1 citations · 1 across the 7 of their papers we have counts for
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Improved Performance of Stochastic Gradients with Gaussian Smoothing
Andrew Starnes, Clayton Webster
This paper formalizes and analyzes Gaussian smoothing applied to two prominent optimization methods: Stochastic Gradient Descent (GSmoothSGD) and Adam (GSmoothAdam) in deep learnin…
Gaussian smoothing gradient descent for minimizing functions (GSmoothGD)
Andrew Starnes, Anton Dereventsov, Clayton Webster
This work analyzes the convergence of a class of smoothing-based gradient descent methods when applied to optimization problems. In particular, Gaussian smoothing is employed to de…
Increasing Entropy to Boost Policy Gradient Performance on Personalization Tasks
Andrew Starnes, Anton Dereventsov, Clayton Webster
In this effort, we consider the impact of regularization on the diversity of actions taken by policies generated from reinforcement learning agents trained using a policy gradient.…
Modeling Non-deterministic Human Behaviors in Discrete Food Choices
Andrew Starnes, Anton Dereventsov, E. Susanne Blazek +1
We establish a non-deterministic model that predicts a user's food preferences from their demographic information. Our simulator is based on NHANES dataset and domain expert knowle…