Interactive Visualization to empower domain experts to align ML model behaviors with their knowledge.

Overview

An interactive visualization system designed to helps domain experts responsibly edit Generalized Additive Models (GAMs).

build pypi license arxiv badge

For more information, check out our manuscript:

GAM Changer: Editing Generalized Additive Models with Interactive Visualization. Zijie J. Wang, Alex Kale, Harsha Nori, Peter Stella, Mark Nunnally, Duen Horng Chau, Mihaela Vorvoreanu, Jennifer Wortman Vaughan, Rich Caruana. Research2Clinics Workshop at NeurIPS, 2021.

Live Demo

For a live demo, visit: http://interpret.ml/gam-changer/

Running Locally

Clone or download this repository:

git clone [email protected]:interpretml/gam-changer.git

# use degit if you don't want to download commit histories
degit interpretml/gam-changer.git

Install the dependencies:

npm install

Then run GAM Changer:

npm run dev

Navigate to localhost:5000. You should see GAM Changer running in your browser :)

Credits

GAM Changer is created by Jay Wang, Alex Kale, Harsha Nori, Peter Stella, Mark Nunnally, Polo Chau, Mickey Vorvoreanu, Jenn Wortman Vaughan, and Rich Caruana, which was the result of a research collaboration between Microsoft Research, NYU Langone Health, Georgia Tech and University of Washington. Jay Wang and Alex Kale were summer interns at Microsoft Research.

We thank Steven Drucker, Adam Fourney, Saleema Amershi, Dean Carignan, Rob DeLine, and the InterpretML team for their support and constructive feedback.

Citation

@article{wangGAMChangerEditing2021,
  title = {{{GAM Changer}}: {{Editing Generalized Additive Models}} with {{Interactive Visualization}}},
  shorttitle = {{{GAM Changer}}},
  author = {Wang, Zijie J. and Kale, Alex and Nori, Harsha and Stella, Peter and Nunnally, Mark and Chau, Duen Horng and Vorvoreanu, Mihaela and Vaughan, Jennifer Wortman and Caruana, Rich},
  year = {2021},
  month = dec,
  journal = {arXiv:2112.03245 [cs]},
  url = {https://interpret.ml/gam-changer},
  archiveprefix = {arXiv}
}

License

The software is available under the MIT License.

Contact

If you have any questions, feel free to open an issue or contact Jay Wang.

Owner
InterpretML
If a tree fell in your random forest, would anyone notice?
InterpretML
Implementation of the state-of-the-art vision transformers with tensorflow

ViT Tensorflow This repository contains the tensorflow implementation of the state-of-the-art vision transformers (a category of computer vision model

Mohammadmahdi NouriBorji 2 Mar 16, 2022
Semi-Supervised Learning with Ladder Networks in Keras. Get 98% test accuracy on MNIST with just 100 labeled examples !

Semi-Supervised Learning with Ladder Networks in Keras This is an implementation of Ladder Network in Keras. Ladder network is a model for semi-superv

Divam Gupta 101 Sep 07, 2022
SimplEx - Explaining Latent Representations with a Corpus of Examples

SimplEx - Explaining Latent Representations with a Corpus of Examples Code Author: Jonathan Crabbé ( Jonathan Crabbé 14 Dec 15, 2022

Retinal vessel segmentation based on GT-UNet

Retinal vessel segmentation based on GT-UNet Introduction This project is a retinal blood vessel segmentation code based on UNet-like Group Transforme

Kent0n 27 Dec 18, 2022
Neighborhood Reconstructing Autoencoders

Neighborhood Reconstructing Autoencoders The official repository for Neighborhood Reconstructing Autoencoders (Lee, Kwon, and Park, NeurIPS 2021). T

Yonghyeon Lee 24 Dec 14, 2022
Easy to use Audio Tagging in PyTorch

Audio Classification, Tagging & Sound Event Detection in PyTorch Progress: Fine-tune on audio classification Fine-tune on audio tagging Fine-tune on s

sithu3 15 Dec 22, 2022
Escaping the Gradient Vanishing: Periodic Alternatives of Softmax in Attention Mechanism

