Zobrazeno 1 - 10
of 996
pro vyhledávání: '"Vincent Vanhoucke"'
Autor:
Andrew E Bruno, Patrick Charbonneau, Janet Newman, Edward H Snell, David R So, Vincent Vanhoucke, Christopher J Watkins, Shawn Williams, Julie Wilson
Publikováno v:
PLoS ONE, Vol 13, Iss 6, p e0198883 (2018)
The Machine Recognition of Crystallization Outcomes (MARCO) initiative has assembled roughly half a million annotated images of macromolecular crystallization experiments from various sources and setups. Here, state-of-the-art machine learning algori
Externí odkaz:
https://doaj.org/article/7621afe3250a43f389b137d5292eab35
Autor:
Laura Downs, Anthony Francis, Nate Koenig, Brandon Kinman, Ryan Hickman, Krista Reymann, Thomas B. McHugh, Vincent Vanhoucke
Interactive 3D simulations have enabled breakthroughs in robotics and computer vision, but simulating the broad diversity of environments needed for deep learning requires large corpora of photo-realistic 3D object models. To address this need, we pr
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b50bfc9d4cc0eabf60c0ccaf71fea8b7
http://arxiv.org/abs/2204.11918
http://arxiv.org/abs/2204.11918
Publikováno v:
ICRA
Mapping and localization, preferably from a small number of observations, are fundamental tasks in robotics. We address these tasks by combining spatial structure (differentiable mapping) and end-to-end learning in a novel neural network architecture
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::9f2988a2a7a3bd8e73e787fb617bcabf
Publikováno v:
IROS
For applications in e-commerce, warehouses, healthcare, and home service, robots are often required to search through heaps of objects to grasp a specific target object. For mechanical search, we introduce X-Ray, an algorithm based on learned occupan
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::4924844e989680072f65b5fc94c8afea
Autor:
David R. So, Christopher J. Watkins, Shawn P. Williams, Julie Wilson, Vincent Vanhoucke, Andrew E. Bruno, Edward H. Snell, Janet Newman, Patrick Charbonneau
Publikováno v:
PLoS ONE, Vol 13, Iss 6, p e0198883 (2018)
PLoS ONE
PLoS ONE
The Machine Recognition of Crystallization Outcomes (MARCO) initiative has assembled roughly half a million annotated images of macromolecular crystallization experiments from various sources and setups. Here, state-of-the-art machine learning algori
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::d6114206f4cdd4f389418a232f31698d
https://eprints.whiterose.ac.uk/131489/1/crystallisation_classification.pdf
https://eprints.whiterose.ac.uk/131489/1/crystallisation_classification.pdf
Autor:
Vincent Vanhoucke, Danijar Hafner, Steven Bohez, Jie Tan, Tingnan Zhang, Atil Iscen, Yunfei Bai, Erwin Coumans
Publikováno v:
Robotics: Science and Systems
Designing agile locomotion for quadruped robots often requires extensive expertise and tedious manual tuning. In this paper, we present a system to automate this process by leveraging deep reinforcement learning techniques. Our system can learn quadr
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::cdba5c4a915482b7384fe60d0baff2d9
YouTube-BoundingBoxes: A Large High-Precision Human-Annotated Data Set for Object Detection in Video
Publikováno v:
CVPR
We introduce a new large-scale data set of video URLs with densely-sampled object bounding box annotations called YouTube-BoundingBoxes (YT-BB). The data set consists of approximately 380,000 video segments about 19s long, automatically selected to f
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::854adcab72b2ff2ad9e32473dff9f66f
http://arxiv.org/abs/1702.00824
http://arxiv.org/abs/1702.00824
Autor:
Alex Irpan, Sergey Levine, Vincent Vanhoucke, Konstantinos Bousmalis, Paul Wohlhart, Peter Pastor, Laura Downs, Julian Ibarz, Yunfei Bai, Matthew Kelcey, Mrinal Kalakrishnan, Kurt Konolige
Publikováno v:
ICRA
Instrumenting and collecting annotated visual grasping datasets to train modern machine learning algorithms can be extremely time-consuming and expensive. An appealing alternative is to use off-the-shelf simulators to render synthetic data for which
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::6c7f9eb38a322db476b954fd724bbbc1
Publikováno v:
CVPR
Convolutional networks are at the core of most state of-the-art computer vision solutions for a wide variety of tasks. Since 2014 very deep convolutional networks started to become mainstream, yielding substantial gains in various benchmarks. Althoug
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::01ba150cc936b662255ef8c6d508333c
https://doi.org/10.1109/cvpr.2016.308
https://doi.org/10.1109/cvpr.2016.308