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Found 4 papers, 2 papers with code
Measuring and Improving the Use of Graph Information in Graph Neural Networks
Graph neural networks (GNNs) have been widely used for representation learning on graph data.
A Representation Learning Framework for Property Graphs
Representation learning on graphs, also called graph embedding, has demonstrated its significant impact on a series of machine learning applications such as classification, prediction and recommendation.
Understanding and Improving Proximity Graph based Maximum Inner Product Search
Then we explain the good performance of ip-NSW as matching the norm bias of the MIPS problem - large norm items have big in-degrees in the ip-NSW proximity graph and a walk on the graph spends the majority of computation on these items, thus effectively avoids unnecessary computation on small norm items.
Efficient Private SCO for Heavy-Tailed Data via Clipping
In this paper, we resolve this issue and derive the first high-probability bounds for the private stochastic method with clipping.
