169 Repositories
Python uncertainty-metrics Libraries
My Solutions to 120 commonly asked data science interview questions.
Data_Science_Interview_Questions Introduction 👋 Here are the answers to 120 Data Science Interview Questions The above answer some is modified based
Symmetry and Uncertainty-Aware Object SLAM for 6DoF Object Pose Estimation
SUO-SLAM This repository hosts the code for our CVPR 2022 paper "Symmetry and Uncertainty-Aware Object SLAM for 6DoF Object Pose Estimation". ArXiv li
On Uncertainty, Tempering, and Data Augmentation in Bayesian Classification
Understanding Bayesian Classification This repository hosts the code to reproduce the results presented in the paper On Uncertainty, Tempering, and Da
(CVPR 2022 - oral) Multi-View Depth Estimation by Fusing Single-View Depth Probability with Multi-View Geometry
Multi-View Depth Estimation by Fusing Single-View Depth Probability with Multi-View Geometry Official implementation of the paper Multi-View Depth Est
Provide baselines and evaluation metrics of the task: traffic flow prediction
Note: This repo is adpoted from https://github.com/UNIMIBInside/Smart-Mobility-Prediction. Due to technical reasons, I did not fork their code. Introd
Repository for "Improving evidential deep learning via multi-task learning," published in AAAI2022
Improving evidential deep learning via multi task learning It is a repository of AAAI2022 paper, “Improving evidential deep learning via multi-task le
Bcc2telegraf: An integration that sends ebpf-based bcc histogram metrics to telegraf daemon
bcc2telegraf bcc2telegraf is an integration that sends ebpf-based bcc histogram
Nflmetrics - Johns Hopkins Spring 2022 Sports Analytics research project about NFL Draft Metrics
nflmetrics GitHub repo for Johns Hopkins Spring 2022 Sports Analytics research p
fair-test is a library to build and deploy FAIR metrics tests APIs supporting the specifications used by the FAIRMetrics working group.
☑️ FAIR test fair-test is a library to build and deploy FAIR metrics tests APIs supporting the specifications used by the FAIRMetrics working group. I
Object detection evaluation metrics using Python.
Object detection evaluation metrics using Python.
Companion code for the paper Theoretical characterization of uncertainty in high-dimensional linear classification
Companion code for the paper Theoretical characterization of uncertainty in high-dimensional linear classification Usage The required packages are lis
Image-to-image regression with uncertainty quantification in PyTorch
Image-to-image regression with uncertainty quantification in PyTorch. Take any dataset and train a model to regress images to images with rigorous, distribution-free uncertainty quantification.
Crowd-Kit is a powerful Python library that implements commonly-used aggregation methods for crowdsourced annotation and offers the relevant metrics and datasets
Crowd-Kit: Computational Quality Control for Crowdsourcing Documentation Crowd-Kit is a powerful Python library that implements commonly-used aggregat
A Bayesian cognition approach for belief updating of correlation judgement through uncertainty visualizations
Overview Code and supplemental materials for Karduni et al., 2020 IEEE Vis. "A Bayesian cognition approach for belief updating of correlation judgemen
SweiNet is an uncertainty-quantifying shear wave speed (SWS) estimator for ultrasound shear wave elasticity (SWE) imaging.
SweiNet SweiNet is an uncertainty-quantifying shear wave speed (SWS) estimator for ultrasound shear wave elasticity (SWE) imaging. SweiNet takes as in
DimReductionClustering - Dimensionality Reduction + Clustering + Unsupervised Score Metrics
Dimensionality Reduction + Clustering + Unsupervised Score Metrics Introduction
Projecting interval uncertainty through the discrete Fourier transform
Projecting interval uncertainty through the discrete Fourier transform This repo
Automatically capture your Ookla Speedtest metrics and display them in a Grafana dashboard
Speedtest All-In-One Automatically capture your Ookla Speedtest metrics and display them in a Grafana dashboard. Getting Started About This Code This
FAIR Enough Metrics is an API for various FAIR Metrics Tests, written in python
☑️ FAIR Enough metrics for research FAIR Enough Metrics is an API for various FAIR Metrics Tests, written in python, conforming to the specifications
TensorFlow implementation for Bayesian Modeling and Uncertainty Quantification for Learning to Optimize: What, Why, and How
Bayesian Modeling and Uncertainty Quantification for Learning to Optimize: What, Why, and How TensorFlow implementation for Bayesian Modeling and Unce
The open-source and free to use Python package miseval was developed to establish a standardized medical image segmentation evaluation procedure
miseval: a metric library for Medical Image Segmentation EVALuation The open-source and free to use Python package miseval was developed to establish
Official repository for the paper "On Evaluation Metrics for Graph Generative Models"
On Evaluation Metrics for Graph Generative Models Authors: Rylee Thompson, Boris Knyazev, Elahe Ghalebi, Jungtaek Kim, Graham Taylor This is the offic
Natural Posterior Network: Deep Bayesian Predictive Uncertainty for Exponential Family Distributions
Natural Posterior Network This repository provides the official implementation o
On Evaluation Metrics for Graph Generative Models
On Evaluation Metrics for Graph Generative Models Authors: Rylee Thompson, Boris Knyazev, Elahe Ghalebi, Jungtaek Kim, Graham Taylor This is the offic
A collection of automation aids to connect various database systems into Lookout for Metrics
A collection of automation aids to connect various database systems into Lookout for Metrics
Add you own metrics to your celery backend
Add you own metrics to your celery backend
Data collection, enhancement, and metrics calculation.
