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ANODE: Unconditionally Accurate Memory-Efficient Gradients for Neural
  ODEs

ANODE: Unconditionally Accurate Memory-Efficient Gradients for Neural ODEs

27 February 2019
A. Gholami
Kurt Keutzer
George Biros
ArXivPDFHTML

Papers citing "ANODE: Unconditionally Accurate Memory-Efficient Gradients for Neural ODEs"

50 / 98 papers shown
Title
Integration Flow Models
Integration Flow Models
Jingjing Wang
Dan Zhang
Joshua Luo
Yin Yang
Feng Luo
145
0
0
28 Apr 2025
Training Stiff Neural Ordinary Differential Equations with Explicit
  Exponential Integration Methods
Training Stiff Neural Ordinary Differential Equations with Explicit Exponential Integration Methods
Colby Fronk
Linda R. Petzold
65
2
0
02 Dec 2024
Efficient, Accurate and Stable Gradients for Neural ODEs
Efficient, Accurate and Stable Gradients for Neural ODEs
Sam McCallum
James Foster
34
4
0
15 Oct 2024
DFM: Interpolant-free Dual Flow Matching
DFM: Interpolant-free Dual Flow Matching
Denis A. Gudovskiy
Tomoyuki Okuno
Yohei Nakata
AI4CE
44
0
0
11 Oct 2024
Training Stiff Neural Ordinary Differential Equations with Implicit
  Single-Step Methods
Training Stiff Neural Ordinary Differential Equations with Implicit Single-Step Methods
Colby Fronk
Linda R. Petzold
32
4
0
08 Oct 2024
A Unified Framework for Neural Computation and Learning Over Time
A Unified Framework for Neural Computation and Learning Over Time
S. Melacci
Alessandro Betti
Michele Casoni
Tommaso Guidi
Matteo Tiezzi
Marco Gori
AI4TS
28
0
0
18 Sep 2024
Sequential-in-time training of nonlinear parametrizations for solving
  time-dependent partial differential equations
Sequential-in-time training of nonlinear parametrizations for solving time-dependent partial differential equations
Huan Zhang
Yifan Chen
Eric Vanden-Eijnden
Benjamin Peherstorfer
39
2
0
01 Apr 2024
Systematic construction of continuous-time neural networks for linear
  dynamical systems
Systematic construction of continuous-time neural networks for linear dynamical systems
Chinmay Datar
Adwait Datar
Felix Dietrich
W. Schilders
AI4TS
36
1
0
24 Mar 2024
Differential Equations for Continuous-Time Deep Learning
Differential Equations for Continuous-Time Deep Learning
Lars Ruthotto
AI4TS
AI4CE
SyDa
BDL
37
7
0
08 Jan 2024
Stability-Informed Initialization of Neural Ordinary Differential
  Equations
Stability-Informed Initialization of Neural Ordinary Differential Equations
Theodor Westny
Arman Mohammadi
Daniel Jung
Erik Frisk
23
0
0
27 Nov 2023
Evolutionary algorithms as an alternative to backpropagation for
  supervised training of Biophysical Neural Networks and Neural ODEs
Evolutionary algorithms as an alternative to backpropagation for supervised training of Biophysical Neural Networks and Neural ODEs
James Hazelden
Yuhan Helena Liu
Eli Shlizerman
E. Shea-Brown
41
2
0
17 Nov 2023
Distribution learning via neural differential equations: a nonparametric
  statistical perspective
Distribution learning via neural differential equations: a nonparametric statistical perspective
Youssef Marzouk
Zhi Ren
Sven Wang
Jakob Zech
29
11
0
03 Sep 2023
Persistent learning signals and working memory without continuous
  attractors
Persistent learning signals and working memory without continuous attractors
Il Memming Park
Ábel Ságodi
Piotr Sokól
21
8
0
24 Aug 2023
Faster Training of Neural ODEs Using Gauß-Legendre Quadrature
Faster Training of Neural ODEs Using Gauß-Legendre Quadrature
Alexander Norcliffe
M. Deisenroth
23
3
0
21 Aug 2023
Trainability, Expressivity and Interpretability in Gated Neural ODEs
Trainability, Expressivity and Interpretability in Gated Neural ODEs
T. Kim
T. Can
K. Krishnamurthy
AI4CE
35
4
0
12 Jul 2023
Correcting auto-differentiation in neural-ODE training
Correcting auto-differentiation in neural-ODE training
