machine learning algorithm such as a neural network depends in a non-trivial way on the structure of the data distribution. To analyze the influ…
Out-of-Distribution Generalization in Kernel Regression
Abdulkadir Canatar, Blake Bordelon, Cengiz Pehlevan
In real word applications, data generating process for training a machine learning model often differs from what the model encounters in the test stage. Understanding how and wheth…
A Theory of Neural Tangent Kernel Alignment and Its Influence on Training
Haozhe Shan, Blake Bordelon
The training dynamics and generalization properties of neural networks (NN) can be precisely characterized in function space via the neural tangent kernel (NTK). Structural changes…