Da-Cheng Juan
Da-Cheng Juan is a software engineer at Google Research. Da-Cheng has worked on large-scale, semi-supervised learning with Expander, as well as personalized recommendation for computational advertising. Prior to joining Google, Da-Cheng received his Ph.D. from Carnegie Mellon University in 2014. His research interests include machine learning, convex optimization, and data mining.
Authored Publications
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Google
Sufficient Context: A New Lens on Retrieval Augmented Generation Systems
Hailey Joren
Jianyi Zhang
Chun-Sung Ferng
Ankur Taly
International Conference on Learning Representations (ICLR) (2025)
SLED: Self Logits Evolution Decoding for Improving Factuality in Large Language Models
Jianyi Zhang
Chun-Sung Ferng
Heinrich Jiang
Yiran Chen
NeurIPS (2024)
DreamSync: Aligning Text-to-Image Generation with Image Understanding Models
Jiao Sun
Yushi Hu
Deqing Fu
Royi Rassin
Su Wang
Charles Herrmann
Ranjay Krishna
Synthetic Data for Computer Vision Workshop @ CVPR 2024
Substance or Style: What Does Your Image Embedding Know?
Charles Herrmann
Chun-Sung Ferng
Dilip Krishnan
NeurIPS 2023 Workshop on Distribution Shifts (DistShift) New Frontiers with Foundation Models
OmniNet: Omnidirectional Representations from Transformers
Yi Tay
Vamsi Aribandi
ICML 2021
Neural Structured Learning in TensorFlow: Hands-On Tutorial at KDD
Chun-Sung Ferng
George Yu
(2020), pp. 3501-3502
