2 citations · 4 across the 9 of their papers we have counts for
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cs.LG2026
DTA-GT: Direction- and Topology-Aware Graph Transformer for Neural Network Representation Learning
Yuxiang Zeng, Kun Xie, Yang Wang +5
Architecture attribute predictors reduce the cost of neural architecture search by estimating accuracy or latency from a small set of evaluated candidates. The key difficulty is th…
cs.LG2024★ 1 cited
FedFa: A Fully Asynchronous Training Paradigm for Federated Learning
Haotian Xu, Zhaorui Zhang, Sheng Di +3
Federated learning has been identified as an efficient decentralized training paradigm for scaling the machine learning model training on a large number of devices while guaranteei…
cs.LG2023
SGOOD: Substructure-enhanced Graph-Level Out-of-Distribution Detection
Zhihao Ding, Jieming Shi, Shiqi Shen +4
Graph-level representation learning is important in a wide range of applications. Existing graph-level models are generally built on i.i.d. assumption for both training and testing…