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Learning Differential Equations that are Easy to Solve
v1v2 (latest)

Learning Differential Equations that are Easy to Solve

Neural Information Processing Systems (NeurIPS), 2020
9 July 2020
Jacob Kelly
J. Bettencourt
Matthew J. Johnson
David Duvenaud
ArXiv (abs)PDFHTML

Papers citing "Learning Differential Equations that are Easy to Solve"

50 / 81 papers shown
Deep Neural Networks Inspired by Differential Equations
Deep Neural Networks Inspired by Differential Equations
Y. Liu
Lianfang Wang
Kuilin Qin
Qinghua Zhang
Faqiang Wang
Li-min Cui
Jun Liu
Yuping Duan
T. Zeng
AI4TSAI4CE
247
1
0
09 Oct 2025
The Vanishing Gradient Problem for Stiff Neural Differential Equations
The Vanishing Gradient Problem for Stiff Neural Differential Equations
Colby Fronk
Linda R. Petzold
234
2
0
02 Aug 2025
Continuous-Time Attention: PDE-Guided Mechanisms for Long-Sequence Transformers
Continuous-Time Attention: PDE-Guided Mechanisms for Long-Sequence Transformers
Yukun Zhang
Xueqing Zhou
AI4TS
205
1
0
27 May 2025
Theoretical Guarantees for High Order Trajectory Refinement in Generative Flows
Theoretical Guarantees for High Order Trajectory Refinement in Generative Flows
Chengyue Gong
Xiaoyu Li
Yingyu Liang
Jiangxuan Long
Zhenmei Shi
Zhao Song
Yu Tian
312
9
0
12 Mar 2025
HOFAR: High-Order Augmentation of Flow Autoregressive Transformers
HOFAR: High-Order Augmentation of Flow Autoregressive Transformers
Yingyu Liang
Zhizhou Sha
Zhenmei Shi
Zhao Song
Mingda Wan
503
4
0
11 Mar 2025
SDE Matching: Scalable and Simulation-Free Training of Latent Stochastic Differential Equations
SDE Matching: Scalable and Simulation-Free Training of Latent Stochastic Differential Equations
Grigory Bartosh
Dmitry Vetrov
C. A. Naesseth
599
9
0
04 Feb 2025
Modeling Neural Networks with Privacy Using Neural Stochastic Differential Equations
Modeling Neural Networks with Privacy Using Neural Stochastic Differential Equations
Sanghyun Hong
Fan Wu
A. Gruber
Kookjin Lee
369
0
0
12 Jan 2025
Training Stiff Neural Ordinary Differential Equations with Explicit
  Exponential Integration Methods
Training Stiff Neural Ordinary Differential Equations with Explicit Exponential Integration MethodsChaos (Chaos), 2024
Colby Fronk
Linda R. Petzold
411
7
0
02 Dec 2024
On the relationship between Koopman operator approximations and neural ordinary differential equations for data-driven time-evolution predictions
On the relationship between Koopman operator approximations and neural ordinary differential equations for data-driven time-evolution predictionsChaos (Chaos), 2024
Jake Buzhardt
C. Ricardo Constante-Amores
Michael D. Graham
454
7
0
20 Nov 2024
Training Stiff Neural Ordinary Differential Equations with Implicit
  Single-Step Methods
Training Stiff Neural Ordinary Differential Equations with Implicit Single-Step MethodsChaos (Chaos), 2024
Colby Fronk
Linda R. Petzold
292
14
0
08 Oct 2024
The Extrapolation Power of Implicit Models
The Extrapolation Power of Implicit Models
Juliette Decugis
