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Name: Kevin Chen
Type: User
Bio: Data Scientist + ML Researcher
Twitter: kevrchen
Location: Washington D.C.
Name: Kevin Chen
Type: User
Bio: Data Scientist + ML Researcher
Twitter: kevrchen
Location: Washington D.C.
We present a coupled Variational Auto-Encoder (VAE) method that improves the accuracy and robustness of the probabilistic inferences on represented data. The new method models the dependency between input feature vectors (images) and weighs the outliers with a higher penalty by generalizing the original loss function to the coupled entropy function, using the principles of nonlinear statistical coupling. We evaluate the performance of the coupled VAE model using the MNIST dataset. Compared with the traditional VAE algorithm, the output images generated by the coupled VAE method are clearer and less blurry. The visualization of the input images embedded in 2D latent variable space provides a deeper insight into the structure of new model with coupled loss function: the latent variable has a smaller deviation and the output values are generated by a more compact latent space. We analyze the histograms of probabilities for the input images using the generalized mean metrics, in which increased geometric mean illustrates that the average likelihood of input data is improved. Increases in the -2/3 mean, which is sensitive to outliers, indicates improved robustness. The decisiveness, measured by the arithmetic mean of the likelihoods, is unchanged and -2/3 mean shows that the new model has better robustness.
Repo for the Deep Reinforcement Learning Nanodegree program
End-to-end deep learning predictive modeling package for time-series data.
Google Research
A toolkit for developing and comparing reinforcement learning algorithms.
A Udacity Machine Learning Engineer (MLE) Nanodegree project where I classify images of dogs and even humans and objects into types of dog breed based on resemblance using Convolutional Neural Networks (CNNs).
Code and resources for Machine Learning for Algorithmic Trading, 2nd edition.
Training and Deploying Machine Learning Models with Containers
NeuralProphet: A simple forecasting package
PlayGround: AI Research into Multi-Agent Learning.
Pipeline between Python ETL code and PostgreSQL data queries.
Developing applications on top of IOTA Tangle made easy! Using Python (pyota).
Teach a Quadcopter How to Fly!
My Udacity Machine Learning Engineer (MLE) Nanodegree projects.
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.