Variational Animal Motion Embedding (VAME): Quick Start Tutorial

Описание к видео Variational Animal Motion Embedding (VAME): Quick Start Tutorial

In this video, I will guide you through the VAME (Variational Animal Motion Embedding) pipeline setup. This video is a quick-start and setup tutorial to get you started quickly.

Vame allows for unsupervised learning, which means it can identify meaningful patterns (motifs) without relying on labeled datasets. This approach enables the analysis of high-dimensional behavioral time series data, multiscale representations of behaviors, and much more. The VAME is also robust to certain forms of noise, and its ability to extract meaningful behavioral motifs is definitely a welcomed feature.

Many features of VAME cannot be covered in a short video like this. Therefore, please check the VAME GitHub repo to learn more:


Code Snippets: https://github.com/LINCellularNeurosc...

VAME: https://github.com/LINCellularNeurosc...


VAME Config.yaml (quick explanation): https://github.com/LINCellularNeurosc...


VAME Workflow: https://github.com/LINCellularNeurosc...

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