32 citations · 55 across the 5 of their papers we have counts for
5 papers
Minimum Description Length Control
Ted Moskovitz, Ta-Chu Kao, Maneesh Sahani +1
We propose a novel framework for multitask reinforcement learning based on the minimum description length (MDL) principle. In this approach, which we term MDL-control (MDL-C), the…
Natural continual learning: success is a journey, not (just) a destination
Ta-Chu Kao, Kristopher T. Jensen, Gido M. van de Ven +2
Biological agents are known to learn many different tasks over the course of their lives, and to be able to revisit previous tasks and behaviors with little to no loss in performan…
Automatic differentiation of Sylvester, Lyapunov, and algebraic Riccati equations
Ta-Chu Kao, Guillaume Hennequin
Sylvester, Lyapunov, and algebraic Riccati equations are the bread and butter of control theorists. They are used to compute infinite-horizon Gramians, solve optimal control proble…
Manifold GPLVMs for discovering non-Euclidean latent structure in neural data
Kristopher T. Jensen, Ta-Chu Kao, Marco Tripodi +1
A common problem in neuroscience is to elucidate the collective neural representations of behaviorally important variables such as head direction, spatial location, upcoming moveme…
Layer Communities in Multiplex Networks
Ta-Chu Kao, Mason A. Porter
Multiplex networks are a type of multilayer network in which entities are connected to each other via multiple types of connections. We propose a method, based on computing pairwis…