Published
iLQR-VAE: control-based learning of input-driven dynamics with applications to neural data
Marine Schimel, Ta-Chu Kao, Kristopher T. Jensen, Guillaume Hennequin
ICLR, 2022
[link]
Natural Continual Learning: success is a journey, not (just) a destination
Ta-Chu Kao, Kristopher T. Jensen, Gido M. van de Ven, Alberto Bernacchia and Guillaume Hennequin.
NeurIPs, 2021
[link | code]
Scalable Bayesian GPFA with automatic relevance determination and discrete noise models
Kristopher T. Jensen, Ta-Chu Kao, Jasmine T. Stone, and Guillaume Hennequin.
NeurIPs, 2021
[link]
Optimal anticipatory control as a theory of motor preparation: a thalamo-cortical model
Ta-Chu Kao, Mahdieh S. Sadabadi and Guillaume Hennequin.
Neuron, 2020
[link | code]
Manifold GPLVMs for discoverying non-Euclidean latent structure in neural data
Kristopher T. Jensen, Ta-Chu Kao, and Guillaume Hennequin.
NeurIPs, 2020
[link]
Neuroscience out of control: control-theoretic perspectives on neural circuit dynamics
Ta-Chu Kao and Guillaume Hennequin
Current Opinion in Neurobiology, 2019
[notebook | pdf]
OwlDE: making ODEs first-class Owl citizens
Marcello Seri and Ta-Chu Kao
Journal of Open Source Software, 2019
[link]
Null ain’t dull: new perspectives on the motor cortex
Ta-Chu Kao and Guillaume Hennequin
Trends in Cognitive Sciences, 2018
[link]
Layer Communities in Multiplex Networks
Ta-Chu Kao and Mason A. Porter
Journal of Statistical Physics, 2017
[link]
Preprints
Automatic differentiation of Sylvester, Lyapunov, and algebraic Riccati equations
Ta-Chu Kao and Guillaume Hennequin
arXiv
link