Tuning a variational autoencoder for data accountability problem in the Mars Science Laboratory ground data system

• URL: http://arxiv.org/abs/2006.03962v1
• Date: Sat, 6 Jun 2020 20:25:07 GMT
• Title: Tuning a variational autoencoder for data accountability problem in the Mars Science Laboratory ground data system
• Authors: Dounia Lakhmiri, Ryan Alimo and Sebastien Le Digabel
• Abstract summary: $Delta$-MADS is a derivative-free optimization method applied for tuning the architecture of a variational autoencoder trained to detect the data with missing patches. This work presents $Delta$-MADS, a derivative-free optimization method applied for tuning the architecture of a variational autoencoder trained to detect the data with missing patches.
• Score: 3.867363075280544
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The Mars Curiosity rover is frequently sending back engineering and science data that goes through a pipeline of systems before reaching its final destination at the mission operations center making it prone to volume loss and data corruption. A ground data system analysis (GDSA) team is charged with the monitoring of this flow of information and the detection of anomalies in that data in order to request a re-transmission when necessary. This work presents $\Delta$-MADS, a derivative-free optimization method applied for tuning the architecture and hyperparameters of a variational autoencoder trained to detect the data with missing patches in order to assist the GDSA team in their mission.

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