3 papers
cs.LG2026
The Global Empirical NTK: Self-Referential Bias and Dimensionality of Gradient Descent Learning
James Hazelden, Laura Driscoll, Eli Shlizerman +1
In training a neural network with gradient descent (GD), each iteration induces a linear operator that governs first-order updates to a model's internal state variables. We define…
cs.LG2026
Center-Manifold Reduction of Learning at Bifurcations: Interference and Rich Learning in Recurrent Neural Networks
James Hazelden, Eric Shea-Brown
Rich learning in recurrent neural networks often proceeds through sudden transitions in latent dynamics, but there is little theory predicting how gradient descent behaves during t…
q-bio.NC2026
Identifying Connectivity Distributions from Neural Dynamics Using Flows
Timothy Doyeon Kim, Ulises Pereira-Obilinovic, Yiliu Wang +2
Connectivity structure shapes neural computation, but inferring this structure from population recordings is degenerate: multiple connectivity structures can generate identical dyn…