no code implementations • NeurIPS 2012 • Jake Bouvrie, Jean-Jeacques Slotine
To learn reliable rules that can generalize to novel situations, the brain must be capable of imposing some form of regularization.
no code implementations • 3 Apr 2012 • Jake Bouvrie, Boumediene Hamzi
We introduce a data-based approach to estimating key quantities which arise in the study of nonlinear control systems and random nonlinear dynamical systems.
no code implementations • 14 Aug 2011 • Jake Bouvrie, Boumediene Hamzi
We introduce a data-driven order reduction method for nonlinear control systems, drawing on recent progress in machine learning and statistical dimensionality reduction.
no code implementations • NeurIPS 2009 • Jake Bouvrie, Lorenzo Rosasco, Tomaso Poggio
A goal of central importance in the study of hierarchical models for object recognition -- and indeed the visual cortex -- is that of understanding quantitatively the trade-off between invariance and selectivity, and how invariance and discrimination properties contribute towards providing an improved representation useful for learning from data.