no code implementations • ICLR 2019 • Jeng-Hau Lin, Yunfan Yang, Rajesh K. Gupta, Zhuowen Tu
Memory and computation efficient deep learning architectures are crucial to the continued proliferation of machine learning capabilities to new platforms and systems.
no code implementations • 19 Mar 2018 • Jeng-Hau Lin, Yunfan Yang, Rajesh Gupta, Zhuowen Tu
In this paper, we tackle the problem us- ing a strategy different from the existing literature by proposing local binary pattern networks or LBPNet, that is able to learn and perform binary operations in an end-to-end fashion.
no code implementations • 15 Jul 2017 • Jeng-Hau Lin, Tianwei Xing, Ritchie Zhao, Zhiru Zhang, Mani Srivastava, Zhuowen Tu, Rajesh K. Gupta
State-of-the-art convolutional neural networks are enormously costly in both compute and memory, demanding massively parallel GPUs for execution.