This project explores the Lottery Ticket Hypothesis: the conjecture that neural networks contain much smaller sparse subnetworks capable of training to full accuracy.In the course of this project, we have demonstrated that these subnetworks existed at initialization in small networks and early in training in larger networks. Neural Networks and the Lottery Ticket Hypothesis 2019-05-07 - 3 minutes read. The Lottery Ticket Hypothesis: Finding Small, Trainable Neural Networks TFUG, NN論文を肴に酒を飲む会 #6 2018/7/5 紹介者：進矢陽介 2018/7/10 追記・修正 2. 【宝くじ仮説】The Lottery Ticket Hypothesis: Finding Small, Trainable Neural Networks 1.
Frankle & Carbin (2019) — The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks Paper | Code While the traditional literature has been able to show that a fully trained dense network can be pruned to little parameters without degrading performance too much, for a long time it has been impossible to successfully train a sparse sub-network from scratch.
Neural Network Pruning: a group of methods to reduce the memory footprint of a neural network by removing redundant parts. Title: The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks Authors: Jonathan Frankle , Michael Carbin (Submitted on 9 Mar 2018 ( v1 ), last revised 4 Mar 2019 (this version, v5))
Deep neural networks, computer systems loosely modeled after the human brain, are found in the majority of AI-based products.
Pruning Procedure: Train the network; Remove the redundant parts; Finetune the network; The pruned network is generally 90-95% lighter and just as accurate as the original model. Learn more about this International Conference on Learning Representations paper: "The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks."
The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks.
As you would expect, neural networks aren’t simple systems — they tend to be very large, require huge datasets and expensive equipment, and can take multiple days to train.
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