The Test, Test, Test: To find the winning ticket, the authors employ iterative pruning. Also you can modify parameters and make your own experiments as well. - Frankle & Carbin (2019, p.2)
However, contemporary … Lottery Ticket Hypothesis. networks contain subnetworks (winning tickets) that—when trained in isolation— reach test accuracy comparable to the original network in a similar number of iterations. Based on these results, Frankle and Carbin propose the lottery ticket hypothesis: dense neural networks contain sparse subnetworks capable of training to commensurate accuracy at similar speed. The neural networks have many parameters, but recent studies say that neural networks so “sparse” that only a few parameters actually affect accuracy. Using the lottery ticket hypothesis, we can now easily explain the observation that large neural networks are more performant than small ones, but that we can still prune them after training without much of a loss in performance. The size of a neural network depends on the number of parameters it contains.
What are your chances if you purchase n tickets?
Title: The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks. The Test, Test, Test: To find the winning ticket, the authors employ iterative pruning. The lottery ticket hypothesis, initially proposed by researchers Jonathan Frankle and Michael Carbin at MIT, suggests that by training deep neural networks (DNNs) from “lucky” initializations, often referred to as "winning lottery tickets,” we can train networks which are 10-100x smaller with minimal losses --- or even while achieving gains --- in performance. Neural network compression techniques are able to reduce the parameter counts of trained networks by over 90 inference performance--without compromising accuracy. The Lottery Ticket Hypothesis: Finding Small, Trainable Neural Networks. They start with a large neural network, train it a bit, set the smallest-magnitude weights to zero (these weights are no longer trainable), rewind the trainable parameters to their original initialized values, and train again.
untrained network; when it matches the accuracy of the original network, it is called a winning ticket. Download PDF Abstract: 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. The Lottery Ticket Hypothesis: A randomly-initialized, dense neural network contains a subnetwork that is initialised such that — when trained in isolation — it can match the test accuracy of the original network after training for at most the same number of iterations.
Implementation of the Master Thesis: Identifying Winning Lottery Tickets in Small Neural Network Architectures by Murat Aksu Experiments notebook can be used to reproduce the results (using the same random seed). However, training subnetworks would be like buying only the winning tickets.
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