no code implementations • 4 Jun 2019 • Baohua Sun, Lin Yang, Michael Lin, Wenhan Zhang, Patrick Dong, Charles Young, Jason Dong
In this paper, we implemented the text classification and sentiment analysis applications on mobile devices using CNN-DSA chips.
no code implementations • 25 May 2019 • Baohua Sun, Lin Yang, Michael Lin, Charles Young, Patrick Dong, Wenhan Zhang, Jason Dong
In this paper, we propose the SuperCaptioning method, which borrows the idea of two-dimensional word embedding from Super Characters method, and processes the information of language and vision together in one single CNN model.
no code implementations • 7 May 2019 • Baohua Sun, Lin Yang, Michael Lin, Charles Young, Jason Dong, Wenhan Zhang, Patrick Dong
The recent work of Super Characters method using two-dimensional word embedding achieved state-of-the-art results in text classification tasks, showcasing the promise of this new approach.
5 code implementations • 26 Feb 2019 • Baohua Sun, Lin Yang, Wenhan Zhang, Michael Lin, Patrick Dong, Charles Young, Jason Dong
Tabular data is the most commonly used form of data in industry.
no code implementations • WS 2018 • Baohua Sun, Lin Yang, Patrick Dong, Wenhan Zhang, Jason Dong, Charles Young
We propose a method named Super Characters for sentiment classification.
no code implementations • 30 Apr 2018 • Baohua Sun, Lin Yang, Patrick Dong, Wenhan Zhang, Jason Dong, Charles Young
Furthermore, in order to better support real-world deployment for various application scenarios, especially with low-end mobile and embedded platforms and MCUs (Microcontroller Units), we also designed algorithms to fully utilize the CNN-DSA accelerator efficiently by reducing the dependency on external accelerator computation resources, including implementation of Fully-Connected (FC) layers within the accelerator and compression of extracted features from the CNN-DSA accelerator.