Discretize-Optimize vs. Optimize-Discretize for Time-Series Regression and Continuous Normalizing Flows

27 May 2020

Papers citing "Discretize-Optimize vs. Optimize-Discretize for Time-Series Regression and Continuous Normalizing Flows"

11 / 11 papers shown

Title
Sensitivity-Constrained Fourier Neural Operators for Forward and Inverse Problems in Parametric Differential Equations Abdolmehdi Behroozi Chaopeng Shen and Daniel Kifer AI4CE 25 0 0 13 May 2025
OT-Transformer: A Continuous-time Transformer Architecture with Optimal Transport Regularization Kelvin Kan Xingjian Li Stanley Osher 99 2 0 30 Jan 2025
Sequential-in-time training of nonlinear parametrizations for solving time-dependent partial differential equations Huan Zhang Yifan Chen Eric Vanden-Eijnden Benjamin Peherstorfer 42 2 0 01 Apr 2024
Trainability, Expressivity and Interpretability in Gated Neural ODEs T. Kim T. Can K. Krishnamurthy AI4CE 35 4 0 12 Jul 2023
Locally Regularized Neural Differential Equations: Some Black Boxes Were Meant to Remain Closed! Avik Pal Alan Edelman Chris Rackauckas 24 3 0 03 Mar 2023
Comparison of neural closure models for discretised PDEs Hugo Melchers D. Crommelin B. Koren Vlado Menkovski B. Sanderse 24 15 0 26 Oct 2022
COMET Flows: Towards Generative Modeling of Multivariate Extremes and Tail Dependence Andrew McDonald Pang-Ning Tan Lifeng Luo 21 9 0 02 May 2022
Multiple shooting for training neural differential equations on time series Evren Mert Turan J. Jäschke AI4TS 40 23 0 14 Sep 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
JFB: Jacobian-Free Backpropagation for Implicit Networks Samy Wu Fung Howard Heaton Qiuwei Li Daniel McKenzie Stanley Osher W. Yin FedML 35 84 0 23 Mar 2021
Learning Differential Equations that are Easy to Solve Jacob Kelly J. Bettencourt Matthew J. Johnson David Duvenaud 30 111 0 09 Jul 2020