Yewei Xu
Shi Chen
Qin Li
Stephen J. Wright
11
1
0
03 Jun 2023
Learning PDE Solution Operator for Continuous Modeling of Time-Series
Learning PDE Solution Operator for Continuous Modeling of Time-Series
Yesom Park
Jaemoo Choi
Changyeon Yoon
Changhoon Song
Myung-joo Kang
AI4TS
AI4CE
27
3
0
02 Feb 2023
On backpropagating Hessians through ODEs
On backpropagating Hessians through ODEs
Axel Ciceri
T. Fischbacher
11
0
0
19 Jan 2023
Generalized Neural Closure Models with Interpretability
Generalized Neural Closure Models with Interpretability
Abhinava Gupta
Pierre FJ Lermusiaux
AI4CE
22
9
0
15 Jan 2023
Offline Supervised Learning V.S. Online Direct Policy Optimization: A
  Comparative Study and A Unified Training Paradigm for Neural Network-Based
  Optimal Feedback Control
Offline Supervised Learning V.S. Online Direct Policy Optimization: A Comparative Study and A Unified Training Paradigm for Neural Network-Based Optimal Feedback Control
Yue Zhao
Jiequn Han
OffRL
17
6
0
29 Nov 2022
torchode: A Parallel ODE Solver for PyTorch
torchode: A Parallel ODE Solver for PyTorch
Marten Lienen
Stephan Günnemann
LRM
19
11
0
22 Oct 2022
Deep Learning Aided Laplace Based Bayesian Inference for Epidemiological
  Systems
Deep Learning Aided Laplace Based Bayesian Inference for Epidemiological Systems
Wai Meng Kwok
S. Dass
G. Streftaris
19
3
0
17 Oct 2022
Parameter-varying neural ordinary differential equations with
  partition-of-unity networks
Parameter-varying neural ordinary differential equations with partition-of-unity networks
Kookjin Lee
N. Trask
22
2
0
01 Oct 2022
Federated Learning of Neural ODE Models with Different Iteration Counts
Federated Learning of Neural ODE Models with Different Iteration Counts
Yuto Hoshino
Hiroki Kawakami
Hiroki Matsutani
FedML
18
0
0
19 Aug 2022
Reachability Analysis of a General Class of Neural Ordinary Differential
  Equations
Reachability Analysis of a General Class of Neural Ordinary Differential Equations
Diego Manzanas Lopez
Patrick Musau
Nathaniel P. Hamilton
Taylor T. Johnson
23
14
0
13 Jul 2022
A memory-efficient neural ODE framework based on high-level adjoint
  differentiation
A memory-efficient neural ODE framework based on high-level adjoint differentiation
Hong Zhang
Wenjun Zhao
25
5
0
02 Jun 2022
A scalable deep learning approach for solving high-dimensional dynamic
  optimal transport
A scalable deep learning approach for solving high-dimensional dynamic optimal transport
Wei Wan
Yuejin Zhang
Chenglong Bao
Bin Dong
Zuoqiang Shi
19
5
0
16 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
14
6
0
19 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 Speed
Shian Du
Yihong Luo
Wei Chen
Jian Xu
Delu Zeng
24
7
0
19 Mar 2022
Continuous-Time Meta-Learning with Forward Mode Differentiation
Continuous-Time Meta-Learning with Forward Mode Differentiation
T. Deleu
David Kanaa
Leo Feng
Giancarlo Kerg
Yoshua Bengio
Guillaume Lajoie
Pierre-Luc Bacon
23
19
0
02 Mar 2022
Bounded nonlinear forecasts of partially observed geophysical systems
  with physics-constrained deep learning
Bounded nonlinear forecasts of partially observed geophysical systems with physics-constrained deep learning
Said Ouala
Steven L. Brunton
A. Pascual
Bertrand Chapron
F. Collard
L. Gaultier
Ronan Fablet
PINN
AI4TS
AI4CE
18
10
0
11 Feb 2022
Latent Time Neural Ordinary Differential Equations
Latent Time Neural Ordinary Differential Equations
Srinivas Anumasa
P. K. Srijith
BDL
22
5
0
23 Dec 2021
Improving Robustness and Uncertainty Modelling in Neural Ordinary
  Differential Equations
Improving Robustness and Uncertainty Modelling in Neural Ordinary Differential Equations
Srinivas Anumasa
P. K. Srijith
OOD
UQCV
BDL
14
11
0
23 Dec 2021
Neural Point Process for Learning Spatiotemporal Event Dynamics
Neural Point Process for Learning Spatiotemporal Event Dynamics
