ANODE: Unconditionally Accurate Memory-Efficient Gradients for Neural ODEs

27 February 2019

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

50 / 98 papers shown

Title
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 Colby Fronk Linda R. Petzold 65 2 0 02 Dec 2024
Efficient, Accurate and Stable Gradients for Neural ODEs Sam McCallum James Foster 32 4 0 15 Oct 2024
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 Colby Fronk Linda R. Petzold 32 4 0 08 Oct 2024
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 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 Chinmay Datar Adwait Datar Felix Dietrich W. Schilders AI4TS 36 1 0 24 Mar 2024
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 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 James Hazelden Yuhan Helena Liu Eli Shlizerman E. Shea-Brown 39 2 0 17 Nov 2023
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 Il Memming Park Ábel Ságodi Piotr Sokól 21 8 0 24 Aug 2023
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 T. Kim T. Can K. Krishnamurthy AI4CE 35 4 0 12 Jul 2023
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 Yesom Park Jaemoo Choi Changyeon Yoon Changhoon Song Myung-joo Kang AI4TS AI4CE 27 3 0 02 Feb 2023
On backpropagating Hessians through ODEs Axel Ciceri T. Fischbacher 11 0 0 19 Jan 2023
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 Yue Zhao Jiequn Han OffRL 17 6 0 29 Nov 2022
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 Wai Meng Kwok S. Dass G. Streftaris 19 3 0 17 Oct 2022
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 Yuto Hoshino Hiroki Kawakami Hiroki Matsutani FedML 18 0 0 19 Aug 2022
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 Hong Zhang Wenjun Zhao 25 5 0 02 Jun 2022
A scalable deep learning approach for solving high-dimensional dynamic optimal transport Wei Wan Yuejin Zhang Chenglong Bao Bin Dong Zuoqiang Shi 19 4 0 16 May 2022
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 Shian Du Yihong Luo Wei Chen Jian Xu Delu Zeng 24 7 0 19 Mar 2022
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 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 Srinivas Anumasa P. K. Srijith BDL 19 5 0 23 Dec 2021
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 Zihao Zhou Xingyi Yang Ryan A. Rossi Handong Zhao Rose Yu 3DPC 28 32 0 12 Dec 2021
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 Marin Bilovs Johanna Sommer Syama Sundar Rangapuram Tim Januschowski Stephan Günnemann AI4TS 16 70 0 25 Oct 2021
Equivariant Finite Normalizing Flows A. Bose Marcus A. Brubaker I. Kobyzev DRL 29 8 0 16 Oct 2021
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 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 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 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 Weiming Zhi Tin Lai Lionel Ott Edwin V. Bonilla Fabio Ramos OOD 23 4 0 04 Jul 2021
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 Kookjin Lee Nathaniel Trask P. Stinis AI4CE 44 42 0 23 Jun 2021
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 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 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 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 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 S. Dutta Peter Rivera-Casillas Matthew W. Farthing AI4CE 15 13 0 22 Apr 2021