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Found 2 papers, 1 papers with code
Deep graph kernel point processes
Point process models are widely used for continuous asynchronous event data, where each data point includes time and additional information called "marks", which can be locations, nodes, or event types.
Neural Stein critics with staged $L^2$-regularization
In this paper, we investigate the role of $L^2$ regularization in training a neural network Stein critic so as to distinguish between data sampled from an unknown probability distribution and a nominal model distribution.
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