Lightning-UQ-Box: Uncertainty Quantification for Neural Networks with PyTorch and Lightning
-
Updated
Jun 11, 2024 - Python
Lightning-UQ-Box: Uncertainty Quantification for Neural Networks with PyTorch and Lightning
General-purpose library for fitting models to data with correlated Gaussian-distributed noise
Uncertainty Toolbox: a Python toolbox for predictive uncertainty quantification, calibration, metrics, and visualization
Gaussian Processes for Experimental Sciences
A collection of Methods and Models for various architectures of Artificial Neural Networks
Bayesian Neural Network in PyTorch
Bayesian Convolutional Neural Network with Variational Inference based on Bayes by Backprop in PyTorch.
Awesome resources on normalizing flows.
On the Effects of Quantisation on Model Uncertainty in Bayesian Neural Networks
Open Source Photometric classification https://supernnova.readthedocs.io
A library for Bayesian neural network layers and uncertainty estimation in Deep Learning extending the core of PyTorch
Bayesian deep learning for remaining useful life estimation via Stein variational gradient descent
Code for the research paper Meta-learning with hierarchical models based on similarity of causal mechanisms
Reimplementation of Sparse Variational Dropout in Keras-Core/Keras 3.0
ComBiNet: Compact Convolutional Bayesian Neural Network for Image Segmentation
Information Bound and its Applications in Bayesian Neural Networks
A simple and extensible library to create Bayesian Neural Network layers on PyTorch.
AIS algorithm for BNN inference
[ACM MM 2020] Uncertainty-based Traffic Accident Anticipation
FPGA-based hardware acceleration for dropout-based Bayesian Neural Networks.
Add a description, image, and links to the bayesian-neural-networks topic page so that developers can more easily learn about it.
To associate your repository with the bayesian-neural-networks topic, visit your repo's landing page and select "manage topics."