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Yuanbang Liang
(e/fe)
Timau a rolau for Yuanbang Liang
Cydymaith Ymchwil
Cyhoeddiad
2026
- Wang, Y. et al., 2026. GRADE: probing knowledge gaps in LLMs through gradient subspace dynamics. Presented at: COLM 2026: The Third Annual Conference on Language Modeling San Francisco, California 6 - 9 October 2026. COLM 2026: The Third Annual Conference on Language Modeling. Open Review:
2025
- Liang, Y. 2025. Hubness awareness sampling for deep generative models in generation and evaluation. PhD Thesis , Cardiff University.
2024
- Liang, Y. et al. 2024. Deep generative model based rate-distortion for image downscaling assessment. Presented at: The IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2024 Seattle, WA, USA 17-21 June 2024. Proceedings of the Conference on Computer Vision and Pattern Recognition. IEEE. , pp.19363-19372. (10.1109/CVPR52733.2024.01832)
- Liang, Y. et al. 2024. Efficient precision and recall metrics for assessing generative models using hubness-aware sampling. Presented at: The Forty-first International Conference on Machine Learning (ICML) Vienna, Austria 21-27 July 2024. Vol. 235., pp.29682-29699.
2023
- Song, S. et al. 2023. Feature proliferation — the "cancer" in StyleGAN and its treatments. Presented at: International Conference on Computer Vision (ICCV) 2023 Paris, France October 1 - 6, 2023. Proceedings of IEEE/CVF International Conference on Computer Vision. IEEE. , pp.2360-2370. (10.1109/ICCV51070.2023.00224)
2022
- Liang, Y. et al. 2022. Exploring and exploiting hubness priors for high-quality GAN latent sampling. Presented at: The 39th International Conference on Machine Learning (ICML 2022) Baltimore, Maryland USA 17-23 July 2022. Vol. 162.ML Research Press. , pp.13271-13284.
Conferences
- Wang, Y. et al., 2026. GRADE: probing knowledge gaps in LLMs through gradient subspace dynamics. Presented at: COLM 2026: The Third Annual Conference on Language Modeling San Francisco, California 6 - 9 October 2026. COLM 2026: The Third Annual Conference on Language Modeling. Open Review:
- Liang, Y. et al. 2024. Deep generative model based rate-distortion for image downscaling assessment. Presented at: The IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2024 Seattle, WA, USA 17-21 June 2024. Proceedings of the Conference on Computer Vision and Pattern Recognition. IEEE. , pp.19363-19372. (10.1109/CVPR52733.2024.01832)
- Liang, Y. et al. 2024. Efficient precision and recall metrics for assessing generative models using hubness-aware sampling. Presented at: The Forty-first International Conference on Machine Learning (ICML) Vienna, Austria 21-27 July 2024. Vol. 235., pp.29682-29699.
- Song, S. et al. 2023. Feature proliferation — the "cancer" in StyleGAN and its treatments. Presented at: International Conference on Computer Vision (ICCV) 2023 Paris, France October 1 - 6, 2023. Proceedings of IEEE/CVF International Conference on Computer Vision. IEEE. , pp.2360-2370. (10.1109/ICCV51070.2023.00224)
- Liang, Y. et al. 2022. Exploring and exploiting hubness priors for high-quality GAN latent sampling. Presented at: The 39th International Conference on Machine Learning (ICML 2022) Baltimore, Maryland USA 17-23 July 2022. Vol. 162.ML Research Press. , pp.13271-13284.
Thesis
- Liang, Y. 2025. Hubness awareness sampling for deep generative models in generation and evaluation. PhD Thesis , Cardiff University.
Ymchwil
Mae fy niddordebau ymchwil yn canolbwyntio ar ddysgu peirianyddol a'i gymwysiadau mewn golwg gyfrifiadurol, graffeg gyfrifiadurol a chynhyrchu cynnwys. Mae fy ymchwil cyfredol yn troi o amgylch ffenomen hybiau i ddatgelu'r berthynas rhwng y dosbarthiad hyper-ddimensiwn a'r modelau cynhyrchiol. Gyda'm canfyddiadau diweddar, mae cydberthynas gref rhwng manifold y model a'r dosbarthiad samplu mewn dimensiwn hyper.