kejiejiang Goto Github PK
Name: Kejie Jiang
Type: User
Bio: 勤学如春起之苗,不见其增,日有所长。
Name: Kejie Jiang
Type: User
Bio: 勤学如春起之苗,不见其增,日有所长。
Accelerated sampling framework with autoencoder-based method
Code and hyperparameters for the paper "Generative Adversarial Networks"
A wizard's guide to Adversarial Autoencoders
Attention based model for learning to solve different routing problems
Automated Machine Learning with scikit-learn
A complete list of papers on anomaly detection.
A collection of AWESOME things about domian adaptation
A framework for data augmentation for 2D and 3D image classification and segmentation
Building a Bayesian deep learning classifier
Tutorials and examples of advanced sampling methods for solving Bayesian Model Updating Problems
Benchmark TU1402
code for "Isolating Sources of Disentanglement in Variational Autoencoders".
Pytorch implementation of β-VAE
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.
A Python Toolbox for Machine Learning Model Combination
:mortar_board: Path to a free self-taught education in Computer Science!
PyTorch Implement of Context Encoders: Feature Learning by Inpainting
An implement by Pytorch
Convolutional LSTM for Precipitation Nowcasting
Implementation of Convolutional LSTM in PyTorch.
Matlab code for my paper "Copula Variational Bayes inference via information geometry", submitted to IEEE Trans. on information theory, 2018
Code for "Learning data-driven discretizations for partial differential equations"
Destruction and Construction Learning for Fine-grained Image Recognition
PyTorch Implementation for Deep Metric Learning Pipelines
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.
Deep Hidden Physics Models: Deep Learning of Nonlinear Partial Differential Equations
neural networks to learn Koopman eigenfunctions
A declarative, efficient, and flexible JavaScript library for building user interfaces.
🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
An Open Source Machine Learning Framework for Everyone
The Web framework for perfectionists with deadlines.
A PHP framework for web artisans
Bring data to life with SVG, Canvas and HTML. 📊📈🎉
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
Some thing interesting about web. New door for the world.
A server is a program made to process requests and deliver data to clients.
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
Some thing interesting about visualization, use data art
Some thing interesting about game, make everyone happy.
We are working to build community through open source technology. NB: members must have two-factor auth.
Open source projects and samples from Microsoft.
Google ❤️ Open Source for everyone.
Alibaba Open Source for everyone
Data-Driven Documents codes.
China tencent open source team.