In domain generalization (DG), the target domain is unknown when the mod...
A fundamental challenge to providing edge-AI services is the need for a
...
Locally supervised learning aims to train a neural network based on a lo...
Few-shot learning allows machines to classify novel classes using only a...
Mixup is a data augmentation method that generates new data points by mi...
Federated learning has been spotlighted as a way to train neural network...
Few-shot learning allows machines to classify novel classes using only a...
Learning novel concepts while preserving prior knowledge is a long-stand...
We tackle a novel few-shot learning challenge, which we call few-shot
se...
Few-shot learning aims to handle previously unseen tasks using only a sm...
In wireless caching networks, a user generally has a concrete purpose of...
Recent advances in large-scale distributed learning algorithms have enab...
Handling previously unseen tasks after given only a few training example...
Coded distributed computing has been considered as a promising technique...
Coded computation is a framework which provides redundancy in distribute...
In this paper, we propose bi-directional cooperative non-orthogonal mult...
In this paper, we propose a joint dynamic power control and user pairing...
This paper considers one-hop device-to-device (D2D)-assisted wireless ca...
We propose a meta learning algorithm utilizing a linear transformer that...
Coding for distributed computing supports low-latency computation by
rel...
Capacity of the distributed storage system (DSS) is often discussed in t...
Clustered distributed storage models real data centers where intra- and
...
Distributed storage systems suffer from significant repair traffic gener...