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Implicit-depth models such as Deep Equilibrium Networks have recently been shown to match or exceed the performance of traditional deep networks while being much more memory efficient.
Generalization of back propagation to recurrent and higher order neural networks
F. J. Pineda · 1988
Earlier work this paper cites.
A learning rule for asynchronous perceptrons with feedback in a combinatorial environment
L. B. Almeida · 1990
Earlier work this paper cites.
Long short-term memory
S. Hochreiter and J. Schmidhuber · 1997
Earlier work this paper cites.
Learning multiple layers of features from tiny images
A. Krizhevsky and G. Hinton · 2009
Earlier work this paper cites.
Mnist handwritten digit database
Y. LeCun, C. Cortes, and C. Burges · 2010
Earlier work this paper cites.
Convex analysis and monotone operator theory in Hilbert spaces , volume 408
H. H. Bauschke, P. L. Combettes, et al · 2011
Earlier work this paper cites.
Reading digits in natural images with unsupervised feature learning
Y. Netzer, T. Wang, A. Coates, A. Bissacco, B. Wu, and A. Y. Ng · 2011
Earlier work this paper cites.
Adam: A method for stochastic optimization
D. P. Kingma and J. Ba · 2015
Earlier work this paper cites.
S. Gould, B. Fernando, A. Cherian, P. Anderson, R. S. Cruz, and E. Guo · 2016
Earlier work this paper cites.
Composing graphical models with neural networks for structured representations and fast inference
M. Johnson, D. K. Duvenaud, A. Wiltschko, R. P. Adams, and S. R. Datta · 2016
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Primer on monotone operator methods
E. K. Ryu and S. Boyd · 2016
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OptNet: Differentiable optimization as a layer in neural networks
B. Amos and J. Z. Kolter · 2017
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Differentiable mpc for end-to-end planning and control
B. Amos, I. Jimenez, J. Sacks, B. Boots, and J. Z. Kolter · 2018
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Neural ordinary differential equations
T. Q. Chen, Y. Rubanova, J. Bettencourt, and D. K. Duvenaud · 2018
Cited alongside, same era.
Reviving and improving recurrent back-propagation
R. Liao, Y. Xiong, E. Fetaya, L. Zhang, K. Yoon, X. Pitkow, R. Urtasun, and R. Zemel · 2018
Cited alongside, same era.
Deep layers as stochastic solvers
A. Bibi, B. Ghanem, V. Koltun, and R. Ranftl · 2019
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Augmented neural odes
E. Dupont, A. Doucet, and Y. W. Teh · 2019
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L. El Ghaoui, F. Gu, B. Travacca, and A. Askari · 2019
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Deep declarative networks: A new hope
S. Gould, R. Hartley, and D. Campbell · 2019
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Meta-learning with implicit gradients
A. Rajeswaran, C. Finn, S. M. Kakade, and S. Levine · 2019
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Contracting implicit recurrent neural networks: Stable models with improved trainability
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What game are we playing? End-to-end learning in normal and extensive form games
C. K. Ling, F. Fang, and J. Z. Kolter · 2018
Cited alongside, same era.
Differentiable convex optimization layers
A. Agrawal, B. Amos, S. Barratt, S. Boyd, S. Diamond, and J. Z. Kolter · 2019
Cited alongside, same era.
Deep equilibrium models
S. Bai, J. Z. Kolter, and V. Koltun · 2019
Cited alongside, same era.
M. Revay and I. R. Manchester · 2019
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Super-convergence: Very fast training of neural networks using large learning rates
L. N. Smith and N. Topin · 2019
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Deep neural network structures solving variational inequalities
P. L. Combettes and J.-C. Pesquet · 2020
Closest in time.
Stable and expressive recurrent vision models, 2020
D. Linsley, A. K. Ashok, L. N. Govindarajan, R. Liu, and T. Serre · 2020
Closest in time.