kriging
PulseAugur coverage of kriging — every cluster mentioning kriging across labs, papers, and developer communities, ranked by signal.
1 day(s) with sentiment data
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New GP-Refiner framework accelerates Diffusion Transformers
Researchers have developed a new framework called GP-Refiner to accelerate Diffusion Transformers, a dominant paradigm in generative AI. This plug-and-play method uses Gaussian Process Regression to dynamically correct …
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Researchers advise against using Gaussian kernels in machine learning
A recent paper argues against the widespread use of the Gaussian kernel in machine learning tasks like regression and classification. The authors contend that this kernel, also known as the squared exponential or radial…
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New Bryson-Frazier smoother enhances Gaussian Process regression stability
Researchers have developed a modified Bryson-Frazier (MBF) smoother for temporal Gaussian Process regression. This method offers a more numerically stable alternative to the Rauch-Tung-Striebel (RTS) smoother by avoidin…
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New generative GPR model robustly handles outliers
Researchers have developed a new generative Gaussian Process Regression (GPR) model designed to overcome the significant distortion caused by outliers in traditional GPR methods. This novel approach models observation-s…
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New SpotOptim Python package released for black-box function optimization
The SpotOptim Python package has been released, offering a framework for optimizing expensive black-box functions. It utilizes a Kriging-based approach with Expected Improvement and supports various variable types, nois…
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New factor graph method boosts Gaussian process regression scalability
Researchers have developed a novel factor graph approach to address the scalability challenges in multi-output Gaussian process regression. This new method expresses the regression problem as a factor graph, enabling ef…
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New framework formalizes structure encoding in AI representations
Researchers have introduced a new framework called Legendre dynamics, which formalizes how internal representations in learning systems can encode underlying physical or statistical structure. This approach uses Legendr…
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New Adaptive Nyström Method Enhances Gaussian Process Regression Scalability
Researchers have developed an adaptive Nyström method to improve the scalability of Gaussian Process Regression (GPR). This new approach greedily selects landmark points to minimize approximation errors, outperforming r…
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New Bayesian Online Learning Framework Aggregates Experts for Adaptive Prediction
Researchers have developed a new framework for Bayesian online learning that addresses the challenge of fixed inferential choices by treating update rules as experts. This aggregation method competes with the best exper…
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Gaussian Process Regression Enhances Monte Carlo Tree Search for Continuous Actions
Researchers have developed a new method for Monte Carlo Tree Search (MCTS) that utilizes Gaussian Process Regression to improve performance in environments with continuous action spaces. This approach aims to better agg…
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New Gaussian Process method solves complex wave problems with uncertainty quantification
Researchers have developed a novel method for solving complex wave propagation problems governed by the Helmholtz equation, particularly in dissipative media where the squared wavenumber is complex. This new approach ex…
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Machine learning boosts wind power forecast accuracy
Researchers have developed advanced machine learning techniques to improve wind power forecasting accuracy. A comparative analysis of conformalized quantile regression, natural gradient boosting, and conditional diffusi…
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Machine learning model outperforms physics-based simulation for hydraulic clutch control
This paper introduces a data-driven method for modeling hydraulic clutch control pressure, addressing nonlinear behaviors like hysteresis and latch transitions. By incorporating current derivative information and using …
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New SHARC framework enhances explainability for ML risk models in finance
A new research paper introduces SHARC, an explainability framework designed for machine learning risk models used in regulatory capital estimation. SHARC addresses the 'black box' problem by applying SHapley Additive ex…
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New data-driven models predict pressure losses in turbulent flows
Researchers have developed two new data-driven models, one using kriging and the other a neural network (NN), to predict pressure losses in turbulent flows across perforated plates. These models were trained on experime…
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New GAIA framework enhances LLM instruction tuning with global data selection
Researchers have developed GAIA (Global Adaptive Instruction tuning via Gaussian processes), a novel framework for selecting high-quality data for Large Language Model (LLM) instruction tuning. Unlike existing methods t…
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New kriging and neural network models predict pressure losses
Researchers have developed two new data-driven models, one using kriging and another employing artificial neural networks (NN), to predict pressure losses in turbulent flows across perforated plates. These models were t…
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New framework for nonlinear system identification introduced
Researchers have introduced Orthogonal Discrepancy Kernels (ODKs), a novel semi-parametric framework designed for nonlinear system identification. This approach effectively separates discrepancy functions from physics-b…
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New F2NARX model offers significant efficiency and accuracy gains for dynamical systems
Researchers have introduced a new Function-on-Function Nonlinear AutoRegressive model with eXogenous inputs (F2NARX), which enhances predictive efficiency and accuracy for complex dynamical systems. This novel framework…
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New AI Framework Enhances Control for Multi-Fuel Engines
Researchers have developed a new data-driven control framework for multi-fuel compression ignition (CI) engines to address challenges in achieving consistent combustion phasing. The system utilizes Gaussian Process Regr…