The codes reproduce the figures and statistics in the paper, "Controlling for multiple covariates," by Mark Tygert.

Related tags

Deep Learningmetamulti
Overview

The accompanying codes reproduce all figures and statistics presented in "Controlling for multiple covariates" by Mark Tygert. This repository also provides the LaTeX and BibTeX sources required for replicating the paper.

Be sure to pip install hilbertcurve prior to running any of this software (the codes depend on HilbertCurve). Also be sure to gunzip codes/cup98lrn.txt prior to running codes/kddcup98.py.

The main files in the repository are the following:

tex/multidim.pdf PDF version of the paper

tex/multidim.tex LaTeX source for the paper

tex/multidim.bib BibTeX source for the paper

tex/diffs0.pdf tex/diffs1.pdf tex/sums0.pdf tex/sums1.pdf tex/partition.pdf Graphics for Subsection 2.3 of the paper

codes/acs.py Python script for processing the American Community Survey

codes/psam_h06.csv Microdata from the 2019 American Community Survey of the U.S. Census Bureau

codes/kddcup98.py Python script for processing the KDD Cup 1998 data

codes/cup98lrn.txt.gz Data from the 1998 KDD Cup

codes/synthetic.py Python script for generating and processing synthetic examples

codes/hilbert.pdf Plot of an approximation with 255 line segments to the Hilbert curve in 2D

codes/disjoint.py Functions for plotting differences between two subpops. with disjoint scores (redistributed from the GitHub repo fbcddisgraph)

codes/disjoint.py Functions for plotting differences of a subpop. from the full population (redistributed from the GitHub repo fbcdgraph)

codes/subpop_weighted.py Functions for plotting differences of a subpop. from the full pop. with weights (redistributed from the GitHub repo fbcdgraph)

Regenerating all the figures requires running in the directory codes acs.py, kddcup98.py, and synthetic.py; issue the commands

cd codes
pip install hilbertcurve
gunzip cup98lrn.txt.gz
python acs.py --var 'MV'
python acs.py --var 'NOC'
python acs.py --var 'MV+NOC'
python acs.py --var 'NOC+MV'
python kddcup98.py
python synthetic.py

Copyright license

This metamulti software is licensed under the (MIT-type) copyright LICENSE file in the root directory of this source tree.

Owner
Meta Research
Meta Research
GitHub Repository
A PyTorch implementation of EfficientDet.

A PyTorch impl of EfficientDet faithful to the original Google impl w/ ported weights

Ross Wightman 1.4k Jan 07, 2023
Adaptive FNO transformer - official Pytorch implementation

Adaptive Fourier Neural Operators: Efficient Token Mixers for Transformers This repository contains PyTorch implementation of the Adaptive Fourier Neu

NVIDIA Research Projects 77 Dec 29, 2022
The official TensorFlow implementation of the paper Action Transformer: A Self-Attention Model for Short-Time Pose-Based Human Action Recognition

Action Transformer A Self-Attention Model for Short-Time Human Action Recognition This repository contains the official TensorFlow implementation of t

PIC4SeRCentre 20 Jan 03, 2023
Fully Convlutional Neural Networks for state-of-the-art time series classification

Deep Learning for Time Series Classification As the simplest type of time series data, univariate time series provides a reasonably good starting poin

Stephen 572 Dec 23, 2022
Improving the robustness and performance of biomedical NLP models through adversarial training

RobustBioNLP Improving the robustness and performance of biomedical NLP models through adversarial training In this repository you can find suppliment

Milad Moradi 3 Sep 20, 2022
A wrapper around SageMaker ML Lineage Tracking extending ML Lineage to end-to-end ML lifecycles, including additional capabilities around Feature Store groups, queries, and other relevant artifacts.

