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Found 8 papers, 0 papers with code
Structural-Entropy-Based Sample Selection for Efficient and Effective Learning
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.
RuleExplorer: A Scalable Matrix Visualization for Understanding Tree Ensemble Classifiers
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
Recent studies have indicated that foundation models, such as BERT and GPT, excel in adapting to a variety of downstream tasks.
A Unified Interactive Model Evaluation for Classification, Object Detection, and Instance Segmentation in Computer Vision
Existing model evaluation tools mainly focus on evaluating classification models, leaving a gap in evaluating more complex models, such as object detection.
A Unified Understanding of Deep NLP Models for Text Classification
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.
Diagnosing Ensemble Few-Shot Classifiers
The base learners and labeled samples (shots) in an ensemble few-shot classifier greatly affect the model performance.
Interactive Steering of Hierarchical Clustering
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).
Diagnosing Concept Drift with Visual Analytics
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.
