Recurrent GAN for imputation of time series data. Implemented in TensorFlow 2 on Wikipedia Web Traffic Forecast dataset from Kaggle.

Natural Language Processing Tutorial for Deep Learning Researchers

ASSISTments Data Mining Competition 2017 - Dario Anongba Varela Optional Semester Project

In this project, we will predict the performance of student ability using machine learning based on KDD Cup 2010 dataset. And we have got much more than full score on it.

Creating MAR, MCAR and MNAR missingness and handling missingness with different techniques. Prediction of house prices by using different machine learning algortihms and comparing results.

Code for paper EDDI: Efficient Dynamic Discovery of High-Value Information with Partial VAE

PyTorch implementation of VIGAN

Graph Imputation Neural Network

Denoising Adversairal Autoencoders

Autoencoders for missing imputation on small data sets ,自编码器在小数据集上的缺失插补方法

This is a thesis project about comparing imputation performances between deep learning methods and conventional statistical methods. In this project, GAIN and VAE with One-Hot and trainable embeddi…

Exploratory Data Analysis, Dealing with Missing Values, Data Munging, Ensembled Regression Model using Stacked Regressor, XGBoost and microsoft Lightxgb

Data imputations library to preprocess datasets with missing data

Matt Brems' Missing Data Workshop

Missing data visualization module for Python.

The Belk Endowment Educational Attainment Data Repository for North Carolina Public Schools

PyTorch implementation of "MIDA: Multiple Imputation using Denoising Autoencoders"

Tianchi visualizaiton competition

Missing Data Imputation for Python

Code for the paper "Generating Multi-Categorical Samples with Generative Adversarial Networks"

Implementations of a number of generative models in Tensorflow 2. GAN, VAE, Seq2Seq, VAEGAN, GAIA, Spectrogram Inversion. Everything is self contained in a jupyter notebook for easy export to colab.

A single jupyter notebook multi gpu VAE-GAN example with latent space algebra and receptive field visualizations.

Code and hyperparameters for the paper "Generative Adversarial Networks"

Deep Convolutional Generative Adversarial Networks

Generative image model with learned similarity measures

AttGAN: Facial Attribute Editing by Only Changing What You Want (IEEE TIP 2019)

A command-line utility program for automating the trivial, frequently occurring data preparation tasks: missing value interpolation, outlier removal, and encoding categorical variables.