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Found 8 papers, 3 papers with code
Variational Inference for Neyman-Scott Processes
Neyman-Scott processes (NSPs) have been applied across a range of fields to model points or temporal events with a hierarchy of clusters.
Deep Neyman-Scott Processes
We consider a deep Neyman-Scott process in this paper, for which the building components of a network are all Poisson processes.
Convolutional Deep Exponential Families
We describe convolutional deep exponential families (CDEFs) in this paper.
Automated Measurement of Quasar Redshift with a Gaussian Process
Our technique is an extension of an earlier Gaussian process method for detecting damped Lyman-alpha absorbers (DLAs) in quasar spectra with known redshifts.
Astrophysics of Galaxies Instrumentation and Methods for Astrophysics
CDeepEx: Contrastive Deep Explanations
The second question asks, "Why did you not choose answer B over A?"
Intrusion Detection using Continuous Time Bayesian Networks
For HIDS, we develop a novel learning method to deal with the finite resolution of system log file time stamps, without losing the benefits of our continuous time model.
Expectation Maximization and Complex Duration Distributions for Continuous Time Bayesian Networks
A CTBN is a directed (possibly cyclic) dependency graph over a set of variables, each of which represents a finite state continuous time Markov process whose transition model is a function of its parents.
Expectation Propagation for Continuous Time Bayesian Networks
Continuous time Bayesian networks (CTBNs) describe structured stochastic processes with finitely many states that evolve over continuous time.
