google / TrustScoreLinks
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).
☆177Updated 2 years ago
Alternatives and similar repositories for TrustScore
Users that are interested in TrustScore are comparing it to the libraries listed below
Sorting:
- ☆125Updated 4 years ago
- Using / reproducing ACD from the paper "Hierarchical interpretations for neural network predictions" 🧠 (ICLR 2019)☆129Updated 4 years ago
- ☆135Updated 6 years ago
- This repository contains the full code for the "Towards fairness in machine learning with adversarial networks" blog post.☆119Updated 4 years ago
- Codebase for "Deep Learning for Case-based Reasoning through Prototypes: A Neural Network that Explains Its Predictions" (to appear in AA…☆76Updated 8 years ago
- Algorithms for abstention, calibration and domain adaptation to label shift.☆37Updated 5 years ago
- A lightweight implementation of removal-based explanations for ML models.☆59Updated 4 years ago
- Tools for training explainable models using attribution priors.☆125Updated 4 years ago
- Codes for reproducing the contrastive explanation in “Explanations based on the Missing: Towards Contrastive Explanations with Pertinent…☆54Updated 7 years ago
- Code repository for our paper "Failing Loudly: An Empirical Study of Methods for Detecting Dataset Shift": https://arxiv.org/abs/1810.119…☆107Updated last year
- Code for using CDEP from the paper "Interpretations are useful: penalizing explanations to align neural networks with prior knowledge" ht…☆128Updated 4 years ago
- This is a benchmark to evaluate machine learning local explanaitons quality generated from any explainer for text and image data☆30Updated 4 years ago
- Code/figures in Right for the Right Reasons☆57Updated 5 years ago
- Keras implementation for DASP: Deep Approximate Shapley Propagation (ICML 2019)☆62Updated 6 years ago
- Efficient and Diverse Batch Acquisition for Deep Bayesian Active Learning.☆247Updated last year
- Experiments for AAAI anchor paper☆66Updated 7 years ago
- ☆32Updated 4 years ago
- PyTorch implementation of parity loss as constraints function to realize the fairness of machine learning.☆73Updated 2 years ago
- Calibration library and code for the paper: Verified Uncertainty Calibration. Ananya Kumar, Percy Liang, Tengyu Ma. NeurIPS 2019 (Spotlig…☆152Updated 3 years ago
- Causal Explanation (CXPlain) is a method for explaining the predictions of any machine-learning model.☆132Updated 5 years ago
- A deep learning framework for building multimodal multi-task learning systems.☆112Updated 2 years ago
- Distance Metric Learning Algorithms for Python☆175Updated 4 years ago
- Supervised Local Modeling for Interpretability☆29Updated 7 years ago
- (ICML 2021) Mandoline: Model Evaluation under Distribution Shift☆30Updated 4 years ago
- DeViSE model (zero-shot learning) trained on ImageNet and deployed on AWS using Docker☆47Updated 6 years ago
- Modular Python Toolbox for Fairness, Accountability and Transparency Forensics☆76Updated 2 years ago
- ☆44Updated 5 years ago
- An implementation of MixMatch with PyTorch☆36Updated 4 years ago
- Active and Sample-Efficient Model Evaluation☆26Updated 7 months ago
- Code for the paper "Calibrating Deep Neural Networks using Focal Loss"☆161Updated 2 years ago