Zobrazeno 1 - 9
of 9
pro vyhledávání: '"Gungor Polatkan"'
Autor:
Gungor Polatkan, Sara Smoot, Gee Jeffrey Douglas, Kirill Talanine, Deepak Kumar, Onkar Anant Dalal, Konstantin Salomatin, Rohan Ramanath
Publikováno v:
KDD
One of the most well-established applications of machine learning is in deciding what content to show website visitors. When observation data comes from high-velocity, user-generated data streams, machine learning methods perform a balancing act betw
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ec56bb94ae65c8b36e8d6a68bbef4481
https://doi.org/10.1145/3447548.3467172
https://doi.org/10.1145/3447548.3467172
Autor:
Tcheprasov Vladislav, Konstantin Salomatin, Deepak Kumar, Sneha Chaudhari, Shivani Rao, Joshi Mahesh S, Gungor Polatkan, Gee Jeffrey Douglas
Publikováno v:
CIKM
We present the evolution of a large-scale content recommendation platform for LinkedIn Learning, serving 645M+ LinkedIn users across several different channels (e.g., desktop, mobile). We address challenges and complexities from both algorithms and i
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::98c3c1fe253c0d3656f8cb09fa0814a9
https://doi.org/10.1145/3357384.3357817
https://doi.org/10.1145/3357384.3357817
Harness the power of social media to predict customer behavior and improve sales Social media is the biggest source of Big Data. Because of this, 90% of Fortune 500 companies are investing in Big Data initiatives that will help them predict consumer
Publikováno v:
IEEE Transactions on Pattern Analysis and Machine Intelligence. 37:346-358
Super-resolution methods form high-resolution images from low-resolution images. In this paper, we develop a new Bayesian nonparametric model for super-resolution. Our method uses a beta-Bernoulli process to learn a set of recurring visual patterns,
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::46e16b3514f6529cf4fddc8ef289c7c9
https://doi.org/10.1109/tpami.2014.2321404
https://doi.org/10.1109/tpami.2014.2321404
Publikováno v:
IEEE Transactions on Pattern Analysis and Machine Intelligence. 35:1887-1901
Unsupervised multi-layered (“deep”) models are considered for general data, with a particular focus on imagery. The model is represented using a hierarchical convolutional factor-analysis construction, with sparse factor loadings and scores. The
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::fa1d22da8af597159b25b54a4d1cbf8c
https://doi.org/10.1109/tpami.2013.19
https://doi.org/10.1109/tpami.2013.19
Autor:
Gungor Polatkan, Joel VanderWerf, Benedicte Bougler, Steven E Shladover, Mustafa Ergen, Raja Sengupta
Publikováno v:
The Lens
Cooperative vehicle systems (CVS) can provide intelligent transportation systems services such as probe vehicle information and hazard warnings by exchanging data among suitably equipped vehicles as they travel. The sensitivity of the performance of
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::dfa3b2c32803e2b42e24e65a433316f5
https://doi.org/10.3141/2000-15
https://doi.org/10.3141/2000-15
Publikováno v:
ICIP
In this paper, computer-based techniques for stylistic analysis of paintings are applied to the five panels of the 14th century Peruzzi Altarpiece by Giotto di Bondone. Features are extracted by combining a dual-tree complex wavelet transform with a
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::5ea9ede4e1ef65722bf4c5a655c45e35
http://arxiv.org/abs/1401.6638
http://arxiv.org/abs/1401.6638
Publikováno v:
ICIP
This paper examines whether machine learning and image analysis tools can be used to assist art experts in the authentication of unknown or disputed paintings. Recent work on this topic [1] has presented some promising initial results. Our reexaminat
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::d502cd2acc7900169ddc59283da1da0a
https://doi.org/10.1109/icip.2009.5413338
https://doi.org/10.1109/icip.2009.5413338