Closed-form Continuous-time Neural Networks
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Updated
Jul 5, 2024 - Python
Closed-form Continuous-time Neural Networks
Tensorflow implementation of Ordinary Differential Equation Solvers with full GPU support
Code for the paper "Learning Differential Equations that are Easy to Solve"
Code for "Infinitely Deep Bayesian Neural Networks with Stochastic Differential Equations"
Implementation of (2018) Neural Ordinary Differential Equations on Keras
Regularized Neural ODEs (RNODE)
Code for our RSS'21 paper: "Hamiltonian-based Neural ODE Networks on the SE(3) Manifold For Dynamics Learning and Control"
LT-OCF: Learnable-Time ODE-based Collaborative Filtering, CIKM'21
NDE: Climate Modeling with Neural Diffusion Equation, ICDM'21
Official PyTorch implementation for the paper Minimizing Trajectory Curvature of ODE-based Generative Models, ICML 2023
Code for "Time-Reversal Symmetric ODE Network (NeurIPS 2020)"
Code and figures for "Neural network differential equations for ion channel modelling".
Thesis repository on neural ordinary differential equations used for coarse-graining molecular dynamics
The official PyTorch implementation of "Learning to Simulate Daily Activities via Modeling Dynamic Human Needs" (WWW'23)
A toolbox for learning with neural ODEs.
This repository contains the code for training and visualizing the fully-differentiable Kuramoto model, "KuraNet".
Adversarially Robust Prototypical Few-shot Segmentation with Neural-ODEs
Using Neural ODEs to fit a networked UIV model to an SIR model of an epidemic.
CVPR2021 paper "Learning Parallel Dense Correspondence from Spatio-Temporal Descriptorsfor Efficient and Robust 4D Reconstruction"
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