Semantical Information Graph Model toward Fast Information Valuation in Large Teamwork. - Yulin Zhang 001; Yang Xu 003; Haixiao Hu; Xianggen Liu (ECAI, 2014)

Object-oriented Neural Programming (OONP) for Document Understanding. - Zhengdong Lu; Xianggen Liu; Haotian Cui; Yukun Yan; Daqi Zheng (ACL (1), 2018)

Jumper: Learning When to Make Classification Decision in Reading. - Xianggen Liu; Lili Mou; Haotian Cui; Zhengdong Lu; Sen Song (IJCAI, 2018)

Deep-learning Based Modeling of Fault Detachment Stability for Power Grid. - Haotian Cui; Xianggen Liu; Yanhao Huang (CoRR, 2018)

JUMPER: Learning When to Make Classification Decisions in Reading. - Xianggen Liu; Lili Mou; Haotian Cui; Zhengdong Lu; Sen Song (CoRR, 2018)

Shift-based Primitives for Efficient Convolutional Neural Networks. - Huasong Zhong; Xianggen Liu; Yihui He; Yuchun Ma (CoRR, 2018)

AddressNet: Shift-Based Primitives for Efficient Convolutional Neural Networks. - Yihui He; Xianggen Liu; Huasong Zhong; Yuchun Ma (WACV, 2019)

Unsupervised Paraphrasing by Simulated Annealing. - Xianggen Liu; Lili Mou; Fandong Meng; Hao Zhou 012; Jie Zhou 016; Sen Song (ACL, 2020)

A Chance-Constrained Generative Framework for Sequence Optimization. - Xianggen Liu; Qiang Liu 001; Sen Song; Jian Peng 001 (ICML, 2020)

Pre-training of Graph Neural Network for Modeling Effects of Mutations on Protein-Protein Binding Affinity. - Xianggen Liu; Yunan Luo; Sen Song; Jian Peng 001 (CoRR, 2020)

Finding decision jumps in text classification. - Xianggen Liu; Lili Mou; Haotian Cui; Zhengdong Lu; Sen Song (Neurocomputing, 2020)

Pairwise Half-graph Discrimination: A Simple Graph-level Self-supervised Strategy for Pre-training Graph Neural Networks. - Pengyong Li; Jun Wang 123; Ziliang Li; Yixuan Qiao; Xianggen Liu; Fei Ma; Peng Gao 015; Sen Song; Guotong Xie (IJCAI, 2021)

Riboexp: an interpretable reinforcement learning framework for ribosome density modeling. - Hailin Hu 002; Xianggen Liu; An Xiao; Yangyang Li; Chengdong Zhang; Tao Jiang 001; Dan Zhao 004; Sen Song; Jianyang Zeng 001 (Briefings Bioinform., 2021)

TrimNet: learning molecular representation from triplet messages for biomedicine. - Pengyong Li; Yuquan Li; Chang-Yu Hsieh; Shengyu Zhang 002; Xianggen Liu; Huanxiang Liu; Sen Song; Xiaojun Yao (Briefings Bioinform., 2021)

Pairwise Half-graph Discrimination: A Simple Graph-level Self-supervised Strategy for Pre-training Graph Neural Networks. - Pengyong Li; Jun Wang 123; Ziliang Li; Yixuan Qiao; Xianggen Liu; Fei Ma; Peng Gao 015; Seng Song; Guotong Xie (CoRR, 2021)

Simulated annealing for optimization of graphs and sequences. - Xianggen Liu; Pengyong Li; Fandong Meng; Hao Zhou 012; Huasong Zhong; Jie Zhou 016; Lili Mou; Sen Song (Neurocomputing, 2021)

Deep geometric representations for modeling effects of mutations on protein-protein binding affinity. - Xianggen Liu; Yunan Luo; Pengyong Li; Sen Song; Jian Peng 001 (PLoS Comput. Biol., 2021)

Abstract Rule Learning for Paraphrase Generation. - Xianggen Liu; Wenqiang Lei; Jiancheng Lv 001; Jizhe Zhou (IJCAI, 2022)

Learning Robust Rule Representations for Abstract Reasoning via Internal Inferences. - Wenbo Zhang; Likai Tang; Site Mo; Xianggen Liu; Sen Song (NeurIPS, 2022)

Vector-Quantized Prompt Learning for Paraphrase Generation. - Haotian Luo; Yixin Liu; Peidong Liu; Xianggen Liu (EMNLP (Findings), 2023)

GPT-NAS: Evolutionary Neural Architecture Search with the Generative Pre-Trained Model. - Caiyang Yu; Xianggen Liu; Wentao Feng; Chenwei Tang; Jiancheng Lv 001 (CoRR, 2023)

IML-ViT: Image Manipulation Localization by Vision Transformer. - Xiaochen Ma 001; Bo Du; Xianggen Liu; Ahmed Y. Al Hammadi; Jizhe Zhou (CoRR, 2023)

Weakly Supervised Reasoning by Neuro-Symbolic Approaches. - Xianggen Liu; Zhengdong Lu; Lili Mou (CoRR, 2023)

Contrastive Learning of Subject-Invariant EEG Representations for Cross-Subject Emotion Recognition. - Xinke Shen; Xianggen Liu; Xin Hu; Dan Zhang 014; Sen Song (IEEE Trans. Affect. Comput., 2023)

Weakly Supervised Reasoning by Neuro-Symbolic Approaches. - Xianggen Liu; Zhengdong Lu; Lili Mou (Compendium of Neurosymbolic Artificial Intelligence, 2023)

MSGNet: Learning Multi-Scale Inter-series Correlations for Multivariate Time Series Forecasting. - Wanlin Cai; Yuxuan Liang; Xianggen Liu; Jianshuai Feng; Yuankai Wu (AAAI, 2024)

Create! Don't Repeat: A Paradigm Shift in Multi-Label Augmentation through Label Creative Generation. - Letian Wang; Xianggen Liu; Jiancheng Lv 001 (NAACL-HLT, 2024)

DrugLLM: Open Large Language Model for Few-shot Molecule Generation. - Xianggen Liu; Yan Guo; Haoran Li; Jin Liu; Shudong Huang; Bowen Ke; Jiancheng Lv 001 (CoRR, 2024)

Bridging the Gap between Learning and Inference for Diffusion-Based Molecule Generation. - Peidong Liu; Wenbo Zhang; Xue Zhe; Jiancheng Lv; Xianggen Liu (CoRR, 2024)

TimeSQL: Improving multivariate time series forecasting with multi-scale patching and smooth quadratic loss. - Site Mo; Haoxin Wang; Bixiong Li; Songhai Fan; Yuankai Wu; Xianggen Liu (Inf. Sci., 2024)

MolEM: a unified generative framework for molecular graphs and sequential orders. - Hanwen Zhang; Deng Xiong; Xianggen Liu; Jiancheng Lv 001 (Briefings Bioinform., 2025)

GPT-NAS: Neural Architecture Search Meets Generative Pre-Trained Transformer Model. - Caiyang Yu; Xianggen Liu; Yifan Wang; Yun Liu; Wentao Feng; Xiong Deng; Chenwei Tang; Jiancheng Lv 001 (Big Data Min. Anal., 2025)