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Publication search results
found 74 matches
- 2024
- Sean P. Walton, Ben J. Evans, Alma A. M. Rahat, James Stovold, Jakub Vincalek:
Does mapping elites illuminate search spaces? A large-scale user study of MAP-Elites applied to human-AI collaborative design. CoRR abs/2402.07911 (2024) - Henry W. Sprueill, Carl Edwards, Khushbu Agarwal, Mariefel V. Olarte, Udishnu Sanyal, Conrad Johnston, Hongbin Liu, Heng Ji, Sutanay Choudhury:
ChemReasoner: Heuristic Search over a Large Language Model's Knowledge Space using Quantum-Chemical Feedback. CoRR abs/2402.10980 (2024) - 2023
- Christian Meyenburg, Uschi Dolfus, Hans Briem, Matthias Rarey:
Galileo: Three-dimensional searching in large combinatorial fragment spaces on the example of pharmacophores. J. Comput. Aided Mol. Des. 37(1): 1-16 (2023) - Yuchen Zhuang, Xiang Chen, Tong Yu, Saayan Mitra, Victor Bursztyn, Ryan A. Rossi, Somdeb Sarkhel, Chao Zhang:
ToolChain*: Efficient Action Space Navigation in Large Language Models with A* Search. CoRR abs/2310.13227 (2023) - 2022
- Qingxia Shang, Yuxiao Huang, Junwei Dong, Yaqing Hou, Yu Wang, Min Li, Liang Feng:
Multi-space evolutionary search with dynamic resource allocation strategy for large-scale optimization. Neural Comput. Appl. 34(10): 7673-7689 (2022) - Michal Witold Przewozniczek, Marcin M. Komarnicki:
Empirical linkage learning for non-binary discrete search spaces in the optimization of a large-scale real-world problem. GECCO Companion 2022: 35-36 - Alcides Fonseca, Máximo Oliveira:
Figra: Evaluating a larger search space for Cardumen in Automatic Program Repair. APR@ICSE 2022: 24-30 - Nilesh Gupta, Patrick H. Chen, Hsiang-Fu Yu, Cho-Jui Hsieh, Inderjit S. Dhillon:
ELIAS: End-to-End Learning to Index and Search in Large Output Spaces. NeurIPS 2022 - Nilesh Gupta, Patrick H. Chen, Hsiang-Fu Yu, Cho-Jui Hsieh, Inderjit S. Dhillon:
End-to-End Learning to Index and Search in Large Output Spaces. CoRR abs/2210.08410 (2022) - 2021
- Chu-Pan Wong:
Beyond Configurable Systems: Applying Variational Execution to Tackle Large Search Spaces. Carnegie Mellon University, USA, 2021 - Louis Bellmann, Patrick Penner, Matthias Rarey:
Topological Similarity Search in Large Combinatorial Fragment Spaces. J. Chem. Inf. Model. 61(1): 238-251 (2021) - Dongyu Meng, Michele Guerriero, Aravind Machiry, Hojjat Aghakhani, Priyanka Bose, Andrea Continella, Christopher Kruegel, Giovanni Vigna:
Bran: Reduce Vulnerability Search Space in Large Open Source Repositories by Learning Bug Symptoms. AsiaCCS 2021: 731-743 - Liang Feng, Qingxia Shang, Yaqing Hou, Kay Chen Tan, Yew-Soon Ong:
Multi-Space Evolutionary Search for Large-Scale Optimization. CoRR abs/2102.11693 (2021) - 2020
- Reinaldo Gomes Morais, Nadia Nedjah, Luiza de Macedo Mourelle:
A novel metaheuristic inspired by Hitchcock birds' behavior for efficient optimization of large search spaces of high dimensionality. Soft Comput. 24(8): 5633-5655 (2020) - João Guilherme Cavalcanti Costa, Yi Mei, Mengjie Zhang:
Adaptive Search Space through Evolutionary Hyper-Heuristics for the Large-Scale Vehicle Routing Problem. SSCI 2020: 2415-2422 - 2018
- Lidong Bing, Zheng-Yu Niu, Piji Li, Wai Lam, Haifeng Wang:
Learning a unified embedding space of web search from large-scale query log. Knowl. Based Syst. 150: 38-48 (2018) - Sébastien Vérel, Fabio Daolio, Gabriela Ochoa, Marco Tomassini:
Sampling Local Optima Networks of Large Combinatorial Search Spaces: The QAP Case. PPSN (2) 2018: 257-268 - Matias Martinez, Martin Monperrus:
Ultra-Large Repair Search Space with Automatically Mined Templates: The Cardumen Mode of Astor. SSBSE 2018: 65-86 - 2017
- Emilio Carrizosa, Vanesa Guerrero, Dolores Romero Morales:
Visualizing proportions and dissimilarities by Space-filling maps: A Large Neighborhood Search approach. Comput. Oper. Res. 78: 369-380 (2017) - Arjun Satish, Ramesh C. Jain, Amarnath Gupta:
Discovering Context to Prune Large and Complex Search Spaces. CONTEXT 2017: 81-95 - 2016
- Tzu-Hsien Wu, Hao Shyng, Jerry Chi-Yuan Chou, Bin Dong, Kesheng Wu:
Indexing Blocks to Reduce Space and Time Requirements for Searching Large Data Files. CCGrid 2016: 398-402 - Alexander Cohen, Brenden M. Lake:
Searching large hypothesis spaces by asking questions. CogSci 2016 - Bilal Kartal:
Stochastic Planning in Large Search Spaces. IJCAI 2016: 4000-4001 - 2015
- Michal Przewozniczek, Róza Goscien, Krzysztof Walkowiak, Miroslaw Klinkowski:
Towards solving practical problems of large solution space using a novel pattern searching hybrid evolutionary algorithm - An elastic optical network optimization case study. Expert Syst. Appl. 42(21): 7781-7796 (2015) - David Churchill, Michael Buro:
Hierarchical Portfolio Search: Prismata's Robust AI Architecture for Games with Large Search Spaces. AIIDE 2015: 16-22 - Zongzhang Zhang, David Hsu, Wee Sun Lee, Zhan Wei Lim, Aijun Bai:
PLEASE: Palm Leaf Search for POMDPs with Large Observation Spaces. ICAPS 2015: 249-258 - Hendrik Nienhoff, Ursula Hübner, Andreas Frey, Mareike Przysucha, Michio Kimura:
Restricted Versus Unrestricted Search Space: Experience from Mining a Large Japanese Database. MedInfo 2015: 1072 - Zongzhang Zhang, David Hsu, Wee Sun Lee, Zhan Wei Lim, Aijun Bai:
PLEASE: Palm Leaf Search for POMDPs with Large Observation Spaces. SOCS 2015: 238-239 - Alexander Thomasian:
Singular Value Decomposition, Clustering, and Indexing for Similarity Search for Large Data Sets in High-Dimensional Spaces. Big Data - Algorithms, Analytics, and Applications 2015: 39-70 - 2014
- Fahad Saeed, Jason D. Hoffert, Mark A. Knepper:
CAMS-RS: Clustering Algorithm for Large-Scale Mass Spectrometry Data Using Restricted Search Space and Intelligent Random Sampling. IEEE ACM Trans. Comput. Biol. Bioinform. 11(1): 128-141 (2014)
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