2 code implementations • NeurIPS 2021 • Cole Hurwitz, Akash Srivastava, Kai Xu, Justin Jude, Matthew G. Perich, Lee E. Miller, Matthias H. Hennig
These approaches, however, are limited in their ability to capture the underlying neural dynamics (e. g. linear) and in their ability to relate the learned dynamics back to the observed behaviour (e. g. no time lag).
no code implementations • 3 Feb 2021 • Cole Hurwitz, Nina Kudryashova, Arno Onken, Matthias H. Hennig
Modern recording technologies now enable simultaneous recording from large numbers of neurons.
no code implementations • 25 Oct 2020 • Akash Srivastava, Yamini Bansal, Yukun Ding, Cole Hurwitz, Kai Xu, Bernhard Egger, Prasanna Sattigeri, Josh Tenenbaum, David D. Cox, Dan Gutfreund
Current autoencoder-based disentangled representation learning methods achieve disentanglement by penalizing the (aggregate) posterior to encourage statistical independence of the latent factors.
no code implementations • 9 Sep 2020 • Seungwook Han, Akash Srivastava, Cole Hurwitz, Prasanna Sattigeri, David D. Cox
First, we generate images in low-frequency bands by training a sampler in the wavelet domain.