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MALI: A memory efficient and reverse accurate integrator for Neural ODEs
v1v2 (latest)

MALI: A memory efficient and reverse accurate integrator for Neural ODEs

International Conference on Learning Representations (ICLR), 2021
9 February 2021
Juntang Zhuang
Nicha Dvornek
S. Tatikonda
James S. Duncan
ArXiv (abs)PDFHTML

Papers citing "MALI: A memory efficient and reverse accurate integrator for Neural ODEs"

33 / 33 papers shown
VNODE: A Piecewise Continuous Volterra Neural Network
VNODE: A Piecewise Continuous Volterra Neural Network
Siddharth Roheda
Aniruddha Bala
Rohit Chowdhury
Rohan Jaiswal
252
0
0
29 Sep 2025
Reversible Deep Equilibrium Models
Reversible Deep Equilibrium Models
Sam McCallum
Kamran Arora
James Foster
286
4
0
16 Sep 2025
Latent Stochastic Interpolants
Latent Stochastic Interpolants
Saurabh Singh
Dmitry Lagun
DiffMBDL
254
1
0
02 Jun 2025
Rex: A Family of Reversible Exponential (Stochastic) Runge-Kutta Solvers
Rex: A Family of Reversible Exponential (Stochastic) Runge-Kutta Solvers
Zander W. Blasingame
Chen Liu
DiffM
404
0
0
12 Feb 2025
Efficient, Accurate and Stable Gradients for Neural ODEs
Efficient, Accurate and Stable Gradients for Neural ODEs
Sam McCallum
James Foster
505
9
0
15 Oct 2024
DFM: Interpolant-free Dual Flow Matching
DFM: Interpolant-free Dual Flow Matching
Denis A. Gudovskiy
Tomoyuki Okuno
Yohei Nakata
AI4CE
329
0
0
11 Oct 2024
PGODE: Towards High-quality System Dynamics Modeling
PGODE: Towards High-quality System Dynamics ModelingInternational Conference on Machine Learning (ICML), 2023
Xiao Luo
Yiyang Gu
Huiyu Jiang
Hang Zhou
Jinsheng Huang
Wei Ju
Zhiping Xiao
Ming Zhang
Luke Huan
AI4CE
367
10
0
11 Nov 2023
Long-term Time Series Forecasting based on Decomposition and Neural
  Ordinary Differential Equations
Long-term Time Series Forecasting based on Decomposition and Neural Ordinary Differential Equations
S. Lim
Jaehyeon Park
Seojin Kim
Hyowon Wi
Haksoo Lim
Jinsung Jeon
Jeongwhan Choi
Noseong Park
AI4TSAI4CE
304
3
0
08 Nov 2023
Faster Training of Neural ODEs Using Gauß-Legendre Quadrature
Faster Training of Neural ODEs Using Gauß-Legendre Quadrature
Alexander Norcliffe
M. Deisenroth
217
6
0
21 Aug 2023
Embracing the chaos: analysis and diagnosis of numerical instability in
  variational flows
Embracing the chaos: analysis and diagnosis of numerical instability in variational flowsNeural Information Processing Systems (NeurIPS), 2023
Zuheng Xu
Trevor Campbell
279
6
0
12 Jul 2023
Critical Sampling for Robust Evolution Operator Learning of Unknown
  Dynamical Systems
Critical Sampling for Robust Evolution Operator Learning of Unknown Dynamical SystemsIEEE Transactions on Artificial Intelligence (IEEE TAI), 2023
Ce Zhang
Kailiang Wu
Zhihai He
297
1
0
15 Apr 2023
Semi-Equivariant Conditional Normalizing Flows
Semi-Equivariant Conditional Normalizing Flows
Eyal Rozenberg
Daniel Freedman
227
0
0
13 Apr 2023
Learning Subgrid-scale Models with Neural Ordinary Differential
  Equations
Learning Subgrid-scale Models with Neural Ordinary Differential Equations
Shinhoo Kang
Emil M. Constantinescu
AI4CE
405
8
0
20 Dec 2022
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
Prediction-based One-shot Dynamic Parking Pricing
Prediction-based One-shot Dynamic Parking PricingInternational Conference on Information and Knowledge Management (CIKM), 2022
Seoyoung Hong
H. Shin
Jeongwhan Choi
Noseong Park
177
5
0
30 Aug 2022
Closed-Form Diffeomorphic Transformations for Time Series Alignment
Closed-Form Diffeomorphic Transformations for Time Series AlignmentInternational Conference on Machine Learning (ICML), 2022
Iñigo Martinez
E. Viles
Igor García Olaizola
AI4TS
181
11
0
16 Jun 2022
A memory-efficient neural ODE framework based on high-level adjoint
  differentiation
