👽 Out-of-Distribution Detection with PyTorch

Model Agnostic Confidence Estimator (MACEST) - A Python library for calibrating Machine Learning models' confidence scores

A list of papers that studies out-of-distribution (OOD) detection and misclassification detection (MisD)

📈 SiRE (Simulation-Informed Revenue Extrapolation with Confidence Estimate for Scaleup Companies Using Scarce Time-Series Data), accepted by CIKM'2022 🗽

Learning from scratch a confidence measure

PyTorch implementation of our ECCV 2022 paper "Rethinking Confidence Calibration for Failure Prediction"

Official pytorch implementation of the paper [Adaptive confidence thresholding for monocular depth estimation]

Confidence Estimation for Black Box Automatic Speech Recognition Systems Using Lattice Recurrent Neural Networks https://arxiv.org/abs/1910.11933 or https://ieeexplore.ieee.org/document/9053264

Free WordPress Plugin: This sample size calculator enables you to calculate the minimum sample size and the margin of error. Learn about sample size, the margin of error, & confidence interval. www.calculator.io/sample-size-calculator/

Demo code for GACE: Geometry Aware Confidence Enhancement

This repo contains code to perform Bootstrap Confidence Intervals estimation (a.k.a. Monte Carlo Confidence Interval or Empirical Confidence Interval estimation) for Machine Learing models.

Benchmark for "Offline Policy Comparison with Confidence"

A Robustness-based Confidence Measure for Hybrid System Falsification

Code for "Confidence-Driven Hierarchical Classification of Cultivated Plant Stresses"

In recent years, the ability to assess the uncertainty of depth estimates in the context of dense stereo matching has received increased attention due to its potential to detect erroneous estimates. Especially, the introduction of deep learning approaches greatly improved general performance, with feature extraction from multiple modalities prov…

Computation of Reliability Statistics: Reliability, Confidence, Assurance

Source code for predicting confidence scores for the samples in t-sne embeddings.

number of times an experiment should be repeated for a 95% probability

Simple evaluation of classification confidence intervals.