Alicia Y. Tsai
Max Emerling
Ashwin Ganesh
L. Ghaoui
244
0
0
19 Jul 2024
Nuclear Norm Regularization for Deep Learning
Nuclear Norm Regularization for Deep LearningNeural Information Processing Systems (NeurIPS), 2024
Christopher Scarvelis
Justin Solomon
232
8
0
23 May 2024
Zero-Shot Transfer of Neural ODEs
Zero-Shot Transfer of Neural ODEsNeural Information Processing Systems (NeurIPS), 2024
Tyler Ingebrand
Adam J. Thorpe
Ufuk Topcu
272
12
0
14 May 2024
Neural Flow Diffusion Models: Learnable Forward Process for Improved Diffusion Modelling
Neural Flow Diffusion Models: Learnable Forward Process for Improved Diffusion Modelling
Grigory Bartosh
Dmitry Vetrov
C. A. Naesseth
DiffM
489
36
0
19 Apr 2024
Scaling physics-informed hard constraints with mixture-of-experts
Scaling physics-informed hard constraints with mixture-of-experts
N. Chalapathi
Yiheng Du
Aditi Krishnapriyan
AI4CE
315
29
0
20 Feb 2024
Sequential Flow Straightening for Generative Modeling
Sequential Flow Straightening for Generative Modeling
Jongmin Yoon
Juho Lee
326
0
0
09 Feb 2024
Rademacher Complexity of Neural ODEs via Chen-Fliess Series
Rademacher Complexity of Neural ODEs via Chen-Fliess Series
Joshua Hanson
Maxim Raginsky
350
6
0
30 Jan 2024
Amortized Reparametrization: Efficient and Scalable Variational
  Inference for Latent SDEs
Amortized Reparametrization: Efficient and Scalable Variational Inference for Latent SDEsNeural Information Processing Systems (NeurIPS), 2023
Kevin Course
P. Nair
282
12
0
16 Dec 2023
Building symmetries into data-driven manifold dynamics models for complex flows: application to two-dimensional Kolmogorov flow
Building symmetries into data-driven manifold dynamics models for complex flows: application to two-dimensional Kolmogorov flow
Carlos E. Pérez De Jesús
Alec J. Linot
Michael D. Graham
AI4CE
383
3
0
15 Dec 2023
On Tuning Neural ODE for Stability, Consistency and Faster Convergence
On Tuning Neural ODE for Stability, Consistency and Faster ConvergenceSN Computer Science (SCS), 2023
Sheikh Waqas Akhtar
ODL
263
2
0
04 Dec 2023
Zero Coordinate Shift: Whetted Automatic Differentiation for
  Physics-informed Operator Learning
Zero Coordinate Shift: Whetted Automatic Differentiation for Physics-informed Operator Learning
Kuangdai Leng
Mallikarjun Shankar
Jeyan Thiyagalingam
494
5
0
01 Nov 2023
Faster Training of Neural ODEs Using Gauß-Legendre Quadrature
Faster Training of Neural ODEs Using Gauß-Legendre Quadrature
Alexander Norcliffe
M. Deisenroth
218
7
0
21 Aug 2023
Trainability, Expressivity and Interpretability in Gated Neural ODEs
Trainability, Expressivity and Interpretability in Gated Neural ODEsInternational Conference on Machine Learning (ICML), 2023
T. Kim
T. Can
K. Krishnamurthy
AI4CE
463
6
0
12 Jul 2023
Physics-Informed Machine Learning for Modeling and Control of Dynamical
  Systems
Physics-Informed Machine Learning for Modeling and Control of Dynamical SystemsAmerican Control Conference (ACC), 2023
Truong X. Nghiem
Ján Drgoňa
Colin N. Jones
Zoltán Nagy
Roland Schwan
...
