Search Results for author:
Found 5 papers, 4 papers with code
Examining Policy Entropy of Reinforcement Learning Agents for Personalization Tasks
This effort is focused on examining the behavior of reinforcement learning systems in personalization environments and detailing the differences in policy entropy associated with the type of learning algorithm utilized.
An adaptive stochastic gradient-free approach for high-dimensional blackbox optimization
In this work, we propose a novel adaptive stochastic gradient-free (ASGF) approach for solving high-dimensional nonconvex optimization problems based on function evaluations.
Analysis of The Ratio of $\ell_1$ and $\ell_2$ Norms in Compressed Sensing
We first propose a novel criterion that guarantees that an $s$-sparse signal is the local minimizer of the $\ell_1/\ell_2$ objective; our criterion is interpretable and useful in practice.
A nonlocal feature-driven exemplar-based approach for image inpainting
We present a nonlocal variational image completion technique which admits simultaneous inpainting of multiple structures and textures in a unified framework.
A Polynomial-Based Approach for Architectural Design and Learning with Deep Neural Networks
In this effort we propose a novel approach for reconstructing multivariate functions from training data, by identifying both a suitable network architecture and an initialization using polynomial-based approximations.
