L2X - Code for replicating the experiments in the paper Learning to Explain: An Information-Theoretic Perspective on Model Interpretation.

Overview

L2X

Code for replicating the experiments in the paper Learning to Explain: An Information-Theoretic Perspective on Model Interpretation at ICML 2018, by Jianbo Chen, Mitchell Stern, Martin J. Wainwright, Michael I. Jordan.

Dependencies

The code for L2X runs with Python and requires Tensorflow of version 1.2.1 or higher and Keras of version 2.0 or higher. Please pip install the following packages:

  • numpy
  • tensorflow
  • keras
  • pandas
  • nltk

Or you may run the following and in shell to install the required packages:

git clone https://github.com/Jianbo-Lab/L2X
cd L2X
sudo pip install -r requirements.txt

See README.md in respective folders for details.

Citation

If you use this code for your research, please cite our paper:

@arxiv{chen2018learning,
title = {Learning to Explain: An Information-Theoretic Perspective on Model Interpretation},
author = {Chen, Jianbo and Song, Le and Wainwright, Martin J and Jordan, Michael I}, 
journal={arXiv preprint arXiv:1802.07814}, 
year = {2018}  
}
Owner
Jianbo Chen
Quantitative researcher at Citadel Securities; PhD in Statistics at UC Berkeley working with Michael I. Jordan and Martin J. Wainwright.
Jianbo Chen
⬛ Python Individual Conditional Expectation Plot Toolbox

⬛ PyCEbox Python Individual Conditional Expectation Plot Toolbox A Python implementation of individual conditional expecation plots inspired by R's IC

Austin Rochford 140 Dec 30, 2022
Auralisation of learned features in CNN (for audio)

AuralisationCNN This repo is for an example of auralisastion of CNNs that is demonstrated on ISMIR 2015. Files auralise.py: includes all required func

Keunwoo Choi 39 Nov 19, 2022
An Empirical Review of Optimization Techniques for Quantum Variational Circuits

QVC Optimizer Review Code for the paper "An Empirical Review of Optimization Techniques for Quantum Variational Circuits". Each of the python files ca

Owen Lockwood 5 Jun 28, 2022
L2X - Code for replicating the experiments in the paper Learning to Explain: An Information-Theoretic Perspective on Model Interpretation.

L2X Code for replicating the experiments in the paper Learning to Explain: An Information-Theoretic Perspective on Model Interpretation at ICML 2018,

Jianbo Chen 113 Sep 06, 2022
A game theoretic approach to explain the output of any machine learning model.

SHAP (SHapley Additive exPlanations) is a game theoretic approach to explain the output of any machine learning model. It connects optimal credit allo

Scott Lundberg 18.3k Jan 08, 2023
A Practical Debugging Tool for Training Deep Neural Networks

Cockpit is a visual and statistical debugger specifically designed for deep learning!

31 Aug 14, 2022
JittorVis - Visual understanding of deep learning model.

JittorVis - Visual understanding of deep learning model.

182 Jan 06, 2023
Interactive convnet features visualization for Keras

Quiver Interactive convnet features visualization for Keras The quiver workflow Video Demo Build your model in keras model = Model(...) Launch the vis

Keplr 1.7k Dec 21, 2022
Lucid library adapted for PyTorch

Lucent PyTorch + Lucid = Lucent The wonderful Lucid library adapted for the wonderful PyTorch! Lucent is not affiliated with Lucid or OpenAI's Clarity

Lim Swee Kiat 520 Dec 26, 2022
Pytorch Feature Map Extractor

MapExtrackt Convolutional Neural Networks Are Beautiful We all take our eyes for granted, we glance at an object for an instant and our brains can ide

Lewis Morris 40 Dec 07, 2022
Python Library for Model Interpretation/Explanations

Skater Skater is a unified framework to enable Model Interpretation for all forms of model to help one build an Interpretable machine learning system

Oracle 1k Dec 27, 2022
A library that implements fairness-aware machine learning algorithms

Themis ML themis-ml is a Python library built on top of pandas and sklearnthat implements fairness-aware machine learning algorithms. Fairness-aware M

Niels Bantilan 105 Dec 30, 2022
Interpretability and explainability of data and machine learning models

AI Explainability 360 (v0.2.1) The AI Explainability 360 toolkit is an open-source library that supports interpretability and explainability of datase

1.2k Dec 29, 2022
Visualize a molecule and its conformations in Jupyter notebooks/lab using py3dmol

Mol Viewer This is a simple package wrapping py3dmol for a single command visualization of a RDKit molecule and its conformations (embed as Conformer

Benoît BAILLIF 1 Feb 11, 2022
Visualizer for neural network, deep learning, and machine learning models

Netron is a viewer for neural network, deep learning and machine learning models. Netron supports ONNX, TensorFlow Lite, Keras, Caffe, Darknet, ncnn,

Lutz Roeder 20.9k Dec 28, 2022
Bias and Fairness Audit Toolkit

The Bias and Fairness Audit Toolkit Aequitas is an open-source bias audit toolkit for data scientists, machine learning researchers, and policymakers

Data Science for Social Good 513 Jan 06, 2023
Model analysis tools for TensorFlow

TensorFlow Model Analysis TensorFlow Model Analysis (TFMA) is a library for evaluating TensorFlow models. It allows users to evaluate their models on

1.2k Dec 26, 2022
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.

Jacob Gildenblat 6.5k Jan 01, 2023
A collection of infrastructure and tools for research in neural network interpretability.

Lucid Lucid is a collection of infrastructure and tools for research in neural network interpretability. We're not currently supporting tensorflow 2!

4.5k Jan 07, 2023
Code for "High-Precision Model-Agnostic Explanations" paper

Anchor This repository has code for the paper High-Precision Model-Agnostic Explanations. An anchor explanation is a rule that sufficiently “anchors”

Marco Tulio Correia Ribeiro 735 Jan 05, 2023