Results 11 to 20 of about 28,661,875 (311)
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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Deep Mixture Point Processes: Spatio-temporal Event Prediction with Rich Contextual Information [PDF]
Knowledge Discovery and Data Mining, 2019 Predicting when and where events will occur in cities, like taxi pick-ups, crimes, and vehicle collisions, is a challenging and important problem with many applications in fields such as urban planning, transportation optimization and location-based ...
Maya Okawa +5 more
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DECOMPOSING IMAGES INTO TRIANGLES BY DELAUNAY POINT PROCESSES [PDF]
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2020 We propose a method for decomposing images into triangles. Contrary to superpixel methods, our output representation both preserves the geometric information disseminated in input images, and has an attractive storage capacity.
D. Chai
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Rank-Based Mixture Models for Temporal Point Processes
Frontiers in Applied Mathematics and Statistics, 2022 Temporal point process, an important area in stochastic process, has been extensively studied in both theory and applications. The classical theory on point process focuses on time-based framework, where a conditional intensity function at each given ...
Yang Chen, Yijia Ma, Wei Wu
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Point processes with Gaussian boson sampling. [PDF]
Physical Review E, 2019 Random point patterns are ubiquitous in nature, and statistical models such as point processes, i.e., algorithms that generate stochastic collections of points, are commonly used to simulate and interpret them.
S. Jahangiri +3 more
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Revealing Spectrum Features of Stochastic Neuron Spike Trains
Mathematics, 2020 Power spectra of spike trains reveal important properties of neuronal behavior. They exhibit several peaks, whose shape and position depend on applied stimuli and intrinsic biophysical properties, such as input current density and channel noise.
Simone Orcioni +3 more
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A Variational Auto-Encoder Model for Stochastic Point Processes [PDF]
Computer Vision and Pattern Recognition, 2019 We propose a novel probabilistic generative model for action sequences. The model is termed the Action Point Process VAE (APP-VAE), a variational auto-encoder that can capture the distribution over the times and categories of action sequences.
Nazanin Mehrasa +5 more
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Axioms, 2020 A plethora of applications from mathematical programming, such as minimax, and mathematical programming, penalization, fixed point to mention a few can be framed as equilibrium problems.
Nopparat Wairojjana +3 more
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DETECTING LINEAR FEATURES BY SPATIAL POINT PROCESSES [PDF]
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2016 This paper proposes a novel approach for linear feature detection. The contribution is twofold: a novel model for spatial point processes and a new method for linear feature detection.
D. Chai, A. Schmidt, C. Heipke
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