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Theoretical/algorithmic challenges for DE
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Deep Neural Networks via Monotone Operators
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- 1 Intro
- 2 The deep learning revolution recent examp
- 3 Deep Learning The story we all tell: deep learning algorithms build hierarchical models of input date, where the earlier layers create simple features and layer layers create high- level abstractions…
- 4 This talk
- 5 Outline
- 6 From deep networks to DEQs
- 7 Long history of related work
- 8 Implementing DEQS
- 9 The DEQ forward pass
- 10 How to train your DEQ
- 11 How to train your DED Compute gradients analytically via implicit function theorem
- 12 More information on implicit layers
- 13 Language modeling: WikiText-103
- 14 Multiscale deep equilibrium models
- 15 ImageNet Top-1 Accuracy
- 16 Citiscapes mlou
- 17 Visualization of Segmentation
- 18 Theoretical/algorithmic challenges for DE
- 19 Key result
- 20 Proof sketch for simpler case
- 21 Monotone operator equilibrium network
- 22 Initial study: CIFAR10
- 23 Additional points on monotone DEOS
- 24 Final thoughts
