no code implementations • 8 Aug 2022 • Neil Fendley, Cash Costello, Eric Nguyen, Gino Perrotta, Corey Lowman
Training reinforcement learning agents that continually learn across multiple environments is a challenging problem.
no code implementations • 28 Jan 2022 • Susama Agarwala, Ben Dees, Corey Lowman
We study the map learned by a family of autoencoders trained on MNIST, and evaluated on ten different data sets created by the random selection of pixel values according to ten different distributions.
no code implementations • 27 Jan 2022 • Benjamin Dees, Susama Agarwala, Corey Lowman
In this paper, we investigate the evolution of autoencoders near their initialization.
1 code implementation • 2 Nov 2021 • Nicholas Kantack, Nina Cohen, Nathan Bos, Corey Lowman, James Everett, Timothy Endres
Specifically, an AI examines human actions and calculates variations on the human strategy that lead to better performance.
no code implementations • 13 Jul 2021 • Susama Agarwala, Benjamin Dees, Andrew Gearhart, Corey Lowman
We study the deformation of the input space by a trained autoencoder via the Jacobians of the trained weight matrices.