More than 40 million people use GitHub to discover, fork, and contribute to over 100 million projects. GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together. Lottery Ticket Hypothesis A randomly-initialized, dense neural network contains a subnetwork that is initialized 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. The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks. You will need to specify all the same hyperparameters …
Lottery Ticket Hypothesis A randomly-initialized, dense neural network contains a subnetwork that is initialized 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.
Their paper aims to explore why we find large, … Sign up TensorFlow reimplementation of The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks Jonathan Frankle, Michael Carbin 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. This codebase was developed by Jonathan Frankle and David Bieber at Google during the summer of 2018. 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. The Lottery Ticket Hypothesis Authors. GitHub is where people build software.
Background. For more details, see The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks. A lottery ticket experiment involves repeatedly training the network to completion, pruning weights, rewinding unpruned weights to their value at initialization, and retraining. ICLR 2019 • Jonathan Frankle • Michael Carbin. - Frankle & Carbin (2019, p.2) To run a lottery ticket experiment, use the lottery subcommand. This library reimplements and extends the work of Frankle and Carbin in "The Lottery Ticket Hypothesis: Finding Small, Trainable Neural Networks" (https://arxiv.org/abs/1803.03635). 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.
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