skip to main content
US FlagAn official website of the United States government
dot gov icon
Official websites use .gov
A .gov website belongs to an official government organization in the United States.
https lock icon
Secure .gov websites use HTTPS
A lock ( lock ) or https:// means you've safely connected to the .gov website. Share sensitive information only on official, secure websites.

Explore Research Products in the PAR
It may take a few hours for recently added research products to appear in PAR search results.


  • Home
  • Search Results
  • Page 1 of 60

Search for: All records

Creators/Authors contains: "Wang, D."

Note: When clicking on a Digital Object Identifier (DOI) number, you will be taken to an external site maintained by the publisher. Some full text articles may not yet be available without a charge during the embargo (administrative interval).
What is a DOI Number?

Some links on this page may take you to non-federal websites. Their policies may differ from this site.

  1. ; ; ; ; (, arXiv preprints)
    Free, publicly-accessible full text available May 26, 2026
  2. ; ; ; ; ; ; (, arXiv preprints)
    Finetuned large language models (LLMs) have shown remarkable performance in financial tasks, such as sentiment analysis and information retrieval. Due to privacy concerns, finetuning and deploying financial LLMs (FinLLMs) locally are crucial for institutions and individuals. In this paper, we employ quantized low-rank adaptation (QLoRA) to finetune FinLLMs, which leverage low-rank structure and quantization technique to significantly reduce computational requirements while maintaining model performance. We also employ data and pipeline parallelism to enable local finetuning on commodity GPUs. Experiments on financial datasets validate the efficacy of our approach in yielding notable improvements over the base models. 
    more » « less
    Free, publicly-accessible full text available December 16, 2025
  3. ; ; ; ; ; ; ; (, Proceedings of the Conference on Robot Learning)
    Free, publicly-accessible full text available October 1, 2025
  4. ; ; ; ; ; ; ; (, Proceedings of the 30th IEEE Real-Time and Embedded Technology and Applications Symposium (RTAS'24))
    Full Text Available 
  5. (, Proc. IEEE/OPTICA Optical Fiber Communication Conference (OFC’23))
    Accepted Manuscript Full Text Available
  6. (, in Proc. IEEE/OPTICA Optical Fiber Communication Conference (OFC’23), Th2A.1, Mar. 2023)
    We implement and test transfer learning-based gain models across 16 ROADM EDFAs, which achieve less than 0.17/0.30 dB mean absolute error for booster/pre-amplifier gain prediction using only 0.5% of the full target EDFA dataset. 
    more » « less
    Accepted Manuscript Full Text Available
  7. ; (, Experimental Biology Conference)
    Full Text Available 
  8. ; ; (, International conference on learning representations (ICLR))
    Accepted Manuscript Full Text Available
  9. ; ; ; ; ; ; (, International Conference on Learning Representations)
    Accepted Manuscript Full Text Available
  10. (, Proceedings of the AAAI Conference on Artificial Intelligence)
    null (Ed.)
    Accepted Manuscript Full Text Available