Gaussian process
PulseAugur coverage of Gaussian process — every cluster mentioning Gaussian process across labs, papers, and developer communities, ranked by signal.
- used by DagsHub 70%
- used by Bayesian optimization 70%
- used by alphaXiv 70%
- used by Gotit.pub 70%
- used by ScienceCast 70%
- used by CatalyzeX 70%
- developed Markov chain Monte Carlo 70%
- instance of alphaXiv 70%
- instance of Gotit.pub 70%
- instance of reproducing kernel Hilbert space 70%
- instance of ScienceCast 60%
- other Markov chain Monte Carlo 50%
13 day(s) with sentiment data
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Bayesian optimization efficiently finds strong experts in LLMs
Researchers have developed a new method using Bayesian optimization to efficiently identify strong single experts within large language models, a process known as gradient-free post-training. This approach, which applie…
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New multitask scanning probe microscopy autonomously selects measurements
Researchers have developed a new method called multitask scanning probe microscopy that uses a Gaussian process to autonomously select the next measurement location and experimental protocol. This closed-loop workflow i…
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New method Adaptive KappaSharp enhances Preferential Bayesian Optimization
Researchers have developed Adaptive KappaSharp, a novel method to improve the efficiency of Preferential Bayesian Optimization (PBO). PBO is used to optimize objectives based on pairwise user comparisons. The new techni…
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New SGP-RI model enables decentralized indoor localization for IoT devices
Researchers have developed a decentralized indoor localization framework using a Sparse Gaussian Process with Reduced-dimensional Inputs (SGP-RI) model. This approach allows Internet of Things (IoT) devices to perform r…
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Neuro-symbolic control architecture enhances laser powder bed fusion quality
Researchers have developed a novel neuro-symbolic closed-loop control architecture for laser powder bed fusion (LPBF) additive manufacturing. This system integrates symbolic reasoning with statistical learning, using an…
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Tensor completion accelerates lattice structure design in materials science
Researchers have developed a novel approach using tensor completion as a surrogate model to accelerate the design of optimal lattice structures for specific mechanical properties. This method addresses challenges in mat…
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New Gaussian Process model enhances analysis of self-exciting count data
Researchers have developed a new statistical model called the Gaussian Process Discrete Hawkes Process (GP-DHP) designed for analyzing discrete-time count data where past events influence future occurrences. This semipa…
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New research paper questions machine learning benchmark limitations
A new research paper published on arXiv explores the limitations of temporal-aggregate learning in machine learning benchmarks. The study, titled "The Label Defines the Timescale: Trait-State Limits of Temporal-Aggregat…
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New Bayesian Optimization techniques tackle complex scientific and engineering problems · 4 sources tracked
Recent research papers explore advancements in Bayesian Optimization (BO) techniques for complex problems. One study introduces "Out-Of-The-Loop" MF-BO, which incorporates historical high-fidelity data to improve optimi…
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Machine learning models show promise in discrete choice tasks for policy
A new research paper analyzes the effectiveness of various machine learning models in complex discrete choice tasks, particularly for policy-making and preference elicitation. The study found that semi-parametric and no…
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BART model converges to Gaussian process, revealing theoretical underpinnings
Researchers have demonstrated that the Bayesian Additive Regression Trees (BART) model, known for its high performance in prediction and causal inference, converges to a Gaussian process (GP) as the number of trees incr…
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New research explores expected improvement policy for optimization in RKHS
This paper investigates the expected improvement (EI) policy for optimizing deterministic objective functions within Reproducing Kernel Hilbert Spaces (RKHS). The researchers analyze the performance of EI using Gaussian…
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Quantum models show no advantage over classical baselines in time-series forecasting
Researchers have developed and evaluated four conditional energy-based forecasting architectures, including classical and quantum-classical hybrid models, for time-series forecasting. Their evaluation, which included a …
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New CBGP framework improves Gaussian process models for critical applications
Researchers have introduced a new framework called Covariance-Boosted Gaussian Process (CBGP) designed to improve the accuracy and reliability of nonstationary Gaussian process models. This method addresses issues of ov…
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New Deep Sigma-Point Process Enhances SAR Imagery RCS Modeling
Researchers have developed a Deep Sigma-Point Process (DSPP) model to improve radar cross-section (RCS) modeling for spaceborne synthetic aperture radar (SAR) imagery. This new model utilizes a hierarchical Gaussian pro…
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Withdrawn paper details novel neural feature maps for scalable Gaussian process inference
A research paper introduced a novel Gaussian process (GP) framework utilizing neural feature maps to create sophisticated kernels. This method allows for efficient and accurate exact GP inference, applicable to various …
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New Bayesian Framework Integrates Dimension Reduction for Gaussian Process Models
Researchers have developed a new Bayesian framework designed to address the challenges of Gaussian Process (GP) modeling with high-dimensional inputs. This novel approach integrates dimensionality reduction directly int…
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New framework auto-tunes SVMs on quantum annealers
This paper introduces a novel framework for optimizing Support Vector Machines (SVMs) that utilize Quadratic Unconstrained Binary Optimization (QUBO) models on quantum-inspired annealers. The framework employs Optuna fo…
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New ALAS kernel family enhances Bayesian optimization flexibility
Researchers have introduced ALAS, a novel family of Gaussian Process kernels designed for flexible Bayesian optimization. ALAS utilizes symmetric alpha-stable spectral components, allowing it to adapt its effective smoo…
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New neural diffusion method enables efficient spatial simulation
Researchers have introduced Neural Conditional Simulation (NCS), a novel method for simulating spatial processes. NCS utilizes neural diffusion models to generate samples from predictive distributions, which are often i…