Ning MIAO

Prof. Ning MIAO, 苗寧

  • LAU-16-222

Calculated based on number of publications stored in Pure and citations from Scopus
20192025

Research activity per year

Personal profile

Author IDs

ORCID iD: 0009-0002-4489-2090
Scopus Author ID: 57216622967

Impact

Qualifications (Brief)

BS and MS (Peking University), PhD (University of Oxford)

Research Interests/Areas

Large language models, especially their reasoning ability; Generative models; Natural language processing

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Collaborations from the last five years

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  • SelfCheck: Using LLMs to Zero-Shot Check Their Own Step-by-Step Reasoning

    Miao, N., Teh, Y. W. & Rainforth, T., Oct 2024, 12th International Conference on Learning Representations (ICLR 2024). International Conference on Learning Representations, ICLR, 16 p. (International Conference on Learning Representations, ICLR).

    Research output: Chapters, Conference Papers, Creative and Literary WorksRGC 32 - Refereed conference paper (with host publication)peer-review

    15 Citations (Scopus)
  • Learning Instance-Specific Augmentations by Capturing Local Invariances

    Miao, N., Rainforth, T., Mathieu, E., Dubois, Y., Teh, Y. W., Foster, A. & Kim, H., 2023, ICML'23: Proceedings of the 40th International Conference on Machine Learning. Krause, A., Cho, K., Engelhardt, B., Sabato, S. & Scarlett, J. (eds.). JMLR.org, p. 24720-24736 1028. (Proceedings of Machine Learning Research; vol. 202).

    Research output: Chapters, Conference Papers, Creative and Literary WorksRGC 32 - Refereed conference paper (with host publication)peer-review

    Open Access
    3 Citations (Scopus)
  • ON INCORPORATING INDUCTIVE BIASES INTO VAES

    Miao, N., Mathieu, E., Siddharth, N., Teh, Y. W. & Rainforth, T., 2022, The Tenth International Conference on Learning Representations: ICLR 2022. International Conference on Learning Representations, ICLR, (ICLR - International Conference on Learning Representations).

    Research output: Chapters, Conference Papers, Creative and Literary WorksRGC 32 - Refereed conference paper (with host publication)peer-review

    Open Access
    1 Citation (Scopus)
  • Dispersed exponential family mixture VAEs for interpretable text generation

    Shi, W., Zhou, H., Miao, N. & Li, L., Jul 2020, 37th International Conference on Machine Learning (ICML 2020): Proceedings of Machine Learning Research. Daumé III, H. & Singh, A. (eds.). International Machine Learning Society (IMLS), Vol. 119. p. 8799-8810 (International Conference on Machine Learning, ICML; vol. PartF168147-12).

    Research output: Chapters, Conference Papers, Creative and Literary WorksRGC 32 - Refereed conference paper (with host publication)peer-review

    Open Access
    12 Citations (Scopus)
  • Do you have the right scissors? Tailoring pre-trained language models via Monte-Carlo methods

    Miao, N., Song, Y., Zhou, H. & Li, L., Jul 2020, ACL 2020 - The 58th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference. Jurafsky, D., Chai, J., Schluter, N. & Tetreault, J. (eds.). Stroudsburg, PA: Association for Computational Linguistics, p. 3436-3441 (Proceedings of the Annual Meeting of the Association for Computational Linguistics).

    Research output: Chapters, Conference Papers, Creative and Literary WorksRGC 32 - Refereed conference paper (with host publication)peer-review

    4 Citations (Scopus)