9 papers
Task-Aware QUBO Allocation for Mixed-Precision Quantization
Osama Orabi, Artur Zagitov, Hadi Salloum +2
Mixed-precision quantization requires discrete allocation of weight and activation bit-widths, followed by recovery of the selected network. We develop a task-aware quadratic uncon…
Higher-Order vs. Quadratic Binary Optimization: Which Is Better for Probability Optimization with Tensor Sampling?
Hadi Salloum, Kirill Novoselov, Aleksandr Pochtarev +1
This article explores the comparative strengths of Higher-Order Unconstrained Binary Optimization (HUBO) and Quadratic Unconstrained Binary Optimization (QUBO) models in the contex…
Neural Network Pruning via QUBO Optimization
Osama Orabi, Artur Zagitov, Hadi Salloum +3
Neural network pruning can be formulated as a combinatorial optimization problem, yet most existing approaches rely on greedy heuristics that ignore complex interactions between fi…
Benchmarking Reinforcement Learning via Stochastic Converse Optimality: Generating Systems with Known Optimal Policies
Sinan Ibrahim, Grégoire Ouerdane, Hadi Salloum +3
The objective comparison of Reinforcement Learning (RL) algorithms is notoriously complex as outcomes and benchmarking of performances of different RL approaches are critically sen…
Diversity-Aware Adaptive Collocation for Physics-Informed Neural Networks via Sparse QUBO Optimization and Hybrid Coresets
Hadi Salloum, Maximilian Mifsud Bonici, Sinan Ibrahim +2
Physics-Informed Neural Networks (PINNs) enforce governing equations by penalizing PDE residuals at interior collocation points, but standard collocation strategies - uniform sampl…
SCOPE: Smooth Convex Optimization for Planned Evolution of Deformable Linear Objects
Ali Jnadi, Hadi Salloum, Yaroslav Kholodov +2
We present SCOPE, a fast and efficient framework for modeling and manipulating deformable linear objects (DLOs). Unlike conventional energy-based approaches, SCOPE leverages convex…
Quantum-Inspired Episode Selection for Monte Carlo Reinforcement Learning via QUBO Optimization
Hadi Salloum, Ali Jnadi, Yaroslav Kholodov +1
Monte Carlo (MC) reinforcement learning suffers from high sample complexity, especially in environments with sparse rewards, large state spaces, and correlated trajectories. We add…
Speeding up the Goemans-Williamson randomized procedure by difference-of-convex optimization
Hadi Salloum, Roland Hildebrand, Nhat Trung Nguyen +4
We present a novel approach to accelerate the Goemans-Williamson (GW) randomized rounding procedure for quadratic unconstrained binary optimization (QUBO) problems. Instead of solv…
Training-Free Out-Of-Distribution Segmentation With Foundation Models
Laith Nayal, Hadi Salloum, Ahmad Taha +2
Detecting unknown objects in semantic segmentation is crucial for safety-critical applications such as autonomous driving. Large vision foundation models, including DINOv2, InternI…