Results 1 to 10 of about 30,785,448 (215)
Deconvolution of calcium imaging data using marked point processes. [PDF]
PLoS Computational Biology, 2020 Calcium imaging has been widely used for measuring spiking activities of neurons. When using calcium imaging, we need to extract summarized information from the raw movie beforehand.
Ryohei Shibue, Fumiyasu Komaki
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Spatio-temporal Diffusion Point Processes [PDF]
Knowledge Discovery and Data Mining, 2023 Spatio-temporal point process (STPP) is a stochastic collection of events accompanied with time and space. Due to computational complexities, existing solutions for STPPs compromise with conditional independence between time and space, which consider the
Yuan Yuan +4 more
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Neural Temporal Point Processes: A Review [PDF]
International Joint Conference on Artificial Intelligence, 2021 Temporal point processes (TPP) are probabilistic generative models for continuous-time event sequences. Neural TPPs combine the fundamental ideas from point process literature with deep learning approaches, thus enabling construction of flexible and ...
Oleksandr Shchur +3 more
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Determinantal Point Processes in Randomized Numerical Linear Algebra [PDF]
Notices of the American Mathematical Society, 2020 Randomized Numerical Linear Algebra (RandNLA) uses randomness to develop improved algorithms for matrix problems that arise in scientific computing, data science, machine learning, etc. Determinantal Point Processes (DPPs), a seemingly unrelated topic in
Michal Derezinski, Michael W. Mahoney
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Information criteria for inhomogeneous spatial point processes [PDF]
Australian & New Zealand journal of statistics (Print), 2020 The theoretical foundation for a number of model selection criteria is established in the context of inhomogeneous point processes and under various asymptotic settings: infill, increasing domain and combinations of these.
Achmad Choiruddin +2 more
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Point Processes in a Metric Space and Their Applications
Mathematics, 2022 Point processes are important in extreme value theory due to their equivalent formulations of two popular models in various applications: the block maxima models and the peak-over-threshold model.
Yuwei Zhao
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Rain process models and convergence to point processes [PDF]
Nonlinear Processes in Geophysics, 2023 A variety of stochastic models have been used to describe time series of precipitation or rainfall. Since many of these stochastic models are simplistic, it is desirable to develop connections between the stochastic models and the underlying physics of ...
S. Hottovy, S. N. Stechmann
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Further results on why a point process is effective for estimating correlation between brain regions
Papers in Physics, 2020 Signals from brain functional magnetic resonance imaging (fMRI) can be efficiently represented by a sparse spatiotemporal point process, according to a recently introduced heuristic signal processing scheme.
I. Cifre +4 more
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Determinantal Point Processes for Machine Learning [PDF]
Found. Trends Mach. Learn., 2012 Determinantal point processes (DPPs) are elegant probabilistic models of repulsion that arise in quantum physics and random matrix theory. In contrast to traditional structured models like Markov random fields, which become intractable and hard to ...
Alex Kulesza, B. Taskar
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Exponential Inequality of Marked Point Processes
Mathematics, 2023 This paper presents the uniform concentration inequality for the stochastic integral of marked point processes. We developed a new chaining method to obtain the results.
Chen Li, Yuping Song
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