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. Stabilizing the Lottery Ticket Hypothesis / The Lottery Ticket Hypothesis at Scale. The lottery ticket hypothesis: Finding sparse, trainable neural networks. The sole empirical evidence in support of the lottery ticket hypothesis is a series of experiments using a procedure called iterative magnitude pruning (IMP). We use the lenet-300-100 architecture [] as described in Figure 1.We follow the outline from Section 1: after randomly initializing and training a network, we prune the network and reset the remaining connections to their original initializations. 2017. TITLE of the paper: Authors: Presenter and Presentation Files: Date for Presentation: Link: The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks TETRIS: Scalable and Efficient Neural Network Acceleration with 3D Memory. This chat discusses some of these papers such as Linear Mode Connectivity, Comparing and Rewinding and Fine-tuning in Neural Network Pruning, and more (full list of papers linked below). The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks Jonathan Frankle, Michael Carbin. Convolutional sequence to sequence learning The lottery ticket hypothesis (Frankle & Carbin, 2019) conjectures that neural networks contain sparse subnetworks that are capable of training in isolation from initialization to full accuracy. The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks @inproceedings{Frankle2018TheLT, title={The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks}, author={Jonathan Frankle and Michael Carbin}, booktitle={ICLR}, year={2018} } Jonathan Frankle and Michael Carbin. The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks. Corpus ID: 53388625. The Lottery Ticket Hypothesis with Jonathan Frankle - YouTube
The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks. The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks. The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks . The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks Authors: Jonathan Frankle, Michael Carbin Institution: Massachusetts Institute of Technology 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- Google Scholar; Mingyu Gao, Jing Pu, Xuan Yang, Mark Horowitz, and Christos Kozyrakis. In International Conference on Learning Representations (ICLR).
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