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Found 28 papers, 8 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.
Inverse Design of Photonic Crystal Surface Emitting Lasers is a Sequence Modeling Problem
By modeling the inverse design of PCSEL as a sequential decision-making problem, RL approaches can construct a satisfactory PCSEL structure from scratch.
Exploring Large Language Model based Intelligent Agents: Definitions, Methods, and Prospects
Intelligent agents stand out as a potential path toward artificial general intelligence (AGI).
Joint DOA estimation and distorted sensor detection under entangled low-rank and row-sparse constraints
The problem of joint direction-of-arrival estimation and distorted sensor detection has received a lot of attention in recent decades.
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.
Attentional Graph Neural Networks for Robust Massive Network Localization
To tap the potential of GNNs in regression, this paper integrates GNNs with attention mechanism, a technique that revolutionized sequential learning tasks with its adaptability and robustness, to tackle a challenging nonlinear regression problem: network localization.
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.
Bayesian-Boosted MetaLoc: Efficient Training and Guaranteed Generalization for Indoor Localization
Existing localization approaches utilizing environment-specific channel state information (CSI) excel under specific environment but struggle to generalize across varied environments.
ProAgent: Building Proactive Cooperative Agents with Large Language Models
Building agents with adaptive behavior in cooperative tasks stands as a paramount goal in the realm of multi-agent systems.
Towards Flexibility and Interpretability of Gaussian Process State-Space Model
The Gaussian process state-space model (GPSSM) has garnered considerable attention over the past decade.
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.
MetaLoc: Learning to Learn Wireless Localization
The framework introduces two paradigms for the optimization of meta-parameters: a centralized paradigm that simplifies the process by sharing data from all historical environments, and a distributed paradigm that maintains data privacy by training meta-parameters for each specific environment separately.
Rethinking Bayesian Learning for Data Analysis: The Art of Prior and Inference in Sparsity-Aware Modeling
However, a come back of Bayesian methods is taking place that sheds new light on the design of deep neural networks, which also establish firm links with Bayesian models and inspire new paths for unsupervised learning, such as Bayesian tensor decomposition.
Fast Generic Interaction Detection for Model Interpretability and Compression
The ability of discovering feature interactions in a black-box model is vital to explainable deep learning.
Recent Advances in Data-Driven Wireless Communication Using Gaussian Processes: A Comprehensive Survey
Empowered by big data and machine learning, next-generation data-driven communication systems will be intelligent with the characteristics of expressiveness, scalability, interpretability, and especially uncertainty modeling, which can confidently involve diversified latent demands and personalized services in the foreseeable future.
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.
Optimally Combining Classifiers for Semi-Supervised Learning
In this paper, we find these two methods have complementary properties and larger diversity, which motivates us to propose a new semi-supervised learning method that is able to adaptively combine the strengths of Xgboost and transductive support vector machine.
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.
Scalable Learning Paradigms for Data-Driven Wireless Communication
The marriage of wireless big data and machine learning techniques revolutionizes the wireless system by the data-driven philosophy.
Gaussian Processes for Analyzing Positioned Trajectories in Sports
Then, a modeling approach is proposed to analyze the kinetic flow of both individual and clusters of skiers.
A General $\mathcal{O}(n^2)$ Hyper-Parameter Optimization for Gaussian Process Regression with Cross-Validation and Non-linearly Constrained ADMM
Hyper-parameter optimization remains as the core issue of Gaussian process (GP) for machine learning nowadays.
Linear Multiple Low-Rank Kernel Based Stationary Gaussian Processes Regression for Time Series
Gaussian processes (GP) for machine learning have been studied systematically over the past two decades and they are by now widely used in a number of diverse applications.
How Effectively Can Indoor Wireless Positioning Relieve Visual Tracking Pains: A Camera-Rao Bound Viewpoint
Visual tracking is fragile in some difficult scenarios, for instance, appearance ambiguity and variation, occlusion can easily degrade most of visual trackers to some extent.
Wireless Traffic Prediction with Scalable Gaussian Process: Framework, Algorithms, and Verification
First, to the best of our knowledge, this paper is the first to empower GP regression with the alternating direction method of multipliers (ADMM) for parallel hyper-parameter optimization in the training phase, where such a scalable training framework well balances the local estimation in baseband units (BBUs) and information consensus among BBUs in a principled way for large-scale executions.
Multitask Gaussian Process with Hierarchical Latent Interactions
The function interaction is modeled by using cross convolution of latent functions.
Compressible Spectral Mixture Kernels with Sparse Dependency Structures for Gaussian Processes
Then, we propose a novel SM kernel with a dependency structure (SMD) by using cross-convolution between the SM components.
