6 citations · 7 across the 7 of their papers we have counts for
7 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…
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…
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…
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…
Local Methods with Adaptivity via Scaling
Savelii Chezhegov, Sergey Skorik, Nikolas Khachaturov +5
The rapid development of machine learning and deep learning has introduced increasingly complex optimization challenges that must be addressed. Indeed, training modern, advanced mo…
Activations and Gradients Compression for Model-Parallel Training
Mikhail Rudakov, Aleksandr Beznosikov, Yaroslav Kholodov +1
Large neural networks require enormous computational clusters of machines. Model-parallel training, when the model architecture is partitioned sequentially between workers, is a po…