1 code implementation • 19 Jun 2019 • Hsin-Pai Cheng, Tunhou Zhang, Yukun Yang, Feng Yan, Shi-Yu Li, Harris Teague, Hai Li, Yiran Chen
Designing neural architectures for edge devices is subject to constraints of accuracy, inference latency, and computational cost.
no code implementations • 13 Jul 2022 • Mukul Gagrani, Corrado Rainone, Yang Yang, Harris Teague, Wonseok Jeon, Herke van Hoof, Weiliang Will Zeng, Piero Zappi, Christopher Lott, Roberto Bondesan
Recent works on machine learning for combinatorial optimization have shown that learning based approaches can outperform heuristic methods in terms of speed and performance.
1 code implementation • 27 Apr 2023 • Burak Bartan, Haoming Li, Harris Teague, Christopher Lott, Bistra Dilkina
The deployment and training of neural networks on edge computing devices pose many challenges.
no code implementations • 26 Mar 2024 • Kartikeya Bhardwaj, Nilesh Prasad Pandey, Sweta Priyadarshi, Kyunggeun Lee, Jun Ma, Harris Teague
Large generative models such as large language models (LLMs) and diffusion models have revolutionized the fields of NLP and computer vision respectively.