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A promising trend in deep learning replaces traditional feedforward networks with implicit networks.
ANODE: Unconditionally accurate memory-efficient gradients for neural ODEs
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Implicitly defined layers in neural networks
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PNKH-B: A projected Newton-Krylov method for large-scale bound-constrained optimization
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Projecting to Manifolds via Unsupervised Learning
Heaton, H.; Fung, S. W.; Lin, A. T.; Osher, S.; and Yin, W. 2020 · 2008
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Visualizing data using t-SNE
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Learning Multiple Layers of Features from Tiny Images
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Manifold models for signals and images
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On the role of sparse and redundant representations in image processing
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MNIST handwritten digit database
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Differentiable Implicit Layers
Look, A.; Doneva, S.; Kandemir, M.; Gemulla, R.; and Peters, J. 2020 · 2010
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Reading digits in natural images with unsupervised feature learning
Netzer, Y.; Wang, T.; Coates, A.; Bissacco, A.; Wu, B.; and Ng, A. Y. 2011 · 2011
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Why are hypergeometric series important and do they have a geometric or heuristic motivation?
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Matrix computations , volume 3
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Computational methods in geophysical electromagnetics
Haber, E. 2014 · 2014
Sorting out Lipschitz function approximation
Anil, C.; Lucas, J.; and Grosse, R. 2019 · 2019
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Deep equilibrium models
Bai, S.; Kolter, J. Z.; and Koltun, V. 2019 · 2019
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Augmented Neural ODEs
Dupont, E.; Doucet, A.; and Teh, Y. W. 2019 · 2019
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Cubic regularization for differentiable games
Li, S.; Xie, Y.; Li, Q.; and Tang, G. ???? · 2019
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Meta-Learning with Implicit Gradients
Rajeswaran, A.; Finn, C.; Kakade, S. M.; and Levine, S. 2019 · 2019
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Deep neural networks motivated by partial differential equations
Ruthotto, L.; and Haber, E. 2019 · 2019
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Adam: A Method for Stochastic Optimization
Kingma, D. P.; and Ba, J. 2015 · 2015
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Deep residual learning for image recognition
He, K.; Zhang, X.; Ren, S.; and Sun, J. 2016 · 2016
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Scalable gradient-based tuning of continuous regularization hyperparameters
Luketina, J.; Berglund, M.; Greff, K.; and Raiko, T. 2016 · 2016
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Parseval networks: Improving robustness to adversarial examples
Cisse, M.; Bojanowski, P.; Grave, E.; Dauphin, Y.; and Usunier, N. 2017 · 2017
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Model-agnostic meta-learning for fast adaptation of deep networks
Finn, C.; Abbeel, P.; and Levine, S. 2017 · 2017
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Stable architectures for deep neural networks
Haber, E.; and Ruthotto, L. 2017 · 2017
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Udell, M.; and Townsend, A. 2019 · 2019
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Adaptively truncating backpropagation through time to control gradient bias
Aicher, C.; Foti, N. J.; and Fox, E. B. 2020 · 2020
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Multiscale Deep Equilibrium Models
Bai, S.; Koltun, V.; and Kolter, J. Z. 2020 · 2020
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Multigrid optimization for large-scale ptychographic phase retrieval
Fung, S. W.; and Wendy, Z. 2020 · 2020
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Universal approximation with deep narrow networks
Kidger, P.; and Lyons, T. 2020 · 2020
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Almost Surely Stable Deep Dynamics
Lawrence, N.; Loewen, P.; Forbes, M.; Backstrom, J.; and Gopaluni, B. 2020 · 2020
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Optimizing millions of hyperparameters by implicit differentiation
Lorraine, J.; Vicol, P.; and Duvenaud, D. 2020 · 2020
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Contracting implicit recurrent neural networks: Stable models with improved trainability
Revay, M.; and Manchester, I. 2020 · 2020
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A machine learning framework for solving high-dimensional mean field game and mean field control problems
Ruthotto, L.; Osher, S. J.; Li, W.; Nurbekyan, L.; and Fung, S. W. 2020 · 2020
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Monotone operator equilibrium networks
Winston, E.; and Kolter, J. Z. 2020 · 2020
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Is Attention Better Than Matrix Decomposition?
Geng, Z.; Guo, M.-H.; Chen, H.; Li, X.; Wei, K.; and Lin, Z. 2021 · 2021
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Deep Equilibrium Architectures for Inverse Problems in Imaging
Gilton, D.; Ongie, G.; and Willett, R. 2021 · 2021
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Regularisation of neural networks by enforcing Lipschitz continuity
Gouk, H.; Frank, E.; Pfahringer, B.; and Cree, M. J. 2021 · 2021
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Differentiable Forward and Backward Fixed-Point Iteration Layers
Jeon, Y.; Lee, M.; and Choi, J. Y. 2021 · 2021
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OT-Flow: Fast and Accurate Continuous Normalizing Flows via Optimal Transport
Onken, D.; Wu Fung, S.; Li, X.; and Ruthotto, L. 2021 · 2021
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An Introduction to Deep Generative Modeling
Ruthotto, L.; and Haber, E. 2021 · 2021
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