Abstract: This article proposes utilizing a single deep reinforcement learning model to solve combinatorial multiobjective optimization problems. We use the well-known multiobjective traveling ...

The Machine Learning for Combinatorial Optimization Competition (ML4CO): Results and Insights Maxime Gasse, Simon Bowly, Quentin Cappart, Jonas Charfreitag, Laurent Charlin, Didier Chételat, Antonia ...

Abstract: Solving combinatorial optimization problems on current noisy quantum devices is currently being advocated for (and restricted to) binary polynomial optimization with equality constraints via ...

0-1 Knapsack Problem Goal: Select a subset of items to maximize total profit without exceeding a weight capacity. Deterministic: Uses nominal weights. Robust: Handles uncertainty in item weights using ...

Quantum AI workloads will benefit from the integration of quantum computers in data centers and high-performance computing ...

The Sports Analytics Research Group employs quantitative analysis to give teams the hard numbers they need to perform better ...

The physical AI platform, named AI Experimentalist, translates research goals from natural language into experiments at scale.

SummaryRFIC design is a complex “dark art” that limits progress in wireless technologies like 5G, autonomous vehicles, and ...

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A slew of start-ups and academic labs are leaning on AI agents and bots, rather than humans, to speed up their chemistry ...