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Deep Neural Networks Motivated by Partial Differential Equations
v1v2 (latest)

Deep Neural Networks Motivated by Partial Differential Equations

12 April 2018
Lars Ruthotto
E. Haber
    AI4CE
ArXiv (abs)PDFHTML

Papers citing "Deep Neural Networks Motivated by Partial Differential Equations"

50 / 244 papers shown
Meta-Solver for Neural Ordinary Differential Equations
Meta-Solver for Neural Ordinary Differential Equations
Julia Gusak
A. Katrutsa
Talgat Daulbaev
A. Cichocki
Ivan Oseledets
186
2
0
15 Mar 2021
ResNet-LDDMM: Advancing the LDDMM Framework using Deep Residual Networks
ResNet-LDDMM: Advancing the LDDMM Framework using Deep Residual NetworksIEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2021
Boulbaba Ben Amor
Sylvain Arguillere
Ling Shao
163
32
0
16 Feb 2021
Momentum Residual Neural Networks
Momentum Residual Neural NetworksInternational Conference on Machine Learning (ICML), 2021
Michael E. Sander
Pierre Ablin
Mathieu Blondel
Gabriel Peyré
308
64
0
15 Feb 2021
MALI: A memory efficient and reverse accurate integrator for Neural ODEs
MALI: A memory efficient and reverse accurate integrator for Neural ODEsInternational Conference on Learning Representations (ICLR), 2021
Juntang Zhuang
Nicha Dvornek
S. Tatikonda
James S. Duncan
218
58
0
09 Feb 2021
Mimetic Neural Networks: A unified framework for Protein Design and
  Folding
Mimetic Neural Networks: A unified framework for Protein Design and FoldingFrontiers in Bioinformatics (Front. Bioinform.), 2021
Moshe Eliasof
Tue Boesen
E. Haber
C. Keasar
Eran Treister
AI4CE
142
12
0
07 Feb 2021
Accuracy and Architecture Studies of Residual Neural Network solving
  Ordinary Differential Equations
Accuracy and Architecture Studies of Residual Neural Network solving Ordinary Differential Equations
Changxin Qiu
Aaron Bendickson
Joshua Kalyanapu
Jue Yan
142
1
0
10 Jan 2021
Residual networks classify inputs based on their neural transient
  dynamics
Residual networks classify inputs based on their neural transient dynamics
F. Lagzi
145
0
0
08 Jan 2021
Hybrid FEM-NN models: Combining artificial neural networks with the
  finite element method
Hybrid FEM-NN models: Combining artificial neural networks with the finite element methodJournal of Computational Physics (JCP), 2021
Sebastian K. Mitusch
S. Funke
M. Kuchta
AI4CE
251
119
0
04 Jan 2021
Towards Natural Robustness Against Adversarial Examples
Towards Natural Robustness Against Adversarial Examples
Haoyu Chu
Shikui Wei
Yao-Min Zhao
AAML
87
1
0
04 Dec 2020
Kinetics-Informed Neural Networks
Kinetics-Informed Neural NetworksCatalysis Today (CT), 2020
G. S. Gusmão
Adhika Retnanto
Shashwati C. da Cunha
A. Medford
167
32
0
30 Nov 2020
Parameterized Neural Ordinary Differential Equations: Applications to
  Computational Physics Problems
Parameterized Neural Ordinary Differential Equations: Applications to Computational Physics ProblemsProceedings of the Royal Society A (Proc. R. Soc. A), 2020
Kookjin Lee
E. Parish
223
83
0
28 Oct 2020
Improving seasonal forecast using probabilistic deep learning
Improving seasonal forecast using probabilistic deep learningJournal of Advances in Modeling Earth Systems (JAMES), 2020
B. Pan
G. Anderson
André Goncalves
Donald D. Lucas
C. Bonfils
Jiwoo Lee
BDLAI4Cl
208
39
0
27 Oct 2020
Robust Neural Networks inspired by Strong Stability Preserving
  Runge-Kutta methods
Robust Neural Networks inspired by Strong Stability Preserving Runge-Kutta methodsEuropean Conference on Computer Vision (ECCV), 2020
Byungjoo Kim
Bryce Chudomelka
Jinyoung Park
Jaewoo Kang
Youngjoon Hong
Hyunwoo J. Kim
AAML
123
6
0
20 Oct 2020
A Principle of Least Action for the Training of Neural Networks
A Principle of Least Action for the Training of Neural Networks
Skander Karkar
Ibrahhim Ayed
Emmanuel de Bézenac
Patrick Gallinari
AI4CE
346
10
0
17 Sep 2020
Deep Learning in Photoacoustic Tomography: Current approaches and future
  directions
Deep Learning in Photoacoustic Tomography: Current approaches and future directionsJournal of Biomedical Optics (JBO), 2020
A. Hauptmann
B. Cox
211
142
0
16 Sep 2020
