191 Open source projects that are alternatives of or similar to mllp

Fit interpretable models. Explain blackbox machine learning.

A curated list of awesome machine learning interpretability resources.

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

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

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

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

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

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

Fast approximate Shapley values in R

Data generator for Arena - interactive XAI dashboard

FairPut - Machine Learning Fairness Framework with LightGBM — Explainability, Robustness, Fairness (by @firmai)

Adaptive, interpretable wavelets across domains (NeurIPS 2021)

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

Model interpretability and understanding for PyTorch

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).

[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.

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

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

Papers about explainability of GNNs

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

Visualization tool for Graph Neural Networks

A Julia package for interpretable machine learning with stochastic Shapley values

Collection of NLP model explanations and accompanying analysis tools

Implementation of the paper "Shapley Explanation Networks"

Package 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.

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

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

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

Predictive AI layer for existing databases.

Debugging, monitoring and visualization for Python Machine Learning and Data Science

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

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

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

MindsDB server allows you to consume and expose MindsDB workflows, through http.

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

For calculating global feature importance using Shapley values.

Examples of Data Science projects and Artificial Intelligence use cases

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

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

A lightweight implementation of removal-based explanations for ML models.

A curated list of awesome open source libraries to deploy, monitor, version and scale your machine learning

H2O.ai Machine Learning Interpretability Resources

ExpMRC: Explainability Evaluation for Machine Reading Comprehension

XAI Tutorial for the Explainable AI track in the ALPS winter school 2021

Code for NeurIPS 2019 paper ``Self-Critical Reasoning for Robust Visual Question Answering''

Concept Bottleneck Models, ICML 2020

A game theoretic approach to explain the output of any machine learning model.

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

Molecular Explanation Generator

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.

A place to store some examples which use Trillian APIs to build things.

Essential NLP & ML, short & fast pure Python code

Official compliant tool suite for implementing the Transparency and Consent Framework (TCF) v2.0. The essential toolkit for CMPs.

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

A Domain Specific Language (DSL) for building language patterns. These can be later compiled into spaCy patterns, pure regex, or any other format

NeurIPS 2018. Linear-time model comparison tests.

Official Implementation of Remembering for the Right Reasons (ICLR 2021)

Code for "Distributed, Egocentric Representations of Graphs for Detecting Critical Structures" (ICML 2019)

Papers from top conferences and journals for event extraction in recent years

Machine Learning in one line of code