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Found 3 papers, 1 papers with code
Learning Disentangled Behaviour Patterns for Wearable-based Human Activity Recognition
To address this issue, in this work, we proposed a Behaviour Pattern Disentanglement (BPD) framework, which can disentangle the behavior patterns from the irrelevant noises such as personal styles or environmental noises, etc.
Wasserstein Distance Guided Cross-Domain Learning
Assuming the source and target domains data(e. g. images) come from a joint distribution but follow on different marginal distributions, the domain adaptation work aims to infer the joint distribution from the source and target domain to learn the domain invariant features.
Orchestrating the Development Lifecycle of Machine Learning-Based IoT Applications: A Taxonomy and Survey
Machine Learning (ML) and Internet of Things (IoT) are complementary advances: ML techniques unlock complete potentials of IoT with intelligence, and IoT applications increasingly feed data collected by sensors into ML models, thereby employing results to improve their business processes and services.