Large-time asymptotics in deep learning
Large-time asymptotics in deep learning
Carlos Esteve
Borjan Geshkovski
Dario Pighin
Enrique Zuazua
557
39
0
06 Aug 2020
Continuous-in-Depth Neural Networks
Continuous-in-Depth Neural Networks
A. Queiruga
N. Benjamin Erichson
D. Taylor
Michael W. Mahoney
281
54
0
05 Aug 2020
Wasserstein-based Projections with Applications to Inverse Problems
Wasserstein-based Projections with Applications to Inverse Problems
Howard Heaton
Samy Wu Fung
A. Lin
Stanley Osher
W. Yin
239
3
0
05 Aug 2020
flexgrid2vec: Learning Efficient Visual Representations Vectors
flexgrid2vec: Learning Efficient Visual Representations Vectors
Ali Hamdi
D. Kim
Flora D. Salim
SSLGNN
235
8
0
30 Jul 2020
ResNet After All? Neural ODEs and Their Numerical Solution
ResNet After All? Neural ODEs and Their Numerical Solution
Katharina Ott
P. Katiyar
Philipp Hennig
Michael Tiemann
308
34
0
30 Jul 2020
Train Like a (Var)Pro: Efficient Training of Neural Networks with
  Variable Projection
Train Like a (Var)Pro: Efficient Training of Neural Networks with Variable ProjectionSIAM Journal on Mathematics of Data Science (SIMODS), 2020
Elizabeth Newman
Lars Ruthotto
Joseph L. Hart
B. V. B. Waanders
AAML
239
24
0
26 Jul 2020
PDO-eConvs: Partial Differential Operator Based Equivariant Convolutions
PDO-eConvs: Partial Differential Operator Based Equivariant Convolutions
Zhengyang Shen
Lingshen He
Zhouchen Lin
Jinwen Ma
200
52
0
20 Jul 2020
Learning Differential Equations that are Easy to Solve
Learning Differential Equations that are Easy to SolveNeural Information Processing Systems (NeurIPS), 2020
Jacob Kelly
J. Bettencourt
Matthew J. Johnson
David Duvenaud
273
123
0
09 Jul 2020
Lipschitz Recurrent Neural Networks
Lipschitz Recurrent Neural Networks
N. Benjamin Erichson
Omri Azencot
A. Queiruga
Liam Hodgkinson
Michael W. Mahoney
345
124
0
22 Jun 2020
STEER: Simple Temporal Regularization For Neural ODEs
STEER: Simple Temporal Regularization For Neural ODEs
Arna Ghosh
Harkirat Singh Behl
Emilien Dupont
Juil Sock
Vinay P. Namboodiri
BDLAI4TS
310
84
0
18 Jun 2020
A Shooting Formulation of Deep Learning
A Shooting Formulation of Deep Learning
François-Xavier Vialard
Roland Kwitt
Susan Wei
Marc Niethammer
178
15
0
18 Jun 2020
Go with the Flow: Adaptive Control for Neural ODEs
Go with the Flow: Adaptive Control for Neural ODEs
Mathieu Chalvidal
Matthew Ricci
Rufin VanRullen
Thomas Serre
270
2
0
16 Jun 2020
Learning continuous-time PDEs from sparse data with graph neural
  networks
Learning continuous-time PDEs from sparse data with graph neural networks
V. Iakovlev
Markus Heinonen
Harri Lähdesmäki
AI4CE
312
78
0
16 Jun 2020
On Second Order Behaviour in Augmented Neural ODEs
On Second Order Behaviour in Augmented Neural ODEsNeural Information Processing Systems (NeurIPS), 2020
Alexander Norcliffe
Cristian Bodnar
Ben Day
Nikola Simidjievski
Pietro Lio
240
105
0
12 Jun 2020
Learning normalizing flows from Entropy-Kantorovich potentials
Learning normalizing flows from Entropy-Kantorovich potentials
Chris Finlay
Augusto Gerolin
Adam M. Oberman
Aram-Alexandre Pooladian
1.6K
26
0
10 Jun 2020
DiffGCN: Graph Convolutional Networks via Differential Operators and
  Algebraic Multigrid Pooling
DiffGCN: Graph Convolutional Networks via Differential Operators and Algebraic Multigrid Pooling
Moshe Eliasof
Eran Treister
256
27
0
07 Jun 2020
Structure preserving deep learning
Structure preserving deep learning
E. Celledoni
Matthias Joachim Ehrhardt
Christian Etmann
R. McLachlan
B. Owren
Carola-Bibiane Schönlieb
Ferdia Sherry
AI4CE
216
47
0
05 Jun 2020
Adaptive Checkpoint Adjoint Method for Gradient Estimation in Neural ODE
Adaptive Checkpoint Adjoint Method for Gradient Estimation in Neural ODEInternational Conference on Machine Learning (ICML), 2020
Juntang Zhuang
Nicha Dvornek
Xiaoxiao Li
S. Tatikonda
X. Papademetris
James Duncan
BDL
227
121
0
03 Jun 2020
Continuous-time system identification with neural networks: Model
  structures and fitting criteria
Continuous-time system identification with neural networks: Model structures and fitting criteriaEuropean Journal of Control (EJC), 2020
Marco Forgione
