~/kforcode$ ls research/

Research

Peer-reviewed work at the intersection of deep learning and applied systems: Document AI, privacy, clinical summarization, and parameter-efficient fine-tuning.

3 publications

  1. paper_012024
    EMNLP 2024 (Main)

    De-Identification of Sensitive Personal Data in IIT-CDIP-Derived Datasets

    Kaushal Prajapati et al.

    A pipeline for detecting and redacting sensitive personal data in large document-image corpora derived from IIT-CDIP, enabling privacy-preserving research on real-world Document AI at scale.

    nlpdocument-aiprivacy
    read paper ↗
  2. paper_022024
    Elsevier — Applied Soft Computing

    Parameter-Efficient Fine-Tuning for Hospital Discharge Summarization

    Kaushal Prajapati et al.

    PEFT methods applied to clinical summarization, matching full fine-tuning quality on hospital discharge summaries at a fraction of the trainable parameters and compute.

    peftllmhealthcare
    read paper ↗
  3. paper_032025
    Elsevier — Applied Soft Computing

    DoRA+: Enhancing Weight-Decomposed Low-Rank Adaptation

    Kaushal Prajapati et al.

    An extension to weight-decomposed low-rank adaptation (DoRA) that improves the magnitude/direction decomposition for more effective and stable parameter-efficient fine-tuning.

    lorapeftdeep-learning
    read paper ↗
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