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Accelerating distributed reinforcement learning with in-switch computing.

, , , , , and . ISCA, page 279-291. ACM, (2019)

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Choosing Parameters of Kernel Subspace LDA for Recognition of Face Images Under Pose and Illumination Variations., , , and . IEEE Trans. Syst. Man Cybern. Part B, 37 (4): 847-862 (2007)Discriminative Local Learning Projection for Face Recognition., , , and . IJPRAI, 25 (1): 83-97 (2011)Wavelet Kernel Construction for Kernel Discriminant Analysis on Face Recognition., , , and . CVPR Workshops, page 47. IEEE Computer Society, (2006)Face Classification based on Shannon Wavelet Kernel and Modified Fisher Criterion., , , and . FGR, page 467-474. IEEE Computer Society, (2006)A Framework of Face Tracking with Classification Using CAMShift-C and LBP., , , and . ICIG, page 217-222. IEEE Computer Society, (2009)Component-based LDA Method for Face Recognition with One Training Sample., , , and . AMFG, page 120-126. IEEE Computer Society, (2003)Linear and Nonlinear Feature-Based Fusion Algorithms for Face Recognition., , , , and . Int. J. Wavelets Multiresolution Inf. Process., 4 (4): 659-676 (2006)Kernel parameter optimization of Kernel-based LDA methods., , , , , and . IJCNN, page 3840-3846. IEEE, (2008)A Novel Fisher Criterion Based St-Subspace Linear Discriminant Method for Face Recognition., , , and . CIS (1), volume 3801 of Lecture Notes in Computer Science, page 933-940. Springer, (2005)Kernel Subspace LDA with Optimized Kernel Parameters on Face Recognition., , , and . FGR, page 327-332. IEEE Computer Society, (2004)