21 citations · 52 across the 13 of their papers we have counts for
13 papers
Gated QKAN-FWP: Scalable Quantum-inspired Sequence Learning
Kuo-Chung Peng, Samuel Yen-Chi Chen, Jiun-Cheng Jiang +16
Fast Weight Programmers (FWPs) encode temporal dependencies through dynamically updated parameters rather than recurrent hidden states. Quantum FWPs (QFWPs) extend this idea with v…
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…
How Embeddings Shape Graph Neural Networks: Classical vs Quantum-Oriented Node Representations
Nouhaila Innan, Antonello Rosato, Alberto Marchisio +1
Node embeddings act as the information interface for graph neural networks, yet their empirical impact is often reported under mismatched backbones, splits, and training budgets. T…
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…
Hyperdimensional Computing for ADHD Classification using EEG Signals
Federica Colonnese, Antonello Rosato, Francesco Di Luzio +1
Following the recent interest in applying the Hyperdimensional Computing paradigm in medical context to power up the performance of general machine learning applied to biomedical d…
Q-SCALE: Quantum computing-based Sensor Calibration for Advanced Learning and Efficiency
Lorenzo Bergadano, Andrea Ceschini, Pietro Chiavassa +4
In a world burdened by air pollution, the integration of state-of-the-art sensor calibration techniques utilizing Quantum Computing (QC) and Machine Learning (ML) holds promise for…
From Graphs to Qubits: A Critical Review of Quantum Graph Neural Networks
Andrea Ceschini, Francesco Mauro, Francesca De Falco +7
Quantum Graph Neural Networks (QGNNs) represent a novel fusion of quantum computing and Graph Neural Networks (GNNs), aimed at overcoming the computational and scalability challeng…
An Explainable Fast Deep Neural Network for Emotion Recognition
Francesco Di Luzio, Antonello Rosato, Massimo Panella
In the context of artificial intelligence, the inherent human attribute of engaging in logical reasoning to facilitate decision-making is mirrored by the concept of explainability,…
Quasi-Chaotic Oscillators Based on Modular Quantum Circuits
Andrea Ceschini, Antonello Rosato, Massimo Panella
Digital circuits based on residue number systems have been considered to produce a pseudo-random behavior. The present work is an initial step towards the complete implementation o…
On Effects of Compression with Hyperdimensional Computing in Distributed Randomized Neural Networks
Antonello Rosato, Massimo Panella, Evgeny Osipov +1
A change of the prevalent supervised learning techniques is foreseeable in the near future: from the complex, computational expensive algorithms to more flexible and elementary tra…
Hyperdimensional Computing for Efficient Distributed Classification with Randomized Neural Networks
Antonello Rosato, Massimo Panella, Denis Kleyko
In the supervised learning domain, considering the recent prevalence of algorithms with high computational cost, the attention is steering towards simpler, lighter, and less comput…
Perceptron Theory Can Predict the Accuracy of Neural Networks
Denis Kleyko, Antonello Rosato, E. Paxon Frady +2
Multilayer neural networks set the current state of the art for many technical classification problems. But, these networks are still, essentially, black boxes in terms of analyzin…