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Found 8 papers, 5 papers with code
Preventing Model Collapse in Gaussian Process Latent Variable Models
Gaussian process latent variable models (GPLVMs) are a versatile family of unsupervised learning models, commonly used for dimensionality reduction.
Regularization-Based Efficient Continual Learning in Deep State-Space Models
Deep state-space models (DSSMs) have gained popularity in recent years due to their potent modeling capacity for dynamic systems.
Ensemble Kalman Filtering Meets Gaussian Process SSM for Non-Mean-Field and Online Inference
The Gaussian process state-space models (GPSSMs) represent a versatile class of data-driven nonlinear dynamical system models.
Sparsity-Aware Distributed Learning for Gaussian Processes with Linear Multiple Kernel
This paper presents a novel GP linear multiple kernel (LMK) and a generic sparsity-aware distributed learning framework to optimize the hyper-parameters.
Towards Efficient Modeling and Inference in Multi-Dimensional Gaussian Process State-Space Models
The Gaussian process state-space model (GPSSM) has attracted extensive attention for modeling complex nonlinear dynamical systems.
Output-Dependent Gaussian Process State-Space Model
Gaussian process state-space model (GPSSM) is a fully probabilistic state-space model that has attracted much attention over the past decade.
Graph Neural Network for Large-Scale Network Localization
In this work, we adopt GNN for a classic but challenging nonlinear regression problem, namely the network localization.
FedLoc: Federated Learning Framework for Data-Driven Cooperative Localization and Location Data Processing
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.