A memory-efficient neural ODE framework based on high-level adjoint differentiationIEEE Transactions on Artificial Intelligence (IEEE TAI), 2022
Hong Zhang
Wenjun Zhao
306
6
0
02 Jun 2022
Do Residual Neural Networks discretize Neural Ordinary Differential
  Equations?
Do Residual Neural Networks discretize Neural Ordinary Differential Equations?Neural Information Processing Systems (NeurIPS), 2022
Michael E. Sander
Pierre Ablin
Gabriel Peyré
319
38
0
29 May 2022
EXIT: Extrapolation and Interpolation-based Neural Controlled
  Differential Equations for Time-series Classification and Forecasting
EXIT: Extrapolation and Interpolation-based Neural Controlled Differential Equations for Time-series Classification and ForecastingThe Web Conference (WWW), 2022
Sheo Yon Jhin
Jaehoon Lee
Minju Jo
Seung-Uk Kook
Jinsung Jeon
Jihyeon Hyeong
Jayoung Kim
Noseong Park
AI4TS
376
29
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 SpeedComputer Vision and Pattern Recognition (CVPR), 2022
Shian Du
Yihong Luo
Wei Chen
Jian Xu
Delu Zeng
317
12
0
19 Mar 2022
Characteristic Neural Ordinary Differential Equations
Characteristic Neural Ordinary Differential EquationsInternational Conference on Learning Representations (ICLR), 2021
Xingzi Xu
Ali Hasan
Khalil Elkhalil
Jie Ding
Vahid Tarokh
BDL
483
3
0
25 Nov 2021
How Does Momentum Benefit Deep Neural Networks Architecture Design? A
  Few Case Studies
How Does Momentum Benefit Deep Neural Networks Architecture Design? A Few Case Studies
Bao Wang
Hedi Xia
T. Nguyen
Stanley Osher
AI4CE
244
13
0
13 Oct 2021
Heavy Ball Neural Ordinary Differential Equations
Heavy Ball Neural Ordinary Differential EquationsNeural Information Processing Systems (NeurIPS), 2021
Hedi Xia
Vai Suliafu
H. Ji
T. Nguyen
Andrea L. Bertozzi
Stanley J. Osher
Bao Wang
227
70
0
10 Oct 2021
Second-Order Neural ODE Optimizer
Second-Order Neural ODE Optimizer
Guan-Horng Liu
T. Chen
Evangelos A. Theodorou
223
19
0
29 Sep 2021
Attentive Neural Controlled Differential Equations for Time-series
  Classification and Forecasting
Attentive Neural Controlled Differential Equations for Time-series Classification and Forecasting
Sheo Yon Jhin
H. Shin
Seoyoung Hong
Solhee Park
Noseong Park
AI4TS
207
38
0
04 Sep 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
217
4
0
04 Jul 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
317
64
0
23 Jun 2021
Distributional Gradient Matching for Learning Uncertain Neural Dynamics
  Models
Distributional Gradient Matching for Learning Uncertain Neural Dynamics ModelsNeural Information Processing Systems (NeurIPS), 2021
Lenart Treven
Philippe Wenk
Florian Dorfler
Andreas Krause
OOD
216
3
0
22 Jun 2021
Neural Controlled Differential Equations for Online Prediction Tasks
Neural Controlled Differential Equations for Online Prediction Tasks
James Morrill
Patrick Kidger
Lingyi Yang
Terry Lyons
AI4TS
233
55
0
21 Jun 2021
Variational multiple shooting for Bayesian ODEs with Gaussian processes
Variational multiple shooting for Bayesian ODEs with Gaussian processesConference on Uncertainty in Artificial Intelligence (UAI), 2021
Pashupati Hegde
Çağatay Yıldız
Harri Lähdesmäki
Samuel Kaski
Markus Heinonen
406
20
0
21 Jun 2021
Efficient and Accurate Gradients for Neural SDEs
Efficient and Accurate Gradients for Neural SDEsNeural Information Processing Systems (NeurIPS), 2021
Patrick Kidger
James Foster
Xuechen Li
Terry Lyons
DiffM
566
91
0
27 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 HeuristicsInternational Conference on Machine Learning (ICML), 2021
Avik Pal
Yingbo Ma
Viral B. Shah
Chris Rackauckas
250
44
0
09 May 2021
Symplectic Adjoint Method for Exact Gradient of Neural ODE with Minimal
  Memory
Symplectic Adjoint Method for Exact Gradient of Neural ODE with Minimal MemoryNeural Information Processing Systems (NeurIPS), 2021
Takashi Matsubara
Yuto Miyatake
Takaharu Yaguchi
245
28
0
19 Feb 2021
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