suinleelab / attributionpriors
Tools for training explainable models using attribution priors.
☆123Updated 4 years ago
Alternatives and similar repositories for attributionpriors:
Users that are interested in attributionpriors are comparing it to the libraries listed below
- Code for using CDEP from the paper "Interpretations are useful: penalizing explanations to align neural networks with prior knowledge" ht…☆127Updated 4 years ago
- Using / reproducing ACD from the paper "Hierarchical interpretations for neural network predictions" 🧠 (ICLR 2019)☆128Updated 3 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
- A repository for explaining feature attributions and feature interactions in deep neural networks.☆186Updated 3 years ago
- Algorithms for abstention, calibration and domain adaptation to label shift.☆36Updated 4 years ago
- Code for the paper "Calibrating Deep Neural Networks using Focal Loss"☆160Updated last year
- ☆133Updated 5 years ago
- ☆125Updated 3 years ago
- ☆51Updated 4 years ago
- A lightweight implementation of removal-based explanations for ML models.☆59Updated 3 years ago
- Code for ICML 2018 paper on "Fast and Scalable Bayesian Deep Learning by Weight-Perturbation in Adam" by Khan, Nielsen, Tangkaratt, Lin, …☆113Updated 6 years ago
- Contains code for the NeurIPS 2019 paper "Practical Deep Learning with Bayesian Principles"☆244Updated 5 years ago
- Code/figures in Right for the Right Reasons☆55Updated 4 years ago
- Codes for reproducing the contrastive explanation in “Explanations based on the Missing: Towards Contrastive Explanations with Pertinent…☆54Updated 6 years ago
- Repo for the Tutorials of Day1-Day3 of the Nordic Probabilistic AI School 2021 (https://probabilistic.ai/)☆48Updated 3 years ago
- Reusable BatchBALD implementation☆78Updated last year
- Bayesianize: A Bayesian neural network wrapper in pytorch☆88Updated 10 months 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
- Pitfalls of In-Domain Uncertainty Estimation and Ensembling in Deep Learning, ICLR 2020☆235Updated 2 years ago
- A PyTorch library for two-sample tests☆238Updated last year
- Reliable Uncertainty Estimates in Deep Neural Networks using Noise Contrastive Priors☆62Updated 4 years ago
- ☆109Updated 2 years ago
- CHOP: An optimization library based on PyTorch, with applications to adversarial examples and structured neural network training.☆77Updated last year
- Rethinking Bias-Variance Trade-off for Generalization of Neural Networks☆49Updated 4 years ago
- Explaining a black-box using Deep Variational Information Bottleneck Approach☆46Updated 2 years ago
- A way to achieve uniform confidence far away from the training data.☆37Updated 3 years ago
- Toy datasets to evaluate algorithms for domain generalization and invariance learning.☆42Updated 3 years ago
- Codebase for Learning Invariances in Neural Networks☆94Updated 2 years ago
- Self-Explaining Neural Networks☆40Updated 5 years ago
- Simple data balancing baselines for worst-group-accuracy benchmarks.☆42Updated last year