Compressing deep neural networks by matrix product operators. - Ze-Feng Gao; Song Cheng; Rong-Qiang He; Zhi-Yuan Xie; Hui-Hai Zhao; Zhong-Yi Lu; Tao Xiang 004 (CoRR, 2019)

Compressing LSTM Networks by Matrix Product Operators. - Ze-Feng Gao; Xingwei Sun; Lan Gao; Junfeng Li; Zhong-Yi Lu (CoRR, 2020)

A Model Compression Method With Matrix Product Operators for Speech Enhancement. - Xingwei Sun; Ze-Feng Gao; Zhong-Yi Lu; Junfeng Li; Yonghong Yan 002 (IEEE ACM Trans. Audio Speech Lang. Process., 2020)

Enabling Lightweight Fine-tuning for Pre-trained Language Model Compression based on Matrix Product Operators. - Peiyu Liu 002; Ze-Feng Gao; Wayne Xin Zhao; Zhi-Yuan Xie; Zhong-Yi Lu; Ji-Rong Wen (ACL/IJCNLP (1), 2021)

Parameter-Efficient Mixture-of-Experts Architecture for Pre-trained Language Models. - Ze-Feng Gao; Peiyu Liu 002; Wayne Xin Zhao; Zhong-Yi Lu; Ji-Rong Wen (COLING, 2022)

Small Pre-trained Language Models Can be Fine-tuned as Large Models via Over-Parameterization. - Ze-Feng Gao; Kun Zhou 002; Peiyu Liu 002; Wayne Xin Zhao; Ji-Rong Wen (ACL (1), 2023)

Enhancing Scalability of Pre-trained Language Models via Efficient Parameter Sharing. - Peiyu Liu 002; Ze-Feng Gao; Yushuo Chen; Xin Zhao 018; Ji-Rong Wen (EMNLP (Findings), 2023)

Scaling Pre-trained Language Models to Deeper via Parameter-efficient Architecture. - Peiyu Liu 002; Ze-Feng Gao; Yushuo Chen; Wayne Xin Zhao; Ji-Rong Wen (CoRR, 2023)

AI-accelerated Discovery of Altermagnetic Materials. - Ze-Feng Gao; Shuai Qu; Bocheng Zeng; Yang Liu 005; Ji-Rong Wen; Hao Sun 002; Peng-Jie Guo; Zhong-Yi Lu (CoRR, 2023)

Unlocking Data-free Low-bit Quantization with Matrix Decomposition for KV Cache Compression. - Peiyu Liu 002; Ze-Feng Gao; Xin Zhao 018; Yipeng Ma; Tao Wang; Ji-Rong Wen (ACL (1), 2024)

Enhancing Parameter-efficient Fine-tuning with Simple Calibration Based on Stable Rank. - Peiyu Liu 002; Ze-Feng Gao; Xiao Zhang 001; Wayne Xin Zhao; Ji-Rong Wen (LREC/COLING, 2024)

Do Emergent Abilities Exist in Quantized Large Language Models: An Empirical Study. - Peiyu Liu 002; Zikang Liu 001; Ze-Feng Gao; Dawei Gao; Wayne Xin Zhao; Yaliang Li; Bolin Ding; Ji-Rong Wen (LREC/COLING, 2024)

Compression Image Dataset Based on Multiple Matrix Product States. - Ze-Feng Gao; Peiyu Liu 002; Wayne Xin Zhao; Zhi-Yuan Xie; Ji-Rong Wen; Zhong-Yi Lu (FICC (2), 2024)

Over-parameterized Student Model via Tensor Decomposition Boosted Knowledge Distillation. - Yu-Liang Zhan; Zhong-Yi Lu; Hao Sun; Ze-Feng Gao (NeurIPS, 2024)

YuLan: An Open-source Large Language Model. - Yutao Zhu 001; Kun Zhou 002; Kelong Mao; Wentong Chen; Yiding Sun; Zhipeng Chen 001; Qian Cao 001; Yihan Wu; Yushuo Chen; Feng Wang; Lei Zhang; Junyi Li; et al. (CoRR, 2024)

Discovering symbolic expressions with parallelized tree search. - Kai Ruan; Ze-Feng Gao; Yike Guo; Hao Sun 002; Ji-Rong Wen; Yang Liu 005 (CoRR, 2024)

AI-accelerated discovery of high critical temperature superconductors. - Xiao-Qi Han; Zhenfeng Ouyang; Peng-Jie Guo; Hao Sun; Ze-Feng Gao; Zhong-Yi Lu (CoRR, 2024)

AI-driven inverse design of materials: Past, present and future. - Xiao-Qi Han; Xin-De Wang; Meng-Yuan Xu; Zhen Feng; Bo-Wen Yao; Peng-Jie Guo; Ze-Feng Gao; Zhong-Yi Lu (CoRR, 2024)

Strong phonon-mediated high temperature superconductivity in Li2AuH6 under ambient pressure. - Zhenfeng Ouyang; Bo-Wen Yao; Xiao-Qi Han; Peng-Jie Guo; Ze-Feng Gao; Zhong-Yi Lu (CoRR, 2025)

InvDesFlow-AL: Active Learning-based Workflow for Inverse Design of Functional Materials. - Xiao-Qi Han; Peng-Jie Guo; Ze-Feng Gao; Hao Sun; Zhong-Yi Lu (CoRR, 2025)

HTSC-2025: A Benchmark Dataset of Ambient-Pressure High-Temperature Superconductors for AI-Driven Critical Temperature Prediction. - Xiao-Qi Han; Ze-Feng Gao; Xin-De Wang; Zhenfeng Ouyang; Peng-Jie Guo; Zhong-Yi Lu (CoRR, 2025)

Perovskite-R1: A Domain-Specialized LLM for Intelligent Discovery of Precursor Additives and Experimental Design. - Xin-De Wang; Zhi-Rui Chen; Peng-Jie Guo; Ze-Feng Gao; Cheng Mu; Zhong-Yi Lu (CoRR, 2025)