Codebase for Inducing Causal Structure for Interpretable Neural Networks

Last update: Oct 10, 2022

Overview

Interchange Intervention Training (IIT)

Codebase for Inducing Causal Structure for Interpretable Neural Networks

Release Notes

12/01/2021: Code and Paper are initially released. Stay tuned for complete implementation for all case studies.

Installation

First clone the directory. Then run the following command to initialize the submodules:

git submodule init; git submodule update

Each time pulling from the repository, should do it for both the main repository and the submodules. You can also do it via our script:

bash pull.sh

Usage

You will find examples where we implement interchange intervention training (IIT) under ./examples folder.

Owner

Zen

GitHub Repository

MapReader: A computer vision pipeline for the semantic exploration of maps at scale

MapReader A computer vision pipeline for the semantic exploration of maps at scale MapReader is an end-to-end computer vision (CV) pipeline designed b

25 Dec 26, 2022

[CVPR 2021] MetaSAug: Meta Semantic Augmentation for Long-Tailed Visual Recognition

MetaSAug: Meta Semantic Augmentation for Long-Tailed Visual Recognition (CVPR 2021) arXiv Prerequisite PyTorch = 1.2.0 Python3 torchvision PIL argpar

51 Nov 11, 2022

An open source Jetson Nano baseboard and tools to design your own.

My Jetson Nano Baseboard This basic baseboard gives the user the foundation and the flexibility to design their own baseboard for the Jetson Nano. It

57 Dec 29, 2022

Combining Latent Space and Structured Kernels for Bayesian Optimization over Combinatorial Spaces

This repository contains source code for the paper Combining Latent Space and Structured Kernels for Bayesian Optimization over Combinatorial Spaces a

9 Nov 21, 2022

On Effective Scheduling of Model-based Reinforcement Learning

On Effective Scheduling of Model-based Reinforcement Learning Code to reproduce the experiments in On Effective Scheduling of Model-based Reinforcemen

8 Oct 07, 2022

Implementation of the ivis algorithm as described in the paper Structure-preserving visualisation of high dimensional single-cell datasets.

285 Jan 04, 2023

RLBot Python bindings for the Rust crate rl_ball_sym

RLBot Python bindings for rl_ball_sym 0.6 Prerequisites: Rust & Cargo Build Tools for Visual Studio RLBot - Verify that the file %localappdata%\RLBotG

2 Nov 25, 2022

Credit fraud detection in Python using a Jupyter Notebook

Credit-Fraud-Detection - Credit fraud detection in Python using a Jupyter Notebook , using three classification models (Random Forest, Gaussian Naive Bayes, Logistic Regression) from the sklearn libr

4 Dec 28, 2021

🚀 PyTorch Implementation of "Progressive Distillation for Fast Sampling of Diffusion Models(v-diffusion)"

PyTorch Implementation of "Progressive Distillation for Fast Sampling of Diffusion Models(v-diffusion)" Unofficial PyTorch Implementation of Progressi

58 Dec 19, 2022

Code for our NeurIPS 2021 paper 'Exploiting the Intrinsic Neighborhood Structure for Source-free Domain Adaptation'

Exploiting the Intrinsic Neighborhood Structure for Source-free Domain Adaptation (NeurIPS 2021) Code for our NeurIPS 2021 paper 'Exploiting the Intri

53 Dec 25, 2022

IAUnet: Global Context-Aware Feature Learning for Person Re-Identification

IAUnet This repository contains the code for the paper: IAUnet: Global Context-Aware Feature Learning for Person Re-Identification Ruibing Hou, Bingpe

30 Jul 14, 2022

Python package for missing-data imputation with deep learning

MIDASpy Overview MIDASpy is a Python package for multiply imputing missing data using deep learning methods. The MIDASpy algorithm offers significant

77 Dec 03, 2022

PyTorch Implementation for Deep Metric Learning Pipelines

Easily Extendable Basic Deep Metric Learning Pipeline Karsten Roth ([email

543 Jan 04, 2023

Code for Environment Inference for Invariant Learning (ICML 2020 UDL Workshop Paper)

Environment Inference for Invariant Learning This code accompanies the paper Environment Inference for Invariant Learning, which appears at ICML 2021.

40 Dec 09, 2022

Drone-based Joint Density Map Estimation, Localization and Tracking with Space-Time Multi-Scale Attention Network

DroneCrowd Paper Detection, Tracking, and Counting Meets Drones in Crowds: A Benchmark. Introduction This paper proposes a space-time multi-scale atte

98 Nov 16, 2022

This is an official PyTorch implementation of Task-Adaptive Neural Network Search with Meta-Contrastive Learning (NeurIPS 2021, Spotlight).

NeurIPS 2021 (Spotlight): Task-Adaptive Neural Network Search with Meta-Contrastive Learning This is an official PyTorch implementation of Task-Adapti

15 Nov 21, 2022

Codebase for Inducing Causal Structure for Interpretable Neural Networks

Related tags

Overview

Interchange Intervention Training (IIT)

Release Notes

Installation

Usage

Owner

Zen

MapReader: A computer vision pipeline for the semantic exploration of maps at scale

[CVPR 2021] MetaSAug: Meta Semantic Augmentation for Long-Tailed Visual Recognition

An open source Jetson Nano baseboard and tools to design your own.

Combining Latent Space and Structured Kernels for Bayesian Optimization over Combinatorial Spaces

On Effective Scheduling of Model-based Reinforcement Learning

Implementation of the ivis algorithm as described in the paper Structure-preserving visualisation of high dimensional single-cell datasets.

RLBot Python bindings for the Rust crate rl_ball_sym

Credit fraud detection in Python using a Jupyter Notebook

🚀 PyTorch Implementation of "Progressive Distillation for Fast Sampling of Diffusion Models(v-diffusion)"

Code for our NeurIPS 2021 paper 'Exploiting the Intrinsic Neighborhood Structure for Source-free Domain Adaptation'

IAUnet: Global Context-Aware Feature Learning for Person Re-Identification

Python package for missing-data imputation with deep learning

PyTorch Implementation for Deep Metric Learning Pipelines

Code for Environment Inference for Invariant Learning (ICML 2020 UDL Workshop Paper)

Drone-based Joint Density Map Estimation, Localization and Tracking with Space-Time Multi-Scale Attention Network

This is an official PyTorch implementation of Task-Adaptive Neural Network Search with Meta-Contrastive Learning (NeurIPS 2021, Spotlight).

In this project, we create and implement a deep learning library from scratch.

Object Tracking and Detection Using OpenCV

Tzer: TVM Implementation of "Coverage-Guided Tensor Compiler Fuzzing with Joint IR-Pass Mutation (OOPSLA'22)“.

Code for Parameter Prediction for Unseen Deep Architectures (NeurIPS 2021)