Fetching the paper…
Reading the bibliography…
We introduce a new family of deep neural network models.
Beitrag zur näherungsweisen Integration totaler Differentialgleichungen
W. Kutta · 1901
Earlier work this paper cites.
Intensitätsschwankungen im fernsprechverker
Conny Palm · 1943
Earlier work this paper cites.
Theory of ordinary differential equations
Earl A Coddington and Norman Levinson · 1955
Earlier work this paper cites.
The mathematical theory of optimal processes
Lev Semenovich Pontryagin, EF Mishchenko, VG Boltyanskii, and RV Gamkrelidze · 1962
Earlier work this paper cites.
Solving Ordinary Differential Equations I – Nonstiff Problems
E. Hairer, S.P. Nørsett, and G. Wanner · 1987
Earlier work this paper cites.
A theoretical framework for back-propagation
Yann LeCun, D Touresky, G Hinton, and T Sejnowski · 1988
Earlier work this paper cites.
Gradient calculations for dynamic recurrent neural networks: A survey
Barak A Pearlmutter · 1995
Earlier work this paper cites.
Gradient-based learning applied to document recognition
Yann LeCun, Léon Bottou, Yoshua Bengio, and Patrick Haffner · 1998
Earlier work this paper cites.
Stable fluids
Jos Stam · 1999
Earlier work this paper cites.
Computationally efficient convolved multiple output Gaussian processes
Mauricio A Álvarez and Neil D Lawrence · 2011
Earlier work this paper cites.
Neural networks for machine learning lecture 6a overview of mini-batch gradient descent, 2012
Geoffrey Hinton, Nitish Srivastava, and Kevin Swersky · 2012
Earlier work this paper cites.
A general-purpose software framework for dynamic optimization
Joel Andersson · 2013
Earlier work this paper cites.
Automated derivation of the adjoint of high-level transient finite element programs
Patrick Farrell, David Ham, Simon Funke, and Marie Rognes · 2013
Earlier work this paper cites.
NICE: Non-linear independent components estimation
Laurent Dinh, David Krueger, and Yoshua Bengio · 2014
Earlier work this paper cites.
Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
Earlier work this paper cites.
Auto-encoding variational Bayes
Diederik P. Kingma and Max Welling · 2014
Earlier work this paper cites.
Stochastic backpropagation and approximate inference in deep generative models
Danilo J Rezende, Shakir Mohamed, and Daan Wierstra · 2014
Earlier work this paper cites.
Probabilistic ODE solvers with Runge-Kutta means
Michael Schober, David Duvenaud, and Philipp Hennig · 2014
Earlier work this paper cites.
Fatode: a library for forward, adjoint, and tangent linear integration of ODEs
Hong Zhang and Adrian Sandu · 2014
Cited alongside, same era.
The Stan math library: Reverse-mode automatic differentiation in c++
Bob Carpenter, Matthew D Hoffman, Marcus Brubaker, Daniel Lee, Peter Li, and Michael Betancourt · 2015
Cited alongside, same era.
Autograd: Reverse-mode differentiation of native Python
Dougal Maclaurin, David Duvenaud, and Ryan P Adams · 2015
Cited alongside, same era.
Variational inference with normalizing flows
Danilo Jimenez Rezende and Shakir Mohamed · 2015
Cited alongside, same era.
Doctor AI: Predicting clinical events via recurrent neural networks
Edward Choi, Mohammad Taha Bahadori, Andy Schuetz, Walter F. Stewart, and Jimeng Sun · 2016
Cited alongside, same era.
Time-dependent representation for neural event sequence prediction
Yang Li · 2017
Later among the works it cites.
PDE-Net: Learning PDEs from Data
Z. Long, Y. Lu, X. Ma, and B. Dong · 2017
Later among the works it cites.
Yiping Lu, Aoxiao Zhong, Quanzheng Li, and Bin Dong · 2017
Later among the works it cites.
The neural Hawkes process: A neurally self-modulating multivariate point process
Hongyuan Mei and Jason M Eisner · 2017
Later among the works it cites.
Fast derivatives of likelihood functionals for ODE based models using adjoint-state method
Valdemar Melicher, Tom Haber, and Wim Vanroose · 2017
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Recurrent marked temporal point processes: Embedding event history to vector
Nan Du, Hanjun Dai, Rakshit Trivedi, Utkarsh Upadhyay, Manuel Gomez-Rodriguez, and Le Song · 2016
Cited alongside, same era.
Adaptive computation time for recurrent neural networks
Alex Graves · 2016
Cited alongside, same era.
David Ha, Andrew Dai, and Quoc V Le · 2016
Cited alongside, same era.
Variable computation in recurrent neural networks
Yacine Jernite, Edouard Grave, Armand Joulin, and Tomas Mikolov · 2016
Cited alongside, same era.
Improved variational inference with inverse autoregressive flow
Diederik P Kingma, Tim Salimans, Rafal Jozefowicz, Xi Chen, Ilya Sutskever, and Max Welling · 2016
Cited alongside, same era.
Directly modeling missing data in sequences with RNNs: Improved classification of clinical time series
Zachary C Lipton, David Kale, and Randall Wetzel · 2016
Cited alongside, same era.
Improving variational auto-encoders using Householder flow
Jakub M Tomczak and Max Welling · 2016
Cited alongside, same era.
Automatic differentiation in pytorch
Adam Paszke, Sam Gross, Soumith Chintala, Gregory Chanan, Edward Yang, Zachary DeVito, Zeming Lin, Alban Desmaison, Luca Antiga, and Adam Lerer · 2017
Later among the works it cites.
What-if reasoning with counterfactual Gaussian processes
Peter Schulam and Suchi Saria · 2017
Later among the works it cites.
CasADi – A software framework for nonlinear optimization and optimal control
Joel A E Andersson, Joris Gillis, Greg Horn, James B Rawlings, and Moritz Diehl · 2018
Closest in time.
Automatic differentiation in machine learning: a survey
Atilim Gunes Baydin, Barak A Pearlmutter, Alexey Andreyevich Radul, and Jeffrey Mark Siskind · 2018
Closest in time.
Sylvester normalizing flows for variational inference
Rianne van den Berg, Leonard Hasenclever, Jakub M Tomczak, and Max Welling · 2018
Closest in time.
Multi-level residual networks from dynamical systems view
Bo Chang, Lili Meng, Eldad Haber, Frederick Tung, and David Begert · 2018
Closest in time.
Recurrent neural networks for multivariate time series with missing values
Zhengping Che, Sanjay Purushotham, Kyunghyun Cho, David Sontag, and Yan Liu · 2018
Closest in time.
Hidden physics models: Machine learning of nonlinear partial differential equations
M. Raissi and G. E. Karniadakis · 2018
Closest in time.
Deep neural networks motivated by partial differential equations
Lars Ruthotto and Eldad Haber · 2018
Closest in time.
Black-box Variational Inference for Stochastic Differential Equations
T. Ryder, A. Golightly, A. S. McGough, and D. Prangle · 2018
Closest in time.
Optimization and uncertainty analysis of ODE models using second order adjoint sensitivity analysis
Paul Stapor, Fabian Froehlich, and Jan Hasenauer · 2018
Closest in time.
Latent-space physics: Towards learning the temporal evolution of fluid flow
Steffen Wiewel, Moritz Becher, and Nils Thuerey · 2018
Closest in time.