l3_data_collection Data collection, enhancement, and metrics calculation. Summary Repository containing code for QuantDAO's JDT data collection task.
📚 A collection of all the Deep Learning Metrics that I came across which are not accuracy/loss.
📚 A collection of all the Deep Learning Metrics that I came across which are not accuracy/loss.
A Python application that helps users determine their calorie intake, and automatically generates customized weekly meal and workout plans based on metrics computed using their physical parameters
A Python application that helps users determine their calorie intake, and automatically generates customized weekly meal and workout plans based on metrics computed using their physical parameters
Data-Uncertainty Guided Multi-Phase Learning for Semi-supervised Object Detection
An official implementation of paper Data-Uncertainty Guided Multi-Phase Learning for Semi-supervised Object Detection
Face uncertainty quantification or estimation using PyTorch.
Face-uncertainty-pytorch This is a demo code of face uncertainty quantification or estimation using PyTorch. The uncertainty of face recognition is af
Semantic similarity computation with different state-of-the-art metrics
Semantic similarity computation with different state-of-the-art metrics Description • Installation • Usage • License Description TaxoSS is a semantic
Whisper is a file-based time-series database format for Graphite.
Whisper Overview Whisper is one of three components within the Graphite project: Graphite-Web, a Django-based web application that renders graphs and
Pytorch implementations of Bayes By Backprop, MC Dropout, SGLD, the Local Reparametrization Trick, KF-Laplace, SG-HMC and more
Bayesian Neural Networks Pytorch implementations for the following approximate inference methods: Bayes by Backprop Bayes by Backprop + Local Reparame
Most popular metrics used to evaluate object detection algorithms.
Most popular metrics used to evaluate object detection algorithms.
Metrics-advisor - Analyze reshaped metrics from TiDB cluster Prometheus and give some advice about anomalies and correlation.
metrics-advisor Analyze reshaped metrics from TiDB cluster Prometheus and give some advice about anomalies and correlation. Team freedeaths mashenjun
A library for uncertainty quantification based on PyTorch
Torchuq [logo here] TorchUQ is an extensive library for uncertainty quantification (UQ) based on pytorch. TorchUQ currently supports 10 representation
Official PyTorch implementation of "Uncertainty-Based Offline Reinforcement Learning with Diversified Q-Ensemble" (NeurIPS'21)
Uncertainty-Based Offline Reinforcement Learning with Diversified Q-Ensemble This is the code for reproducing the results of the paper Uncertainty-Bas
An efficient PyTorch implementation of the evaluation metrics in recommender systems.
recsys_metrics An efficient PyTorch implementation of the evaluation metrics in recommender systems. Overview • Installation • How to use • Benchmark
Final Project for the CS238: Decision Making Under Uncertainty course at Stanford University in Autumn '21.
Final Project for the CS238: Decision Making Under Uncertainty course at Stanford University in Autumn '21. We optimized wind turbine placement in a wind farm, subject to wake effects, using Q-learning.
C/C++ Dependency Analyzer: a rewrite of John Lakos' dep_utils (adep/cdep/ldep) from
cppdep performs dependency analysis among components/packages/package groups of a large C/C++ project. This is a rewrite of dep_utils(adep/cdep/ldep), which is provided by John Lakos' book "Large-Scale C++ Software Design", Addison Wesley (1996).
