Zobrazeno 1 - 6
of 6
pro vyhledávání: '"Ross Goroshin"'
Autor:
Demis Hassabis, Raia Hadsell, Stephen Gaffney, Ross Goroshin, Timothy P. Lillicrap, Charles Beattie, Helen King, Caswell Barry, Piotr Mirowski, Neil C. Rabinowitz, Charles Blundell, Benigno Uria, Andrea Banino, Koray Kavukcuoglu, Joseph Modayil, Alexander Pritzel, Amir Sadik, Thomas Degris, Greg Wayne, Dharshan Kumaran, Stig Petersen, Brian Hu Zhang, Fabio Viola, Martin J. Chadwick, Razvan Pascanu, Hubert Soyer
Publikováno v:
Nature. 557:429-433
Deep neural networks have achieved impressive successes in fields ranging from object recognition to complex games such as Go1,2. Navigation, however, remains a substantial challenge for artificial agents, with deep neural networks trained by reinfor
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::19b8abdc695cb8351ef270f29574661c
https://doi.org/10.1038/s41586-018-0102-6
https://doi.org/10.1038/s41586-018-0102-6
Publikováno v:
ICCV
Current state-of-the-art classification and detection algorithms rely on supervised training. In this work we study unsupervised feature learning in the context of temporally coherent video data. We focus on feature learning from unlabeled video data
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::bdd9a29e2fa031268355b70a02f260b0
http://arxiv.org/abs/1412.6056
http://arxiv.org/abs/1412.6056
Publikováno v:
CVPR
Recent state-of-the-art performance on human-body pose estimation has been achieved with Deep Convolutional Networks (ConvNets). Traditional ConvNet architectures include pooling and sub-sampling layers which reduce computational requirements, introd
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c1345ed6fe8d723b87812efcf5417717
Autor:
Jason C. Isaacs, Ross Goroshin
Publikováno v:
AIPR
The classical paradigm of line and curve detection in images, as prescribed by the Hough transform, breaks down in cluttered and noisy imagery. In this paper we present an "upgraded" and ultimately more robust approach to line detection in images. Th
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::80cb33b4f1ee4a3042553e1a0c46a145
https://doi.org/10.1109/aipr.2010.5759686
https://doi.org/10.1109/aipr.2010.5759686
Autor:
Jason C. Isaacs, Ross Goroshin
Publikováno v:
OCEANS 2010 MTS/IEEE SEATTLE.
The classical paradigm of line and curve detection in images, as prescribed by the Hough transform, breaks down in cluttered and noisy imagery. In this paper we present an “upgraded” and ultimately more robust approach to line detection in images
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::e630ac9f837056a6cc6ee9105dafe101
https://doi.org/10.1109/oceans.2010.5664414
https://doi.org/10.1109/oceans.2010.5664414
Autor:
Ross Goroshin, Jason C. Isaacs
Publikováno v:
SMC
The classical paradigm of line and curve detection in images, as prescribed by the Hough transform, breaks down in cluttered and noisy imagery. In this paper we present an "upgraded" and ultimately more robust approach to line detection in images. Th
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::259cb9bd01390e029cb1d10b8ff81edb
https://doi.org/10.1109/icsmc.2009.5346577
https://doi.org/10.1109/icsmc.2009.5346577