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Creators/Authors contains: "Wang, D"

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  1. ; ; ; ; (, arXiv preprints)
    Free, publicly-accessible full text available May 26, 2026
  2. ; ; ; ; ; ; (, Proc. CVPR)
    Free, publicly-accessible full text available June 16, 2026
  3. ; ; ; ; ; ; (, Proc. CVPR)
    Free, publicly-accessible full text available June 16, 2026
  4. ; ; ; ; ; ; (, 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. 
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    Free, publicly-accessible full text available December 16, 2025
  5. ; ; ; ; ; ; ; (, Proceedings of the Conference on Robot Learning)
    Accepted Manuscript Full Text Available
  6. ; ; ; ; ; ; ; (, NeurIPS)
    Accepted Manuscript Full Text Available
  7. ; ; ; ; ; ; ; (, Proceedings of the 30th IEEE Real-Time and Embedded Technology and Applications Symposium (RTAS'24))
    Full Text Available 
  8. (, Proc. IEEE/OPTICA Optical Fiber Communication Conference (OFC’23))
    Accepted Manuscript Full Text Available
  9. (, 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. 
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    Accepted Manuscript Full Text Available
  10. ; (, Experimental Biology Conference)
    Full Text Available