Reinforcement Learning (RL) remains a central optimisation framework in machine learning. Although RL agents can converge to optimal solutions, the definition o...
While quantum annealing (QA) has been developed for combinatorial optimization, practical QA devices operate at finite temperature and under noise, and their ou...
We study the problem of computing matrix chain multiplications in a distributed computing cluster. In such systems, performance is often limited by the straggle...
In today's complex industrial environments, operators must often navigate through extensive technical manuals to identify troubleshooting procedures that may he...
Neural Combinatorial Optimization (NCO) has mostly focused on learning policies, typically neural networks, that operate on a single candidate solution at a tim...
Context: Traditional software security analysis methods struggle to keep pace with the scale and complexity of modern codebases, requiring intelligent automatio...
Evolving neural network architectures is a computationally demanding process. Traditional methods often require an extensive search through large architectural ...
Autonomous Driving Assistance Systems (ADAS) rely on extensive testing to ensure safety and reliability, yet road scenario datasets often contain redundant case...
With the development of large language models (LLMs) in the field of programming, intelligent programming coaching systems have gained widespread attention. How...
Spike-Timing-Dependent Plasticity (STDP) provides a biologically grounded learning rule for spiking neural networks (SNNs), but its reliance on precise spike ti...
Conversational agents are increasingly used as support tools along mental therapeutic pathways with significant societal impacts. In particular, empathy is a ke...
Spike-timing-dependent plasticity (STDP) provides a biologically-plausible learning mechanism for spiking neural networks (SNNs); however, Hebbian weight update...
