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Detection of Dataset Shifts in Learning-Enabled Cyber-Physical Systems using Variational Autoencoder for Regression

📅 2021
technical research breakthrough
🔵 Rare

📖 Description

Detection of Dataset Shifts in Learning-Enabled Cyber-Physical Systems using Variational Autoencoder for Regression Feiyang Cai Vanderbilt University Nashville, TN [email address redacted] I. Ozdagli Vanderbilt University Nashville, TN [email address redacted] Koutsoukos Vanderbilt University Nashville, TN [email address redacted] Abstract ?Cyber-physical systems (CPSs) use learning-enabled components (LECs) extensively to cope with various complex tasks under high-uncertainty environments. However, the dataset shifts between the training and testing phase may lead the LECs to become ineffective to make large-error predictions, and fur- ther, compromise the safety of the overall system. In our paper, we ?rst provide the formal de?nitions for different types of dataset shifts in learning-enabled CPS. Then, we propose an approach to detect the dataset shifts effectively for regression problems. Our approach is based on the inductive conformal anomaly detection...

📊 Game Impacts

Not verified in game

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Variable Direction Condition
Research proposed: up Always
Papers proposed: up Always
Vibey Doom proposed: up Always

💭 Reactions

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📰 Media Reaction: Not recorded
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🔗 Sources

🏷️ Event Metadata

Think this event's metadata could be improved? Category and tags describe the real-world event and are maintained in pdoom-data. Rarity, game impacts and p(doom) effects are game-mechanical values owned by pdoom1. Each link below goes to the repository that decides that field.

📁 Category Technical Research Breakthrough → Suggest different category
⭐ Rarity 🔵 Rare → Suggest different rarity
🏷️ Tags (0) → Suggest tag changes
📊 Game Impacts (3) 3 variable changes → Suggest impact changes
☢️ p(Doom) Impact Not recorded → Suggest p(doom) change
📝 General Metadata Year, description, reactions → Comprehensive review

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