Zobrazeno 1 - 10
of 24
pro vyhledávání: '"Pokropek, Ernest"'
Autor:
Pokropek, Ernest
A significant amount of sex trafficking victims are being advertised on online adult services, which are currently being flooded with spam. Investigators rely on online adult services to track cases of sex trafficking; however, the ever-increasing vo
Externí odkaz:
http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-335022
In this work, we present a pipeline to reconstruct the 3D pose of a horse from 4 simultaneous surveillance camera recordings. Our environment poses interesting challenges to tackle, such as limited field view of the cameras and a relatively closed an
Externí odkaz:
http://arxiv.org/abs/2306.05311
Most action recognition models today are highly parameterized, and evaluated on datasets with appearance-wise distinct classes. It has also been shown that 2D Convolutional Neural Networks (CNNs) tend to be biased toward texture rather than shape in
Externí odkaz:
http://arxiv.org/abs/2112.12175
Autor:
Pokropek, Artur, Pokropek, Ernest
Publikováno v:
Structural Equation Modeling: A Multidisciplinary Journal 2022
While in recent years a number of new statistical approaches have been proposed to model group differences with a different assumption on the nature of the measurement invariance of the instruments, the tools for detecting local misspecifications of
Externí odkaz:
http://arxiv.org/abs/2107.12757
Autor:
Cachay, Salva Rühling, Erickson, Emma, Bucker, Arthur Fender C., Pokropek, Ernest, Potosnak, Willa, Bire, Suyash, Osei, Salomey, Lütjens, Björn
Deep learning-based models have recently outperformed state-of-the-art seasonal forecasting models, such as for predicting El Ni\~no-Southern Oscillation (ENSO). However, current deep learning models are based on convolutional neural networks which a
Externí odkaz:
http://arxiv.org/abs/2104.05089
Autor:
Cachay, Salva Rühling, Erickson, Emma, Bucker, Arthur Fender C., Pokropek, Ernest, Potosnak, Willa, Osei, Salomey, Lütjens, Björn
Deep learning-based models have recently outperformed state-of-the-art seasonal forecasting models, such as for predicting El Ni\~no-Southern Oscillation (ENSO). However, current deep learning models are based on convolutional neural networks which a
Externí odkaz:
http://arxiv.org/abs/2012.01598
Akademický článek
Tento výsledek nelze pro nepřihlášené uživatele zobrazit.
K zobrazení výsledku je třeba se přihlásit.
K zobrazení výsledku je třeba se přihlásit.
Autor:
Cachay, Salva R��hling, Erickson, Emma, Bucker, Arthur Fender C., Pokropek, Ernest, Potosnak, Willa, Bire, Suyash, Osei, Salomey, L��tjens, Bj��rn
Deep learning-based models have recently outperformed state-of-the-art seasonal forecasting models, such as for predicting El Ni��o-Southern Oscillation (ENSO). However, current deep learning models are based on convolutional neural networks whic
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::16e7076855b53c582135e3675a9ffe32
Autor:
Cachay, Salva R��hling, Erickson, Emma, Bucker, Arthur Fender C., Pokropek, Ernest, Potosnak, Willa, Osei, Salomey, L��tjens, Bj��rn
Deep learning-based models have recently outperformed state-of-the-art seasonal forecasting models, such as for predicting El Ni��o-Southern Oscillation (ENSO). However, current deep learning models are based on convolutional neural networks whic
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::f68f7b69ac652db3bddc74ebc6c7631f
Akademický článek
Tento výsledek nelze pro nepřihlášené uživatele zobrazit.
K zobrazení výsledku je třeba se přihlásit.
K zobrazení výsledku je třeba se přihlásit.