Search Results for author:
Found 6 papers, 0 papers with code
SparseDM: Toward Sparse Efficient Diffusion Models
Diffusion models have been extensively used in data generation tasks and are recognized as one of the best generative models.
Toward Efficient Automated Feature Engineering
Specifically, we construct the AFE pipeline based on reinforcement learning setting, where each feature is assigned an agent to perform feature transformation \com{and} selection, and the evaluation score of the produced features in downstream tasks serve as the reward to update the policy.
Learning Moving-Object Tracking with FMCW LiDAR
In this paper, we propose a learning-based moving-object tracking method utilizing our newly developed LiDAR sensor, Frequency Modulated Continuous Wave (FMCW) LiDAR.
SenseMag: Enabling Low-Cost Traffic Monitoring using Non-invasive Magnetic Sensing
Extensive experiment based on real-word field deployment (on the highways in Shenzhen, China) shows that SenseMag significantly outperforms the existing methods in both classification accuracy and the granularity of vehicle types (i. e., 7 types by SenseMag versus 4 types by the existing work in comparisons).
FedDrop: Trajectory-weighted Dropout for Efficient Federated Learning
Federated learning (FL) enables edge clients to train collaboratively while preserving individual's data privacy.
Pay Attention to Features, Transfer Learn Faster CNNs
Deep convolutional neural networks are now widely deployed in vision applications, but a limited size of training data can restrict their task performance.
