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Found 10 papers, 3 papers with code
Federated Transfer Learning with Task Personalization for Condition Monitoring in Ultrasonic Metal Welding
Recently, machine learning models emerged as a promising tool for CM in many manufacturing applications due to their ability to learn complex patterns.
FedCore: Straggler-Free Federated Learning with Distributed Coresets
Federated learning (FL) is a machine learning paradigm that allows multiple clients to collaboratively train a shared model while keeping their data on-premise.
WeldMon: A Cost-effective Ultrasonic Welding Machine Condition Monitoring System
Ultrasonic welding machines play a critical role in the lithium battery industry, facilitating the bonding of batteries with conductors.
Hierarchical Semi-Supervised Contrastive Learning for Contamination-Resistant Anomaly Detection
Anomaly detection aims at identifying deviant samples from the normal data distribution.
Robust and Personalized Federated Learning with Spurious Features: an Adversarial Approach
To this end, we propose a strategy to mitigate the effect of spurious features based on our observation that the global model in the federated learning step has a low accuracy disparity due to statistical heterogeneity.
CrossRoI: Cross-camera Region of Interest Optimization for Efficient Real Time Video Analytics at Scale
Video cameras are pervasively deployed in city scale for public good or community safety (i. e. traffic monitoring or suspected person tracking).
DeepRT: A Soft Real Time Scheduler for Computer Vision Applications on the Edge
Based on this analysis, we propose a GPU scheduler, DeepRT, which provides latency guarantee to the requests while maintaining high overall system throughput.
Robusta: Robust AutoML for Feature Selection via Reinforcement Learning
However, these AutoML pipelines only focus on improving the learning accuracy of benign samples while ignoring the ML model robustness under adversarial attacks.
Identifying Coarse-grained Independent Causal Mechanisms with Self-supervision
Current approaches for learning disentangled representations assume that independent latent variables generate the data through a single data generation process.
Serdab: An IoT Framework for Partitioning Neural Networks Computation across Multiple Enclaves
To address this challenge, we present Serdab, a distributed orchestration framework for deploying deep neural network computation across multiple secure enclaves (e. g., Intel SGX).
