Flowchart of the specific incarnation of the BO algorithm used in the experiments. by Lisa Laux (9367681)

“…To choose the next pipeline configuration to evaluate, the BO algorithm uses an Expected Improvement function to trade off maximisation of QS with the need to fully learn the GP. …”

Parameters of the proposed algorithm. by Heba Askr (15572851)

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The performance results on benchmark test functions and real-world problems. by Mahmood A. Jumaah (22272371)

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Flowchart of one iteration of the CE-Symm algorithm. by Spencer E. Bliven (6672755)

State Preparation in Quantum Algorithms for Fragment-Based Quantum Chemistry by Ruhee D’Cunha (8921372)

“…The localized active space–unitary coupled cluster (LAS–UCC) algorithm iteratively loads a fragment-based multireference wave function onto a quantum computer. …”

Algorithm operation steps. by Junyan Wang (4738518)

Raw task performance on both experiments, with respect to conditions. by Anis Najar (9745561)

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Great machine learning performance with multiple classification algorithms. by Sydney C. Weiser (15383531)

Completion times for different algorithms. by Jianbin Zheng (587000)

“…This paper first analyzes the H-beam processing flow and appropriately simplifies it, develops a reinforcement learning environment for multi-agent scheduling, and applies the rMAPPO algorithm to make scheduling decisions. The effectiveness of the proposed method is then verified on both the physical work cell for riveting and welding and its digital twin platform, and it is compared with other baseline multi-agent reinforcement learning methods (MAPPO, MADDPG, and MASAC). …”

The average cumulative reward of algorithms. by Jianbin Zheng (587000)

“…This paper first analyzes the H-beam processing flow and appropriately simplifies it, develops a reinforcement learning environment for multi-agent scheduling, and applies the rMAPPO algorithm to make scheduling decisions. The effectiveness of the proposed method is then verified on both the physical work cell for riveting and welding and its digital twin platform, and it is compared with other baseline multi-agent reinforcement learning methods (MAPPO, MADDPG, and MASAC). …”

Step by step comparison of enrichment algorithms. by Nicholas Markarian (18251668)

Comparison with the BST algorithm for the AF and CST bundles. by Thomas Durantel (20933812)

Subjects: “…orientation distribution function…”

Functional pattern of the proposed method for depression detection. by Umme Marzia Haque (11846815)

Model construction and validation (A) Construction of the model based on multiple machine learning algorithms. by Chengting Wu (21485612)

IRBMO vs. meta-heuristic algorithms boxplot. by Chenyi Zhu (9383370)

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IRBMO vs. feature selection algorithm boxplot. by Chenyi Zhu (9383370)

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Solving steps. by Mengkai Liu (13919242)

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The performance comparison of the base (CNN+BiLSTM+MHSA) model with different algorithms for solving the problem of imbalanced data classification on the test set. by Wenhui Yan (825510)

Workflow overview illustrating the primary steps of the KRSA pipeline. by Erica A. K. DePasquale (11851483)

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The collective motion, collision avoidance, and CMCA score of the VCMCA algorithm, VCMCA algorithm without CA, and random walk over the simulation steps for <i>n</i> = 30 repetitio... by David L. Krongauz (15447260)