J. Paulson
Andrea Carron
Melanie Zeilinger
Wenceslao Shaw-Cortez
D. Vrabie
PINNAI4CE
302
69
0
24 Jun 2023
Stabilized Neural Differential Equations for Learning Dynamics with
  Explicit Constraints
Stabilized Neural Differential Equations for Learning Dynamics with Explicit ConstraintsNeural Information Processing Systems (NeurIPS), 2023
Alistair J R White
Niki Kilbertus
Maximilian Gelbrecht
Niklas Boers
450
16
0
16 Jun 2023
Generalization bounds for neural ordinary differential equations and
  deep residual networks
Generalization bounds for neural ordinary differential equations and deep residual networksNeural Information Processing Systems (NeurIPS), 2023
Pierre Marion
281
28
0
11 May 2023
Semi-Equivariant Conditional Normalizing Flows
Semi-Equivariant Conditional Normalizing Flows
Eyal Rozenberg
Daniel Freedman
227
0
0
13 Apr 2023
GECCO: Geometrically-Conditioned Point Diffusion Models
GECCO: Geometrically-Conditioned Point Diffusion ModelsIEEE International Conference on Computer Vision (ICCV), 2023
M. Tyszkiewicz
Pascal Fua
Eduard Trulls
DiffM
363
28
0
10 Mar 2023
Locally Regularized Neural Differential Equations: Some Black Boxes Were
  Meant to Remain Closed!
Locally Regularized Neural Differential Equations: Some Black Boxes Were Meant to Remain Closed!International Conference on Machine Learning (ICML), 2023
Avik Pal
Alan Edelman
Chris Rackauckas
295
4
0
03 Mar 2023
Minimizing Trajectory Curvature of ODE-based Generative Models
Minimizing Trajectory Curvature of ODE-based Generative ModelsInternational Conference on Machine Learning (ICML), 2023
Sangyun Lee
Beomsu Kim
Jong Chul Ye
687
83
0
27 Jan 2023
Semi-Equivariant Continuous Normalizing Flows for Target-Aware Molecule
  Generation
Semi-Equivariant Continuous Normalizing Flows for Target-Aware Molecule Generation
Eyal Rozenberg
Daniel Freedman
233
1
0
09 Nov 2022
Sparsity in Continuous-Depth Neural Networks
Sparsity in Continuous-Depth Neural NetworksNeural Information Processing Systems (NeurIPS), 2022
H. Aliee
Till Richter
Mikhail Solonin
I. Ibarra
Fabian J. Theis
Niki Kilbertus
294
16
0
26 Oct 2022
GENIE: Higher-Order Denoising Diffusion Solvers
GENIE: Higher-Order Denoising Diffusion SolversNeural Information Processing Systems (NeurIPS), 2022
Tim Dockhorn
Arash Vahdat
Karsten Kreis
DiffM
505
151
0
11 Oct 2022
Self-Supervised Deep Equilibrium Models for Inverse Problems with
  Theoretical Guarantees
Self-Supervised Deep Equilibrium Models for Inverse Problems with Theoretical Guarantees
Weijie Gan
Chunwei Ying
Parna Eshraghi
Tongyao Wang
C. Eldeniz
Yuyang Hu
Jiaming Liu
Yasheng Chen
Hongyu An
Ulugbek S. Kamilov
198
4
0
07 Oct 2022
Flow Matching for Generative Modeling
Flow Matching for Generative ModelingInternational Conference on Learning Representations (ICLR), 2022
Y. Lipman
Ricky T. Q. Chen
Heli Ben-Hamu
Maximilian Nickel
Matt Le
OOD
1.4K
3,990
0
06 Oct 2022
Homotopy-based training of NeuralODEs for accurate dynamics discovery
Homotopy-based training of NeuralODEs for accurate dynamics discoveryNeural Information Processing Systems (NeurIPS), 2022
Joon-Hyuk Ko
Hankyul Koh
Nojun Park
W. Jhe
446
14
0
04 Oct 2022
Neural Integral Equations
Neural Integral Equations
E. Zappala
Antonio H. O. Fonseca
J. O. Caro
David van Dijk
272
5
0
30 Sep 2022
Survival Mixture Density Networks
Survival Mixture Density NetworksMachine Learning in Health Care (MLHC), 2022
Xintian Han
Mark Goldstein
Rajesh Ranganath
291
11
0
23 Aug 2022
