google / TrustScore
To Trust Or Not To Trust A Classifier. A measure of uncertainty for any trained (possibly black-box) classifier which is more effective than the classifier's own implied confidence (e.g. softmax probability for a neural network).
☆175Updated 2 years ago
Alternatives and similar repositories for TrustScore:
Users that are interested in TrustScore are comparing it to the libraries listed below
- ☆125Updated 3 years ago
- ☆134Updated 5 years ago
- Using / reproducing ACD from the paper "Hierarchical interpretations for neural network predictions" 🧠 (ICLR 2019)☆128Updated 3 years ago
- Codebase for "Deep Learning for Case-based Reasoning through Prototypes: A Neural Network that Explains Its Predictions" (to appear in AA…☆74Updated 7 years ago
- This repository contains the full code for the "Towards fairness in machine learning with adversarial networks" blog post.☆117Updated 4 years ago
- Algorithms for abstention, calibration and domain adaptation to label shift.☆36Updated 4 years ago
- This project contains code for paper Ksenia Konyushkova, Raphael Sznitman, Pascal Fua 'Learning Active Learning from Data', NIPS 2017☆87Updated 2 years ago
- OD-test: A Less Biased Evaluation of Out-of-Distribution (Outlier) Detectors (PyTorch)☆62Updated last year
- Tools for training explainable models using attribution priors.☆123Updated 4 years ago
- ☆264Updated 5 years ago
- Keras implementation for DASP: Deep Approximate Shapley Propagation (ICML 2019)☆61Updated 5 years ago
- Towards Automatic Concept-based Explanations☆159Updated 11 months ago
- Code for using CDEP from the paper "Interpretations are useful: penalizing explanations to align neural networks with prior knowledge" ht…☆127Updated 4 years ago
- code release for Representer point Selection for Explaining Deep Neural Network in NeurIPS 2018☆67Updated 3 years ago
- An implementation of MixMatch with PyTorch☆36Updated 4 years ago
- Calibration library and code for the paper: Verified Uncertainty Calibration. Ananya Kumar, Percy Liang, Tengyu Ma. NeurIPS 2019 (Spotlig…☆146Updated 2 years ago
- Experiments for AAAI anchor paper☆63Updated 7 years ago
- Supervised Local Modeling for Interpretability☆28Updated 6 years ago
- Code repository for our paper "Failing Loudly: An Empirical Study of Methods for Detecting Dataset Shift": https://arxiv.org/abs/1810.119…☆104Updated last year
- Python codes for weakly-supervised learning☆123Updated 4 years ago
- A lightweight implementation of removal-based explanations for ML models.☆59Updated 3 years ago
- Contains code for the NeurIPS 2019 paper "Practical Deep Learning with Bayesian Principles"☆244Updated 5 years ago
- Gold Loss Correction☆87Updated 6 years ago
- Combating hidden stratification with GEORGE☆63Updated 3 years ago
- This is a benchmark to evaluate machine learning local explanaitons quality generated from any explainer for text and image data☆30Updated 3 years ago
- Efficient and Diverse Batch Acquisition for Deep Bayesian Active Learning.☆238Updated 9 months ago
- This is a public collection of papers related to machine learning model interpretability.☆26Updated 3 years ago
- Code/figures in Right for the Right Reasons☆55Updated 4 years ago
- Code for "Evaluating Explainable AI: Which Algorithmic Explanations Help Users Predict Model Behavior?"☆46Updated last year
- Code and data for the experiments in "On Fairness and Calibration"☆51Updated 2 years ago