Expectation Propagation Based Iterative Multi-User Detection for MIMO-IDMA Systems. - Xiangming Meng; Sheng Wu 001; Linling Kuang; Zuyao Ni; Jianhua Lu (VTC Spring, 2014)

Message Passing Approach to Regularized Zero-Forcing Precoding in Multibeam Satellite Systems. - Xiangming Meng; Sheng Wu 001; Linling Kuang; Jianhua Lu (VTC Fall, 2015)

Novel Scheme of Orthogonal Convolutional Coding and Non-Iterative Decoding for Mobile Satellite Communication Systems. - Xiangming Meng; Sheng Wu 001; Linling Kuang; Zuyao Ni; Jianhua Lu (VTC Fall, 2015)

Concise Derivation of Complex Bayesian Approximate Message Passing via Expectation Propagation. - Xiangming Meng; Sheng Wu 001; Linling Kuang; Jianhua Lu (CoRR, 2015)

An Expectation Propagation Perspective on Approximate Message Passing. - Xiangming Meng; Sheng Wu 001; Linling Kuang; Jianhua Lu (IEEE Signal Process. Lett., 2015)

Approximate Message Passing with Nearest Neighbor Sparsity Pattern Learning. - Xiangming Meng; Sheng Wu 001; Linling Kuang; Defeng Huang; Jianhua Lu (CoRR, 2016)

Multi-User Detection for Spatial Modulation via Structured Approximate Message Passing. - Xiangming Meng; Sheng Wu 001; Linling Kuang; Defeng Huang; Jianhua Lu (IEEE Commun. Lett., 2016)

Block Expectation Propagation for Downlink Channel Estimation in Massive MIMO Systems. - Sheng Wu 001; Zuyao Ni; Xiangming Meng; Linling Kuang (IEEE Commun. Lett., 2016)

Low Complexity Receiver for Uplink SCMA System via Expectation Propagation. - Xiangming Meng; Yiqun Wu; Yan Chen 010; Meng Cheng (WCNC, 2017)

Estimation of Sparse Massive MIMO-OFDM Channels With Approximately Common Support. - Xincong Lin; Sheng Wu 001; Linling Kuang; Zuyao Ni; Xiangming Meng; Chunxiao Jiang (IEEE Commun. Lett., 2017)

Spectral-Efficient Band Allocation Scheme for Frequency-Domain Pulse-Shaping-Based SC-FDMA Systems. - Kun Wu 007; Guangliang Ren; Xiangming Meng; Jueying Wu; Qiwei Wang (IEEE Trans. Veh. Technol., 2017)

Compressive Massive Random Access for Massive Machine-Type Communications (mMTC). - Malong Ke; Zhen Gao 001; Yongpeng Wu 001; Xiangming Meng (GlobalSIP, 2018)

A Universal Receiver for Uplink NOMA Systems. - Xiangming Meng; Yiqun Wu; Chao Wang 047; Yan Chen 010 (ICCC Workshops, 2018)

Turbo-Like Iterative Multi-User Receiver Design for 5G Non-Orthogonal Multiple Access. - Xiangming Meng; Yiqun Wu; Chao Wang 047; Yan Chen 010 (VTC Fall, 2018)

Binary Sparse Bayesian Learning Algorithm for One-bit Compressed Sensing. - Jiang Zhu 004; Lin Han; Xiangming Meng; Zhiwei Xu 003 (CoRR, 2018)

Off-grid Variational Bayesian Inference of Line Spectral Estimation from One-bit Samples. - Jiang Zhu 004; Qi Zhang 081; Xiangming Meng (CoRR, 2018)

A Generalized Sparse Bayesian Learning Algorithm for 1-bit DOA Estimation. - Xiangming Meng; Jiang Zhu 004 (IEEE Commun. Lett., 2018)

A Unified Bayesian Inference Framework for Generalized Linear Models. - Xiangming Meng; Sheng Wu 001; Jiang Zhu 004 (IEEE Signal Process. Lett., 2018)

Joint User Activity and Data Detection for NOMA via the Integrated Framework of Expectation Maximization and Expectation Propagation. - Lei Zhang 146; Xiangming Meng; Lei Wang 160; Yan Chen 010 (SPAWC, 2019)

Bilinear Adaptive Generalized Vector Approximate Message Passing. - Xiangming Meng; Jiang Zhu 004 (IEEE Access, 2019)

An AMP-Based Low Complexity Generalized Sparse Bayesian Learning Algorithm. - Jiang Zhu 004; Lin Han; Xiangming Meng (IEEE Access, 2019)

Training Binary Neural Networks using the Bayesian Learning Rule. - Xiangming Meng; Roman Bachmann 001; Mohammad Emtiyaz Khan (ICML, 2020)

Training Restricted Boltzmann Machines with Binary Synapses using the Bayesian Learning Rule. - Xiangming Meng (CoRR, 2020)

