470 Open source projects that are alternatives of or similar to deep-explanation-penalization

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)

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

A curated list of awesome machine learning interpretability resources.

Fit interpretable models. Explain blackbox machine learning.

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

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

[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

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

Interpretable ML package 🔍 for concise, transparent, and accurate predictive modeling (sklearn-compatible).

Molecular Explanation Generator

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

Collection of NLP model explanations and accompanying analysis tools

Implementation of the paper "Shapley Explanation Networks"

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

Papers about explainability of GNNs

XAI - An eXplainability toolbox for machine learning

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

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

For calculating global feature importance using Shapley values.

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

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

Model interpretability and understanding for PyTorch

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

Recurrent Neural Network for generating piano MIDI-files from audio (MP3, WAV, etc.)

A collection of state-of-the-art algorithms for the training, serving and interpretation of Decision Forest models.

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

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

Adaptive, interpretable wavelets across domains (NeurIPS 2021)

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

Predictive AI layer for existing databases.

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

Data generator for Arena - interactive XAI dashboard

Luk.ai Clients - Federated Machine Learning for Everyone!

Moore Machine Networks (MMN): Learning Finite-State Representations of Recurrent Policy Networks

CNN architecture for predicting DNA binding sites for Transcription Factors

PMML scoring library for Spark as SparkML Transformer

Scalable identity resolution, entity resolution, data mastering and deduplication using ML

Cheat Sheets for deep learning and machine learning.

MLApp is a Python library for building scalable data science solutions that meet modern software engineering standards.

A system for development of high-performance, data-intensive, distributed computing, applications, tools, and libraries.

Tilted Empirical Risk Minimization (ICLR '21)

Gaussian Naive Bayes (GaussianNB) classifier

Enabling easy statistical significance testing for deep neural networks.

📒 Experiment tracking tool and model registry

Ready to run docker-compose configuration for ML Flow with Mysql and Minio S3

Code for Neural Inverse Knitting: From Images to Manufacturing Instructions

PyTorch implementation of "Conditional Image Generation with PixelCNN Decoders" by van den Oord et al. 2016

Python package to visualize and cluster partial dependence.

Concept Bottleneck Models, ICML 2020

Polyaxon Core Client & CLI to streamline MLOps

Char RNN Language Model based on Tensorflow

Visit our website for more Mobile and Web applications

Normal & height maps generation from single pictures

Reading notes on deep learning papers---深度学习论文阅读笔记

No description or website provided.

Code for "Free-Lunch Saliency via Attention in Atari Agents"

Azure Machine Learning Cheat Sheets

Official PyTorch and MATLAB implementations of our MICCAI 2020 paper "FocusLiteNN: High Efficiency Focus Quality Assessment for Digital Pathology"