47 Repositories
Python interpretable-ml Libraries
[ICML 2022] The official implementation of Graph Stochastic Attention (GSAT).
Graph Stochastic Attention (GSAT) The official implementation of GSAT for our paper: Interpretable and Generalizable Graph Learning via Stochastic Att
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.
DeepXF: Explainable Forecasting and Nowcasting with State-of-the-art Deep Neural Networks and Dynamic Factor Model Also, verify TS signal similarities
Job-Recommend-Competition - Vectorwise Interpretable Attentions for Multimodal Tabular Data
SiD - Simple Deep Model Vectorwise Interpretable Attentions for Multimodal Tabul
An implementation of IMLE-Net: An Interpretable Multi-level Multi-channel Model for ECG Classification
IMLE-Net: An Interpretable Multi-level Multi-channel Model for ECG Classification The repostiory consists of the code, results and data set links for
Which Style Makes Me Attractive? Interpretable Control Discovery and Counterfactual Explanation on StyleGAN
Interpretable Control Exploration and Counterfactual Explanation (ICE) on StyleGAN Which Style Makes Me Attractive? Interpretable Control Discovery an
LERP : Label-dependent and event-guided interpretable disease risk prediction using EHRs
LERP : Label-dependent and event-guided interpretable disease risk prediction using EHRs This is the code for the LERP. Dataset The dataset used is MI
BERTopic is a topic modeling technique that leverages 🤗 transformers and c-TF-IDF to create dense clusters allowing for easily interpretable topics whilst keeping important words in the topic descriptions
BERTopic BERTopic is a topic modeling technique that leverages 🤗 transformers and c-TF-IDF to create dense clusters allowing for easily interpretable
Pytorch implementation of NEGEV method. Paper: "Negative Evidence Matters in Interpretable Histology Image Classification".
Pytorch 1.10.0 code for: Negative Evidence Matters in Interpretable Histology Image Classification (https://arxiv. org/abs/xxxx.xxxxx) Citation: @arti
GCRC: A Gaokao Chinese Reading Comprehension dataset for interpretable Evaluation
GCRC GCRC: A New Challenging MRC Dataset from Gaokao Chinese for Explainable Eva
Resources for our AAAI 2022 paper: "LOREN: Logic-Regularized Reasoning for Interpretable Fact Verification".
LOREN Resources for our AAAI 2022 paper (pre-print): "LOREN: Logic-Regularized Reasoning for Interpretable Fact Verification". DEMO System Check out o
Sparse Physics-based and Interpretable Neural Networks
Sparse Physics-based and Interpretable Neural Networks for PDEs This repository contains the code and manuscript for research done on Sparse Physics-b
InDuDoNet+: A Model-Driven Interpretable Dual Domain Network for Metal Artifact Reduction in CT Images
InDuDoNet+: A Model-Driven Interpretable Dual Domain Network for Metal Artifact Reduction in CT Images Hong Wang, Yuexiang Li, Haimiao Zhang, Deyu Men
Codebase for Inducing Causal Structure for Interpretable Neural Networks
Interchange Intervention Training (IIT) Codebase for Inducing Causal Structure for Interpretable Neural Networks Release Notes 12/01/2021: Code and Pa
Interpretable and Generalizable Person Re-Identification with Query-Adaptive Convolution and Temporal Lifting
QAConv Interpretable and Generalizable Person Re-Identification with Query-Adaptive Convolution and Temporal Lifting This PyTorch code is proposed in
Discovering Interpretable GAN Controls [NeurIPS 2020]
GANSpace: Discovering Interpretable GAN Controls Figure 1: Sequences of image edits performed using control discovered with our method, applied to thr
The code of NeurIPS 2021 paper "Scalable Rule-Based Representation Learning for Interpretable Classification".
Rule-based Representation Learner This is a PyTorch implementation of Rule-based Representation Learner (RRL) as described in NeurIPS 2021 paper: Scal
Code for reproducing key results in the paper "InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets"
Status: Archive (code is provided as-is, no updates expected) InfoGAN Code for reproducing key results in the paper InfoGAN: Interpretable Representat
Extracting knowledge graphs from language models as a diagnostic benchmark of model performance.
Interpreting Language Models Through Knowledge Graph Extraction Idea: How do we interpret what a language model learns at various stages of training?
Responsible Machine Learning with Python
Examples of techniques for training interpretable ML models, explaining ML models, and debugging ML models for accuracy, discrimination, and security.
moDel Agnostic Language for Exploration and eXplanation
moDel Agnostic Language for Exploration and eXplanation Overview Unverified black box model is the path to the failure. Opaqueness leads to distrust.
Fit interpretable models. Explain blackbox machine learning.
InterpretML - Alpha Release In the beginning machines learned in darkness, and data scientists struggled in the void to explain them. Let there be lig
Code repo for "Towards Interpretable Deep Networks for Monocular Depth Estimation" paper.
InterpretableMDE A PyTorch implementation for "Towards Interpretable Deep Networks for Monocular Depth Estimation" paper. arXiv link: https://arxiv.or
Interpretable-contrastive-word-mover-s-embedding
Interpretable-contrastive-word-mover-s-embedding Paper Datasets Here is a Dropbox link to the datasets used in the paper: https://www.dropbox.com/sh/n
Anuvada: Interpretable Models for NLP using PyTorch
Anuvada: Interpretable Models for NLP using PyTorch So, you want to know why your classifier arrived at a particular decision or why your flashy new d
Constructing interpretable quadratic accuracy predictors to serve as an objective function for an IQCQP problem that represents NAS under latency constraints and solve it with efficient algorithms.
