IEEE

Varying Infimum Gradient Descent Algorithm for Agent–Server Systems With Uncertain Communication Network

Abstract: This article studies agent-server system identification problems by using a varying infimum gradient descent (VI-GD) algorithm. To efficiently use the GD algorithm for the agent-server with ...
www.cs.utexas.edu

FAMO: Fast Adaptive Multitask Optimization

FAMO: Fast Adaptive Multitask Optimization. Bo Liu, Yihao Feng, Peter Stone, and Qiang Liu. @InProceedings{bo_liu_neurips_2023, author = {Bo Liu and Yihao Feng and Peter Stone and Qiang Liu}, title = ...
GitHub

Supervised SNN Based on Gradient Descent in TensorFlow

This repo attempts to proposes a supervised learning algorithm of SNN by using spike sequences with complex spatio-temporal information. We explore an error back ...
IEEE

Analysis of the Gradient-Descent Total Least-Squares Adaptive Filtering Algorithm

Abstract: The gradient-descent total least-squares (GD-TLS) algorithm is a stochastic-gradient adaptive filtering algorithm that compensates for error in both input ...
PNAS

The inverse variance–flatness relation in stochastic gradient descent is critical for finding flat minima

One key ingredient in deep learning is the stochastic gradient descent (SGD) algorithm, which allows neural nets to find generalizable solutions at flat minima of the high-dimensional loss function.
Frontiers

Semi-Stochastic Gradient Descent Methods

In this paper we study the problem of minimizing the average of a large number of smooth convex loss functions. We propose a new method, S2GD (Semi-Stochastic Gradient Descent), which runs for one or ...