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Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
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Related Experiment Video

Updated: May 10, 2025

Defining the Role Of Language in Infants' Object Categorization with Eye-tracking Paradigms
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Defining the Role Of Language in Infants' Object Categorization with Eye-tracking Paradigms

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Hierarchical contrastive learning for multi-label text classification.

Wei Zhang1, Yun Jiang1, Yun Fang1

  • 1Advanced Institution of Information Technology, Peking University, No.233, Yonghui Rd, Hangzhou, 311215, Zhejiang, China.

Scientific Reports
|April 24, 2025
PubMed
Summary
This summary is machine-generated.

Hierarchical contrastive learning (HCL-MTC) enhances multi-label text classification by modeling label hierarchies. This novel method effectively captures semantic relationships, improving classification accuracy on benchmark datasets.

Keywords:
Contrastive learningHierarchical structureMulti-label text classificationMulti-task

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Area of Science:

  • Natural Language Processing
  • Machine Learning
  • Artificial Intelligence

Background:

  • Multi-label text classification is challenging due to complex label hierarchies.
  • Traditional methods often fail to leverage semantic dependencies within label structures.
  • Understanding parent-child and sibling relationships is crucial for accurate classification.

Purpose of the Study:

  • To introduce a novel method, Hierarchical Contrastive Learning for Multi-label Text Classification (HCL-MTC).
  • To effectively model and utilize the hierarchical structure of labels in text classification.
  • To improve the understanding of semantic correlations and distinctions among labels.

Main Methods:

  • Constructing a graph representation to model hierarchical label dependencies.
  • Recasting multi-label text classification as a multi-task learning problem.
  • Incorporating a hierarchical contrastive loss function with a specialized sampling process.

Main Results:

  • HCL-MTC demonstrated substantial performance gains over baseline methods.
  • The model effectively captured correlations and distinctions among labels.
  • Significant improvements were observed on benchmark datasets like RCV1-v2 and WoS.

Conclusions:

  • Hierarchical contrastive learning offers a powerful approach for multi-label text classification.
  • Explicitly modeling label hierarchies significantly enhances classification performance.
  • The proposed HCL-MTC method advances the state-of-the-art in hierarchical text classification.