Search Results for author:
Found 5 papers, 1 papers with code
Continual Reinforcement Learning with TELLA
Training reinforcement learning agents that continually learn across multiple environments is a challenging problem.
Geometric instability of out of distribution data across autoencoder architecture
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.
Eigenvalues of Autoencoders in Training and at Initialization
In this paper, we investigate the evolution of autoencoders near their initialization.
Instructive artificial intelligence (AI) for human training, assistance, and explainability
Specifically, an AI examines human actions and calculates variations on the human strategy that lead to better performance.
Geometry and Generalization: Eigenvalues as predictors of where a network will fail to generalize
We study the deformation of the input space by a trained autoencoder via the Jacobians of the trained weight matrices.
