Network Pruning is a commonly used practise to reduce … If one instead re-initializes the weights back to their original (but now masked) weights, it is possible to recover performance on par (or even better!) … Jonathan Frankle, Gintare Karolina Dziugaite, Daniel M. Roy, Michael Carbin. Linear Mode Connectivity and the Lottery Ticket Hypothesis. Published as a conference paper at ICLR 2019 THE LOTTERY TICKET HYPOTHESIS: FINDING SPARSE, TRAINABLE NEURAL NETWORKS Jonathan Frankle MIT CSAIL jfrankle@csail.mit.edu Michael Carbin MIT CSAIL mcarbin@csail.mit.edu ABSTRACT Neural network pruning techniques can reduce the parameter counts of trained net-

ICLR 2020. Alex Renda, Jonathan Frankle, Michael Carbin. View publication. Frankle, Dziugaite, Roy, & Carbin (2019) - Stabilizing the Lottery Ticket Hypothesis Paper | Code. The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks ... ICLR 2019 • 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. Neural networks become larger and larger and use up to billions of parameters. We present an algorithm to identify winning tickets and a series of experiments that support the lottery ticket hypothesis and the importance of these fortuitous initializations. One limitation of the original lottery ticket paper was that its restriction to small-scale tasks such as MNIST and CIFAR-10. The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks . The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks . Arxiv. Much of the recent success in NLP is due to the large Transformer-based models such as BERT (Devlin et al, 2019). Abstract: The lottery ticket hypothesis proposes that over-parameterization of deep neural networks (DNNs) aids training by increasing the probability of a “lucky” sub-network initialization being present rather than by helping the optimization process (Frankle& Carbin, 2019). The original lottery ticket hypothesis paper (Frankle & Carbin, 2019) first provided insight why this might be the case: After pruning the resulting sub-networks were randomly initialized. Deconstructing Lottery Tickets: Zeros, Signs, and the Supermask, by Zhou et al . In order to scale the LTH to competitive CIFAR-10 architectures, Frankle & Carbin (2019) had to tune learning rate schedules. The Lottery Ticket Hypothesis. Researchers also further studied and extended the lottery ticket hypothesis. Background: Network Pruning Pruning basically means reducing the extent of a neural network by removing superfluous and unwanted parts. View publication. The Lottery Ticket Hypothesis has been presented in the form of a research paper at ICLR 2019 by MIT-IBM Watson AI Lab. An initial commit of the lottery ticket hypothesis experiment infrast… Nov 2, 2018: foundations: An initial commit of the lottery ticket hypothesis experiment infrast… Nov 2, 2018: mnist_fc: An initial commit of the lottery ticket hypothesis experiment infrast… Nov 2, 2018: CONTRIBUTING.md ICLR 2019 • Jonathan Frankle • Michael Carbin. in potentially fewer training iterations. Comparing Rewinding and Fine-Tuning in Neural Network Pruning. We present an algorithm to identify winning tickets and a series of experiments that support the lottery ticket hypothesis and the importance of these fortuitous initializations. Many of their experiments had a bit of the feel of the “ lottery ticket hypothesis ” line of work from ICLR 2019. This paper has been awarded the Best Paper Award in ICLR 2019. The winning tickets we find have won the initialization lottery: their connections have initial weights that make training particularly effective. We consistently find winning tickets that are less than 10-20% of the size of several fully-connected and convolutional feed-forward architectures for MNIST and CIFAR10. Abstract: The lottery ticket hypothesis proposes that over-parameterization of deep neural networks (DNNs) aids training by increasing the probability of a “lucky” sub-network initialization being present rather than by helping the optimization process (Frankle& Carbin, 2019). ICLR 2019. Based on these results, we articulate the "lottery ticket hypothesis:" dense, randomly-initialized, feed-forward networks contain subnetworks ("winning tickets") that - when trained in isolation - reach test accuracy comparable to the original network in a similar number of iterations. August 20, 2019 • Lukas Galke • similar version cross-published in towardsdatascience.com. And the Best Paper Award at ICLR 2019 Goes To: Ordered Neurons: Integrating Tree Structures Into Recurrent Neural Networks (RNNs) The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks; Let’s break down these two incredible papers and … 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.