THE LOTTERY TICKET HYPOTHESIS: FINDING SPARSE, TRAINABLE NEURAL NETWORKS论文笔记,程序员大本营,技术文章内容聚合第一站。
The requirements of real biological neural networks are rather modest in comparison, and one feature that might underlie this austerity is their sparse connectivity. In deep learning, trainable sparse networks that perform well on a specific task are usually constructed using label-dependent pruning ...
Neural network pruning techniques can reduce the parameter counts of trained networks by over 90%, decreasing storage requirements and improving computational performance of inference without compromising accuracy. However, contemporary experience is that the sparse architectures produced by pruning are difficul...
[ICLR19] THE LOTTERY TICKET HYPOTHESIS: FINDING SPARSE, TRAINABLE NEURAL NETWORKS 本篇是ICLR2019的两篇Best Paper之一。另一篇:ORDERED NEURONS: INTEGRATING TREE STRUCTURES INTO RECURRENT NEURAL NETWORKS ABSTRACT 神经网络剪枝技术可以在不影响精度的前提下,将训练网络的参数数减少90%以上,降低存储需求,提高...
the mutant trajectory approaches that of the WT, thus the link-knockdown study becomes equivalent to computing the partial derivative (Eq.2). However, the knockdown (or knockout) trajectory introduced here can also be used in another way—to search for sparse or minimal networks as discussed be...