Period-alternatives-of-Softmax Experimental Demo for our paper 'Escaping the Gradient Vanishing: Periodic Alternatives of Softmax in Attention Mechani

slwang9353 0 Sep 06, 2021
The codes for the work "Swin-Unet: Unet-like Pure Transformer for Medical Image Segmentation"

Swin-Unet The codes for the work "Swin-Unet: Unet-like Pure Transformer for Medical Image Segmentation"(https://arxiv.org/abs/2105.05537). A validatio

869 Jan 07, 2023
Infrastructure as Code (IaC) for a self-hosted version of Gnosis Safe on AWS

Welcome to Yearn Gnosis Safe! Setting up your local environment Infrastructure Deploying Gnosis Safe Prerequisites 1. Create infrastructure for secret

Numan 16 Jul 18, 2022
Optimal Camera Position for a Practical Application of Gaze Estimation on Edge Devices,

Optimal Camera Position for a Practical Application of Gaze Estimation on Edge Devices, Linh Van Ma, Tin Trung Tran, Moongu Jeon, ICAIIC 2022 (The 4th

Linh 11 Oct 10, 2022
Real-Time Social Distance Monitoring tool using Computer Vision

Social Distance Detector A Real-Time Social Distance Monitoring Tool Table of Contents Motivation YOLO Theory Detection Output Tech Stack Functionalit

Pranav B 13 Oct 14, 2022
Minecraft Hack Detection With Python

Minecraft Hack Detection An attempt to try and use crowd sourced replays to find

Kuleen Sasse 3 Mar 26, 2022
Robust Partial Matching for Person Search in the Wild

APNet for Person Search Introduction This is the code of Robust Partial Matching for Person Search in the Wild accepted in CVPR2020. The Align-to-Part

Yingji Zhong 36 Dec 18, 2022
Code for paper entitled "Improving Novelty Detection using the Reconstructions of Nearest Neighbours"

NLN: Nearest-Latent-Neighbours A repository containing the implementation of the paper entitled Improving Novelty Detection using the Reconstructions

Michael (Misha) Mesarcik 4 Dec 14, 2022
source code for https://arxiv.org/abs/2005.11248 "Accelerating Antimicrobial Discovery with Controllable Deep Generative Models and Molecular Dynamics"

Accelerating Antimicrobial Discovery with Controllable Deep Generative Models and Molecular Dynamics This work will be published in Nature Biomedical

International Business Machines 71 Nov 15, 2022
Codebase for Amodal Segmentation through Out-of-Task andOut-of-Distribution Generalization with a Bayesian Model

Codebase for Amodal Segmentation through Out-of-Task andOut-of-Distribution Generalization with a Bayesian Model

Yihong Sun 12 Nov 15, 2022
face_recognization (FaceNet) + TFHE (HNP) + hand_face_detection (Mediapipe)

SuperControlSystem Face_Recognization (FaceNet) 面部识别 (FaceNet) Fully Homomorphic Encryption over the Torus (HNP) 环面全同态加密 (TFHE) Hand_Face_Detection (M

liziyu0104 2 Dec 30, 2021
DA2Lite is an automated model compression toolkit for PyTorch.

DA2Lite (Deep Architecture to Lite) is a toolkit to compress and accelerate deep network models. ⭐ Star us on GitHub — it helps!! Frameworks & Librari

Sinhan Kang 7 Mar 22, 2022
Hyperbolic Hierarchical Clustering.

Hyperbolic Hierarchical Clustering (HypHC) This code is the official PyTorch implementation of the NeurIPS 2020 paper: From Trees to Continuous Embedd

HazyResearch 154 Dec 15, 2022
Code and project page for ICCV 2021 paper "DisUnknown: Distilling Unknown Factors for Disentanglement Learning"

DisUnknown: Distilling Unknown Factors for Disentanglement Learning See introduction on our project page Requirements PyTorch = 1.8.0 torch.linalg.ei

Sitao Xiang 24 May 16, 2022