Gaussian process
PulseAugur coverage of Gaussian process — every cluster mentioning Gaussian process across labs, papers, and developer communities, ranked by signal.
- used by Bayesian optimization 90%
- used by GP-UCB 80%
- instance of alphaXiv 70%
- instance of Gotit.pub 70%
- used by DagsHub 70%
- affiliated with Bayesian optimization 70%
- instance of Bayesian optimization 70%
- used by Gotit.pub 70%
- developed Bayesian optimization 70%
- used by ScienceCast 70%
- used by alphaXiv 70%
- used by CatalyzeX 70%
11 day(s) with sentiment data
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ShapleyOG method accelerates Shapley value estimation in machine learning
Researchers have developed ShapleyOG, a novel method for estimating Shapley values in machine learning that significantly reduces computational overhead. By focusing on the odd component of the game, ShapleyOG requires …
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New hybrid AI framework improves data assimilation with sparse observations
Researchers have developed a novel hybrid framework that combines a convolutional neural network (CNN) with a learnable-kernel Gaussian Process (GP) for data assimilation and bias correction. This approach is designed t…
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New FLAT method improves sample-efficient traffic calibration for digital twins
Researchers have developed FLAT, a novel method for sample-efficient traffic calibration in digital twins. This approach smooths the parameter-error landscape, making the objective learnable from a limited number of sam…
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New research explores advanced manifold methods for complex problem-solving
Two new research papers explore advanced methods for solving complex mathematical problems on manifolds, which are curved spaces that differ from standard Euclidean geometry. The first paper introduces probabilistic mes…
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New Kriging Method Integrates Global PDE Residuals for Enhanced Probabilistic Solving
Researchers have introduced a novel method called PDE-residual-informed Kriging (PRIK) to enhance the probabilistic framework for solving partial differential equations (PDEs) using Gaussian processes. PRIK directly int…
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New Regret Bounds for Gaussian Process Upper Confidence Bound in Bayesian Optimization
This paper presents improved regret bounds for the Gaussian Process Upper Confidence Bound (GP-UCB) algorithm in Bayesian optimization. The research focuses on scenarios with a Matérn kernel, achieving a cumulative regr…
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Graph-informed Kriging boosts Bayesian optimization for wind farm layouts
Researchers have developed a novel Graph-informed Kriging framework to enhance the efficiency of Bayesian optimization for wind farm layouts. This new method embeds reusable aerodynamic interaction information into the …
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New Gaussian Process Model Enhances Materials Constitutive Modeling
Researchers have developed a novel Gaussian Process State-Space Model (GP-SSM) to improve constitutive modeling for history-dependent materials. This new framework replaces traditional fixed functional forms with flexib…
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New Transport Neural Tangent Kernel Enhances Active Learning for Generative Models
Researchers have developed a new active learning framework for conditional generative models, addressing the limitations of existing methods that focus solely on scalar or vector-valued outputs. The proposed transport N…
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AI-powered digital twin predicts cattle heat stress with 92% accuracy
Researchers have developed a novel digital twin framework for precision livestock farming, focusing on predicting core body temperature in dairy cattle to detect heat stress. This framework integrates physics-based simu…
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AutoPipe framework streamlines LLM post-training pipeline configuration
Researchers have developed AutoPipe, a novel framework designed to efficiently configure post-training pipelines for large language models (LLMs). This budget-aware system utilizes a two-phase approach, learning from hi…
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New Diona regression method offers faster predictions than GPs
A new regression method called Direct Inference on Admissible Sets (Diona) has been introduced, which bypasses extensive global training by directly using samples to estimate state and derivative values along with their…
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QuaNAS improves Neural Architecture Search efficiency using prior knowledge
Researchers have developed QuaNAS, a novel approach to Neural Architecture Search (NAS) that leverages prior search knowledge to improve efficiency. Unlike traditional methods that start from scratch, QuaNAS utilizes a …
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New algorithm exploits concavity for efficient contextual bandit optimization · arXiv paper
Researchers have developed a new contextual bandit algorithm that leverages concavity information in reward functions to improve optimization efficiency. This algorithm, detailed in a recent arXiv paper, uses a constrai…
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New method estimates robot policy uncertainty from single model
Researchers have developed a novel method for estimating epistemic uncertainty in Vision-Language-Action (VLA) policies, which are crucial for robots to identify knowledge gaps and request assistance. This new approach,…
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Bayesian Optimization Automates Multi-Agent System Design
Researchers have developed a new Bayesian Evolutionary Optimization framework to automate the design and tuning of multi-agent systems (MAS). This approach uses evolutionary operators to generate workflow graphs and Bay…
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New Bayesian method models evolving time series clusters
Researchers have developed a novel Bayesian nonparametric method for analyzing time series data, capable of modeling an evolving and potentially unbounded number of clusters. This approach utilizes a hierarchical Dirich…
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New method maps transonic flutter boundary with active learning
Researchers have developed a new method for mapping the transonic flutter boundary of airfoils, specifically focusing on Isogai's case A. This method utilizes coupled unsteady Euler simulations and a Gaussian-process su…
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Predictable Scaling framework slashes model exploration compute by 88%
Researchers have developed a framework called Predictable Scaling (PS) to efficiently explore changes in recommendation models. PS uses scaled-down model variants and curve fitting to predict full-scale performance, sig…
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New algorithm learns structure in clinical time series data
Researchers have developed a new structure learning algorithm specifically designed for clinical time series data. This algorithm, named StructGP, models patient time series as stationary Gaussian processes, allowing fo…