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Scalable Learning in Computer Vision
This video was recorded at NIPS Workshops, Whistler 2009. Computer vision is a challenging application area of machine learning. Recent work has shown that large training sets may yield higher performance in vision tasks like object detection. We overview our work in object detection using a scalable, distributed training system capable of training on more than 100 million examples in just a few hours. We also briefly describe recent work with deep learning algorithms that may allow us to apply these architectures to large datasets as well.
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This work is licensed under a Attribution-NonCommercial-NoDerivs 3.0 United States