Dario Piga
230
82
0
03 Jun 2020
Temporal-Differential Learning in Continuous Environments
Temporal-Differential Learning in Continuous Environments
T. Bian
Zhong-Ping Jiang
CLLOffRL
51
1
0
01 Jun 2020
Discretize-Optimize vs. Optimize-Discretize for Time-Series Regression
  and Continuous Normalizing Flows
Discretize-Optimize vs. Optimize-Discretize for Time-Series Regression and Continuous Normalizing Flows
Derek Onken
Lars Ruthotto
BDL
261
59
0
27 May 2020
Fourier Neural Networks as Function Approximators and Differential
  Equation Solvers
Fourier Neural Networks as Function Approximators and Differential Equation Solvers
M. Ngom
O. Marin
204
2
0
27 May 2020
The Random Feature Model for Input-Output Maps between Banach Spaces
The Random Feature Model for Input-Output Maps between Banach Spaces
Nicholas H. Nelsen
Andrew M. Stuart
320
163
0
20 May 2020
Model Reduction and Neural Networks for Parametric PDEs
Model Reduction and Neural Networks for Parametric PDEs
K. Bhattacharya
Bamdad Hosseini
Nikola B. Kovachki
Andrew M. Stuart
491
402
0
07 May 2020
Neural Differential Equations for Single Image Super-resolution
Neural Differential Equations for Single Image Super-resolutionInternational Conference on Learning Representations (ICLR), 2020
Teven Le Scao
127
2
0
02 May 2020
Fractional Deep Neural Network via Constrained Optimization
Fractional Deep Neural Network via Constrained Optimization
Harbir Antil
R. Khatri
R. Löhner
Deepanshu Verma
156
32
0
01 Apr 2020
Deep connections between learning from limited labels & physical
  parameter estimation -- inspiration for regularization
Deep connections between learning from limited labels & physical parameter estimation -- inspiration for regularization
Bas Peters
AI4CE
131
0
0
17 Mar 2020
Interpolation Technique to Speed Up Gradients Propagation in Neural ODEs
Interpolation Technique to Speed Up Gradients Propagation in Neural ODEs
Talgat Daulbaev
A. Katrutsa
L. Markeeva
Julia Gusak
A. Cichocki
Ivan Oseledets
136
8
0
11 Mar 2020
Integrating Scientific Knowledge with Machine Learning for Engineering
  and Environmental Systems
Integrating Scientific Knowledge with Machine Learning for Engineering and Environmental SystemsACM Computing Surveys (ACM CSUR), 2020
J. Willard
X. Jia
Shaoming Xu
M. Steinbach
Vipin Kumar
AI4CE
663
525
0
10 Mar 2020
Alternating the Population and Control Neural Networks to Solve
  High-Dimensional Stochastic Mean-Field Games
Alternating the Population and Control Neural Networks to Solve High-Dimensional Stochastic Mean-Field GamesProceedings of the National Academy of Sciences of the United States of America (PNAS), 2020
A. Lin
Samy Wu Fung
Wuchen Li
L. Nurbekyan
Stanley J. Osher
303
91
0
24 Feb 2020
Stochastic Runge-Kutta methods and adaptive SGD-G2 stochastic gradient
  descent
Stochastic Runge-Kutta methods and adaptive SGD-G2 stochastic gradient descentInternational Conference on Pattern Recognition (ICPR), 2020
I. Ayadi
Gabriel Turinici
ODL
96
9
0
20 Feb 2020
How to train your neural ODE: the world of Jacobian and kinetic
  regularization
How to train your neural ODE: the world of Jacobian and kinetic regularizationInternational Conference on Machine Learning (ICML), 2020
Chris Finlay
J. Jacobsen
L. Nurbekyan
Adam M. Oberman
374
328
0
07 Feb 2020
Translating Diffusion, Wavelets, and Regularisation into Residual
  Networks
Translating Diffusion, Wavelets, and Regularisation into Residual Networks
Tobias Alt
Joachim Weickert
Pascal Peter
DiffM
239
8
0
07 Feb 2020
PDE-NetGen 1.0: from symbolic PDE representations of physical processes
  to trainable neural network representations
PDE-NetGen 1.0: from symbolic PDE representations of physical processes to trainable neural network representations
O. Pannekoucke
Ronan Fablet
AI4CEPINNDiffM
74
8
0
03 Feb 2020
AdvectiveNet: An Eulerian-Lagrangian Fluidic reservoir for Point Cloud
  Processing
AdvectiveNet: An Eulerian-Lagrangian Fluidic reservoir for Point Cloud ProcessingInternational Conference on Learning Representations (ICLR), 2020
Xingzhe He
Helen Lu Cao
Bo Zhu
3DPC
132
10
0
01 Feb 2020
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