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Understanding LLMs: A Comprehensive Overview from Training to Inference.

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Yi Pan

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Characterizations of Communication Overhead for Scalable Random Parallel Algorithms on Multicomputer Systems., и . PDPTA, стр. 474-485. CSREA Press, (1996)An Improved Genetic Algorithm for Multiprocessor Task Scheduling with Non-Negligible Inter-processor Communication Delay., , , , и . MAICS, стр. 165-172. Omnipress, (2003)Fast nearest Neighbor Algorithms on a Linear Array with a Reconfigurable Pipelined Bus System., , и . Parallel Algorithms Appl., 13 (1): 1-25 (1998)Introduction: recent Developments in Parallel and Distributed Data Mining, и . Distributed and Parallel Databases, (2002)This is an introduction to four articles about Knowledge Discovery in Databases, KDD. It makes the statement that the key challenge in data mining is the extraction of knowledge and insight from massive databases. Data mining has the two primary goals of prediction and verification. Prediction is about predicting the unknown values of attributes of interest based on a trained model. The data mining process is being divided into data selection, data cleansing, data transformation, data reduction, data mining, algorithm selection, post processing, and interpolation..Cysteine separations profiles on protein secondary structure infer disulfide connectivity., , , , и . GrC, стр. 663-665. IEEE, (2006)Teaching Parallel Programming Using Both High-Level and Low-Level Languages.. International Conference on Computational Science (3), том 2331 из Lecture Notes in Computer Science, стр. 888-897. Springer, (2002)A Study of Average-Case Speedup and Scalability of Parallel Computations on Static Networks., , , и . PDPTA, стр. 1362-. CSREA Press, (1997)Path selection with class distribution information in the integrated network., , , и . IEEE Communications Letters, 6 (2): 88-90 (2002)A Feature Selection Algorithm Based on Graph Theory and Random Forests for Protein Secondary Structure Prediction., , , , и . ISBRA, том 4463 из Lecture Notes in Computer Science, стр. 590-600. Springer, (2007)NEURONgrid: A Toolkit for Generating Parameter-Space Maps Using NEURON in a Grid Environment., , , , и . ISBRA, том 4463 из Lecture Notes in Computer Science, стр. 182-191. Springer, (2007)