5 papers
Anisotropic Gaussian Smoothing for Gradient-based Optimization
Andrew Starnes, Guannan Zhang, Viktor Reshniak +1
This article introduces a novel family of optimization algorithms - Anisotropic Gaussian Smoothing Gradient Descent (AGS-GD), AGS-Stochastic Gradient Descent (AGS-SGD), and AGS-Ada…
Mamba for Scalable and Efficient Personalized Recommendations
Andrew Starnes, Clayton Webster
In this effort, we propose using the Mamba for handling tabular data in personalized recommendation systems. We present the \textit{FT-Mamba} (Feature Tokenizer\,\,Mamba), a nov…
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.…