Utilizing Citation Network Structure to Predict Citation Counts: A Deep Learning Approach. - Qihang Zhao (CoRR, 2020)

On modeling and predicting popularity dynamics via integrating generative model and rich features. - Xiaodong Feng 001; Qihang Zhao; Jie Ma; Guoyin Jiang (Knowl. Based Syst., 2020)

Prediction of information cascades via content and structure proximity preserved graph level embedding. - Xiaodong Feng 001; Qihang Zhao; Zhen Liu 006 (Inf. Sci., 2021)

RESETBERT4Rec: A Pre-training Model Integrating Time And User Historical Behavior for Sequential Recommendation. - Qihang Zhao (SIGIR, 2022)

TCJA-SNN: Temporal-Channel Joint Attention for Spiking Neural Networks. - Rui-Jie Zhu 003; Qihang Zhao; Tianjing Zhang; Haoyu Deng; Yule Duan; Malu Zhang; Liang-Jian Deng (CoRR, 2022)

AECasN: An information cascade predictor by learning the structural representation of the whole cascade network with autoencoder. - Xiaodong Feng 001; Qihang Zhao; Yunkai Li (Expert Syst. Appl., 2022)

Predicting information diffusion via deep temporal convolutional networks. - Qihang Zhao; Yuzhe Zhang 002; Xiaodong Feng 001 (Inf. Syst., 2022)

Utilizing citation network structure to predict paper citation counts: A Deep learning approach. - Qihang Zhao; Xiaodong Feng 001 (J. Informetrics, 2022)

Uni-Match: A Semantic Unified Model for Query-Product Retrieval. - Zhenyang Zhu; Rui-Jie Zhu 003; Yunrui Ge; Qihang Zhao (Tiny Papers @ ICLR, 2023)

SpikeGPT: Generative Pre-trained Language Model with Spiking Neural Networks. - Rui-Jie Zhu 003; Qihang Zhao; Jason K. Eshraghian (CoRR, 2023)

Both Efficiency and Effectiveness! A Large Scale Pre-ranking Framework in Search System. - Qihang Zhao; Rui-Jie Zhu 003; Liu Yang; Yongming He; Bo Zhou; Luo Cheng (CoRR, 2023)

RWKV: Reinventing RNNs for the Transformer Era. - Bo Peng; Eric Alcaide; Quentin Anthony; Alon Albalak; Samuel Arcadinho; Huanqi Cao; Xin Cheng; Michael Chung; Matteo Grella; Kranthi Kiran G. V.; Xuzheng He; Haowen Hou; et al. (CoRR, 2023)

Universal Normalization Enhanced Graph Representation Learning for Gene Network Prediction. - Zehao Dong; Muhan Zhang; Qihang Zhao; Philip R. O. Payne; Michael A. Province; Carlos Cruchaga; Tianyu Zhao; Yixin Chen 001; Fuhai Li 001 (CoRR, 2023)

Towards popularity prediction of information cascades via degree distribution and deep neural networks. - Xiaodong Feng 001; Qihang Zhao; Rui-Jie Zhu 003 (J. Informetrics, 2023)

Highly Accurate Disease Diagnosis and Highly Reproducible Biomarker Identification with PathFormer. - Zehao Dong; Qihang Zhao; Philip R. O. Payne; Michael A. Province; Carlos Cruchaga; Muhan Zhang; Tianyu Zhao; Yixin Chen 001; Fuhai Li 001 (CoRR, 2024)

Eagle and Finch: RWKV with Matrix-Valued States and Dynamic Recurrence. - Bo Peng; Daniel Goldstein; Quentin Anthony; Alon Albalak; Eric Alcaide; Stella Biderman; Eugene Cheah; Xingjian Du; Teddy Ferdinan; Haowen Hou; Przemyslaw Kazienko; Kranthi Kiran GV; et al. (CoRR, 2024)

Redefining Information Retrieval of Structured Database via Large Language Models. - Mingzhu Wang; Yuzhe Zhang 002; Qihang Zhao; Juanyi Yang; Hong Zhang (CoRR, 2024)

dnaGrinder: a lightweight and high-capacity genomic foundation model. - Qihang Zhao; Chi Zhang; Weixiong Zhang (CoRR, 2024)

SpikeGPT: Generative Pre-trained Language Model with Spiking Neural Networks. - Rui-Jie Zhu 003; Qihang Zhao; Guoqi Li; Jason Eshraghian (Trans. Mach. Learn. Res., 2024)

Joint learning of structural and textual information on propagation network by graph attention networks for rumor detection. - Qihang Zhao; Yuzhe Zhang 002; Xiaodong Feng 001 (Appl. Intell., 2024)

TCJA-SNN: Temporal-Channel Joint Attention for Spiking Neural Networks. - Rui-Jie Zhu 003; Malu Zhang; Qihang Zhao; Haoyu Deng; Yule Duan; Liang-Jian Deng (IEEE Trans. Neural Networks Learn. Syst., 2025)