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Found 4 papers, 2 papers with code
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.
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.
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.
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.
