9 citations · 24 across the 4 of their papers we have counts for
6 papers
System Demo for Transfer Learning across Vision and Text using Domain Specific CNN Accelerator for On-Device NLP Applications
Baohua Sun, Lin Yang, Michael Lin +4
Power-efficient CNN Domain Specific Accelerator (CNN-DSA) chips are currently available for wide use in mobile devices. These chips are mainly used in computer vision applications.…
SuperCaptioning: Image Captioning Using Two-dimensional Word Embedding
Baohua Sun, Lin Yang, Michael Lin +4
Language and vision are processed as two different modal in current work for image captioning. However, recent work on Super Characters method shows the effectiveness of two-dimens…
SuperChat: Dialogue Generation by Transfer Learning from Vision to Language using Two-dimensional Word Embedding and Pretrained ImageNet CNN Models
Baohua Sun, Lin Yang, Michael Lin +4
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 a…
SuperTML: Two-Dimensional Word Embedding for the Precognition on Structured Tabular Data
Baohua Sun, Lin Yang, Wenhan Zhang +4
Tabular data is the most commonly used form of data in industry. Gradient Boosting Trees, Support Vector Machine, Random Forest, and Logistic Regression are typically used for clas…
Super Characters: A Conversion from Sentiment Classification to Image Classification
Baohua Sun, Lin Yang, Patrick Dong +3
We propose a method named Super Characters for sentiment classification. This method converts the sentiment classification problem into image classification problem by projecting t…
Ultra Power-Efficient CNN Domain Specific Accelerator with 9.3TOPS/Watt for Mobile and Embedded Applications
Baohua Sun, Lin Yang, Patrick Dong +3
Computer vision performances have been significantly improved in recent years by Convolutional Neural Networks(CNN). Currently, applications using CNN algorithms are deployed mainl…