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Found 6 papers, 1 papers with code
LiDAR-based Outdoor Crowd Management for Smart Campus on the Edge
We deploy LiDAR sensors on light poles to collect data from the crowd on the campus and leverage edge accelerators to process data locally.
Confidence-based federated distillation for vision-based lane-centering
Machine learning-based steering angle prediction needs to consider the vehicle's limitation in uploading large amounts of potentially private data for model training.
A Contrastive Knowledge Transfer Framework for Model Compression and Transfer Learning
Knowledge Transfer (KT) achieves competitive performance and is widely used for image classification tasks in model compression and transfer learning.
Enabling Deep Learning on Edge Devices through Filter Pruning and Knowledge Transfer
The results show that 1) our model compression method can remove up to 99. 36% parameters of WRN-28-10, while preserving a Top-1 accuracy of over 90% on CIFAR-10; 2) our knowledge transfer method enables the compressed models to achieve more than 90% accuracy on CIFAR-10 and retain good accuracy on old categories; 3) it allows the compressed models to converge within real time (three to six minutes) on the edge for incremental learning tasks; 4) it enables the model to classify unseen categories of data (78. 92% Top-1 accuracy) that it is never trained with.
Convergence of Generalized Belief Propagation Algorithm on Graphs with Motifs
Belief propagation is a fundamental message-passing algorithm for numerous applications in machine learning.
Multi-Agent Decentralized Belief Propagation on Graphs
We consider the problem of interactive partially observable Markov decision processes (I-POMDPs), where the agents are located at the nodes of a communication network.
