Mathematical Advances in Deep Learning - Part I of II | SIAM

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2019 SIAM Conference on Com...
Mathematical Advances in De...

Mathematical Advances in Deep Learning - Part I of II

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CSE19 - MS1-1: Adversarial Regularizers in Inverse Problem

Presentation: Sebastian Lunz, University of Cambridge, United Kingdom, 22 min 50 sec

CSE19 - MS1-1: Adversarial Regularizers in Inverse Problem

Document: CSE19 - MS1-1: Adversarial Regularizers in Inverse Problem

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CSE19 - MS1-2: Machine Learning with Laplacian Smoothing Gradient Decent

Presentation: Bao Wang, Department of Mathematics, UCLA, 15 min 1 sec

CSE19 - MS1-2: Machine Learning with Laplacian Smoothing Gradient Decent

Document: CSE19 - MS1-2: Machine Learning with Laplacian Smoothing Gradient Decent

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CSE19 - MS1-3: Simultaneous Parallel-in-layer Optimization for Training of Deep Residual Networks

Presentation: Stefanie Guenther, Technische Universität Kaiserslautern, Germany, 22 min 57 sec

CSE19 - MS1-3: Simultaneous Parallel-in-layer Optimization for Training of Deep Residual Networks

Document: CSE19 - MS1-3: Simultaneous Parallel-in-layer Optimization for Training of Deep Residual Networks

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CSE19 - MS1-4: Neural Ordinary Differential Equations

Presentation: Ricky T Q Chen, University of Toronto, Canada, 17 min 9 sec

CSE19 - MS1-4: Neural Ordinary Differential Equations

Document: CSE19 - MS1-4: Neural Ordinary Differential Equations