tksmatsubara / symplectic-adjoint-method
Code for "Symplectic Adjoint Method for Exact Gradient of Neural ODE with Minimal Memory," NeurIPS, 2021.
☆16Updated 3 years ago
Alternatives and similar repositories for symplectic-adjoint-method:
Users that are interested in symplectic-adjoint-method are comparing it to the libraries listed below
- Code for "'Hey, that's not an ODE:' Faster ODE Adjoints via Seminorms" (ICML 2021)☆87Updated 2 years ago
- code for "Neural Conservation Laws A Divergence-Free Perspective".☆37Updated 2 years ago
- Nonparametric Differential Equation Modeling☆53Updated last year
- Symplectic Recurrent Neural Networks☆28Updated 2 years ago
- PyTorch implementation of the OT-Flow approach in arXiv:2006.00104☆51Updated 9 months ago
- Refining continuous-in-depth neural networks☆39Updated 3 years ago
- Computing gradients and Hessians of feed-forward networks with GPU acceleration☆18Updated last year
- Experiments from the paper "On Second Order Behaviour in Augmented Neural ODEs"☆58Updated 6 months ago
- Reference implementation of Finite Element Networks as proposed in "Learning the Dynamics of Physical Systems from Sparse Observations wi…☆69Updated 11 months ago
- Repo to the paper "Lie Point Symmetry Data Augmentation for Neural PDE Solvers"☆49Updated last year
- ☆107Updated 3 years ago
- Library for normalizing flows and neural flows.☆24Updated 2 years ago
- This repository contains code released by DiffEqML Research☆90Updated 3 years ago
- Koopman Kernels for Learning Dynamical Systems from Trajectory Data☆26Updated last year
- Stochastic Normalizing Flows☆76Updated 3 years ago
- Convex potential flows☆83Updated 3 years ago
- Second-Order Neural ODE Optimizer, NeurIPS 2021 spotlight☆52Updated 3 years ago
- Re-implementation of Hamiltonian Generative Networks paper☆33Updated 2 years ago
- Port-Hamiltonian Approach to Neural Network Training☆22Updated 5 years ago
- Implementation of the work Variational multiple shooting for Bayesian ODEs with Gaussian processes☆12Updated 2 years ago
- Source code for Large-Scale Wasserstein Gradient Flows (NeurIPS 2021)☆32Updated 2 years ago
- Riemannian Convex Potential Maps☆67Updated 2 years ago
- Code for efficiently sampling functions from GP(flow) posteriors☆72Updated 4 years ago
- repo for paper: Adaptive Checkpoint Adjoint (ACA) method for gradient estimation in neural ODE☆54Updated 4 years ago
- [ICLR 2022] Path integral sampler☆46Updated last year
- Implementation of Action Matching for the Schrödinger equation☆24Updated last year
- Sampling with gradient-based Markov Chain Monte Carlo approaches☆99Updated last year
- Regularized Neural ODEs (RNODE)☆82Updated 3 years ago
- Methods and experiments for assumed density SDE approximations☆11Updated 3 years ago
- Stochastic Gradient Langevin Dynamics for Bayesian learning☆31Updated 3 years ago