A Weakly Supervised Amodal Segmenter with Boundary Uncertainty Estimation
Paper Khoi Nguyen, Sinisa Todorovic "A Weakly Supervised Amodal Segmenter with Boundary Uncertainty Estimation", accepted to ICCV 2021 Our code is mai
Prometheus exporter for metrics from the MyAudi API
Prometheus Audi Exporter This Prometheus exporter exports metrics that it fetches from the MyAudi API. Usage Checkout submodules Install dependencies
Github Traffic Insights as Prometheus metrics.
github-traffic Github Traffic collects your repository's traffic data and exposes it as Prometheus metrics. Grafana dashboard that displays the metric
PyTorch implementation of Uncertainty Estimation via Response Scaling for Pseudo-mask Noise Mitigation in Weakly-supervised Semantic Segmentation
Uncertainty Estimation via Response Scaling for Pseudo-mask Noise Mitigation in Weakly-supervised Semantic Segmentation Introduction This is a PyTorch
Uncertainty Estimation via Response Scaling for Pseudo-mask Noise Mitigation in Weakly-supervised Semantic Segmentation
Uncertainty Estimation via Response Scaling for Pseudo-mask Noise Mitigation in Weakly-supervised Semantic Segmentation Introduction This is a PyTorch
Exports osu! user stats to prometheus metrics for a specified set of users
osu! to prometheus exporter This tool exports osu! user statistics into prometheus metrics for a specified set of user ids. Just copy the config.json.
Hypernetwork-Ensemble Learning of Segmentation Probability for Medical Image Segmentation with Ambiguous Labels
Hypernet-Ensemble Learning of Segmentation Probability for Medical Image Segmentation with Ambiguous Labels The implementation of Hypernet-Ensemble Le
Leverage Twitter API v2 to analyze tweet metrics such as impressions and profile clicks over time.
Tweetmetric Tweetmetric allows you to track various metrics on your most recent tweets, such as impressions, retweets and clicks on your profile. The
Ballcone is a fast and lightweight server-side Web analytics solution.
Ballcone Ballcone is a fast and lightweight server-side Web analytics solution. It requires no JavaScript on your website. Screenshots Design Goals Si
Code for the paper “The Peril of Popular Deep Learning Uncertainty Estimation Methods”
Uncertainty Estimation Methods Code for the paper “The Peril of Popular Deep Learning Uncertainty Estimation Methods” Reference If you use this code,
ZEBRA: Zero Evidence Biometric Recognition Assessment
ZEBRA: Zero Evidence Biometric Recognition Assessment license: LGPLv3 - please reference our paper version: 2020-06-11 author: Andreas Nautsch (EURECO
The best solution of the Weather Prediction track in the Yandex Shifts challenge
yandex-shifts-weather The repository contains information about my solution for the Weather Prediction track in the Yandex Shifts challenge https://re
django app that allows capture application metrics by each user individually
Django User Metrics django app that allows capture application metrics by each user individually, so after you can generate reports with aggregation o
Generalise Prometheus metrics. takes out server specific, replaces variables and such.
Generalise Prometheus metrics. takes out server specific, replaces variables and such. makes it easier to copy from Prometheus console straight to Grafana.
The evaluator covering all of the metrics required by tasks within the DUE Benchmark.
DUE Evaluator The repository contains the evaluator covering all of the metrics required by tasks within the DUE Benchmark, i.e., set-based F1 (for KI
Graph Posterior Network: Bayesian Predictive Uncertainty for Node Classification (NeurIPS 2021)
Graph Posterior Network This is the official code repository to the paper Graph Posterior Network: Bayesian Predictive Uncertainty for Node Classifica
ICCV2021: Code for 'Spatial Uncertainty-Aware Semi-Supervised Crowd Counting'
ICCV2021: Code for 'Spatial Uncertainty-Aware Semi-Supervised Crowd Counting'
Process GPX files (adding sensor metrics, uploading to InfluxDB, etc.) exported from imxingzhe.com
Xingzhe GPX Processor 行者轨迹处理工具 Xingzhe sells cheap GPS bike meters with sensor support including cadence, heart rate and power. But the GPX files expo
A library of metrics for evaluating recommender systems
recmetrics A python library of evalulation metrics and diagnostic tools for recommender systems. **This library is activly maintained. My goal is to c
A comprehensive set of fairness metrics for datasets and machine learning models, explanations for these metrics, and algorithms to mitigate bias in datasets and models.
