5 papers
Learning Encoding-Decoding Direction Pairs to Unveil Concepts of Influence in Deep Vision Networks
Alexandros Doumanoglou, Kurt Driessens, Dimitrios Zarpalas
Empirical evidence shows that deep vision networks often represent concepts as directions in latent space with concept information written along directional components in the vecto…
A 1-bit quantum filter for particle trajectory reconstruction
Xenofon Chiotopoulos, Davide Nicotra, George Scriven +6
The transition to the High-Luminosity Large Hadron Collider (HL-LHC) presents a computational challenge where particle reconstruction complexity may outpace classical computing res…
TrackHHL: A Quantum Computing Algorithm for Track Reconstruction at the LHCb
Xenofon Chiotopoulos, Miriam Lucio Martinez, Davide Nicotra +4
In the future high-luminosity LHC era, high-energy physics experiments face unprecedented computational challenges for event reconstruction. Employing the LHCb vertex locator as a…
Variational Quantum Algorithms for Particle Track Reconstruction
Vincenzo Lipardi, Xenofon Chiotopoulos, Jacco A. de Vries +4
Quantum Computing is a rapidly developing field with the potential to tackle the increasing computational challenges faced in high-energy physics. In this work, we explore the pote…
A Research Agenda for Usability and Generalisation in Reinforcement Learning
Dennis J. N. J. Soemers, Spyridon Samothrakis, Kurt Driessens +1
It is common practice in reinforcement learning (RL) research to train and deploy agents in bespoke simulators, typically implemented by engineers directly in general-purpose progr…