Detection of Accounting Anomalies using Deep Autoencoder Neural Networks - A lab we prepared for NVIDIA's GPU Technology Conference 2018 that will walk you through the detection of accounting anomalies using deep autoencoder neural networks. The majority of the lab content is based on Jupyter Notebook, Python and PyTorch.

State Representation Learning (SRL) zoo with PyTorch - Part of S-RL Toolbox

Focal Frequency Loss for Generative Models

Generative Probabilistic Novelty Detection with Adversarial Autoencoders

rectorch is a pytorch-based framework for state-of-the-art top-N recommendation

PyTorch implementation of DEC (Deep Embedding Clustering)

Structural Deep Clustering Network

Generative Adversarial Networks and Autoencoders for 3D Shapes

Deep learning meets molecular dynamics.

Tensorflow implementation of conditional variational auto-encoder for MNIST

Deep Learning sample programs using PyTorch in C++

Convolutional Autoencoder for Loop Closure

Code & data accompanying the KDD 2017 paper "KATE: K-Competitive Autoencoder for Text"

Autoencoders in Keras

A simple Tensorflow based library for deep and/or denoising AutoEncoder.

R, Python and Mathematica Codes in Machine Learning, Deep Learning, Artificial Intelligence, NLP and Geolocation

Training and evaluating a variational autoencoder for pan-cancer gene expression data

KitNET is a lightweight online anomaly detection algorithm, which uses an ensemble of autoencoders.

Implementation of the stacked denoising autoencoder in Tensorflow

Split-Brain Autoencoders: Unsupervised Learning by Cross-Channel Prediction. In CVPR, 2017.