44 citations · 68 across the 12 of their papers we have counts for
12 papers
Physical Layer Authentication Based on Hierarchical Variational Auto-Encoder for Industrial Internet of Things
Rui Meng, Xiaodong Xu, Bizhu Wang +4
Recently, Physical Layer Authentication (PLA) has attracted much attention since it takes advantage of the channel randomness nature of transmission media to achieve communication…
Efficient Gaussian Process Classification-based Physical-Layer Authentication with Configurable Fingerprints for 6G-Enabled IoT
Rui Meng, Fangzhou Zhu, Xiqi Cheng +6
The future 6G-enabled IoT will facilitate seamless global connectivity among ubiquitous wireless devices, but this advancement also introduces heightened security risks such as spo…
Latent Semantic Diffusion-based Channel Adaptive De-Noising SemCom for Future 6G Systems
Bingxuan Xu, Rui Meng, Yue Chen +3
Compared with the current Shannon's Classical Information Theory (CIT) paradigm, semantic communication (SemCom) has recently attracted more attention, since it aims to transmit th…
Sparse Gaussian processes for solving nonlinear PDEs
Rui Meng, Xianjin Yang
This article proposes an efficient numerical method for solving nonlinear partial differential equations (PDEs) based on sparse Gaussian processes (SGPs). Gaussian processes (GPs)…
Interpretable Research Replication Prediction via Variational Contextual Consistency Sentence Masking
Tianyi Luo, Rui Meng, Xin Eric Wang +1
Research Replication Prediction (RRP) is the task of predicting whether a published research result can be replicated or not. Building an interpretable neural text classifier for R…
Compressed Predictive Information Coding
Rui Meng, Tianyi Luo, Kristofer Bouchard
Unsupervised learning plays an important role in many fields, such as artificial intelligence, machine learning, and neuroscience. Compared to static data, methods for extracting l…
Bayesian Inference in High-Dimensional Time-Serieswith the Orthogonal Stochastic Linear Mixing Model
Rui Meng, Kristofer Bouchard
Many modern time-series datasets contain large numbers of output response variables sampled for prolonged periods of time. For example, in neuroscience, the activities of 100s-1000…
Stochastic Collapsed Variational Inference for Structured Gaussian Process Regression Network
Rui Meng, Herbie Lee, Kristofer Bouchard
This paper presents an efficient variational inference framework for deriving a family of structured gaussian process regression network (SGPRN) models. The key idea is to incorpor…
Spatiotemporal Attention for Multivariate Time Series Prediction and Interpretation
Tryambak Gangopadhyay, Sin Yong Tan, Zhanhong Jiang +2
Multivariate time series modeling and prediction problems are abundant in many machine learning application domains. Accurate interpretation of such prediction outcomes from a mach…
Nonstationary Multivariate Gaussian Processes for Electronic Health Records
Rui Meng, Braden Soper, Herbert Lee +3
We propose multivariate nonstationary Gaussian processes for jointly modeling multiple clinical variables, where the key parameters, length-scales, standard deviations and the corr…
Hierarchical Hidden Markov Jump Processes for Cancer Screening Modeling
Rui Meng, Soper Braden, Jan Nygard +2
Hidden Markov jump processes are an attractive approach for modeling clinical disease progression data because they are explainable and capable of handling both irregularly sampled…
Regularized Sparse Gaussian Processes
Rui Meng, Herbert Lee, Soper Braden +1
Gaussian processes are a flexible Bayesian nonparametric modelling approach that has been widely applied but poses computational challenges. To address the poor scaling of exact in…