Adaptive Asynchronous Control Using Meta-learned Neural Ordinary
  Differential Equations
Adaptive Asynchronous Control Using Meta-learned Neural Ordinary Differential EquationsIEEE Transactions on robotics (TRO), 2022
Achkan Salehi
Steffen Rühl
Stéphane Doncieux
AI4CE
442
4
0
25 Jul 2022
ICE-NODE: Integration of Clinical Embeddings with Neural Ordinary
  Differential Equations
ICE-NODE: Integration of Clinical Embeddings with Neural Ordinary Differential EquationsMachine Learning in Health Care (MLHC), 2022
Asem Alaa
Erik Mayer
Mauricio Barahona
AI4CE
326
9
0
05 Jul 2022
Conditional Permutation Invariant Flows
Conditional Permutation Invariant Flows
Berend Zwartsenberg
Adam Scibior
Matthew Niedoba
Vasileios Lioutas
Yunpeng Liu
Justice Sefas
Setareh Dabiri
J. Lavington
Trevor Campbell
Frank Wood
175
9
0
17 Jun 2022
On the balance between the training time and interpretability of neural
  ODE for time series modelling
On the balance between the training time and interpretability of neural ODE for time series modelling
Yakov Golovanev
A. Hvatov
AI4TS
198
2
0
07 Jun 2022
Online Deep Equilibrium Learning for Regularization by Denoising
Online Deep Equilibrium Learning for Regularization by DenoisingNeural Information Processing Systems (NeurIPS), 2022
Jiaming Liu
Xiaojian Xu
Weijie Gan
Shirin Shoushtari
Ulugbek S. Kamilov
324
33
0
25 May 2022
Proximal Implicit ODE Solvers for Accelerating Learning Neural ODEs
Proximal Implicit ODE Solvers for Accelerating Learning Neural ODEs
Justin Baker
Hedi Xia
Yiwei Wang
E. Cherkaev
A. Narayan
Long Chen
Jack Xin
Andrea L. Bertozzi
Stanley J. Osher
Bao Wang
288
10
0
19 Apr 2022
Deep Equilibrium Optical Flow Estimation
Deep Equilibrium Optical Flow EstimationComputer Vision and Pattern Recognition (CVPR), 2022
Shaojie Bai
Zhengyang Geng
Yash Savani
J. Zico Kolter
313
88
0
18 Apr 2022
TO-FLOW: Efficient Continuous Normalizing Flows with Temporal
  Optimization adjoint with Moving Speed
TO-FLOW: Efficient Continuous Normalizing Flows with Temporal Optimization adjoint with Moving SpeedComputer Vision and Pattern Recognition (CVPR), 2022
Shian Du
Yihong Luo
Wei Chen
Jian Xu
Delu Zeng
319
12
0
19 Mar 2022
Input-to-State Stable Neural Ordinary Differential Equations with
  Applications to Transient Modeling of Circuits
Input-to-State Stable Neural Ordinary Differential Equations with Applications to Transient Modeling of CircuitsConference on Learning for Dynamics & Control (L4DC), 2022
Alan Yang
J. Xiong
Maxim Raginsky
E. Rosenbaum
AI4TS
157
7
0
14 Feb 2022
Continuous Deep Equilibrium Models: Training Neural ODEs faster by
  integrating them to Infinity
Continuous Deep Equilibrium Models: Training Neural ODEs faster by integrating them to InfinityIEEE Conference on High Performance Extreme Computing (HPEC), 2022
Avik Pal
Alan Edelman
Chris Rackauckas
332
10
0
28 Jan 2022
Wassersplines for Neural Vector Field--Controlled Animation
Wassersplines for Neural Vector Field--Controlled Animation
Paul Zhang
Dmitriy Smirnov
Justin Solomon
299
5
0
28 Jan 2022
Taylor-Lagrange Neural Ordinary Differential Equations: Toward Fast
  Training and Evaluation of Neural ODEs
Taylor-Lagrange Neural Ordinary Differential Equations: Toward Fast Training and Evaluation of Neural ODEsInternational Joint Conference on Artificial Intelligence (IJCAI), 2022
Franck Djeumou
Cyrus Neary
Eric Goubault
S. Putot
Ufuk Topcu
AI4TS
270
20
0
14 Jan 2022
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