1 citations · 1 across the 3 of their papers we have counts for
3 papers
cs.LG2024
HGNAS: Hardware-Aware Graph Neural Architecture Search for Edge Devices
Ao Zhou, Jianlei Yang, Yingjie Qi +5
Graph Neural Networks (GNNs) are becoming increasingly popular for graph-based learning tasks such as point cloud processing due to their state-of-the-art (SOTA) performance. Never…
cs.CV2023
Lossy and Lossless (L) Post-training Model Size Compression
Yumeng Shi, Shihao Bai, Xiuying Wei +2
Deep neural networks have delivered remarkable performance and have been widely used in various visual tasks. However, their huge size causes significant inconvenience for transmis…
cs.LG2023★ 1 cited
Hardware-Aware Graph Neural Network Automated Design for Edge Computing Platforms
Ao Zhou, Jianlei Yang, Yingjie Qi +4
Graph neural networks (GNNs) have emerged as a popular strategy for handling non-Euclidean data due to their state-of-the-art performance. However, most of the current GNN model de…