Lei Shi - Publications
Affiliations: | University of California, Berkeley, Berkeley, CA, United States |
Area:
Computational neuroscience/ Visual systemYear | Citation | Score | |||
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2023 | He M, He F, Shi L, Huang X, Suykens JAK. Learning with Asymmetric Kernels: Least Squares and Feature Interpretation. Ieee Transactions On Pattern Analysis and Machine Intelligence. PMID 37028385 DOI: 10.1109/TPAMI.2023.3257351 | 0.419 | |||
2022 | He F, He M, Shi L, Huang X. Global Search and Analysis for the Nonconvex Two-Level l Penalty. Ieee Transactions On Neural Networks and Learning Systems. PMID 36040939 DOI: 10.1109/TNNLS.2022.3201052 | 0.392 | |||
2019 | Liu F, Huang X, Shi L, Yang J, Suykens JAK. A Double-Variational Bayesian Framework in Random Fourier Features for Indefinite Kernels. Ieee Transactions On Neural Networks and Learning Systems. PMID 31514157 DOI: 10.1109/TNNLS.2019.2934729 | 0.413 | |||
2017 | Shi L, Chen H, Zhang SY, Chu TT, Zhao YF, Chen YX, Li YM. Semi-synthesis of murine prion protein by native chemical ligation and chemical activation for preparation of polypeptide-α-thioester. Journal of Peptide Science : An Official Publication of the European Peptide Society. PMID 28429419 DOI: 10.1002/Psc.3008 | 0.507 | |||
2016 | Huang X, Shi L, Suykens JA. Solution Path for Pin-SVM Classifiers With Positive and Negative τ Values. Ieee Transactions On Neural Networks and Learning Systems. PMID 27071202 DOI: 10.1109/TNNLS.2016.2547324 | 0.396 | |||
2015 | Huang X, Shi L, Suykens JA. Support Vector Machine Classifier With Pinball Loss. Ieee Transactions On Pattern Analysis and Machine Intelligence. 36: 984-97. PMID 26353231 DOI: 10.1109/TPAMI.2013.178 | 0.391 | |||
2010 | Shi L, Griffiths TL, Feldman NH, Sanborn AN. Exemplar models as a mechanism for performing Bayesian inference. Psychonomic Bulletin & Review. 17: 443-64. PMID 20702863 DOI: 10.3758/PBR.17.4.443 | 0.635 | |||
2007 | Yao H, Shi L, Han F, Gao H, Dan Y. Rapid learning in cortical coding of visual scenes. Nature Neuroscience. 10: 772-8. PMID 17468750 DOI: 10.1038/Nn1895 | 0.636 | |||
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