Training Deep Nets with Sublinear Memory Cost
《Training Deep Nets with Sublinear Memory Cost》笔记 摘要 我们提出了一种减少深度神经网络训练时内存消耗的系统性方法。具体来说，我们设计了一个算法，训练一个 n n 层网络仅耗费 O(n−−√) O ( n ) 的内存，每个mini-batch只需要一个额外的前向计算成本。由于许多最先进的模型已经达到了GPU显存的上限，我们的算法允许探索更深入更复杂的
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相关文章
- 1. CHAPTER 11-Training Deep Neural Nets-part3
- 2. Deep Convolutional Nets for Semantic Image Segmentation with Deep Gaussian CRFs
- 3. Deep Stereo Matching with Explicit Cost Aggregation Sub-Architecture
- 4. FitNets: Hints for Thin Deep Nets
- 5. BinaryConnect： Training Deep Neural Networks with binary weights during propagations
- 6. TRAINING DEEP NEURAL NETWORKS WITH LOW PRECISION MULTIPLICATIONS
- 7. Distributed Training using Apache MXNet with Horovod
- 8. Linear regression with one variable - Cost function
- 9. Violations Associated with Nets
- 10. Aspect Level Sentiment Classification with Deep Memory Network笔记