Graph neural network for traffic forecasting: A survey
Traffic forecasting is important for the success of intelligent transportation systems. Deep
learning models, including convolution neural networks and recurrent neural networks, have …
learning models, including convolution neural networks and recurrent neural networks, have …
Graph neural network: A comprehensive review on non-euclidean space
This review provides a comprehensive overview of the state-of-the-art methods of graph-
based networks from a deep learning perspective. Graph networks provide a generalized …
based networks from a deep learning perspective. Graph networks provide a generalized …
A metaverse: Taxonomy, components, applications, and open challenges
SM Park, YG Kim - IEEE access, 2022 - ieeexplore.ieee.org
Unlike previous studies on the Metaverse based on Second Life, the current Metaverse is
based on the social value of Generation Z that online and offline selves are not different …
based on the social value of Generation Z that online and offline selves are not different …
Federated learning from pre-trained models: A contrastive learning approach
Federated Learning (FL) is a machine learning paradigm that allows decentralized clients to
learn collaboratively without sharing their private data. However, excessive computation and …
learn collaboratively without sharing their private data. However, excessive computation and …
Free lunch for few-shot learning: Distribution calibration
Learning from a limited number of samples is challenging since the learned model can
easily become overfitted based on the biased distribution formed by only a few training …
easily become overfitted based on the biased distribution formed by only a few training …
Graph meta learning via local subgraphs
Prevailing methods for graphs require abundant label and edge information for learning.
When data for a new task are scarce, meta-learning can learn from prior experiences and …
When data for a new task are scarce, meta-learning can learn from prior experiences and …
Progressive meta-learning with curriculum
Meta-learning offers an effective solution to learn new concepts under scarce supervision
through an episodic-training scheme: a series of target-like tasks sampled from base classes …
through an episodic-training scheme: a series of target-like tasks sampled from base classes …
Learning to collaborate in decentralized learning of personalized models
Learning personalized models for user-customized computer-vision tasks is challenging due
to the limited private-data and computation available on each edge device. Decentralized …
to the limited private-data and computation available on each edge device. Decentralized …
A universal representation transformer layer for few-shot image classification
Few-shot classification aims to recognize unseen classes when presented with only a small
number of samples. We consider the problem of multi-domain few-shot image classification …
number of samples. We consider the problem of multi-domain few-shot image classification …
Graph prototypical networks for few-shot learning on attributed networks
Attributed networks nowadays are ubiquitous in a myriad of high-impact applications, such
as social network analysis, financial fraud detection, and drug discovery. As a central …
as social network analysis, financial fraud detection, and drug discovery. As a central …