August 19-20 | San Diego, CA
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Monday, August 19 • 13:50 - 14:30
AutoML for Efficient Neural Architecture Design - Ligeng Zhu, MIT*

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Deep learning has drawn growing attention in recent years. The impressive performance comes at scale of computational power, which makes it hard to execute especially on mobile devices. Besides, designing a good and efficient neural-net requires a lot of engineering efforts, let alone the following platform/hardware specific deployment. To reduce the human-bandwidth, we propose ProxylessNAS -- an efficient framework that automatically specializes neural architecture for different hardwares. With >74.5% top-1 accuracy, the measured latency of ProxylessNAS is 1.8x faster than MobileNet-v2, a widely used human design for mobile vision.


Ligeng Zhu

Research Assistant, MIT
Ligeng Zhu is a research assistant of Professor Song Han’s group at MIT. His research focuses on efficient machine learning, with a special interest in the design automation. His recent AutoML works aim to automatically search the optimal neural-net architecture for a specific task... Read More →

Monday August 19, 2019 13:50 - 14:30 PDT
Harbor Ballroom E (Track 2)
  Machine Learning
  • Session Slides Included YES