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Neural ordinary differential equations (neural ODEs) are a popular family of continuous-depth deep learning models.
ε \varepsilon -entropy and ε \varepsilon -capacity of sets in functional spaces
A. Kolmogorov and V. Tikhomirov · 1959
Earlier work this paper cites.
Ordinary Differential Equations
V. Arnold · 1992
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Inequalities for Differential and Integral Equations
B. G. Pachpatte and W. Ames · 1997
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Parameter identification in ODE models
P. Deuflhard and S. Röblitz · 2015
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Adam: A Method for Stochastic Optimization
D. P. Kingma and J. Ba · 2015
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Norm-based capacity control in neural networks
B. Neyshabur, R. Tomioka, and N. Srebro · 2015
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Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2016
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Identity mappings in deep residual networks
K. He, X. Zhang, S. Ren, and J. Sun · 2016
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Spectrally-normalized margin bounds for neural networks
P. L. Bartlett, D. J. Foster, and M. J. Telgarsky · 2017
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A proposal on machine learning via dynamical systems
W. E · 2017
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Efficient regression in metric spaces via approximate lipschitz extension
L.-A. Gottlieb, A. Kontorovich, and R. Krauthgamer · 2017
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Stable architectures for deep neural networks
E. Haber and L. Ruthotto · 2017
Earlier work this paper cites.
Y. Lu, A. Zhong, Q. Li, and B. Dong · 2017
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Neural ordinary differential equations
R. T. Q. Chen, Y. Rubanova, J. Bettencourt, and D. K. Duvenaud · 2018
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Size-independent sample complexity of neural networks
N. Golowich, A. Rakhlin, and O. Shamir · 2018
Earlier work this paper cites.
A PAC-bayesian approach to spectrally-normalized margin bounds for neural networks
B. Neyshabur, S. Bhojanapalli, and N. Srebro · 2018
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Nearly-tight VC-dimension and pseudodimension bounds for piecewise linear neural networks
P. L. Bartlett, N. Harvey, C. Liaw, and A. Mehrabian · 2019
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PyTorch: An Imperative Style, High-Performance Deep Learning Library
A. Paszke, S. Gross, F. Massa, A. Lerer, J. Bradbury, G. Chanan, T. Killeen, Z. Lin, N. Gimelshein, L. Antiga, A. Desmaison, A. Kopf, E. Yang, Z. DeVito, M. Raison, A. Tejani, S. Chilamkurthy, B. Steiner, L. Fang, J. Bai, and S. Chintala · 2019
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Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
M. Raissi, P. Perdikaris, and G. E. Karniadakis · 2019
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Neural stochastic differential equations: Deep latent gaussian models in the diffusion limit
B. Tzen and M. Raginsky · 2019
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High-Dimensional Statistics: A Non-Asymptotic Viewpoint
M. J. Wainwright · 2019
Neural-ODE for pharmacokinetics modeling and its advantage to alternative machine learning models in predicting new dosing regimens
J. Lu, K. Deng, X. Zhang, G. Liu, and Y. Guan · 2021
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Estimating lipschitz constants of monotone deep equilibrium models
C. Pabbaraju, E. Winston, and J. Z. Kolter · 2021
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Integrating expert ODEs into neural ODEs: Pharmacology and disease progression
Z. Qian, W. Zame, L. Fleuren, P. Elbers, and M. van der Schaar · 2021
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Stateful ODE-nets using basis function expansions
A. F. Queiruga, N. B. Erichson, L. Hodgkinson, and M. W. Mahoney · 2021
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LEADS: Learning dynamical systems that generalize across environments
Y. Yin, I. Ayed, E. de Bézenac, N. Baskiotis, and P. Gallinari · 2021
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Urban flow prediction with spatial–temporal neural ODEs
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Robust pricing and hedging via neural sdes
P. Gierjatowicz, M. Sabate-Vidales, D. Šiška, L. Szpruch, and Z. Zurič · 2020
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Learning differential equations that are easy to solve
J. Kelly, J. Bettencourt, M. J. Johnson, and D. K. Duvenaud · 2020
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Neural controlled differential equations for irregular time series
P. Kidger, J. Morrill, J. Foster, and T. Lyons · 2020
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Dissecting neural ODEs
S. Massaroli, M. Poli, J. Park, A. Yamashita, and H. Asama · 2020
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Continuous-in-depth neural networks
A. F. Queiruga, N. B. Erichson, D. Taylor, and M. W. Mahoney · 2020
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Neural flows: Efficient alternative to neural ODEs
M. Biloš, J. Sommer, S. S. Rangapuram, T. Januschowski, and S. Günnemann · 2021
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Scaling properties of deep residual networks
A.-S. Cohen, R. Cont, A. Rossier, and R. Xu · 2021
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F. Zhou, L. Li, K. Zhang, and G. Trajcevski · 2021
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Pay attention to your loss : understanding misconceptions about lipschitz neural networks
L. Béthune, T. Boissin, M. Serrurier, F. Mamalet, C. Friedrich, and A. G. Sanz · 2022
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Fitting an immersed submanifold to data via sussmann’s orbit theorem
J. Hanson and M. Raginsky · 2022
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Scaling ResNets in the large-depth regime
P. Marion, A. Fermanian, G. Biau, and J.-P. Vert · 2022
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Do residual neural networks discretize neural ordinary differential equations?
M. E. Sander, P. Ablin, and G. Peyré · 2022
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Deep limits of residual neural networks
M. Thorpe and Y. van Gennip · 2022
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Neural generalized ordinary differential equations with layer-varying parameters
D. Yu, H. Miao, and H. Wu · 2022
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Learning theory from first principles, 2023
F. Bach · 2023
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Notes on existence and uniqueness theorems for ODEs, 2017
J. Luk · 2023
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Implicit regularization of deep residual networks towards neural odes
P. Marion, Y.-H. Wu, M. E. Sander, and B. Gérard · 2023
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