Time series forecasting with PyTorch
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Updated
Jun 17, 2024 - Python
Time series forecasting with PyTorch
Sensitivity Analysis Library in Python. Contains Sobol, Morris, FAST, and other methods.
Uncertainty Toolbox: a Python toolbox for predictive uncertainty quantification, calibration, metrics, and visualization
Learn fast, scalable, and calibrated measures of uncertainty using neural networks!
A 3D vision library from 2D keypoints: monocular and stereo 3D detection for humans, social distancing, and body orientation.
This repo contains a PyTorch implementation of the paper: "Evidential Deep Learning to Quantify Classification Uncertainty"
Uncertainty Quantification 360 (UQ360) is an extensible open-source toolkit that can help you estimate, communicate and use uncertainty in machine learning model predictions.
(ICCV 2019) Uncertainty-aware Face Representation and Recognition
A Library for Uncertainty Quantification.
"In Defense of Pseudo-Labeling: An Uncertainty-Aware Pseudo-label Selection Framework for Semi-Supervised Learning" by Mamshad Nayeem Rizve, Kevin Duarte, Yogesh S Rawat, Mubarak Shah (ICLR 2021)
"What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?", NIPS 2017 (unofficial code).
PyTorch Implementation of QuickNAT and Bayesian QuickNAT, a fast brain MRI segmentation framework with segmentation Quality control using structure-wise uncertainty
My implementation of the paper "Simple and Scalable Predictive Uncertainty estimation using Deep Ensembles"
A collection of Wells/Drilling Engineering tools, focused on well trajectory planning for the time being.
Self-Supervised Learning for OOD Detection (NeurIPS 2019)
Official pytorch implementation of the paper "Bayesian Meta-Learning for the Few-Shot Setting via Deep Kernels" (NeurIPS 2020)
Airport Surface Simulator and Evaluation Tool 2
Sampling nuclear data and uncertainty
This repository contains the code used in the paper: A high-resolution canopy height model of the Earth. Here, we developed a model to estimate canopy top height anywhere on Earth. The model estimates canopy top height for every Sentinel-2 image pixel and was trained using sparse GEDI LIDAR data as a reference.
Density estimation using Gaussian mixtures in the presence of noisy, heterogeneous and incomplete data
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