Search Results for author:
Found 3 papers, 1 papers with code
Byzantine-Robust Federated Learning with Variance Reduction and Differential Privacy
Federated learning (FL) is designed to preserve data privacy during model training, where the data remains on the client side (i. e., IoT devices), and only model updates of clients are shared iteratively for collaborative learning.
Distractor-Aware Fast Tracking via Dynamic Convolutions and MOT Philosophy
A practical long-term tracker typically contains three key properties, i. e. an efficient model design, an effective global re-detection strategy and a robust distractor awareness mechanism.
Attention-based Supply-Demand Prediction for Autonomous Vehicles
Short term temporal dependency is captured with LSTM.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
