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• W2788150902 abstract "We propose a block-diagonal approximation of the positive-curvature Hessian (BDA-PCH) matrix to measure curvature. Our proposed BDAPCH matrix is memory efficient and can be applied to any fully-connected neural networks where the activation and criterion functions are twice differentiable. Particularly, our BDA-PCH matrix can handle non-convex criterion functions. We devise an efficient scheme utilizing the conjugate gradient method to derive Newton directions for mini-batch setting. Empirical studies show that our method outperforms the competing second-order methods in convergence speed." @default.
• W2788150902 created "2018-03-06" @default.
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• W2788150902 date "2018-02-19" @default.
• W2788150902 modified "2023-09-22" @default.
• W2788150902 title "BDA-PCH: Block-Diagonal Approximation of Positive-Curvature Hessian for Training Neural Networks." @default.
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• W2788150902 hasPublicationYear "2018" @default.
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