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View article: Efficient Transformed Gaussian Process State-Space Models for Non-Stationary High-Dimensional Dynamical Systems
Efficient Transformed Gaussian Process State-Space Models for Non-Stationary High-Dimensional Dynamical Systems Open
Gaussian process state-space models (GPSSMs) offer a principled framework for learning and inference in nonlinear dynamical systems with uncertainty quantification. However, existing GPSSMs are limited by the use of multiple independent st…
View article: Multi-View Oriented GPLVM: Expressiveness and Efficiency
Multi-View Oriented GPLVM: Expressiveness and Efficiency Open
The multi-view Gaussian process latent variable model (MV-GPLVM) aims to learn a unified representation from multi-view data but is hindered by challenges such as limited kernel expressiveness and low computational efficiency. To overcome …
View article: Hybrid Data-Driven SSM for Interpretable and Label-Free mmWave Channel Prediction
Hybrid Data-Driven SSM for Interpretable and Label-Free mmWave Channel Prediction Open
Accurate prediction of mmWave time-varying channels is essential for mitigating the issue of channel aging in complex scenarios owing to high user mobility. Existing channel prediction methods have limitations: classical model-based method…
View article: Scalable Random Feature Latent Variable Models
Scalable Random Feature Latent Variable Models Open
Random feature latent variable models (RFLVMs) represent the state-of-the-art in latent variable models, capable of handling non-Gaussian likelihoods and effectively uncovering patterns in high-dimensional data. However, their heavy relian…
View article: Online/Offline Learning to Enable Robust Beamforming: Limited Feedback Meets Deep Generative Models
Online/Offline Learning to Enable Robust Beamforming: Limited Feedback Meets Deep Generative Models Open
Robust beamforming is a pivotal technique in massive multiple-input multiple-output (MIMO) systems as it mitigates interference among user equipment (UE). One current risk-neutral approach to robust beamforming is the stochastic weighted m…
View article: Preventing Model Collapse in Gaussian Process Latent Variable Models
Preventing Model Collapse in Gaussian Process Latent Variable Models Open
Gaussian process latent variable models (GPLVMs) are a versatile family of unsupervised learning models commonly used for dimensionality reduction. However, common challenges in modeling data with GPLVMs include inadequate kernel flexibili…
View article: Regularization-Based Efficient Continual Learning in Deep State-Space Models
Regularization-Based Efficient Continual Learning in Deep State-Space Models Open
Deep state-space models (DSSMs) have gained popularity in recent years due to their potent modeling capacity for dynamic systems. However, existing DSSM works are limited to single-task modeling, which requires retraining with historical t…
View article: Ensemble Kalman Filtering Meets Gaussian Process SSM for Non-Mean-Field and Online Inference
Ensemble Kalman Filtering Meets Gaussian Process SSM for Non-Mean-Field and Online Inference Open
The Gaussian process state-space models (GPSSMs) represent a versatile class of data-driven nonlinear dynamical system models. However, the presence of numerous latent variables in GPSSM incurs unresolved issues for existing variational in…
View article: Sparsity-Aware Distributed Learning for Gaussian Processes with Linear Multiple Kernel
Sparsity-Aware Distributed Learning for Gaussian Processes with Linear Multiple Kernel Open
Gaussian processes (GPs) stand as crucial tools in machine learning and signal processing, with their effectiveness hinging on kernel design and hyper-parameter optimization. This paper presents a novel GP linear multiple kernel (LMK) and …
View article: Towards Efficient Modeling and Inference in Multi-Dimensional Gaussian Process State-Space Models
Towards Efficient Modeling and Inference in Multi-Dimensional Gaussian Process State-Space Models Open
The Gaussian process state-space model (GPSSM) has attracted extensive attention for modeling complex nonlinear dynamical systems. However, the existing GPSSM employs separate Gaussian processes (GPs) for each latent state dimension, leadi…
View article: Output-Dependent Gaussian Process State-Space Model
Output-Dependent Gaussian Process State-Space Model Open
Gaussian process state-space model (GPSSM) is a fully probabilistic state-space model that has attracted much attention over the past decade. However, the outputs of the transition function in the existing GPSSMs are assumed to be independ…
View article: The Impact of SOD3 on Prostatic Diseases: Elevated SOD3 Serves as a Novel Biomarker for the Diagnosis of Chronic Nonbacterial Prostatitis
The Impact of SOD3 on Prostatic Diseases: Elevated SOD3 Serves as a Novel Biomarker for the Diagnosis of Chronic Nonbacterial Prostatitis Open
BACKGROUND: Prostate is the most common gland for the three major diseases in male, such as chronic nonbacterial prostatitis (CNP), benign prostatic hyperplasia (BPH) and prostate cancer (PCa). However, there is lack of ideal biomarker for…
View article: Graph Neural Network for Large-Scale Network Localization
Graph Neural Network for Large-Scale Network Localization Open
Graph neural networks (GNNs) are popular to use for classifying structured data in the context of machine learning. But surprisingly, they are rarely applied to regression problems. In this work, we adopt GNN for a classic but challenging …
View article: FedLoc: Federated Learning Framework for Data-Driven Cooperative Localization and Location Data Processing
FedLoc: Federated Learning Framework for Data-Driven Cooperative Localization and Location Data Processing Open
In this overview paper, data-driven learning model-based cooperative localization and location data processing are considered, in line with the emerging machine learning and big data methods. We first review (1) state-of-the-art algorithms…
View article: Data-Driven Fault Localization in Distribution Systems with Distributed Energy Resources
Data-Driven Fault Localization in Distribution Systems with Distributed Energy Resources Open
The integration of Distributed Energy Resources (DERs) introduces a non-conventional two-way power flow which cannot be captured well by traditional model-based techniques. This brings an unprecedented challenge in terms of the accurate lo…
View article: FedLoc: Federated Learning Framework for Data-Driven Cooperative Localization and Location Data Processing
FedLoc: Federated Learning Framework for Data-Driven Cooperative Localization and Location Data Processing Open
In this overview paper, data-driven learning model-based cooperative localization and location data processing are considered, in line with the emerging machine learning and big data methods. We first review (1) state-of-the-art algorithms…
View article: One-Class Classifier Based Fault Detection in Distribution Systems With Varying Penetration Levels of Distributed Energy Resources
One-Class Classifier Based Fault Detection in Distribution Systems With Varying Penetration Levels of Distributed Energy Resources Open
The integration of Distributed Energy Resources (DERs) into distribution systems greatly increases the system complexity and introduces two-way power flow. Conventional protection schemes are based upon local measurements and simple linear…