4 papers
Contextual Bandits for Resource-Constrained Devices using Probabilistic Learning
Marco Angioli, Kevin Johansson, Antonello Rosato +2
Contextual bandits (CB) are online sequential decision-making problems under partial feedback that underpin many adaptive services. There is a growing demand to deploy CB agents di…
Efficient Hyperdimensional Computing with Modular Composite Representations
Marco Angioli, Christopher J. Kymn, Antonello Rosato +3
The modular composite representation (MCR) is a computing model that represents information with high-dimensional integer vectors using modular arithmetic. Originally proposed as a…
HD-CB: The First Exploration of Hyperdimensional Computing for Contextual Bandits Problems
Marco Angioli, Antonello Rosato, Marcello Barbirotta +3
Hyperdimensional Computing (HDC), also known as Vector Symbolic Architectures, is a computing paradigm that combines the strengths of symbolic reasoning with the efficiency and sca…
Efficient Implementation of LinearUCB through Algorithmic Improvements and Vector Computing Acceleration for Embedded Learning Systems
Marco Angioli, Marcello Barbirotta, Abdallah Cheikh +3
As the Internet of Things expands, embedding Artificial Intelligence algorithms in resource-constrained devices has become increasingly important to enable real-time, autonomous de…