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Related Experiment Video

Updated: Apr 22, 2026

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

1.3K

Semi-supervised domain adaptation on manifolds.

Li Cheng, Sinno Jialin Pan

    IEEE Transactions on Neural Networks and Learning Systems
    |October 15, 2014
    PubMed
    Summary
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    This study introduces a new domain adaptation method for machine learning, handling large source data and limited labeled target data. The approach effectively learns transformations and models, improving generalization on target data.

    Area of Science:

    • Machine Learning
    • Computer Vision
    • Data Science

    Background:

    • Semi-supervised domain adaptation is crucial for real-world problems with varying data distributions.
    • Existing methods often struggle with large source datasets and scarce labeled target data.

    Purpose of the Study:

    • To develop a novel approach for semi-supervised domain adaptation.
    • To effectively learn transformations between source and target domains.
    • To improve model generalization on target data.

    Main Methods:

    • The study models transformations as rotation matrices, leading to an optimization problem with special orthogonal group constraints.
    • An iterative coordinate descent solver is proposed to jointly learn transformation and model parameters.
    • Geodesic updates ensure manifold constraints are satisfied during optimization.

    Related Experiment Videos

    Last Updated: Apr 22, 2026

    Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
    03:14

    Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

    Published on: December 6, 2024

    1.3K

    Main Results:

    • The proposed framework demonstrates competitive performance against state-of-the-art methods.
    • Empirical evaluations on synthetic and real-world datasets validate the approach.
    • The method is general and applicable to various loss functions and prediction tasks.

    Conclusions:

    • The developed method offers an effective solution for semi-supervised domain adaptation.
    • Jointly learning transformations and models under manifold constraints enhances predictive performance.
    • This work advances the field of domain adaptation with a robust and versatile framework.