Junghwan-brian / SDE-Net
implementations sde-net
☆14Updated 4 years ago
Alternatives and similar repositories for SDE-Net:
Users that are interested in SDE-Net are comparing it to the libraries listed below
- Code for: "Neural Controlled Differential Equations for Online Prediction Tasks"☆38Updated 2 years ago
- Stochastic Gradient Langevin Dynamics for Bayesian learning☆30Updated 3 years ago
- PyTorch implementation of the NCDSSM models presented in the ICML '23 paper "Neural Continuous-Discrete State Space Models for Irregularl…☆24Updated last year
- Official code for the ICLR 2021 paper Neural ODE Processes☆71Updated 2 years ago
- Experiments from the paper "On Second Order Behaviour in Augmented Neural ODEs"☆57Updated 5 months ago
- Personal implementation of "Variational Inference with Normalizing Flows" by [Rezende, et al., 2015] in PyTorch☆22Updated 5 years ago
- code for "Neural Jump Ordinary Differential Equations"☆29Updated 2 years ago
- Code used for the AAAI 2020 paper "System Identification with Time-Aware Neural Sequence Models"☆17Updated 5 years ago
- AISTATS paper 'Uncertainty in Neural Networks: Approximately Bayesian Ensembling'☆88Updated 4 years ago
- Methods and experiments for assumed density SDE approximations☆11Updated 3 years ago
- Example code of Sparse Gaussian Process Attention (ICLR 2023)☆24Updated 8 months ago
- ☆15Updated 2 years ago
- Code for paper: SDE-Net: Equipping Deep Neural network with Uncertainty Estimates☆109Updated 4 years ago
- Code for "Function Space Particle Optimization for Bayesian Neural Networks"☆17Updated 2 years ago
- Differentiable ODE solvers with full GPU support and O(1)-memory backpropagation.☆58Updated 4 years ago
- Approximate Inference Turns Deep Networks into Gaussian Processes (dnn2gp)☆48Updated 5 years ago
- Pytorch implementation of neural processes and variants☆27Updated 7 months ago
- 🤿 Implementation of doubly stochastic deep Gaussian Process using GPflow and TensorFlow 2.0☆26Updated last year
- Heterogeneous Multi-output Gaussian Processes☆52Updated 4 years ago
- This repository is the implementation of Deep Dirichlet Process Mixture Models (UAI 2022)☆13Updated 2 years ago
- Training quantile models☆43Updated 3 months ago
- Bayesian Neural Network in PyTorch☆84Updated 10 months ago
- [ICLR 2022] "Bayesian Modeling and Uncertainty Quantification for Learning to Optimize: What, Why, and How" by Yuning You, Yue Cao, Tianl…☆13Updated 2 years ago
- Refining continuous-in-depth neural networks☆39Updated 3 years ago
- A pytorch implementation of MCDO(Monte-Carlo Dropout methods)☆56Updated 6 years ago
- Continual Gaussian Processes☆32Updated last year
- Code for Accelerated Linearized Laplace Approximation for Bayesian Deep Learning (ELLA, NeurIPS 22')☆16Updated 2 years ago
- A PyTorch implementation of "Meta-Amortized Variational Inference and Learning" (https://arxiv.org/abs/1902.01950)☆14Updated 4 years ago
- Continuous-Time Modeling of Counterfactual Outcomes Using Neural Controlled Differential Equations (ICML 2022)☆27Updated 2 years ago
- PyTorch code of "Modeling Continuous Stochastic Processes with Dynamic Normalizing Flows" (NeurIPS 2020)☆48Updated 4 years ago