42 Open source projects that are alternatives of or similar to pyCeterisParibus

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

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

Predictive AI layer for existing databases.

Fit interpretable models. Explain blackbox machine learning.

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

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

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

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

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

Fast approximate Shapley values in R

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

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

Papers about explainability of GNNs

A repository for explaining feature attributions and feature interactions in deep neural networks.

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

This is a Pytorch implementation of GraphLIME

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

The official code of Relevance-CAM

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

Implementation of the paper "Shapley Explanation Networks"

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

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

Variants of Vision Transformer and its downstream tasks

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

No description or website provided.

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

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

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

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

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

Molecular Explanation Generator

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.

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

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

Examples of Data Science projects and Artificial Intelligence use cases

ExpMRC: Explainability Evaluation for Machine Reading Comprehension

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.

Essential NLP & ML, short & fast pure Python code

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

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

Machine Learning Finite State Machine Models from Data with Genetic Algorithms