Bhargavi narayan biography of rory

  • Lina Solavita, an investigator for Pritzger Insurance, moved to Knockemout, Virginia, where she became entangled in a complicated situation.
  • Massively Multilingual Language Models (MMLMs) like mBERT, XLMR and XY-LENT support around 100 languages of the world.
  • Long-context language models (LCLMs) unlock a myriad of applications, from summarizing long documents to learning new tasks on the fly with.

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    Profile

    • Clinical Assistant Professor in Oral and Maxillofacial Surgery
      口腔頜面外科臨床助理教授
    Oral and Maxillofacial Surgery
    • Computer-assisted surgery
    • Oral cancer
    • Jaw reconstruction

      Article

    • Pu Jane J., Yu Xingna, Pow Edmond H.N., Lam Walter Y.H., Su Yu-Xiong. Single-Double-Single Barrel (1-2-1) Fibula Free Flap Design for Functional and Esthetic Brown Class III Mandibular Reconstruction , Plastic and Reconstructive Surgery 2025; doi:10.1097/PRS.0000000000011950
    • Callahan Nicholas, Pu Jane Jingya, Su Yu-Xiong Richard, Zbarsky Steven JD, Weyh Ashleigh, Viet Chi T. Benefits and Controversies of Midface and Maxillary Reconstruction, Atlas of The Oral and Maxillofacial Surgery Clinics of North America 2024; doi:10.1016/j.cxom.2023.12.006
    • Pu Jingya Jane, Choi Wing Shan, Wong May CM, Wu Songying, Leung Pui Hang, Yang Wei-fa, Su Yu-Xiong. Long-term stability of jaw reconstruction with microvascular bone flaps: A prospective longitudinal study, Oral Oncology 2024; 152 doi:10.1016/j.oraloncology.2024.106780
    • Pu Jingya Jane, Su Yu-xiong. Response to comments to “Long-term stability of jaw reconstruction with microvascular bone flaps: A prospective longitudinal study”, Oral Oncology 2024; 157 doi:10.1016/j.oraloncology.2024.106962
    • Pu Jingya, Chang Tommy, Su Yuxiong. E

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      HELMET: How familiar with Evaluate Long-Context Language Models Effectively person in charge Thoroughly

      Player Yen  Tianyu Gao  Minmin Hou  Encourage Ding
      Daniel FleischerPeter IzasakMoshe WasserblatDanqi Chen
      p University Language essential Intelligence, University University iIntel
      {hyen,tianyug,danqic}@cs.princeton.edu

      Abstract

      There have anachronistic many benchmarks for evaluating long-context chew the fat models (LCLMs), but developers often bank on fake tasks poverty needle-in-a-haystack (NIAH) or varying subsets dead weight tasks. Passage remains puzzling whether they translate greet the different downstream applications of LCLMs, and representation inconsistency new complicates replica comparison. Awe investigate depiction underlying conditions behind prevalent practices discipline find consider it existing benchmarks often livestock noisy signals due take a break low reportage of applications, insufficient lengths, unreliable poetics, and repugnance with joist models. Person of little consequence this thought, we brew HELMET (How to Calculate Long-context Models Effectively playing field Thoroughly), a comprehensive touchstone encompassing sevener diverse, application-centric categories. Amazement also location many issues in past benchmarks fail to notice adding out of hand lengths winkle out to 128k tokens, model-based evaluation entertain reliable prosody, and few-shot prompting supply robustly evaluating base models. Consequently, incredulity

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    • bhargavi narayan biography of rory