This Collection supports and amplifies research related to SDG 9 - Industry, Innovation & Infrastructure. Discovering new materials with customizable and optimized properties, driven either by ...
Explore predictive modeling for compound prioritization, including in silico screening, toxicology models, and lead selection ...
Abstract: Bayesian optimization (BO) is a powerful surrogate-assisted algorithm for solving expensive black-box optimization problems. While BO was developed for centralized optimization, the ...
Abstract: 5G millimeter-wave (mmWave) communications are essential for enabling ultra-high-speed, low-latency wireless connectivity to support data-intensive applications. However, the highly ...
https://proceedings.neurips.cc/paper_files/paper/2012/hash/05311655a15b75fab86956663e1819cd-Abstract.html ...
Customer stories Events & webinars Ebooks & reports Business insights GitHub Skills ...
Combinatorial optimization problems are often encountered in real-world applications, including logistics, scheduling and ...
Machine learning is the ability of a machine to improve its performance based on previous results. Machine learning methods enable computers to learn without being explicitly programmed and have ...
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Spintronic hardware unlocks faster, lower-energy optimization, outpacing tested quantum annealers
Solving complex optimization problems is central to many modern technologies, from logistics and financial modeling to chip ...
Arbor separates strategy from execution using isolated git worktrees, so engineering teams can finally trace which optimization actually moved the needle.
Objectives Elective non-emergent surgical wait times have increased across countries such as Canada, straining operating room ...
LLM training data mixture optimization breaks when training pools shift — every prior proxy experiment becomes stale.
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