The lottery ticket hypothesis: finding sparse, trainable neural networks. In International Conference on Learning Representations (ICLR), 2019. 概 网络稀疏性中的 Lottery Ticket Hypothesis. 动机 在此之前的网络的 pruning 通常采取这样的操作: 一个较大的网络 f(⋅;θ)f(⋅;θ); 进行一次完整的训练...
ICLR 2019最佳论文:The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks,程序员大本营,技术文章内容聚合第一站。
[ICLR19] THE LOTTERY TICKET HYPOTHESIS: FINDING SPARSE, TRAINABLE NEURAL NETWORKS,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 ...
event-based sensors and spiking neural networks, provide a powerful computational framework to explore the principles of insect-inspired obstacle avoidance on a real robotic platform. Event-based sensors inherit precise timing information in their sparse output, which enables real-time OF computation ...
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 below. The network shown as topology #1 in Fig. 2c contains both of the elementary adaptation motifs: negative feedback loop and ...
[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...