Fetching the paper…
Reading the bibliography…
We compare the discretize-optimize (Disc-Opt) and optimize-discretize (Opt-Disc) approaches for time-series regression and continuous normalizing flows (CNFs) using neural ODEs.
The use of adjoint systems in the problem of differential corrections for trajectories
G. A. Bliss · 1919
Earlier work this paper cites.
A stochastic approximation method
H. Robbins and S. Monro · 1951
Earlier work this paper cites.
A stochastic estimator of the trace of the influence matrix for laplacian smoothing splines
M. F. Hutchinson · 1989
Earlier work this paper cites.
A simple weight decay can improve generalization
A. Krogh and J. A. Hertz · 1992
Earlier work this paper cites.
Discrete- vs. Continuous-time Nonlinear Signal Processing of Cu Electrodissolution Data
R. Rico-Martínez, K. Krischer, and I. G. Kevrekidis · 1992
Earlier work this paper cites.
Identification of distributed parameter systems: A neural net based approach
R. González-García, R. Rico-Martínez, and I. G. Kevrekidis · 1998
Earlier work this paper cites.
Perspectives in flow control and optimization , volume 5
M. D. Gunzburger · 2003
Earlier work this paper cites.
Numerical optimization
J. Nocedal and S. Wright · 2006
Earlier work this paper cites.
Understanding the difficulty of training deep feedforward neural networks
X. Glorot and Y. Bengio · 2010
Earlier work this paper cites.
A First Course in Numerical Methods
U. M. Ascher and C. Greif · 2011
Earlier work this paper cites.
Deep autoregressive networks
K. Gregor, I. Danihelka, A. Mnih, C. Blundell, and D. Wierstra · 2014
Earlier work this paper cites.
Adam: A method for stochastic optimization
D. P. Kingma and J. Ba · 2014
Earlier work this paper cites.
Auto-encoding variational bayes
D. P. Kingma and M. Welling · 2014
Earlier work this paper cites.
Variational inference with normalizing flows
D. J. Rezende and S. Mohamed · 2015
Cited alongside, same era.
Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2016
Cited alongside, same era.
A proposal on machine learning via dynamical systems
W. E · 2017
Cited alongside, same era.
Stable architectures for deep neural networks
E. Haber and L. Ruthotto · 2017
Cited alongside, same era.
Maximum principle based algorithms for deep learning
Q. Li, L. Chen, C. Tai, and E. Weinan · 2017
Cited alongside, same era.
Deep multi-scale convolutional neural network for dynamic scene deblurring
S. Nah, T. H. Kim, and K. M. Lee · 2017
Cited alongside, same era.
Deep learning as optimal control problems: Models and numerical methods
M. Benning, E. Celledoni, M. J. Ehrhardt, B. Owren, and C.-B. Schönlieb · 2019
Later among the works it cites.
Machine learning for fluid mechanics
S. L. Brunton, B. R. Noack, and P. Koumoutsakos · 2019
Later among the works it cites.
ANODE: Unconditionally accurate memory-efficient gradients for neural odes
A. Gholami, K. Keutzer, and G. Biros · 2019
Later among the works it cites.
FFJORD: Free-form continuous dynamics for scalable reversible generative models
W. Grathwohl, R. T. Chen, J. Betterncourt, I. Sutskever, and D. Duvenaud · 2019
Later among the works it cites.
Layer-Parallel Training of Deep Residual Neural Networks
S. Günther, L. Ruthotto, J. B. Schroder, E. C. Cyr, and N. R. Gauger · 2019
Later among the works it cites.
Equivariant flows: sampling configurations for multi-body systems with symmetric energies
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
G. Papamakarios, T. Pavlakou, and I. Murray · 2017
Cited alongside, same era.
Image segmentation with pyramid dilated convolution based on resnet and u-net
Q. Zhang, Z. Cui, X. Niu, S. Geng, and Y. Qiao · 2017
Cited alongside, same era.
Reversible architectures for arbitrarily deep residual neural networks
B. Chang, L. Meng, E. Haber, L. Ruthotto, D. Begert, and E. Holtham · 2018
Cited alongside, same era.
Neural ordinary differential equations
T. Q. Chen, Y. Rubanova, J. Bettencourt, and D. K. Duvenaud · 2018
Cited alongside, same era.
Beyond finite layer neural networks: Bridging deep architectures and numerical differential equations
Y. Lu, A. Zhong, Q. Li, and B. Dong · 2018
Cited alongside, same era.
Deep hidden physics models: Deep learning of nonlinear partial differential equations
M. Raissi · 2018
Cited alongside, same era.
J. Köhler, L. Klein, and F. Noé · 2019
Later among the works it cites.
DiffEqFlux.jl - A julia library for neural differential equations
C. Rackauckas, M. Innes, Y. Ma, J. Bettencourt, L. White, and V. Dixit · 2019
Later among the works it cites.
Deep neural networks motivated by partial differential equations
L. Ruthotto and E. Haber · 2019
Later among the works it cites.
Transport analysis of infinitely deep neural network
S. Sonoda and N. Murata · 2019
Later among the works it cites.
Discrete processes and their continuous limits
U. M. Ascher · 2020
Closest in time.
C. Finlay, J.-H. Jacobsen, L. Nurbekyan, and A. M. Oberman · 2020
Closest in time.
Universal differential equations for scientific machine learning
C. Rackauckas, Y. Ma, J. Martensen, C. Warner, K. Zubov, R. Supekar, D. Skinner, A. Ramadhan, and A. Edelman · 2020
Closest in time.