Results 31 to 40 of about 245,725 (186)
The Bregman Chord Divergence [PDF]
, 2019 Distances are fundamental primitives whose choice significantly impacts the performances of algorithms in machine learning and signal processing. However selecting the most appropriate distance for a given task is an endeavor. Instead of testing one by one the entries of an ever-expanding dictionary of {\em ad hoc} distances, one rather prefers to ...
Frank Nielsen, Richard Nock
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Minimax quantum state estimation under Bregman divergence [PDF]
Quantum, 2019 We investigate minimax estimators for quantum state tomography under general Bregman divergences. First, generalizing the work of Koyama et al. [Entropy 19, 618 (2017)] for relative entropy, we find that given any estimator for a quantum state, there ...
Maria Quadeer +2 more
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Learning to Approximate a Bregman Divergence
CoRR, 2019 19 pages, 4 ...
Ali Siahkamari +4 more
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Update of Prior Probabilities by Minimal Divergence
Entropy, 2021 The present paper investigates the update of an empirical probability distribution with the results of a new set of observations. The update reproduces the new observations and interpolates using prior information.
Jan Naudts
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Inexact Bregman iteration with an application to Poisson data reconstruction [PDF]
, 2013 This work deals with the solution of image restoration problems by an iterative regularization method based on the Bregman iteration. Any iteration of this scheme requires to exactly compute the minimizer of a function.
Ruggiero, V +3 more
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Neural Bregman Divergences for Distance Learning
CoRR, 2022 Many metric learning tasks, such as triplet learning, nearest neighbor retrieval, and visualization, are treated primarily as embedding tasks where the ultimate metric is some variant of the Euclidean distance (e.g., cosine or Mahalanobis), and the algorithm must learn to embed points into the pre-chosen space.
Fred Lu, Edward Raff, Francis Ferraro
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Hessian geometric structure of chemical thermodynamic systems with stoichiometric constraints
Physical Review Research, 2022 We establish a Hessian geometric structure in chemical thermodynamics, which describes chemical reaction networks (CRNs) with equilibrium states. In our setup, the ideal gas assumption and mass action kinetics are not required.
Yuki Sughiyama +3 more
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Parametric Bayesian Estimation of Differential Entropy and Relative Entropy
Entropy, 2010 Given iid samples drawn from a distribution with known parametric form, we propose the minimization of expected Bregman divergence to form Bayesian estimates of differential entropy and relative entropy, and derive such estimators for the uniform ...
Maya Gupta, Santosh Srivastava
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Bregman superquantiles. Estimation methods and applications
Dependence Modeling, 2016 In thiswork,we extend some parameters built on a probability distribution introduced before to the casewhere the proximity between real numbers is measured by using a Bregman divergence.
Labopin-Richard T. +3 more
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Divergence Family Contribution to Data Evaluation in Blockchain Via Alpha-EM and Log-EM Algorithms
IEEE Access, 2021 This study interrelates three adjacent topics in data evaluation. The first is the establishment of a relationship between Bregman divergence and probabilistic alpha-divergence.
Yasuo Matsuyama
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