Alternatives and similar repositories for MMD-critic:

Users that are interested in MMD-critic are comparing it to the libraries listed below

• dtak / rrr
  Code/figures in Right for the Right Reasons
  ☆55Updated 4 years ago
• laura-rieger / deep-explanation-penalization
  Code for using CDEP from the paper "Interpretations are useful: penalizing explanations to align neural networks with prior knowledge" ht…
  ☆128Updated 3 years ago
• csinva / hierarchical-dnn-interpretations
  Using / reproducing ACD from the paper "Hierarchical interpretations for neural network predictions" 🧠 (ICLR 2019)
  ☆128Updated 3 years ago
• marcotcr / anchor-experiments
  Experiments for AAAI anchor paper
  ☆62Updated 6 years ago
• google-research-datasets / bam
  ☆49Updated 4 years ago
• Jianbo-Lab / L2X
  ☆124Updated 3 years ago
• OscarcarLi / PrototypeDL
  Codebase for "Deep Learning for Case-based Reasoning through Prototypes: A Neural Network that Explains Its Predictions" (to appear in AA…
  ☆73Updated 7 years ago
• gpleiss / equalized_odds_and_calibration
  Code and data for the experiments in "On Fairness and Calibration"
  ☆50Updated 2 years ago
• chihkuanyeh / Representer_Point_Selection
  code release for Representer point Selection for Explaining Deep Neural Network in NeurIPS 2018
  ☆67Updated 3 years ago
• kundajelab / abstention
  Algorithms for abstention, calibration and domain adaptation to label shift.
  ☆36Updated 4 years ago
• GDPlumb / MAPLE
  Supervised Local Modeling for Interpretability
  ☆28Updated 6 years ago
• 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 t…
  ☆174Updated last year
• yaringal / acquisition_example
  Train a simple convnet on the MNIST dataset and evaluate the BALD acquisition function
  ☆16Updated 7 years ago
• marcoancona / DASP
  Keras implementation for DASP: Deep Approximate Shapley Propagation (ICML 2019)
  ☆60Updated 5 years ago
• amiratag / InterpretationFragility
  Interpretation of Neural Network is Fragile
  ☆36Updated 8 months ago
• VigneshSrinivasan10 / interprettensor
  ☆99Updated 6 years ago
• suinleelab / attributionpriors
  Tools for training explainable models using attribution priors.
  ☆120Updated 3 years ago
• google-research / heldout-influence-estimation
  ☆62Updated 3 years ago
• jcpeterson / cifar-10h
  Original dataset release for CIFAR-10H
  ☆82Updated 4 years ago
• emtiyaz / vadam
  Code for ICML 2018 paper on "Fast and Scalable Bayesian Deep Learning by Weight-Perturbation in Adam" by Khan, Nielsen, Tangkaratt, Lin, …
  ☆110Updated 6 years ago
• ServiceNow / synbols
  The Synbols dataset generator is a ServiceNow Research project that was started at Element AI.
  ☆43Updated last year
• adebayoj / sanity_checks_saliency
  ☆110Updated 2 years ago
• zzzace2000 / FIDO-saliency
  Explaining Image Classifiers by Counterfactual Generation
  ☆28Updated 2 years ago
• IBM / SIC
  Source code for paper Mroueh, Sercu, Rigotti, Padhi, dos Santos, "Sobolev Independence Criterion", NeurIPS 2019
  ☆14Updated 7 months ago
• BlackHC / batchbald_redux
  Reusable BatchBALD implementation
  ☆74Updated 10 months ago
• algofairness / fairness-comparison
  Comparing fairness-aware machine learning techniques.
  ☆159Updated 2 years ago
• amarasovic / interpretability-literature
  ☆264Updated 5 years ago
• ruthcfong / perturb_explanations
  Code for Fong and Vedaldi 2017, "Interpretable Explanations of Black Boxes by Meaningful Perturbation"
  ☆30Updated 5 years ago
• facebookresearch / InvarianceUnitTests
  Toy datasets to evaluate algorithms for domain generalization and invariance learning.
  ☆42Updated 3 years ago
• d909b / cxplain
  Causal Explanation (CXPlain) is a method for explaining the predictions of any machine-learning model.
  ☆130Updated 4 years ago