Search Results for author:
Found 5 papers, 1 papers with code
Proportional Fairness in Federated Learning
With the increasingly broad deployment of federated learning (FL) systems in the real world, it is critical but challenging to ensure fairness in FL, i. e. reasonably satisfactory performances for each of the numerous diverse clients.
An Operator Splitting View of Federated Learning
Over the past few years, the federated learning ($\texttt{FL}$) community has witnessed a proliferation of new $\texttt{FL}$ algorithms.
PePScenes: A Novel Dataset and Baseline for Pedestrian Action Prediction in 3D
To this end, we propose a new pedestrian action prediction dataset created by adding per-frame 2D/3D bounding box and behavioral annotations to the popular autonomous driving dataset, nuScenes.
Graph-SIM: A Graph-based Spatiotemporal Interaction Modelling for Pedestrian Action Prediction
2) We introduce a new dataset that provides 3D bounding box and pedestrian behavioural annotations for the existing nuScenes dataset.
Non-Parametric Graph Learning for Bayesian Graph Neural Networks
A Bayesian framework which targets posterior inference of the graph by considering it as a random quantity can be beneficial.
