The recent “Lottery Ticket Hypothesis” paper by Frankle & Carbin showed that a simple approach to creating sparse networks (keep the large weights) results in models that are trainable from scratch, but only when starting from the same initial weights. 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. This phenomenon offers a novel interpretation of overparameterization, which leads to exponentially more draws from the lottery.
The Lottery Ticket Hypothesis - Paper Recommendation.
To benefit from their existence, one needs to find methods to identify winning tickets … 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. Lottery ticket hypothesis[1] says that the initial value of the parameter after pruning is important, not the structure after pruning. I wanted to highlight a recent paper I came across, which is also a nice follow-up to my earlier post on pruning neural networks:. I recently gave a talk on NAS algorithms at a reading group and discussed papers using evolutionary/genetic algorithms and also briefly commented on their recent applications in reinforcement learning. September 13, 2019. Here are a few of the top of my head: Implications. The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks. Consider a dense, randomly-initialized neural network f(x;W 0) that trains to accuracy a in T iterations. My question stems from a couple of interactions I had from professors at my university. I wanted to highlight a recent paper I came across, which is also a nice follow-up to my earlier post on pruning neural networks:. From this perspective, poorer individuals may purchase more lottery tickets because they cannot afford higher-quality entertainment. Let W t be the weights at iteration t of training. The Lottery Ticket Hypothesis.
We begin by briefly summarizing Frankle and Carbin’s paper, The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks, which we abbreviated as “LT”. Lottery Ticket Hypothesis. The performance of these networks often exceeds the performance of the non-sparse base model, but for reasons that were not well … I wanted to highlight a recent paper I came across, which is also a nice follow-up to my earlier post on pruning neural networks:.