6 citations · 7 across the 2 of their papers we have counts for
4 papers
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…
Sampling-based probabilistic inference emerges from learning in neural circuits with a cost on reliability
Laurence Aitchison, Guillaume Hennequin, Mate Lengyel
Neural responses in the cortex change over time both systematically, due to ongoing plasticity and learning, and seemingly randomly, due to various sources of noise and variability…
Asymptotic scaling properties of the posterior mean and variance in the Gaussian scale mixture model
Rodrigo Echeveste, Guillaume Hennequin, Máté Lengyel
The Gaussian scale mixture model (GSM) is a simple yet powerful probabilistic generative model of natural image patches. In line with the well-established idea that sensory process…