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Partial differential equations (PDEs) are indispensable for modeling many physical phenomena and also commonly used for solving image processing tasks.
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Deep Visual Representation Learning with Target Coding
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Deep Learning
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Deep Relaxation: Partial Differential Equations for Optimizing Deep Neural Networks
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Trainable Nonlinear Reaction Diffusion: A Flexible Framework for Fast and Effective Image Restoration
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An Analysis of Single-Layer Networks in Unsupervised Feature Learning
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Computational optimization of systems governed by partial differential equations
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Deep neural networks for acoustic modeling in speech recognition: The shared views of four research groups
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A Proposal on Machine Learning via Dynamical Systems
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Universal adversarial perturbations
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Deepxplore: Automated whitebox testing of deep learning systems
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Reversible architectures for arbitrarily deep residual neural networks
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Neural ordinary differential equations
T. Q. Chen, Y. Rubanova, J. Bettencourt, and D. Duvenaud · 2018
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