Mingxing Tan, Staff Software Engineer, Google Brain
AutoML for Efficient Vision Learning
This talk will focus on a few recent progresses we have made on AutoML, particularly on neural architecture search for efficient convolutional neural networks. We will first discuss the challenges and solutions in designing network architecture search spaces / algorithms / constraints, as well as hyperparamter auto-tuning. Afterwards, we will discuss how to scale neural networks for better accuracy and efficiency. We will conclude the talk with some representative AutoML applications on image classification, detection, segmentation.
Bio: Mingxing Tan is a researcher at Google Brain, mainly focusing on AutoML research and applications. He has co-authored several popular models like EfficientNet/EfficientDet/MobileNetV3. He finished his Ph.D. at Peking University and post-doc research at Cornell University.