Zihao Zhou
Xingyi Yang
Ryan A. Rossi
Handong Zhao
Rose Yu
3DPC
31
32
0
12 Dec 2021
Layer-Parallel Training of Residual Networks with Auxiliary-Variable
  Networks
Layer-Parallel Training of Residual Networks with Auxiliary-Variable Networks
Qi Sun
Hexin Dong
Zewei Chen
Jiacheng Sun
Zhenguo Li
Bin Dong
27
1
0
10 Dec 2021
Neural Flows: Efficient Alternative to Neural ODEs
Neural Flows: Efficient Alternative to Neural ODEs
Marin Bilovs
Johanna Sommer
Syama Sundar Rangapuram
Tim Januschowski
Stephan Günnemann
AI4TS
16
70
0
25 Oct 2021
Equivariant Finite Normalizing Flows
Equivariant Finite Normalizing Flows
A. Bose
Marcus A. Brubaker
I. Kobyzev
DRL
29
8
0
16 Oct 2021
Meta-Learning with Adjoint Methods
Meta-Learning with Adjoint Methods
Shibo Li
Zheng Wang
A. Narayan
Robert M. Kirby
Shandian Zhe
4
4
0
16 Oct 2021
Second-Order Neural ODE Optimizer
Second-Order Neural ODE Optimizer
Guan-Horng Liu
T. Chen
Evangelos A. Theodorou
26
12
0
29 Sep 2021
Quantized Convolutional Neural Networks Through the Lens of Partial
  Differential Equations
Quantized Convolutional Neural Networks Through the Lens of Partial Differential Equations
Ido Ben-Yair
Gil Ben Shalom
Moshe Eliasof
Eran Treister
MQ
24
5
0
31 Aug 2021
Data-driven reduced order modeling of environmental hydrodynamics using
  deep autoencoders and neural ODEs
Data-driven reduced order modeling of environmental hydrodynamics using deep autoencoders and neural ODEs
S. Dutta
Peter Rivera-Casillas
Orie M. Cecil
Matthew W. Farthing
E. Perracchione
M. Putti
AI4CE
10
7
0
06 Jul 2021
Learning ODEs via Diffeomorphisms for Fast and Robust Integration
Learning ODEs via Diffeomorphisms for Fast and Robust Integration
Weiming Zhi
Tin Lai
Lionel Ott
Edwin V. Bonilla
Fabio Ramos
OOD
23
4
0
04 Jul 2021
Closed-form Continuous-time Neural Models
Closed-form Continuous-time Neural Models
Ramin Hasani
Mathias Lechner
Alexander Amini
Lucas Liebenwein
Aaron Ray
Max Tschaikowski
G. Teschl
Daniela Rus
PINN
AI4TS
31
82
0
25 Jun 2021
Machine learning structure preserving brackets for forecasting
  irreversible processes
Machine learning structure preserving brackets for forecasting irreversible processes
Kookjin Lee
Nathaniel Trask
P. Stinis
AI4CE
44
42
0
23 Jun 2021
Stateful ODE-Nets using Basis Function Expansions
Stateful ODE-Nets using Basis Function Expansions
A. Queiruga
N. Benjamin Erichson
Liam Hodgkinson
Michael W. Mahoney
27
16
0
21 Jun 2021
Causal Navigation by Continuous-time Neural Networks
Causal Navigation by Continuous-time Neural Networks
Charles J. Vorbach
Ramin Hasani
Alexander Amini
Mathias Lechner
Daniela Rus
26
47
0
15 Jun 2021
Incorporating NODE with Pre-trained Neural Differential Operator for
  Learning Dynamics
Incorporating NODE with Pre-trained Neural Differential Operator for Learning Dynamics
Shiqi Gong
Qi Meng
Yue Wang
Lijun Wu
Wei Chen
Zhi-Ming Ma
Tie-Yan Liu
13
2
0
08 Jun 2021
Learning Runge-Kutta Integration Schemes for ODE Simulation and
  Identification
Learning Runge-Kutta Integration Schemes for ODE Simulation and Identification
Said Ouala
L. Debreu
A. Pascual
Bertrand Chapron
F. Collard
L. Gaultier
Ronan Fablet
10
4
0
11 May 2021
Opening the Blackbox: Accelerating Neural Differential Equations by
  Regularizing Internal Solver Heuristics
Opening the Blackbox: Accelerating Neural Differential Equations by Regularizing Internal Solver Heuristics
Avik Pal
Yingbo Ma
Viral B. Shah
Chris Rackauckas
28
36
0
09 May 2021
Neural Ordinary Differential Equations for Data-Driven Reduced Order
  Modeling of Environmental Hydrodynamics
Neural Ordinary Differential Equations for Data-Driven Reduced Order Modeling of Environmental Hydrodynamics
S. Dutta
Peter Rivera-Casillas
Matthew W. Farthing
AI4CE
15
13
0
22 Apr 2021
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