kejiejiang Goto Github PK

accelerated_sampling_with_autoencoder

Accelerated sampling framework with autoencoder-based method

adversarial

Code and hyperparameters for the paper "Generative Adversarial Networks"

adversarial_autoencoder

A wizard's guide to Adversarial Autoencoders

attention-learn-to-route

Attention based model for learning to solve different routing problems

auto-sklearn

Automated Machine Learning with scikit-learn

awesome-anomaly-detection

A complete list of papers on anomaly detection.

awsome-domain-adaptation

A collection of AWESOME things about domian adaptation

batchgenerators

A framework for data augmentation for 2D and 3D image classification and segmentation

bayesian-neural-network-blogpost

Building a Bayesian deep learning classifier

benchmarktu1402

Benchmark TU1402

beta-tcvae

code for "Isolating Sources of Disentanglement in Variational Autoencoders".

beta-vae

Pytorch implementation of β-VAE

binary-autoencoder

cnn-convlstm-time-series

Inspired by the success and computational efficiency of convolutional architectures for various sequential tasks compared to recurrent neural networks. We explored CNN and RCNN autoencoder whose representations can be utilized for the task of time-series classification. Our results surpass existing RNN and DTW-based-classifiers on 11 out of 30 datasets while the existing RNN achieved 8/30.

combo

A Python Toolbox for Machine Learning Model Combination

computer-science

:mortar_board: Path to a free self-taught education in Computer Science!

context_encoder_pytorch

PyTorch Implement of Context Encoders: Feature Learning by Inpainting

convlstm

An implement by Pytorch

convlstm-2

Convolutional LSTM for Precipitation Nowcasting

convlstm_pytorch

Implementation of Convolutional LSTM in PyTorch.

copula-variational-bayes

Matlab code for my paper "Copula Variational Bayes inference via information geometry", submitted to IEEE Trans. on information theory, 2018

data-driven-discretization-1d

Code for "Learning data-driven discretizations for partial differential equations"

dcl

Destruction and Construction Learning for Fine-grained Image Recognition

dec-pytorch

dec-pytorch-1

deep-metric-learning-baselines

PyTorch Implementation for Deep Metric Learning Pipelines

deepad

Detection of Accounting Anomalies in the Latent Space using Adversarial Autoencoder Neural Networks - A lab we prepared for the KDD'19 Workshop on Anomaly Detection in Finance that will walk you through the detection of interpretable accounting anomalies using adversarial autoencoder neural networks. The majority of the lab content is based on Jupyter Notebook, Python and PyTorch.

deephpms

Deep Hidden Physics Models: Deep Learning of Nonlinear Partial Differential Equations

deepkoopman

neural networks to learn Koopman eigenfunctions

deepmind-research

This repository contains implementations and illustrative code to accompany DeepMind publications