AI Fairness 360 (AIF360) The AI Fairness 360 toolkit is an extensible open-source library containg techniques developed by the research community to h
The official implementation of Relative Uncertainty Learning for Facial Expression Recognition
Relative Uncertainty Learning for Facial Expression Recognition The official implementation of the following paper at NeurIPS2021: Title: Relative Unc
Code of Adverse Weather Image Translation with Asymmetric and Uncertainty aware GAN
Adverse Weather Image Translation with Asymmetric and Uncertainty-aware GAN (AU-GAN) Official Tensorflow implementation of Adverse Weather Image Trans
code for our ECCV 2020 paper "A Balanced and Uncertainty-aware Approach for Partial Domain Adaptation"
Code for our ECCV (2020) paper A Balanced and Uncertainty-aware Approach for Partial Domain Adaptation. Prerequisites: python == 3.6.8 pytorch ==1.1.0
Code accompanying the NeurIPS 2021 paper "Generating High-Quality Explanations for Navigation in Partially-Revealed Environments"
Generating High-Quality Explanations for Navigation in Partially-Revealed Environments This work presents an approach to explainable navigation under
Official implementation of "Robust channel-wise illumination estimation"
This repository provides the official implementation of "Robust channel-wise illumination estimation." accepted in BMVC (2021).
Hubble - Network, Service & Security Observability for Kubernetes using eBPF
Network, Service & Security Observability for Kubernetes What is Hubble? Getting Started Features Service Dependency Graph Metrics & Monitoring Flow V
Fluency ENhanced Sentence-bert Evaluation (FENSE), metric for audio caption evaluation. And Benchmark dataset AudioCaps-Eval, Clotho-Eval.
FENSE The metric, Fluency ENhanced Sentence-bert Evaluation (FENSE), for audio caption evaluation, proposed in the paper "Can Audio Captions Be Evalua
Gathers data and displays metrics related to climate change and resource depletion on a PowerBI report.
Apocalypse Status Dashboard Purpose Climate change and resource depletion are grave long-term dangers. The code in this repository will pull data from
Metrics to evaluate quality and efficacy of synthetic datasets.
An Open Source Project from the Data to AI Lab, at MIT Metrics for Synthetic Data Generation Projects Website: https://sdv.dev Documentation: https://
AugMix: A Simple Data Processing Method to Improve Robustness and Uncertainty
AugMix Introduction We propose AugMix, a data processing technique that mixes augmented images and enforces consistent embeddings of the augmented ima
Neural network graphs and training metrics for PyTorch, Tensorflow, and Keras.
HiddenLayer A lightweight library for neural network graphs and training metrics for PyTorch, Tensorflow, and Keras. HiddenLayer is simple, easy to ex
MRQy is a quality assurance and checking tool for quantitative assessment of magnetic resonance imaging (MRI) data.
Front-end View Backend View Table of Contents Description Prerequisites Running Basic Information Measurements User Interface Feedback and usage Descr
reXmeX is recommender system evaluation metric library.
A general purpose recommender metrics library for fair evaluation.
The project covers common metrics for super-resolution performance evaluation.
Super-Resolution Performance Evaluation Code The project covers common metrics for super-resolution performance evaluation. Metrics support The script
Metrinome is an all-purpose tool for working with code complexity metrics.
Overview Metrinome is an all-purpose tool for working with code complexity metrics. It can be used as both a REPL and API, and includes: Converters to
A System Metrics Monitoring Tool Built using Python3 , rabbitmq,Grafana and InfluxDB. Setup using docker compose. Use to monitor system performance with graphical interface of grafana , storage of influxdb and message queuing of rabbitmq
SystemMonitoringRabbitMQGrafanaInflux This repository has code to setup a system monitoring tool The tools used are the follows Python3.6 Docker Rabbi
A compact version of EDI-Vetter, which uses the TLS output to quickly vet transit signals.
A compact version of EDI-Vetter, which uses the TLS output to quickly vet transit signals. All your favorite hits in a simplified format.
HHP-Net: A light Heteroscedastic neural network for Head Pose estimation with uncertainty
HHP-Net: A light Heteroscedastic neural network for Head Pose estimation with uncertainty Giorgio Cantarini, Francesca Odone, Nicoletta Noceti, Federi
Elucidating Robust Learning with Uncertainty-Aware Corruption Pattern Estimation
Elucidating Robust Learning with Uncertainty-Aware Corruption Pattern Estimation Introduction 📋 Official implementation of Explainable Robust Learnin
Regression Metrics Calculation Made easy for tensorflow2 and scikit-learn
Regression Metrics Installation To install the package from the PyPi repository you can execute the following command: pip install regressionmetrics I
Using Streamlit to host a multi-page tool with model specs and classification metrics, while also accepting user input values for prediction.
