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12 search results found

Transformers Interpret ⭐ 1,090

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

Cnn Exposed ⭐ 157

🕵️‍♂️ Interpreting Convolutional Neural Network (CNN) Results.

Hierarchical Dnn Interpretations ⭐ 119

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

Pytorch_explain ⭐ 80

PyTorch Explain: Logic Explained Networks in Python.

Deep Explanation Penalization ⭐ 74

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

Neuro Symbolic Sudoku Solver ⭐ 44

⚙️ Solving sudoku using Deep Reinforcement learning in combination with powerful symbolic representations.

Logic_explained_networks ⭐ 43

Logic Explained Networks is a python repository implementing explainable-by-design deep learning models.

Torchprism ⭐ 40

Principal Image Sections Mapping. Convolutional Neural Network Visualisation and Explanation Framework

Finding semantically meaningful and accurate prompts.

Global Attribution Mapping ⭐ 31

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

Ddsm Visual Primitives ⭐ 31

Using deep learning to discover interpretable representations for mammogram classification and explanation

Repository for our NeurIPS 2022 paper "Concept Embedding Models: Beyond the Accuracy-Explainability Trade-Off"

GitHub repository for DORA: Data-agnOstic Representation Analysis paper. DORA allows to find outlier representations in Deep Neural Networks.

Modeling Uncertainty Local Explainability ⭐ 16

Local explanations with uncertainty 💐!

Dlime_experiments ⭐ 16

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).

This repository contains the implementation of SimplEx, a method to explain the latent representations of black-box models with the help of a corpus of examples. For more details, please read our NeurIPS 2021 paper: 'Explaining Latent Representations with a Corpus of Examples'.

Label Free Xai ⭐ 10

This repository contains the implementation of Label-Free XAI, a new framework to adapt explanation methods to unsupervised models. For more details, please read our ICML 2022 paper: 'Label-Free Explainability for Unsupervised Models'.

Malaria Detection Using Deep Learning Techniques ⭐ 9

Malaria Parasite Detection using Efficient Neural Ensembles. Malaria, a life threatening disease caused by the bite of the Anopheles mosquito infected with the parasite, has been a major burden towards healthcare for years leading to approximately 400,000 deaths globally every year. This study aims to build an efficient system by applying ensemble techniques based on deep learning to automate the detection of the parasite using whole slide images of thin blood smears.

Calimocho ⭐ 5

Explanatory Interactive Machine Learning with Self-explaining Neural Networks

Comprehensible Convolutional Neural Networks via Guided Concept Learning

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