Structure Learning in Inverse Ising Problems Using 𝓁2-Regularized Linear Estimator. - Xiangming Meng; Tomoyuki Obuchi; Yoshiyuki Kabashima (CoRR, 2020)

Vector approximate message passing algorithm for compressed sensing with structured matrix perturbation. - Jiang Zhu 004; Qi Zhang 081; Xiangming Meng; Zhiwei Xu 003 (Signal Process., 2020)

Ising Model Selection Using $ ell_{1}$-Regularized Linear Regression: A Statistical Mechanics Analysis. - Xiangming Meng; Tomoyuki Obuchi; Yoshiyuki Kabashima (NeurIPS, 2021)

Ising Model Selection Using 𝓁1-Regularized Linear Regression. - Xiangming Meng; Tomoyuki Obuchi; Yoshiyuki Kabashima (CoRR, 2021)

On Model Selection Consistency of Lasso for High-Dimensional Ising Models on Tree-like Graphs. - Xiangming Meng; Tomoyuki Obuchi; Yoshiyuki Kabashima (CoRR, 2021)

Advanced NOMA Receivers From a Unified Variational Inference Perspective. - Xiangming Meng; Lei Zhang 146; Chao Wang 047; Lei Wang 160; Yiqun Wu; Yan Chen 010; Wenjin Wang 001 (IEEE J. Sel. Areas Commun., 2021)

Unitary Approximate Message Passing for Sparse Bayesian Learning. - Man Luo 005; Qinghua Guo 001; Ming Jin 001; Yonina C. Eldar; Defeng Huang; Xiangming Meng (IEEE Trans. Signal Process., 2021)

Exact Solutions of a Deep Linear Network. - Liu Ziyin 001; Botao Li; Xiangming Meng (NeurIPS, 2022)

Stochastic Neural Networks with Infinite Width are Deterministic. - Liu Ziyin 001; Hanlin Zhang 002; Xiangming Meng; Yuting Lu; Eric P. Xing; Masahito Ueda (CoRR, 2022)

On Model Selection Consistency of Lasso for High-Dimensional Ising Models. - Xiangming Meng; Tomoyuki Obuchi; Yoshiyuki Kabashima (AISTATS, 2023)

Average case analysis of Lasso under ultra sparse conditions. - Koki Okajima; Xiangming Meng; Takashi Takahashi; Yoshiyuki Kabashima (AISTATS, 2023)

A Unitary Transform Based Generalized Approximate Message Passing. - Jiang Zhu 004; Xiangming Meng; Xupeng Lei; Qinghua Guo 001 (ICASSP, 2023)

Quantized Compressed Sensing with Score-Based Generative Models. - Xiangming Meng; Yoshiyuki Kabashima (ICLR, 2023)

QCM-SGM+: Improved Quantized Compressed Sensing With Score-Based Generative Models for General Sensing Matrices. - Xiangming Meng; Yoshiyuki Kabashima (CoRR, 2023)

Decode-and-Forward vs. Lossy-Forward: Intelligent Reflecting Surface-Assisted Sidelink Transmission. - Meng Cheng; Xiangming Meng (IEEE Trans. Veh. Technol., 2023)

QCS-SGM+: Improved Quantized Compressed Sensing with Score-Based Generative Models. - Xiangming Meng; Yoshiyuki Kabashima (AAAI, 2024)

Diffusion Model Based Posterior Sampling for Noisy Linear Inverse Problems. - Xiangming Meng; Yoshiyuki Kabashima (ACML, 2024)

SemanticMask: A Contrastive View Design for Anomaly Detection in Tabular Data. - Shuting Tao; Tongtian Zhu; Hongwei Wang 001; Xiangming Meng (IJCAI, 2024)

Improving Decoupled Posterior Sampling for Inverse Problems using Data Consistency Constraint. - Zhi Qi; Shihong Yuan; Yuyin Yuan; Linling Kuang; Yoshiyuki Kabashima; Xiangming Meng (CoRR, 2024)

Industrial Data Synthesis Framework for Fault Diagnosis Using Diffusion and Flow-Based Models. - Shuting Tao; Xiangming Meng; Hanrong Zhang; Hongwei Wang (CSCWD, 2025)

SCSA: A Plug-and-Play Semantic Continuous-Sparse Attention for Arbitrary Semantic Style Transfer. - Chunnan Shang; Zhizhong Wang; Hongwei Wang; Xiangming Meng (CVPR, 2025)

FIG: Flow with Interpolant Guidance for Linear Inverse Problems. - Yici Yan; Yichi Zhang; Xiangming Meng; Zhizhen Zhao 001 (ICLR, 2025)

A Transformer Fault Diagnosis Method Based on Data Augmentation and IEVO-CNN. - Liqun Shang; Xiangming Meng; Yulin Tian; Di An (IEEE Trans. Instrum. Meas., 2025)