62 Open source projects that are alternatives of or similar to ShapleyExplanationNetworks

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

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

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A curated list of awesome machine learning interpretability resources.

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Workshop: Explanation and exploration of machine learning models with R and DALEX at eRum 2020

Fast approximate Shapley values in R

The code of AAAI 2020 paper "Transparent Classification with Multilayer Logical Perceptrons and Random Binarization".

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A Julia package for interpretable machine learning with stochastic Shapley values

In this work, we propose a deterministic version of Local Interpretable Model Agnostic Explanations (LIME) and the experimental results on three different medical datasets shows the superiority for Deterministic Local Interpretable Model-Agnostic Explanations (DLIME).

Many Class Activation Map methods implemented in Pytorch for CNNs and Vision Transformers. Including Grad-CAM, Grad-CAM++, Score-CAM, Ablation-CAM and XGrad-CAM

✂️ Repository for our ICLR 2019 paper: Discovery of Natural Language Concepts in Individual Units of CNNs

[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.

No description or website provided.

Predictive AI layer for existing databases.

ISeeU: Visually interpretable deep learning for mortality prediction inside the ICU

Concept Bottleneck Models, ICML 2020

Library implementing state-of-the-art Concept-based and Disentanglement Learning methods for Explainable AI

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

In this part, I've introduced and experimented with ways to interpret and evaluate models in the field of image. (Pytorch)

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A fast Tsetlin Machine implementation employing bit-wise operators, with MNIST demo.

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Examples of Data Science projects and Artificial Intelligence use cases

(Explainable AI) - Learning Non-Monotonic Logic Programs From Statistical Models Using High-Utility Itemset Mining

Variants of Vision Transformer and its downstream tasks

Explaining the output of machine learning models with more accurately estimated Shapley values

Multislice PHATE for tensor embeddings

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

CARLA: A Python Library to Benchmark Algorithmic Recourse and Counterfactual Explanation Algorithms

Reference tables to introduce and organize evaluation methods and measures for explainable machine learning systems

Papers about explainability of GNNs

Official NumPy Implementation of Deep Networks from the Principle of Rate Reduction (2021)

An awesome & curated list for Artificial General Intelligence, an emerging inter-discipline field that combines artificial intelligence and computational cognitive sciences.

This is a Pytorch implementation of GraphLIME

Visualize BERT's self-attention layers on text classification tasks

GAM (Global Attribution Mapping) explains the landscape of neural network predictions across subpopulations

Techniques & resources for training interpretable ML models, explaining ML models, and debugging ML models.

This Repo containes the implemnetation of generating Guided-GradCAM for 3D medical Imaging using Nifti file in tensorflow 2.0. Different input files can be used in that case need to edit the input to the Guided-gradCAM model.

The official code of Relevance-CAM

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

Sample use case for Xavier AI in Healthcare conference: https://www.xavierhealth.org/ai-summit-day2/

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ExpMRC: Explainability Evaluation for Machine Reading Comprehension

Zennit is a high-level framework in Python using PyTorch for explaining/exploring neural networks using attribution methods like LRP.

This project provides responsible AI user interfaces for Fairlearn, interpret-community, and Error Analysis, as well as foundational building blocks that they rely on.

Essential NLP & ML, short & fast pure Python code

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Explains machine learning models fast using the Anchor algorithm originally proposed by marcotcr in 2018

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Implementation of the paper "Language-agnostic representation learning of source code from structure and context".

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