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Exploring N-dimensional latent spaces generated by neural variational autoencoders

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Hackathorn/LS_Workshop

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LS_Workshop

The overall system is called Exploratory Semi-Supervised Machine Learning (ESS-ML). It is composed of following enterprise pipeline to analyze datasets in terms of its latent (or hidden) space, thus revealing relationships useful to understanding and utilizing the data. ESS-ML Pipeline

This repository contains the code for Exploring LS Patterns (Step 6), which takes data from the LS Data Server and preparing it for analysis in the next step - Applying LS Insights to a specific business problem by domain experts. It consists mainly of a Unity3D project that uses the Oculus Quest device to explore high-dimensional spaces for these latent spaces.

Input Data

The input data consists several datasets that specify: (a) overall plot of the latent space, (b) points for data samples, and (c) clusters of points according to various criteria.

Input Data Formats