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Poster
Rigging the Lottery: Making All Tickets Winners
Utku Evci · Trevor Gale · Jacob Menick · Pablo Samuel Castro · Erich Elsen

Thu Jul 16 06:00 AM -- 06:45 AM & Thu Jul 16 05:00 PM -- 05:45 PM (PDT) @
in Poster Session 41 »

Many applications require sparse neural networks due to space or inference time restrictions. There is a large body of work on training dense networks to yield sparse networks for inference, but this limits the size of the largest trainable sparse model to that of the largest trainable dense model. In this paper we introduce a method to train sparse neural networks with a fixed parameter count and a fixed computational cost throughout training, without sacrificing accuracy relative to existing dense-to-sparse training methods. Our method updates the topology of the sparse network during training by using parameter magnitudes and infrequent gradient calculations. We show that this approach requires fewer floating-point operations (FLOPs) to achieve a given level of accuracy compared to prior techniques. We demonstrate state-of-the-art sparse training results on a variety of networks and datasets, including ResNet-50, MobileNets on Imagenet-2012, and RNNs on WikiText-103. Finally, we provide some insights into why allowing the topology to change during the optimization can overcome local minima encountered when the topology remains static.

Keywords: Algorithms · Architectures · Computer Vision · Sparsity and Compressed Sensing · Deep Learning - Algorithms

Author Information

Utku Evci (Google)
Trevor Gale (Google Brain)
Jacob Menick (DeepMind)
Pablo Samuel Castro (Google Brain)

Pablo was born and raised in Quito, Ecuador, and moved to Montreal after high school to study at McGill. He stayed in Montreal for the next 10 years, finished his bachelors, worked at a flight simulator company, and then eventually obtained his masters and PhD at McGill, focusing on Reinforcement Learning. After his PhD Pablo did a 10-month postdoc in Paris before moving to Pittsburgh to join Google. He has worked at Google for almost 6 years, and is currently a research Software Engineer in Google Brain in Montreal, focusing on fundamental Reinforcement Learning research, as well as Machine Learning and Music. Aside from his interest in coding/AI/math, Pablo is an active musician (https://www.psctrio.com), loves running (5 marathons so far, including Boston!), and discussing politics and activism.

Erich Elsen (Google)

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