Search Results for author: Weikai Yang

Found 8 papers, 0 papers with code

Structural-Entropy-Based Sample Selection for Efficient and Effective Learning

no code implementations3 Oct 2024 Tianchi Xie, Jiangning Zhu, Guozu Ma, Minzhi Lin, Wei Chen, Weikai Yang, Shixia Liu

Based on the decomposition, we present $\textbf{S}$tructural-$\textbf{E}$ntropy-based sample $\textbf{S}$election ($\textbf{SES}$), a method that integrates both global and local information to select informative and representative samples.

Active Learning Continual Learning

RuleExplorer: A Scalable Matrix Visualization for Understanding Tree Ensemble Classifiers

no code implementations5 Sep 2024 Zhen Li, Weikai Yang, Jun Yuan, Jing Wu, Changjian Chen, Yao Ming, Fan Yang, HUI ZHANG, Shixia Liu

To ensure the inclusion of anomalous rules, we develop an anomaly-biased model reduction method to prioritize these rules at each hierarchical level.

Foundation Models Meet Visualizations: Challenges and Opportunities

no code implementations9 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.

Fairness

A Unified Understanding of Deep NLP Models for Text Classification

no code implementations19 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.

text-classification Text Classification

Diagnosing Ensemble Few-Shot Classifiers

no code implementations9 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.

Interactive Steering of Hierarchical Clustering

no code implementations21 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).

Clustering

Diagnosing Concept Drift with Visual Analytics

no code implementations28 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.

Drift Detection text-classification +1

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