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Found 5 papers, 1 papers with code
Measuring and Sampling: A Metric-guided Subgraph Learning Framework for Graph Neural Network
We demonstrate the effectiveness and efficiency of MeGuide in training various GNNs on multiple datasets.
Label Contrastive Coding based Graph Neural Network for Graph Classification
Graph classification is a critical research problem in many applications from different domains.
Ripple Walk Training: A Subgraph-based training framework for Large and Deep Graph Neural Network
To deal with these problems, in this paper, we propose a general subgraph-based training framework, namely Ripple Walk Training (RWT), for deep and large graph neural networks.
BGADAM: Boosting based Genetic-Evolutionary ADAM for Neural Network Optimization
To resolve this problem and further maximize the advantages of genetic algorithm with base learners, we propose to implement the boosting strategy for input model training, which can subsequently improve the effectiveness of genetic algorithm.
DEAM: Adaptive Momentum with Discriminative Weight for Stochastic Optimization
Optimization algorithms with momentum, e. g., (ADAM), have been widely used for building deep learning models due to the faster convergence rates compared with stochastic gradient descent (SGD).