IQNAS: Interpretable Integer Quadratic programming Neural Architecture Search Realistic use of neural networks often requires adhering to multiple con
Adaptive, interpretable wavelets across domains (NeurIPS 2021)
Adaptive wavelets Wavelets which adapt given data (and optionally a pre-trained model). This yields models which are faster, more compressible, and mo
Class activation maps for your PyTorch models (CAM, Grad-CAM, Grad-CAM++, Smooth Grad-CAM++, Score-CAM, SS-CAM, IS-CAM, XGrad-CAM, Layer-CAM)
TorchCAM: class activation explorer Simple way to leverage the class-specific activation of convolutional layers in PyTorch. Quick Tour Setting your C
The Body Part Regression (BPR) model translates the anatomy in a radiologic volume into a machine-interpretable form.
Copyright © German Cancer Research Center (DKFZ), Division of Medical Image Computing (MIC). Please make sure that your usage of this code is in compl
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
Class Activation Map methods implemented in Pytorch pip install grad-cam ⭐ Comprehensive collection of Pixel Attribution methods for Computer Vision.
CAPRI: Context-Aware Interpretable Point-of-Interest Recommendation Framework
CAPRI: Context-Aware Interpretable Point-of-Interest Recommendation Framework This repository contains a framework for Recommender Systems (RecSys), a
The official implementation of CSG-Stump: A Learning Friendly CSG-Like Representation for Interpretable Shape Parsing
CSGStumpNet The official implementation of CSG-Stump: A Learning Friendly CSG-Like Representation for Interpretable Shape Parsing Paper | Project page
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
Class Activation Map methods implemented in Pytorch pip install grad-cam ⭐ Tested on many Common CNN Networks and Vision Transformers. ⭐ Includes smoo
PyTorch implementation of Interpretable Explanations of Black Boxes by Meaningful Perturbation
PyTorch implementation of Interpretable Explanations of Black Boxes by Meaningful Perturbation The paper: https://arxiv.org/abs/1704.03296 What makes
The official code for paper "R2D2: Recursive Transformer based on Differentiable Tree for Interpretable Hierarchical Language Modeling".
R2D2 This is the official code for paper titled "R2D2: Recursive Transformer based on Differentiable Tree for Interpretable Hierarchical Language Mode
Towards Interpretable Deep Metric Learning with Structural Matching
DIML Created by Wenliang Zhao*, Yongming Rao*, Ziyi Wang, Jiwen Lu, Jie Zhou This repository contains PyTorch implementation for paper Towards Interpr
Data and Code for ACL 2021 Paper "Inter-GPS: Interpretable Geometry Problem Solving with Formal Language and Symbolic Reasoning"
Introduction Code and data for ACL 2021 Paper "Inter-GPS: Interpretable Geometry Problem Solving with Formal Language and Symbolic Reasoning". We cons
This is the code repository for Interpretable Machine Learning with Python, published by Packt.
Interpretable Machine Learning with Python, published by Packt
Annotated, understandable, and visually interpretable PyTorch implementations of: VAE, BIRVAE, NSGAN, MMGAN, WGAN, WGANGP, LSGAN, DRAGAN, BEGAN, RaGAN, InfoGAN, fGAN, FisherGAN
Overview PyTorch 0.4.1 | Python 3.6.5 Annotated implementations with comparative introductions for minimax, non-saturating, wasserstein, wasserstein g
CausaLM: Causal Model Explanation Through Counterfactual Language Models
CausaLM: Causal Model Explanation Through Counterfactual Language Models Authors: Amir Feder, Nadav Oved, Uri Shalit, Roi Reichart Abstract: Understan
Code for CVPR2021 paper 'Where and What? Examining Interpretable Disentangled Representations'.
PS-SC GAN This repository contains the main code for training a PS-SC GAN (a GAN implemented with the Perceptual Simplicity and Spatial Constriction c
Official NumPy Implementation of Deep Networks from the Principle of Rate Reduction (2021)
Deep Networks from the Principle of Rate Reduction This repository is the official NumPy implementation of the paper Deep Networks from the Principle
Implementation of the paper "Shapley Explanation Networks"
Shapley Explanation Networks Implementation of the paper "Shapley Explanation Networks" at ICLR 2021. Note that this repo heavily uses the experimenta
A Lucid Framework for Transparent and Interpretable Machine Learning Models.
Currently a Beta-Version lucidmode is an open-source, low-code and lightweight Python framework for transparent and interpretable machine learning mod
Interpretable Models for NLP using PyTorch
This repo is deprecated. Please find the updated package here. https://github.com/EdGENetworks/anuvada Anuvada: Interpretable Models for NLP using PyT
[HELP REQUESTED] Generalized Additive Models in Python
pyGAM Generalized Additive Models in Python. Documentation Official pyGAM Documentation: Read the Docs Building interpretable models with Generalized
Highly interpretable classifiers for scikit learn, producing easily understood decision rules instead of black box models
Highly interpretable, sklearn-compatible classifier based on decision rules This is a scikit-learn compatible wrapper for the Bayesian Rule List class
A Python package implementing a new model for text classification with visualization tools for Explainable AI :octocat:
A Python package implementing a new model for text classification with visualization tools for Explainable AI 🍣 Online live demos: http://tworld.io/s