Zobrazeno 1 - 10
of 49
pro vyhledávání: '"Karthik S. Gurumoorthy"'
Autor:
Karthik S. Gurumoorthy, Abhiraj Hinge
Publikováno v:
Machine Learning and Knowledge Discovery in Databases ISBN: 9783031264184
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::a7ec44f34d5f8332871f7a8792d802b9
https://doi.org/10.1007/978-3-031-26419-1_36
https://doi.org/10.1007/978-3-031-26419-1_36
Publikováno v:
2022 30th European Signal Processing Conference (EUSIPCO).
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::0cb97fc40b908e2d15c0e0e92f371ee6
https://doi.org/10.23919/eusipco55093.2022.9909860
https://doi.org/10.23919/eusipco55093.2022.9909860
Publikováno v:
Signal Processing. 165:57-71
In many applications in compressed sensing, the measurement matrix is a Fourier matrix, i.e., it measures the Fourier transform of the underlying signal at some specified ‘base’ frequencies { u i } i = 1 M , where M is the number of measurements.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::cdc5d5fc33cf33fe84293f9f0d4b9f55
https://doi.org/10.1016/j.sigpro.2019.06.033
https://doi.org/10.1016/j.sigpro.2019.06.033
Publikováno v:
SSP
Joint probability density or joint probability mass function (PDF/PMF) estimation is a fundamental machine learning problem. The number of free parameters scales exponentially with respect to the number of random variables. Hence, most work on nonpar
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::601e4ac41b44f32e0fc03f79b9fd5e5c
https://doi.org/10.1109/ssp49050.2021.9513818
https://doi.org/10.1109/ssp49050.2021.9513818
Publikováno v:
Signal Processing. 162:35-53
Recovery error bounds in compressed sensing under Gaussian or uniform bounded noise do not translate easily to the case of Poisson noise. Reasons for this include the signal dependent nature of Poisson noise, and also the fact that the negative log l
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::6b2d3bf45ca20d71527264fdf86313de
https://doi.org/10.1016/j.sigpro.2019.04.004
https://doi.org/10.1016/j.sigpro.2019.04.004
Publikováno v:
Machine Learning and Knowledge Discovery in Databases. Applied Data Science and Demo Track ISBN: 9783030676698
ECML/PKDD (5)
ECML/PKDD (5)
Manifold of product attributes such as dimensions, weight, fragility and liquid content determine the package type used by e-commerce companies to ship products. Sub-optimal package types lead to damaged shipments, incurring huge damage related costs
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::537f6bc5b68b315ef56b42b45a5c1d56
https://doi.org/10.1007/978-3-030-67670-4_18
https://doi.org/10.1007/978-3-030-67670-4_18
Publikováno v:
Machine Learning and Knowledge Discovery in Databases. Applied Data Science Track ISBN: 9783030865139
ECML/PKDD (4)
ECML/PKDD (4)
In this work, we develop an optimal transport (OT) based framework to select informative prototypical examples that best represent a given target dataset. Summarizing a given target dataset via representative examples is an important problem in sever
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::e5c636c29893738646d715b9409aeb4e
https://doi.org/10.1007/978-3-030-86514-6_33
https://doi.org/10.1007/978-3-030-86514-6_33
Publikováno v:
ICDM
Learning from positive ( $P$ ) and unlabeled ( $U$ ) data has a rich history as it finds use in multiple applications. In this paper, we provide a novel framework to tackle this problem in a model agnostic fashion. We say model agnostic since, our so
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::d307e7195171ef66449685c6434c56cd
https://doi.org/10.1109/icdm50108.2020.00019
https://doi.org/10.1109/icdm50108.2020.00019
Autor:
Karthik S. Gurumoorthy, Ajit Rajwade
Publikováno v:
Signal Processing. 188:108186
In this paper, we consider compressive inversion of a signal/image that is sparse in typical orthonormal bases used in image processing, given its measurements that have been corrupted by Poisson noise. The square-root operation is known to convert a
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::f657c945cacf99a9ede16b233bd1c6c3
https://doi.org/10.1016/j.sigpro.2021.108186
https://doi.org/10.1016/j.sigpro.2021.108186
Autor:
Karthik S. Gurumoorthy, Alberto Carrassi, Amit Apte, Christopher K. R. T. Jones, Colin Grudzien
Publikováno v:
SIAM Journal on Control and Optimization. 55:741-759
We prove that for linear, discrete, time-varying, deterministic system (perfect model) with noisy outputs, the Riccati transformation in the Kalman filter asymptotically bounds the rank of the forecast and the analysis error covariance matrices to be
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e373a203cab0e5bdea6983df413cd0ac
https://doi.org/10.1137/15m1025839
https://doi.org/10.1137/15m1025839