IMEXnet - A Forward Stable Deep Neural Network Eldad Haber, Keegan - PowerPoint PPT Presentation

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IMEXnet - A Forward Stable Deep Neural Network Eldad Haber, Keegan Lensink, Eran Treister and Lars Ruthotto Jun 2019 Outline I Why Implicit I Implicit Explicit I Some results Why Implicit I For CNNs - depth is connected to field of view I

IMEXnet - A Forward Stable Deep Neural Network Eldad Haber, Keegan Lensink, Eran Treister and Lars Ruthotto Jun 2019
Outline I Why Implicit I Implicit Explicit I Some results
Why Implicit I For CNN’s - depth is connected to field of view I Stability of the standard networks can be limited I Vanishing/Exploding gradients Goal: Develop a method that can deal with those problems
Deep Networks and ODE’s ˙ Y = σ ( KY + b ) Y j +1 = Y j + h σ ( K j Y j + b j ) . ↔ I Deep Residual Networks equivalent to Forward Euler for ODE’s I Forward Euler have limitation on stability I Require many steps to converge
Semi-Implicit methods Di ff erent stable integration technique that allows large steps Y j +1 = ( I + h K j ) − 1 ( Y j + h σ ( K j Y j + b j ) − K j Y j ) . ˙ Y = σ ( KY + b ) ↔ Implicit methods are used for I Computational Fluid Dynamics I Computational Electromagnetics I Nonlinear dynamics I Computer graphics
Semi-Implicit methods Come to our poster and see how we apply these networks to many data sets

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IMEXnet - A Forward Stable Deep Neural Network Eldad Haber, Keegan - PowerPoint PPT Presentation

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IMEXnet - A Forward Stable Deep Neural Network Eldad Haber, Keegan - PowerPoint PPT Presentation

IMEXnet - A Forward Stable Deep Neural Network Eldad Haber, Keegan Lensink, Eran Treister and Lars Ruthotto Jun 2019 Outline I Why Implicit I Implicit Explicit I Some results Why Implicit I For CNNs - depth is connected to field of view I

Neural Information Retrieval Wassila Lalouani 1 Plan Neural network architectures Neural

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