This paper presents a holistic approach to gradient leakage resilient
di...
Federated Learning (FL) has been gaining popularity as a collaborative
l...
Budgeted adaptive inference with early exits is an emerging technique to...
Ensemble learning is gaining renewed interests in recent years. This pap...
Deep neural networks based object detection models have revolutionized
c...
This paper presents LDP-Fed, a novel federated learning system with a fo...
Federated learning (FL) is an emerging distributed machine learning fram...
The rapid growth of real-time huge data capturing has pushed the deep
le...
Deep neural network (DNN) has demonstrated its success in multiple domai...
Ensemble learning is a methodology that integrates multiple DNN learners...
Deep neural networks (DNNs) have demonstrated impressive performance on ...
Learning Rate (LR) is an important hyper-parameter to tune for effective...
Much sequential data exhibits highly non-uniform information distributio...
Representation learning of pedestrian trajectories transforms variable-l...