12 citations · 22 across the 11 of their papers we have counts for
6 papers · 1 filter
Comparing NARS and Reinforcement Learning: An Analysis of ONA and -Learning Algorithms
Ali Beikmohammadi, Sindri Magnússon
In recent years, reinforcement learning (RL) has emerged as a popular approach for solving sequence-based tasks in machine learning. However, finding suitable alternatives to RL re…
Human-Inspired Framework to Accelerate Reinforcement Learning
Ali Beikmohammadi, Sindri Magnússon
Reinforcement learning (RL) is crucial for data science decision-making but suffers from sample inefficiency, particularly in real-world scenarios with costly physical interactions…
Federated Learning for IoUT: Concepts, Applications, Challenges and Opportunities
Nancy Victor, Rajeswari. C, Mamoun Alazab +5
Internet of Underwater Things (IoUT) have gained rapid momentum over the past decade with applications spanning from environmental monitoring and exploration, defence applications,…
Delay-adaptive step-sizes for asynchronous learning
Xuyang Wu, Sindri Magnusson, Hamid Reza Feyzmahdavian +1
In scalable machine learning systems, model training is often parallelized over multiple nodes that run without tight synchronization. Most analysis results for the related asynchr…
The Internet of Things as a Deep Neural Network
Rong Du, Sindri Magnússon, Carlo Fischione
An important task in the Internet of Things (IoT) is field monitoring, where multiple IoT nodes take measurements and communicate them to the base station or the cloud for processi…
Communication-efficient Variance-reduced Stochastic Gradient Descent
Hossein S. Ghadikolaei, Sindri Magnusson
We consider the problem of communication efficient distributed optimization where multiple nodes exchange important algorithm information in every iteration to solve large problems…