10 citations · 35 across the 13 of their papers we have counts for
6 papers · 1 filter
Solvable models of learning to pursue a moving target
John J. Vastola, Kanaka Rajan
Agents tasked with intercepting a moving target must learn how to reach it, ideally as efficiently as possible. As observers of animal pursuit behavior have noticed, different stra…
A Variational Manifold Embedding Framework for Nonlinear Dimensionality Reduction
John J. Vastola, Samuel J. Gershman, Kanaka Rajan
Dimensionality reduction algorithms like principal component analysis (PCA) are workhorses of machine learning and neuroscience, but each has well-known limitations. Variants of PC…
Gradient Descent as Loss Landscape Navigation: a Normative Framework for Deriving Learning Rules
John J. Vastola, Samuel J. Gershman, Kanaka Rajan
Learning rules -- prescriptions for updating model parameters to improve performance -- are typically assumed rather than derived. Why do some learning rules work better than other…
Generalization through variance: how noise shapes inductive biases in diffusion models
John J. Vastola
How diffusion models generalize beyond their training set is not known, and is somewhat mysterious given two facts: the optimum of the denoising score matching (DSM) objective usua…
Dynamical symmetries in the fluctuation-driven regime: an application of Noether's theorem to noisy dynamical systems
John J. Vastola
Noether's theorem provides a powerful link between continuous symmetries and conserved quantities for systems governed by some variational principle. Perhaps unfortunately, most dy…
The Unreasonable Effectiveness of Gaussian Score Approximation for Diffusion Models and its Applications
Binxu Wang, John J. Vastola
By learning the gradient of smoothed data distributions, diffusion models can iteratively generate samples from complex distributions. The learned score function enables their gene…