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interpretability

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EthicalML / awesome-production-machine-learning

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A curated list of awesome open source libraries to deploy, monitor, version and scale your machine learning

machine-learning data-mining privacy deep-learning interpretability privacy-preserving production-machine-learning mlops privacy-preserving-machine-learning explainability responsible-ai responsible-ml machine-learning-operations ml-ops ml-operations privacy-preserving-ml large-scale-ml production-ml large-scale-machine-learning
  • Updated Aug 19, 2020

stellargraph / stellargraph

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Consolidate example graphs in unit tests

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adocherty
adocherty commented Nov 27, 2019

Description

Currently our unit tests are disorganized and each test creates example StellarGraph graphs in different or similar ways with no sharing of this code.

This issue is to improve the unit tests by making functions to create example graphs available to all unit tests by, for example, making them pytest fixtures at the top level of the tests (see https://docs.pytest.org/en/latest/

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good first issue sg-library

shubhomoydas / ad_examples

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A collection of anomaly detection methods (iid/point-based, graph and time series) including active learning for anomaly detection/discovery, bayesian rule-mining, description for diversity/explanation/interpretability. Analysis of incorporating label feedback with ensemble and tree-based detectors. Includes adversarial attacks with Graph Convolutional Network.

streaming timeseries time-series lstm generative-adversarial-network gan rnn autoencoder ensemble-learning trees active-learning concept-drift graph-convolutional-networks interpretability anomaly-detection adversarial-attacks explaination anogan unsuperivsed nettack
  • Updated Jul 27, 2020
  • Python

jphall663 / interpretable_machine_learning_with_python

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Examples of techniques for training interpretable ML models, explaining ML models, and debugging ML models for accuracy, discrimination, and security.

python data-science machine-learning data-mining h2o gradient-boosting-machine transparency decision-tree fairness lime accountability interpretability interpretable-ai interpretable-ml xai fatml interpretable interpretable-machine-learning iml machine-learning-interpretability
  • Updated Aug 11, 2020
  • Jupyter Notebook
pyss3

sergioburdisso / pyss3

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A Python package implementing a new model for text classification with visualization tools for Explainable AI :octocat:

nlp machine-learning natural-language-processing text-mining data-mining text-classification machine-learning-algorithms artificial-intelligence document-classification sentence-classification interpretability multilabel-classification explainable-artificial-intelligence interpretable-ml xai interpretable-machine-learning document-categorization early-classification text-labeling ss3-classifier
  • Updated Aug 19, 2020
  • Python

chaoyanghe / Awesome-Federated-Learning

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A collection of research papers categorized into broad topics in federated learning.

machine-learning privacy computer-vision semi-supervised-learning transfer-learning wireless-communication distributed-optimization interpretability neural-architecture-search federated-learning continual-learning vertical-federated-learning non-iid decentralized-federated-learning hierarchical-federated-learning adversarial-attack-and-defense communication-efficiency straggler-problem computation-efficiency incentive-mechanism
  • Updated Jul 18, 2020
hierarchical-dnn-interpretations

csinva / hierarchical-dnn-interpretations

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Using / reproducing ACD (ICLR 2019) from the paper "Hierarchical interpretations for neural network predictions"

python data-science machine-learning statistics deep-neural-networks ai deep-learning neural-network jupyter-notebook ml pytorch artificial-intelligence convolutional-neural-networks acd interpretation interpretability feature-importance explainable-ai iclr2019 explainability
  • Updated Jul 23, 2020
  • Jupyter Notebook

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