no code implementations • 9 Oct 2023 • Weikai Yang, Mengchen Liu, Zheng Wang, Shixia Liu
Recent studies have indicated that foundation models, such as BERT and GPT, excel in adapting to a variety of downstream tasks.
no code implementations • 9 Aug 2023 • Changjian Chen, Yukai Guo, Fengyuan Tian, Shilong Liu, Weikai Yang, Zhaowei Wang, Jing Wu, Hang Su, Hanspeter Pfister, Shixia Liu
Existing model evaluation tools mainly focus on evaluating classification models, leaving a gap in evaluating more complex models, such as object detection.
no code implementations • 19 Jun 2022 • Zhen Li, Xiting Wang, Weikai Yang, Jing Wu, Zhengyan Zhang, Zhiyuan Liu, Maosong Sun, HUI ZHANG, Shixia Liu
The rapid development of deep natural language processing (NLP) models for text classification has led to an urgent need for a unified understanding of these models proposed individually.
no code implementations • 9 Jun 2022 • Weikai Yang, Xi Ye, Xingxing Zhang, Lanxi Xiao, Jiazhi Xia, Zhongyuan Wang, Jun Zhu, Hanspeter Pfister, Shixia Liu
The base learners and labeled samples (shots) in an ensemble few-shot classifier greatly affect the model performance.
no code implementations • 21 Sep 2020 • Weikai Yang, Xiting Wang, Jie Lu, Wenwen Dou, Shixia Liu
The novelty of our approach includes 1) automatically constructing constraints for hierarchical clustering using knowledge (knowledge-driven) and intrinsic data distribution (data-driven), and 2) enabling the interactive steering of clustering through a visual interface (user-driven).
no code implementations • 28 Jul 2020 • Weikai Yang, Zhen Li, Mengchen Liu, Yafeng Lu, Kelei Cao, Ross Maciejewski, Shixia Liu
Concept drift is a phenomenon in which the distribution of a data stream changes over time in unforeseen ways, causing prediction models built on historical data to become inaccurate.