Papers Gaussian Processes
“Gaussian Processes” 태그가 달린 논문 2,227편 · 필터 해제
Gaussian Processes for Modelling Spatial Fields with Robot Swarms
Robot swarms, by virtue of their decentralised architecture, are a natural tool for scalable, robust modelling of spatial fields, such as water temperature, wind velocity, or terrain elevation. However, existing methods …
Gaussian ProcessesGeneralized Splines and Gaussian Processes
For finite-dimensional linear inverse problems where the variables are Gaussian, it is well-known that the minimum-mean-square error estimator takes the form of a regularized least-squares data fit. In this chapter, we s…
Gaussian ProcessesCDGP: Contrastive Dual Gaussian Processes for Weakly Supervised Anomaly Segmentation
Industrial visual inspection must both decide whether a product is defective and localize the defect, yet pixel-level masks are costly to collect at scale. Most anomaly-segmentation methods learn only from defect-free im…
Gaussian ProcessesModified Bryson-Frazier Smoothing and Hyperparameter Learning for Temporal Gaussian Process Regression
One-dimensional Gaussian processes with stationary, integrable kernel functions admit exact or arbitrarily accurate state-space representations, enabling linear-time inference through Kalman filtering and Rauch-Tung-Stri…
Gaussian ProcessesOn the Brittleness of Maximum Likelihood Estimation for Gaussian Process Hyperparameter Optimization
Machine learning (ML) has become an indispensable part of modern engineering design workflows. A crucial step in training an ML model is the selection of the loss function which can be systematically formulated via vario…
Hyperparameter OptimizationGaussian ProcessesConformal risk control for model-form uncertainty in parametric non-intrusive reduced-order models
Non-intrusive reduced-order models (NIROMs) have become a standard tool for approximating parametric partial differential equations from computer design of experiments while significantly reducing computational costs. Ho…
Gaussian ProcessesRecursive Gaussian Processes and the Bayesian Brain
Predictive coding offers a powerful framework for cortical computation, yet scalable implementations that respect both Bayesian exactness and neurobiological constraints remain scarce. We bridge this gap by formally conn…
Gaussian ProcessesBayesian InferenceThe Bayesian Reflex: A Predictive Coding Engine for Artificial Intelligence
Predictive coding offers a powerful theory of cortical computation, but corresponding scalable algorithmic implementations for artificial intelligence have remained elusive. This paper introduces the Bayesian reflex, a c…
Gaussian ProcessesContinual LearningCompactly supported radial basis functions as probability density functions
Compactly Supported Radial Basis Functions (CS-RBFs) are a fundamental tool in multivariate approximation theory. However, their use in statistics and probability modeling remains underexplored, having been used mainly t…
Incremental LearningGaussian ProcessesDensity EstimationTransfer Learning Architectures for Scalable Multi-Fidelity Bayesian Optimization
Self-driving laboratories increasingly rely on multi-fidelity Bayesian optimization (MFBO) to balance cheap, approximate evaluations against scarce, expensive ones, with a predictive surrogate at its core. Gaussian proce…
Gaussian ProcessesTransfer LearningCovariance-Boosted Gaussian Processes for Spatiotemporal Irregularities
Nonstationary Gaussian process (GP) models are powerful tools for capturing input-dependent variability by adapting to observed data. However, with limited sampling and highly parameterized covariance structure, they are…
Gaussian ProcessesGNet: A scalable and flexible Gaussian process network with nonparametric neurons
We develop GNet, a scalable and flexible Gaussian process network with nonparametric activation functions modeled by Gaussian processes. To reduce computational and storage costs, we introduce the jointly inverse Kalman …
Gaussian ProcessesDeep Gaussian Processes on Directed Acyclic Graphs
Many real-world processes can be represented as compositions of functions along a directed acyclic graph (DAG). In causal modelling, these correspond to the underlying mechanisms; in engineering, to multiple fidelity lev…
Gaussian ProcessesA unified perspective of Gaussian process approximation for differential equations
The use of Gaussian processes for approximating differential equations has expanded rapidly, leading to a growing, diverse, and fragmented body of numerical methods. We present a unified Bayesian perspective that places …
Gaussian ProcessesDIVO: Continuous-time DVL-Inertial-Visual Odometry for Unmanned Underwater Vehicles
This paper presents a novel acoustic-visual-inertial odometry solution leveraging a continuous-time trajectory estimation framework for unmanned underwater vehicles. Underwater environments present unique challenges for …
Visual LocalizationGaussian ProcessesVisual TrackingVisual OdometryStructured Gaussian Processes for Uncertainty-Aware Classification of High-Dimensional, Small-Sampled Omics Data
Classifying heterogeneous omics data remains a fundamental challenge in computational biology, particularly in high-dimensional, small-sample settings where nonlinear interactions dominate and class imbalance further com…
Gaussian ProcessesLearning from Demonstration via Spatiotemporal Tubes for Unknown Euler-Lagrange Systems
We present STT-LfD, a unified Learning from Demonstration (LfD) framework that integrates motion learning with control for unknown Euler-Lagrange systems. Unlike traditional decoupled approaches that track a fixed refere…
Gaussian ProcessesDynamic Gaussian Processes and the Vanilla-SPDE Exchange
Gaussian process inference is often limited by cubic computational costs, a challenge that becomes more pronounced in spatio-temporal settings where posterior inference is required over dense grids. While state-space SPD…
Gaussian ProcessesOnline Data Selection for Instruction Tuning via Gaussian Processes
With Large Language Model (LLM) pre-training and fine-tuning shifting its focus from data volume to data quality, quality data selection has emerged as a critical research topic. Existing online data selection methods fo…
Gaussian ProcessesUAV-MapFusion: RTK-Aligned Uncertainty-Aware Coarse-to-Fine Multi-Session UAV Mapping
Large-scale point cloud maps are essential for robotics and spatial intelligence tasks. UAVs provide an efficient means for large-scale map acquisition; however, due to limited flight endurance and onboard storage, mappi…
Gaussian Processes