SHAP FOLD(Explainable AI) - Learning Non-Monotonic Logic Programs From Statistical Models Using High-Utility Itemset Mining
Stars: ✭ 35 (+94.44%)
fastshapFast approximate Shapley values in R
Stars: ✭ 79 (+338.89%)
shaprExplaining the output of machine learning models with more accurately estimated Shapley values
Stars: ✭ 95 (+427.78%)
CARLACARLA: A Python Library to Benchmark Algorithmic Recourse and Counterfactual Explanation Algorithms
Stars: ✭ 166 (+822.22%)
ProtoTreeProtoTrees: Neural Prototype Trees for Interpretable Fine-grained Image Recognition, published at CVPR2021
Stars: ✭ 47 (+161.11%)
mllpThe code of AAAI 2020 paper "Transparent Classification with Multilayer Logical Perceptrons and Random Binarization".
Stars: ✭ 15 (-16.67%)
responsible-ai-toolboxThis project provides responsible AI user interfaces for Fairlearn, interpret-community, and Error Analysis, as well as foundational building blocks that they rely on.
Stars: ✭ 615 (+3316.67%)
dlime experimentsIn 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).
Stars: ✭ 21 (+16.67%)
MindsdbPredictive AI layer for existing databases.
Stars: ✭ 4,199 (+23227.78%)
InterpretFit interpretable models. Explain blackbox machine learning.
Stars: ✭ 4,352 (+24077.78%)
TensorwatchDebugging, monitoring and visualization for Python Machine Learning and Data Science
Stars: ✭ 3,191 (+17627.78%)
ml-fairness-frameworkFairPut - Machine Learning Fairness Framework with LightGBM — Explainability, Robustness, Fairness (by @firmai)
Stars: ✭ 59 (+227.78%)
DIGA library for graph deep learning research
Stars: ✭ 1,078 (+5888.89%)
cnn-raccoonCreate interactive dashboards for your Convolutional Neural Networks with a single line of code!
Stars: ✭ 31 (+72.22%)
path explainA repository for explaining feature attributions and feature interactions in deep neural networks.
Stars: ✭ 151 (+738.89%)
ddsm-visual-primitivesUsing deep learning to discover interpretable representations for mammogram classification and explanation
Stars: ✭ 25 (+38.89%)
GraphLIMEThis is a Pytorch implementation of GraphLIME
Stars: ✭ 40 (+122.22%)
zennitZennit is a high-level framework in Python using PyTorch for explaining/exploring neural networks using attribution methods like LRP.
Stars: ✭ 57 (+216.67%)
Relevance-CAMThe official code of Relevance-CAM
Stars: ✭ 21 (+16.67%)
awesome-agi-cocosciAn awesome & curated list for Artificial General Intelligence, an emerging inter-discipline field that combines artificial intelligence and computational cognitive sciences.
Stars: ✭ 81 (+350%)
javaAnchorExplainerExplains machine learning models fast using the Anchor algorithm originally proposed by marcotcr in 2018
Stars: ✭ 17 (-5.56%)
deep-explanation-penalizationCode for using CDEP from the paper "Interpretations are useful: penalizing explanations to align neural networks with prior knowledge" https://arxiv.org/abs/1909.13584
Stars: ✭ 110 (+511.11%)
trulensLibrary containing attribution and interpretation methods for deep nets.
Stars: ✭ 146 (+711.11%)
transformers-interpretModel explainability that works seamlessly with 🤗 transformers. Explain your transformers model in just 2 lines of code.
Stars: ✭ 861 (+4683.33%)
hierarchical-dnn-interpretationsUsing / reproducing ACD from the paper "Hierarchical interpretations for neural network predictions" 🧠 (ICLR 2019)
Stars: ✭ 110 (+511.11%)
megMolecular Explanation Generator
Stars: ✭ 14 (-22.22%)
Deep XFPackage towards building Explainable Forecasting and Nowcasting Models with State-of-the-art Deep Neural Networks and Dynamic Factor Model on Time Series data sets with single line of code. Also, provides utilify facility for time-series signal similarities matching, and removing noise from timeseries signals.
Stars: ✭ 83 (+361.11%)
Transformer-MM-Explainability[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.
Stars: ✭ 484 (+2588.89%)
concept-based-xaiLibrary implementing state-of-the-art Concept-based and Disentanglement Learning methods for Explainable AI
Stars: ✭ 41 (+127.78%)
bert attn vizVisualize BERT's self-attention layers on text classification tasks
Stars: ✭ 41 (+127.78%)
xai-iml-sotaInteresting resources related to Explainable Artificial Intelligence, Interpretable Machine Learning, Interactive Machine Learning, Human in Loop and Visual Analytics.
Stars: ✭ 51 (+183.33%)
expmrcExpMRC: Explainability Evaluation for Machine Reading Comprehension
Stars: ✭ 58 (+222.22%)
3D-GuidedGradCAM-for-Medical-ImagingThis 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.
Stars: ✭ 60 (+233.33%)
graspEssential NLP & ML, short & fast pure Python code
Stars: ✭ 58 (+222.22%)
neuro-symbolic-sudoku-solver⚙️ Solving sudoku using Deep Reinforcement learning in combination with powerful symbolic representations.
Stars: ✭ 60 (+233.33%)
XAIatERUM2020Workshop: Explanation and exploration of machine learning models with R and DALEX at eRum 2020
Stars: ✭ 52 (+188.89%)
datafsmMachine Learning Finite State Machine Models from Data with Genetic Algorithms
Stars: ✭ 14 (-22.22%)
pyCeterisParibusPython library for Ceteris Paribus Plots (What-if plots)
Stars: ✭ 19 (+5.56%)
mindsdb serverMindsDB server allows you to consume and expose MindsDB workflows, through http.
Stars: ✭ 3 (-83.33%)
self critical vqaCode for NeurIPS 2019 paper ``Self-Critical Reasoning for Robust Visual Question Answering''
Stars: ✭ 39 (+116.67%)
WhiteBox-Part1In this part, I've introduced and experimented with ways to interpret and evaluate models in the field of image. (Pytorch)
Stars: ✭ 34 (+88.89%)
diabetes use caseSample use case for Xavier AI in Healthcare conference: https://www.xavierhealth.org/ai-summit-day2/
Stars: ✭ 22 (+22.22%)