79 Open source projects that are alternatives of or similar to Contrastiveexplanation

Moore Machine Networks (MMN): Learning Finite-State Representations of Recurrent Policy Networks

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Model explainability that works seamlessly with 🤗 transformers. Explain your transformers model in just 2 lines of code.

Model interpretability and understanding for PyTorch

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

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Visualization toolkit for neural networks in PyTorch! Demo -->

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Data generator for Arena - interactive XAI dashboard

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Code for using CDEP from the paper "Interpretations are useful: penalizing explanations to align neural networks with prior knowledge" https://arxiv.org/abs/1909.13584

A curated list of awesome machine learning interpretability resources.

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

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Global-Local Capsule Network (GLCapsNet) is a capsule-based architecture able to provide context-based eye fixation prediction for several autonomous driving scenarios, while offering interpretability both globally and locally.

Interesting resources related to XAI (Explainable Artificial Intelligence)

Code for AAAI 2018 accepted paper: "Improving the Adversarial Robustness and Interpretability of Deep Neural Networks by Regularizing their Input Gradients"

Code for SPINE - Sparse Interpretable Neural Embeddings. Jhamtani H.*, Pruthi D.*, Subramanian A.*, Berg-Kirkpatrick T., Hovy E. AAAI 2018

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Algorithms for monitoring and explaining machine learning models

Interesting resources related to Explainable Artificial Intelligence, Interpretable Machine Learning, Interactive Machine Learning, Human in Loop and Visual Analytics.

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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.

Explainable AI framework for data scientists. Explain & debug any blackbox machine learning model with a single line of code.

A collection of state-of-the-art algorithms for the training, serving and interpretation of Decision Forest models.

Interpretable ML package 🔍 for concise, transparent, and accurate predictive modeling (sklearn-compatible).

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ProtoTrees: Neural Prototype Trees for Interpretable Fine-grained Image Recognition, published at CVPR2021

Codebase for "Demystifying Black-box Models with Symbolic Metamodels", NeurIPS 2019.

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