Results 51 to 60 of about 500 (159)
Algorithms, 2022 The family of α-divergences including the oriented forward and reverse Kullback–Leibler divergences is often used in signal processing, pattern recognition, and machine learning, among others.
Frank Nielsen
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Sound offset responses become highly informative in the auditory cortex
The Journal of Physiology, EarlyView.Abstract figure legend Using large datasets of neural responses to diverse sounds recorded across four stages of the mouse auditory system (depicted and colour‐coded on the left), offset responses occurring after the termination of each sound (time window indicated by the orange shaded area superimposed with activity traces) were characterized and it ...
Charly Lamothe +3 more
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Entropy, 2014 Information geometry studies the dually flat structure of a manifold, highlighted by the generalized Pythagorean theorem. The present paper studies a class of Bregman divergences called the (ρ,τ)-divergence.
Shun-ichi Amari
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Entropy, 2022 We consider information-theoretic bounds on the expected generalization error for statistical learning problems in a network setting. In this setting, there are K nodes, each with its own independent dataset, and the models from the K nodes have to be ...
Leighton Pate Barnes +2 more
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Distinctive neurophysiological correlates of sound onset and offset perception in humans
The Journal of Physiology, EarlyView.Abstract figure legend Electroencephalography (EEG) recordings were obtained from participants listening to successive pairs of 1‐2 s noises in a silent background (Study 1) or successive pairs 1‐2 s silent gaps in a noise background (Study 2). Participants heard the same stimuli in the context of either a duration discrimination task (identifying ...
Fatima Ali +3 more
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Quantization and clustering with Bregman divergences
Journal of Multivariate Analysis, 2010 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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Worst-case and smoothed analysis of k-means clustering with Bregman divergences
Journal of Computational Geometry, 2013 The k-means method is the method of choice for clustering large-scale data sets and it performs exceedingly well in practice despite its exponential worst-case running-time.
Bodo Manthey, Heiko Roeglin
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Journal of Geophysical Research: Earth Surface, Volume 131, Issue 8, August 2026.Abstract Lateral erosion of salt marshes via cliff retreat is a primary cause of global marsh loss, driven by interactions between hydrodynamics, sediment, and vegetation. While previous studies show a linear relationship between wave power and cliff retreat, models based on this relationship are largely unvalidated for short‐term (sub‐yearly) and ...
S. Dzimballa +7 more
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Modified Jensen-Bregman LogDet Divergence for Target Detection With Region Covariance Descriptor
IEEE AccessIn this study, we exploit the modified Jensen-Bregman LogDet (MJBLD) divergence to measure the dissimilarity between two region covariance descriptors extracted from an image, and design a target detection method based on this descriptor.
Xiqian Fan, Shaozhu Ye
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Information Geometric Approach on Most Informative Boolean Function Conjecture
Entropy, 2018 Let X n be a memoryless uniform Bernoulli source and Y n be the output of it through a binary symmetric channel. Courtade and Kumar conjectured that the Boolean function f : { 0 , 1 } n → { 0 , 1 } that maximizes the ...
Albert No
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