Technology
A Appendix: Proofs and Algorithms A.1 Proofs of results in Section 4 Proof of Proposition 4.1. Plug B
(Bertsekas, 1999). Algorithm 1. Furthermore, we call ˆ f (), X We can show that | f () ˆ f () |, 8 2 [, ] . Besides, computing the upper bound claimed in Proposition 4.2 requires finding The second equality is from the fact that the objective function is affine w.r.t. Finally, we verify the rest two components. Finally, we verify the rest two components. This finishes the proof of our claim.
Foreground Clustering for Joint Segmentation and Localization in Videos and Images
Thus, any advancements in segmentation and localization algorithm are automatically transferred to the performance of high level tasks (17).
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However,batch-splitting suffers from problems including the inability to train very large models (due to memory constraints), high latency, and inefficiency at small batch sizes. All of these can be solved by more general distribution strategies (model-parallelism). Unfortunately,efficient model-parallel algorithms tend tobe complicated todiscover, describe, and to implement, particularly on large clusters.
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Neural Information Processing Systems http://nips.cc/
Towards Understanding the Importance of Shortcut Connections in Residual Networks
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