Zobrazeno 1 - 10
of 10
pro vyhledávání: '"Eric Tzeng"'
Autor:
Suraj Trivedi, Diana Hylton, Matthew Mueller, Ilona Juan, Christie Mun, Eric Tzeng, Patricia Guan, Maya Filipovic, Sohaib Mandoorah, Alyssa Brezenski, E. Orestes O'Brien, Atul Malhotra, Ulrich Schmidt
Publikováno v:
Cureus, vol 15, iss 2
Introduction The number of subjects infected with the novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) throughout the western hemisphere increased exponentially in the later months of 2020.With this increase in infection, the number
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::8425fae470985650204cbdf75b371280
https://escholarship.org/uc/item/3rx77374
https://escholarship.org/uc/item/3rx77374
Publikováno v:
Essential Echocardiography ISBN: 9783030843489
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::824adc7dd342e22c186b665908d7de86
https://doi.org/10.1007/978-3-030-84349-6_22
https://doi.org/10.1007/978-3-030-84349-6_22
Autor:
Sergey Levine, Kate Saenko, Judy Hoffman, Chelsea Finn, Trevor Darrell, Coline Devin, Eric Tzeng, Pieter Abbeel
Publikováno v:
Springer Proceedings in Advanced Robotics ISBN: 9783030430887
WAFR
WAFR
Real-world robotics problems often occur in domains that differ significantly from the robot’s prior training environment. For many robotic control tasks, real world experience is expensive to obtain, but data is easy to collect in either an instru
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::eeff5807d123bdadf63191e44c471b6d
https://doi.org/10.1007/978-3-030-43089-4_44
https://doi.org/10.1007/978-3-030-43089-4_44
Publikováno v:
KDD
At Pinterest, we utilize image embeddings throughout our search and recommendation systems to help our users navigate through visual content by powering experiences like browsing of related content and searching for exact products for shopping. In th
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::beb17894886e2fc2565a01a84de687d4
Publikováno v:
CVPR
Adversarial learning methods are a promising approach to training robust deep networks, and can generate complex samples across diverse domains. They also can improve recognition despite the presence of domain shift or dataset bias: several adversari
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::1fb8ea88e82045548ddfe2cebdafea85
https://doi.org/10.1109/cvpr.2017.316
https://doi.org/10.1109/cvpr.2017.316
Autor:
Trevor Darrell, Dmitry Kislyuk, Eric Tzeng, Michael Feng, Yushi Jing, Andrew Zhai, Jeff Donahue, Yue Li Du
Publikováno v:
WWW (Companion Volume)
Over the past three years Pinterest has experimented with several visual search and recommendation systems, from enhancing existing products such as Related Pins (2014), to powering new products such as Similar Looks (2015), Flashlight (2016), and Le
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::581bc545b1514cb484c9febef153bee2
Publikováno v:
Large-Scale Visual Geo-Localization ISBN: 9783319257792
Large-Scale Visual Geo-Localization
Large-Scale Visual Geo-Localization
We propose a system for user-aided visual localization of desert imagery without the use of any metadata such as GPS readings, camera focal length, or field-of-view. The system makes use only of publicly available datasets—in particular, digital el
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::08505fdfcf6afd5e176e2305ffdb968f
https://doi.org/10.1007/978-3-319-25781-5_13
https://doi.org/10.1007/978-3-319-25781-5_13
Publikováno v:
ICRA
We consider the problem of learning from demonstrations to manipulate deformable objects. Recent work [1], [2], [3] has shown promising results that enable robotic manipulation of deformable objects through learning from demonstrations. Their approac
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::1c2cad54a3f84c60a78b91bf4703173e
https://doi.org/10.1109/icra.2015.7139644
https://doi.org/10.1109/icra.2015.7139644
Publikováno v:
IROS
Recent work [1], [2] has shown promising results in enabling robotic manipulation of deformable objects through learning from demonstrations. Their method computes a registration from training scene to test scene, and then applies an extrapolation of
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ed9c36986bca3073d04726335b15b135
https://doi.org/10.1109/iros.2014.6943185
https://doi.org/10.1109/iros.2014.6943185
Publikováno v:
CVPR Workshops
We propose a system for user-aided visual localization of desert imagery without the use of any metadata such as GPS readings, camera focal length, or field-of-view. The system makes use only of publicly available digital elevation models (DEMs) to r
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::b596f261fd8c1356d416979361774deb
https://doi.org/10.1109/cvprw.2013.42
https://doi.org/10.1109/cvprw.2013.42