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Dropout: a simple way to prevent neural networks from overfitting.

, , , , and . Journal of Machine Learning Research, 15 (1): 1929-1958 (2014)

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Learning Hand-Eye Coordination for Robotic Grasping with Deep Learning and Large-Scale Data Collection, , , and . (2016)cite arxiv:1603.02199Comment: This is an extended version of "Learning Hand-Eye Coordination for Robotic Grasping with Large-Scale Data Collection," ISER 2016. Draft modified to correct typo in Algorithm 1 and add a link to the publicly available dataset.Pedestrian detection with a Large-Field-Of-View deep network., , and . ICRA, page 704-711. IEEE, (2015)ImageNet Classification with Deep Convolutional Neural Networks, , and . (2012)Learning Hand-Eye Coordination for Robotic Grasping with Large-Scale Data Collection., , , and . ISER, volume 1 of Springer Proceedings in Advanced Robotics, page 173-184. Springer, (2016)Using very deep autoencoders for content-based image retrieval., and . ESANN, (2011)Improving neural networks by preventing co-adaptation of feature detectors, , , , and . (Jul 3, 2012)One weird trick for parallelizing convolutional neural networks. (2014)cite arxiv:1404.5997.Transforming Auto-Encoders, , and . ICANN, (2011)ChauffeurNet: Learning to Drive by Imitating the Best and Synthesizing the Worst., , and . Robotics: Science and Systems, (2019)Real-Time Pedestrian Detection with Deep Network Cascades., , , , and . BMVC, page 32.1-32.12. BMVA Press, (2015)