Predicitng_viability Using Streamlit to host a multi-page tool with model specs and classification metrics, while also accepting user input values for
Cloudkeeper is “housekeeping for clouds” - find leaky resources, manage quota limits, detect drift and clean up.
Cloudkeeper Housekeeping for Clouds! Table of contents Overview Docker based quick start Cloning this repository Component list Contact License Overvi
Regression Metrics Calculation Made easy
Regression Metrics Mean Absolute Error Mean Square Error Root Mean Square Error Root Mean Square Logarithmic Error Root Mean Square Logarithmic Error
Data, model training, and evaluation code for "PubTables-1M: Towards a universal dataset and metrics for training and evaluating table extraction models".
PubTables-1M This repository contains training and evaluation code for the paper "PubTables-1M: Towards a universal dataset and metrics for training a
Attack on Confidence Estimation algorithm from the paper "Disrupting Deep Uncertainty Estimation Without Harming Accuracy"
Attack on Confidence Estimation (ACE) This repository is the official implementation of "Disrupting Deep Uncertainty Estimation Without Harming Accura
Code for the paper "Regularizing Variational Autoencoder with Diversity and Uncertainty Awareness"
DU-VAE This is the pytorch implementation of the paper "Regularizing Variational Autoencoder with Diversity and Uncertainty Awareness" Acknowledgement
Building blocks for uncertainty-aware cycle consistency presented at NeurIPS'21.
UncertaintyAwareCycleConsistency This repository provides the building blocks and the API for the work presented in the NeurIPS'21 paper Robustness vi
Fairness Metrics: All you need to know
Fairness Metrics: All you need to know Testing machine learning software for ethical bias has become a pressing current concern. Recent research has p
Relative Uncertainty Learning for Facial Expression Recognition
Relative Uncertainty Learning for Facial Expression Recognition The official implementation of the following paper at NeurIPS2021: Title: Relative Unc
High-fidelity performance metrics for generative models in PyTorch
High-fidelity performance metrics for generative models in PyTorch
Script to quickly get the metrics from Github repos to analyze.
commit-prefix-analysis Script to quickly get the metrics from Github repos to analyze. Setup Install the Github CLI. You'll know its working when runn
Building blocks for uncertainty-aware cycle consistency presented at NeurIPS'21.
UncertaintyAwareCycleConsistency This repository provides the building blocks and the API for the work presented in the NeurIPS'21 paper Robustness vi
This is the 3D Implementation of 《Inconsistency-aware Uncertainty Estimation for Semi-supervised Medical Image Segmentation》
CoraNet This is the 3D Implementation of 《Inconsistency-aware Uncertainty Estimation for Semi-supervised Medical Image Segmentation》 Environment pytor
BEAMetrics: Benchmark to Evaluate Automatic Metrics in Natural Language Generation
BEAMetrics: Benchmark to Evaluate Automatic Metrics in Natural Language Generation Installing The Dependencies $ conda create --name beametrics python
Continual reinforcement learning baselines: experiment specifications, implementation of existing methods, and common metrics. Easily extensible to new methods.
Continual Reinforcement Learning This repository provides a simple way to run continual reinforcement learning experiments in PyTorch, including evalu
Source code for "Taming Visually Guided Sound Generation" (Oral at the BMVC 2021)
Taming Visually Guided Sound Generation • [Project Page] • [ArXiv] • [Poster] • • Listen for the samples on our project page. Overview We propose to t
rliable is an open-source Python library for reliable evaluation, even with a handful of runs, on reinforcement learning and machine learnings benchmarks.
Open-source library for reliable evaluation on reinforcement learning and machine learning benchmarks. See NeurIPS 2021 oral for details.
Exploring the link between uncertainty estimates obtained via "exact" Bayesian inference and out-of-distribution (OOD) detection.
Uncertainty-based OOD detection Exploring the link between uncertainty estimates obtained by "exact" Bayesian inference and out-of-distribution (OOD)
A fast implementation of bss_eval metrics for blind source separation
fast_bss_eval Do you have a zillion BSS audio files to process and it is taking days ? Is your simulation never ending ? Fear no more! fast_bss_eval i