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SqueezeNext: Hardware-Aware Neural Network Design.

, , , , , , , and . CVPR Workshops, page 1638-1647. Computer Vision Foundation / IEEE Computer Society, (2018)

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Convolutional Monte Carlo Rollouts in Go., and . CoRR, (2015)How to scale distributed deep learning?, , , and . CoRR, (2016)SqueezeDet: Unified, Small, Low Power Fully Convolutional Neural Networks for Real-Time Object Detection for Autonomous Driving., , , and . CoRR, (2016)A Novel Domain Adaptation Framework for Medical Image Segmentation., , , , , , , , and . BrainLes@MICCAI (2), volume 11384 of Lecture Notes in Computer Science, page 289-298. Springer, (2018)SqueezeDet: Unified, Small, Low Power Fully Convolutional Neural Networks for Real-Time Object Detection for Autonomous Driving., , , and . CVPR Workshops, page 446-454. IEEE Computer Society, (2017)Spatially Parallel Convolutions., , and . ICLR (Workshop), OpenReview.net, (2018)Shift: A Zero FLOP, Zero Parameter Alternative to Spatial Convolutions., , , , , , , , and . CoRR, (2017)SqueezeNext: Hardware-Aware Neural Network Design., , , , , , , and . CVPR Workshops, page 1638-1647. Computer Vision Foundation / IEEE Computer Society, (2018)Regret Minimization for Partially Observable Deep Reinforcement Learning., , and . CoRR, (2017)Integrated Model, Batch, and Domain Parallelism in Training Neural Networks., , , , and . SPAA, page 77-86. ACM, (2018)