CORE: Consistent Representation Learning for Face Forgery Detection. - Yunsheng Ni; Depu Meng; Changqian Yu; Chengbin Quan; Dongchun Ren; Youjian Zhao (CVPR Workshops, 2022)

Rethinking Optimization and Architecture for Tiny Language Models. - Yehui Tang; Kai Han 002; Fangcheng Liu; Yunsheng Ni; Yuchuan Tian; Zheyuan Bai; Yi-Qi Hu; Sichao Liu; Shangling Jui; Yunhe Wang 001 (ICML, 2024)

Kangaroo: Lossless Self-Speculative Decoding for Accelerating LLMs via Double Early Exiting. - Fangcheng Liu; Yehui Tang; Zhenhua Liu 003; Yunsheng Ni; Duyu Tang; Kai Han 002; Yunhe Wang 001 (NeurIPS, 2024)

Kangaroo: Lossless Self-Speculative Decoding via Double Early Exiting. - Fangcheng Liu; Yehui Tang; Zhenhua Liu 003; Yunsheng Ni; Kai Han 002; Yunhe Wang 001 (CoRR, 2024)

EMS-SD: Efficient Multi-sample Speculative Decoding for Accelerating Large Language Models. - Yunsheng Ni; Chuanjian Liu; Yehui Tang; Kai Han 002; Yunhe Wang 001 (NAACL (Long Papers), 2025)

Pangu Ultra: Pushing the Limits of Dense Large Language Models on Ascend NPUs. - Yichun Yin; Wenyong Huang; Kaikai Song; Yehui Tang; Xueyu Wu; Wei Guo 006; Peng Guo; Yaoyuan Wang; Xiaojun Meng; Yasheng Wang; Dong Li 016; Can Chen; et al. (CoRR, 2025)

Pangu Ultra MoE: How to Train Your Big MoE on Ascend NPUs. - Yehui Tang; Yichun Yin; Yaoyuan Wang; Hang Zhou; Yu Pan; Wei Guo 006; Zhiguo Zhang; Miao Rang; Fangcheng Liu; Naifu Zhang; Binghan Li; Yonghan Dong; et al. (CoRR, 2025)