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Fed-DART and FACT: A solution for Federated Learning in a production environment
Federated Learning as a decentralized artificial intelligence (AI) solution solves a variety of problems in industrial applications.
Toward Unsupervised Test Scenario Extraction for Automated Driving Systems from Urban Naturalistic Road Traffic Data
Therefore, a method for extracting multimodal urban traffic scenarios from naturalistic road traffic data in an unsupervised manner, minimizing the amount of (potentially biased) prior expert knowledge, is proposed.
HALF: Holistic Auto Machine Learning for FPGAs
To efficiently implement DNNs on a specific FPGA platform for a given cost criterion, e. g. energy efficiency, an enormous amount of design parameters has to be considered from the topology down to the final hardware implementation.
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