2 citations · 2 across the 3 of their papers we have counts for
3 papers
eess.SY2024
FTISS Adaptive Bearing-Only Formation Tracking Control with Unknown Disturbance Rejection
Hong Liang Cheah, Mohammad Deghat
This paper proposes a finite-time input-to-state stable (FTISS) bearing-only formation control law that rejects unknown constant disturbances. Unlike existing finite-time bearing-b…
cs.RO2024★ 2 cited
How Can LLMs and Knowledge Graphs Contribute to Robot Safety? A Few-Shot Learning Approach
Abdulrahman Althobaiti, Angel Ayala, JingYing Gao +4
Large Language Models (LLMs) are transforming the robotics domain by enabling robots to comprehend and execute natural language instructions. The cornerstone benefits of LLM includ…
cs.LG2024
AA-DLADMM: An Accelerated ADMM-based Framework for Training Deep Neural Networks
Zeinab Ebrahimi, Gustavo Batista, Mohammad Deghat
Stochastic gradient descent (SGD) and its many variants are the widespread optimization algorithms for training deep neural networks. However, SGD suffers from inevitable drawbacks…