ML Lineage Helper This library is a wrapper around the SageMaker SDK to support ease of lineage tracking across the ML lifecycle. Lineage artifacts in

AWS Samples 12 Nov 01, 2022
Parallel and High-Fidelity Text-to-Lip Generation; AAAI 2022 ; Official code

Parallel and High-Fidelity Text-to-Lip Generation This repository is the official PyTorch implementation of our AAAI-2022 paper, in which we propose P

Zhying 77 Dec 21, 2022
A scikit-learn-compatible module for estimating prediction intervals.

MAPIE - Model Agnostic Prediction Interval Estimator MAPIE allows you to easily estimate prediction intervals (or prediction sets) using your favourit

588 Jan 04, 2023
A3C LSTM Atari with Pytorch plus A3G design

NEWLY ADDED A3G A NEW GPU/CPU ARCHITECTURE OF A3C FOR SUBSTANTIALLY ACCELERATED TRAINING!! RL A3C Pytorch NEWLY ADDED A3G!! New implementation of A3C

David Griffis 532 Jan 02, 2023
Multi-tool reverse engineering collaboration solution.

CollaRE v0.3 Intorduction CollareRE is a tool for collaborative reverse engineering that aims to allow teams that do need to use more then one tool du

105 Nov 27, 2022
It helps user to learn Pick-up lines and share if he has a better one

Pick-up-Lines-Generator(Open Source) It helps user to learn Pick-up lines Share and Add one or many to the DataBase Unique SQLite DataBase AI Undercon

knock_nott 0 May 04, 2022
Fiddle is a Python-first configuration library particularly well suited to ML applications.

Fiddle Fiddle is a Python-first configuration library particularly well suited to ML applications. Fiddle enables deep configurability of parameters i

Google 227 Dec 26, 2022
[CIKM 2019] Code and dataset for "Fi-GNN: Modeling Feature Interactions via Graph Neural Networks for CTR Prediction"

FiGNN for CTR prediction The code and data for our paper in CIKM2019: Fi-GNN: Modeling Feature Interactions via Graph Neural Networks for CTR Predicti

Big Data and Multi-modal Computing Group, CRIPAC 75 Dec 30, 2022
"Moshpit SGD: Communication-Efficient Decentralized Training on Heterogeneous Unreliable Devices", official implementation

Moshpit SGD: Communication-Efficient Decentralized Training on Heterogeneous Unreliable Devices This repository contains the official PyTorch implemen

Yandex Research 21 Oct 18, 2022
This is the official Pytorch-version code of FlatGCN (Flattened Graph Convolutional Networks for Recommendation).

FlatGCN This is the official Pytorch-version code of FlatGCN (Flattened Graph Convolutional Networks for Recommendation, submitted to ICASSP2022). Req

Dreamer 2 Aug 09, 2022
Links to works on deep learning algorithms for physics problems, TUM-I15 and beyond

Links to works on deep learning algorithms for physics problems, TUM-I15 and beyond

Nils Thuerey 1.3k Jan 08, 2023
TorchIO is a Medical image preprocessing and augmentation toolkit for deep learning. Part of the PyTorch Ecosystem.

Medical image preprocessing and augmentation toolkit for deep learning. Part of the PyTorch Ecosystem.

Fernando Pérez-García 1.6k Jan 06, 2023
Implementation of Neonatal Seizure Detection using EEG signals for deploying on edge devices including Raspberry Pi.

NeonatalSeizureDetection Description Link: https://arxiv.org/abs/2111.15569 Citation: @misc{nagarajan2021scalable, title={Scalable Machine Learn

Vishal Nagarajan 11 Nov 08, 2022
banditml is a lightweight contextual bandit & reinforcement learning library designed to be used in production Python services.

banditml is a lightweight contextual bandit & reinforcement learning library designed to be used in production Python services. This library is developed by Bandit ML and ex-authors of Facebook's app

Bandit ML 51 Dec 22, 2022
2D&3D human pose estimation

Human Pose Estimation Papers [CVPR 2016] - 201511 [IJCAI 2016] - 201602 Other Action Recognition with Joints-Pooled 3D Deep Convolutional Descriptors

133 Jan 02, 2023