Practical Algorithm
Practical algorithm research focuses on developing and improving algorithms for diverse applications, prioritizing efficiency, accuracy, and interpretability. Current research emphasizes areas like efficient model training and inference (e.g., low-bit quantization for LLMs, distributed algorithms for large datasets), robust optimization techniques (e.g., evolutionary algorithms, Q-learning variants), and methods for handling noisy data or dynamic environments. These advancements have significant implications across various fields, including machine learning, robotics, and data analysis, by enabling more efficient and reliable solutions to complex problems.
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Papers - Page 5
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Digestion Algorithm in Hierarchical Symbolic Forests: A Fast Text Normalization Algorithm and Semantic Parsing Framework for Specific Scenarios and Lightweight Deployment
Kevin YouExtreme Multi-label Completion for Semantic Document Labelling with Taxonomy-Aware Parallel Learning
Julien Audiffren, Christophe Broillet, Ljiljana Dolamic, Philippe Cudré-MaurouxPASCO (PArallel Structured COarsening): an overlay to speed up graph clustering algorithms
Etienne Lasalle (OCKHAM), Rémi Vaudaine (OCKHAM), Titouan Vayer (OCKHAM), Pierre Borgnat (Phys-ENS), Rémi Gribonval (OCKHAM)+2
December 17, 2024
Voter Priming Campaigns: Strategies, Equilibria, and Algorithms
Jonathan Shaki, Yonatan Aumann, Sarit KrausGPgym: A Remote Service Platform with Gaussian Process Regression for Online Learning
Xiaobing Dai, Zewen YangOpen-Set Heterogeneous Domain Adaptation: Theoretical Analysis and Algorithm
Thai-Hoang Pham, Yuanlong Wang, Changchang Yin, Xueru Zhang, Ping Zhang