Papers GPR
“GPR” 태그가 달린 논문 270편 · 필터 해제
Sample-Constrained Black Box Optimization for Audio Personalization
We consider the problem of personalizing audio to maximize user experience. Briefly, we aim to find a filter $h^*$, which applied to any music or speech, will maximize the user's satisfaction. This is a black-box optimiz…
GPRFast ground penetrating radar dual-parameter full waveform inversion method accelerated by hybrid compilation of CUDA kernel function and PyTorch
This study proposes a high-performance dual-parameter full waveform inversion framework (FWI) for ground-penetrating radar (GPR), accelerated through the hybrid compilation of CUDA kernel functions and PyTorch. The metho…
Computational EfficiencyGPRGPUTrustworthy Prediction with Gaussian Process Knowledge Scores
Probabilistic models are often used to make predictions in regions of the data space where no observations are available, but it is not always clear whether such predictions are well-informed by previously seen data. In …
Anomaly DetectionGPRImputationPredictionPredicting Stock Market Crash with Bayesian Generalised Pareto Regression
This paper develops a Bayesian Generalised Pareto Regression (GPR) model to forecast extreme losses in Indian equity markets, with a focus on the Nifty 50 index. Extreme negative returns, though rare, can cause significa…
GPRregressionOvercoming Overfitting in Reinforcement Learning via Gaussian Process Diffusion Policy
One of the key challenges that Reinforcement Learning (RL) faces is its limited capability to adapt to a change of data distribution caused by uncertainties. This challenge arises especially in RL systems using deep neur…
GPRReinforcement Learning (RL)Fast Derivative Valuation from Volatility Surfaces using Machine Learning
We introduce a fast and flexible Machine Learning (ML) framework for pricing derivative products whose valuation depends on volatility surfaces. By parameterizing volatility surfaces with the 5-parameter stochastic volat…
GPRA Multi-output Gaussian Process Regression with Negative Transfer Mitigation for Generating Boundary Test Scenarios of Multi-UAV Systems
Adaptive sampling based on Gaussian process regression (GPR) has already been applied with considerable success to generate boundary test scenarios for multi-UAV systems (MUS). One of the key techniques in such researche…
GPRA fast sound power prediction tool for genset noise using machine learning
This paper investigates the application of machine learning regression algorithms Kernel Ridge Regression (KRR), Huber Regressor (HR), and Gaussian Process Regression (GPR) for predicting sound power levels of gensets, o…
GPRMarketingNoise EstimationregressionEmpirical 3D Channel Modeling for Cellular-Connected UAVs: A Triple-Layer Machine Learning Approach
This work proposes an empirical air to ground (A2G) propagation model specifically designed for cellular connected unmanned aerial vehicles (UAVs). An in depth aerial drive test was carried out within an operating Long T…
feature selectionGPRA Novel Shape-Aware Topological Representation for GPR Data with DNN Integration
Ground Penetrating Radar (GPR) is a widely used Non-Destructive Testing (NDT) technique for subsurface exploration, particularly in infrastructure inspection and maintenance. However, conventional interpretation methods …
GPRobject-detectionObject DetectionTopological Data AnalysisEfficient Implementation of Gaussian Process Regression Accelerated Saddle Point Searches with Application to Molecular Reactions
The task of locating first order saddle points on high-dimensional surfaces describing the variation of energy as a function of atomic coordinates is an essential step for identifying the mechanism and estimating the rat…
GPRSTRIDE: Sparse Techniques for Regression in Deep Gaussian Processes
Gaussian processes (GPs) have gained popularity as flexible machine learning models for regression and function approximation with an in-built method for uncertainty quantification. However, GPs suffer when the amount of…
Gaussian ProcessesGPRregressionUncertainty QuantificationCell Library Characterization for Composite Current Source Models Based on Gaussian Process Regression and Active Learning
The composite current source (CCS) model has been adopted as an advanced timing model that represents the current behavior of cells for improved accuracy and better capability than traditional non-linear delay models (NL…
Active LearningGPRBayesian full waveform inversion with sequential surrogate model refinement
Bayesian formulations of inverse problems are attractive for their ability to incorporate prior knowledge and update probabilistic models as new data become available. Markov chain Monte Carlo (MCMC) methods sample poste…
Dimensionality ReductionGPREnsuring Reproducibility in Generative AI Systems for General Use Cases: A Framework for Regression Testing and Open Datasets
Reproducibility and reliability remain pressing challenges for generative AI systems whose behavior can drift with each model update or prompt revision. We introduce GPR-bench, a lightweight, extensible benchmark that op…
Code GenerationGPRInformation RetrievalPrompt Engineering+1Transition States Energies from Machine Learning: An Application to Reverse Water-Gas Shift on Single-Atom Alloys
Obtaining accurate transition state (TS) energies is a bottleneck in computational screening of complex materials and reaction networks due to the high cost of TS search methods and first-principles methods such as densi…
GPRReservoir-enhanced Segment Anything Model for Subsurface Diagnosis
Urban roads and infrastructure, vital to city operations, face growing threats from subsurface anomalies like cracks and cavities. Ground Penetrating Radar (GPR) effectively visualizes underground conditions employing el…
Anomaly DetectionGPRmodelInverted Gaussian Process Optimization for Nonparametric Koopman Operator Discovery
The Koopman Operator Theory opens the door for application of rich linear systems theory for computationally efficient modeling and optimal control of nonlinear systems by providing a globally linear representation for c…
GPROperator learningUncertainty QuantificationThe impact of external uncertainties on the extreme return connectedness between food, fossil energy, and clean energy markets
We investigate the extreme return connectedness between the food, fossil energy, and clean energy markets using the quantile connectedness approach, which combines the traditional spillover index with quantile regression…
CPUGPRquantile regressionMarsLGPR: Mars Rover Localization with Ground Penetrating Radar
In this work, we propose the use of Ground Penetrating Radar (GPR) for rover localization on Mars. Precise pose estimation is an important task for mobile robots exploring planetary surfaces, as they operate in GPS-denie…
GPRPose EstimationPose PredictionVisual Odometry