A survey of on-device learning in TinyML organized by distribution change regimes, highlighting influences on applications, hardware, and solutions plus a gap between benchmarks and deployments.
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Filtering algorithms reconstruct trajectories of in-silico particles in a stirred tank reactor from noisy IMU data with errors below 10%.
citing papers explorer
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What changes after deployment? A survey on On-device Learning in TinyML
A survey of on-device learning in TinyML organized by distribution change regimes, highlighting influences on applications, hardware, and solutions plus a gap between benchmarks and deployments.
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Tracking in-silico Lagrangian sensors in a lab-scale stirred tank reactor
Filtering algorithms reconstruct trajectories of in-silico particles in a stirred tank reactor from noisy IMU data with errors below 10%.