A game theoretic approach to explain the output of any machine learning model.
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Updated
Aug 19, 2024 - Jupyter Notebook
A game theoretic approach to explain the output of any machine learning model.
A curated list of awesome open source libraries to deploy, monitor, version and scale your machine learning
Fit interpretable models. Explain blackbox machine learning.
Responsible AI Toolbox is a suite of tools providing model and data exploration and assessment user interfaces and libraries that enable a better understanding of AI systems. These interfaces and libraries empower developers and stakeholders of AI systems to develop and monitor AI more responsibly, and take better data-driven actions.
🔅 Shapash: User-friendly Explainability and Interpretability to Develop Reliable and Transparent Machine Learning Models
[CVPR 2021] Official PyTorch implementation for Transformer Interpretability Beyond Attention Visualization, a novel method to visualize classifications by Transformer based networks.
XAI - An eXplainability toolbox for machine learning
Making decision trees competitive with neural networks on CIFAR10, CIFAR100, TinyImagenet200, Imagenet
[ICCV 2021- Oral] Official PyTorch implementation for Generic Attention-model Explainability for Interpreting Bi-Modal and Encoder-Decoder Transformers, a novel method to visualize any Transformer-based network. Including examples for DETR, VQA.
Power Tools for AI Engineers With Deadlines
Visualization toolkit for neural networks in PyTorch! Demo -->
Official implementation of Score-CAM in PyTorch
Can we use explanations to improve hate speech models? Our paper accepted at AAAI 2021 tries to explore that question.
Papers about explainability of GNNs
CARLA: A Python Library to Benchmark Algorithmic Recourse and Counterfactual Explanation Algorithms
P-NET, Biologically informed deep neural network for prostate cancer classification and discovery
Explainable AI framework for data scientists. Explain & debug any blackbox machine learning model with a single line of code. We are looking for co-authors to take this project forward. Reach out @ ms8909@nyu.edu
Neural network visualization toolkit for tf.keras
💡 Adversarial attacks on explanations and how to defend them
Causal Explanation (CXPlain) is a method for explaining the predictions of any machine-learning model.
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