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Found 3 papers, 1 papers with code
KubeEdge-Sedna v0.3: Towards Next-Generation Automatically Customized AI Engineering Scheme
Edge-cloud collaborative lifelong learning adapts to data heterogeneity at different edge locations through (1) multi-task transfer learning to achieve accurate prediction of "thousands of people and thousands of faces"; (2) incremental processing of unknown tasks, the more systems learn and the smarter systems are with small samples, gradually realize AI engineering and automation; (3) Use the cloud-side knowledge base to remember new situational knowledge to avoid catastrophic forgetting; (4) The edge-cloud collaborative architecture enables data security compliance and edge AI services to be offline autonomy while applying cloud resources.
MLink: Linking Black-Box Models from Multiple Domains for Collaborative Inference
The cost efficiency of model inference is critical to real-world machine learning (ML) applications, especially for delay-sensitive tasks and resource-limited devices.
On-edge Multi-task Transfer Learning: Model and Practice with Data-driven Task Allocation
We then show that task allocation with task importance for MTL (TATIM) is a variant of the NP-complete Knapsack problem, where the complicated computation to solve this problem needs to be conducted repeatedly under varying contexts.
