3 papers
quant-ph2026
A Trainable-Embedding Quantum Physics-Informed Framework for Multi-Species Reaction-Diffusion Systems
Ban Q. Tran, Nahid Binandeh Dehaghani, A. Pedro Aguiar +2
Physics-informed neural networks (PINNs) and hybrid quantum-classical extensions provide a promising framework for solving partial differential equations (PDEs) by embedding physic…
cs.LG2026
Parameter-efficient Multi-Task and Multi-Domain Learning using Factorized Tensor Networks
Yash Garg, Nebiyou Yismaw, Rakib Hyder +2
Multi-task and multi-domain learning methods seek to learn multiple tasks/domains, jointly or one after another, using a single unified network. The primary challenge and opportuni…
cs.AR2025
MARVEL: An End-to-End Framework for Generating Model-Class Aware Custom RISC-V Extensions for Lightweight AI
Ajay Kumar M, Cian O'Mahoney, Pedro Kreutz Werle +5
Deploying deep neural networks (DNNs) on resource-constrained IoT devices remains a challenging problem, often requiring hardware modifications tailored to individual AI models. Ex…