84 citations · 347 across the 30 of their papers we have counts for
5 papers · 1 filter
A data-centric approach for assessing progress of Graph Neural Networks
Tianqi Zhao, Ngan Thi Dong, Alan Hanjalic +1
Graph Neural Networks (GNNs) have achieved state-of-the-art results in node classification tasks. However, most improvements are in multi-class classification, with less focus on t…
AGALE: A Graph-Aware Continual Learning Evaluation Framework
Tianqi Zhao, Alan Hanjalic, Megha Khosla
In recent years, continual learning (CL) techniques have made significant progress in learning from streaming data while preserving knowledge across sequential tasks, particularly…
Multi-label Node Classification On Graph-Structured Data
Tianqi Zhao, Ngan Thi Dong, Alan Hanjalic +1
Graph Neural Networks (GNNs) have shown state-of-the-art improvements in node classification tasks on graphs. While these improvements have been largely demonstrated in a multi-cla…
S2IGAN: Speech-to-Image Generation via Adversarial Learning
Xinsheng Wang, Tingting Qiao, Jihua Zhu +2
An estimated half of the world's languages do not have a written form, making it impossible for these languages to benefit from any existing text-based technologies. In this paper,…
Are Nearby Neighbors Relatives?: Testing Deep Music Embeddings
Jaehun Kim, Julián Urbano, Cynthia C. S. Liem +1
Deep neural networks have frequently been used to directly learn representations useful for a given task from raw input data. In terms of overall performance metrics, machine learn…