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 Gotit.pub 70%
- affiliated with Bayesian optimization 70%
- used by alphaXiv 70%
- used by ScienceCast 70%
- instance of ScienceCast 70%
- instance of alphaXiv 70%
- used by CatalyzeX 70%
- used by reproducing kernel Hilbert space 70%
- developed Markov chain Monte Carlo 70%
- competes with deep neural network 70%
- instance of reproducing kernel Hilbert space 70%
10 day(s) with sentiment data
-
New Bayesian Model Enhances EEG Brain-Computer Interface Accuracy
Researchers have developed a novel sparse Bayesian regression framework to improve the performance of electroencephalography (EEG)-based P300 brain-computer interfaces (BCIs). This method explicitly models interactions …
-
Pseudo-label augmentation boosts affect sensing in small groups
Researchers have developed a pseudo-label augmentation technique to improve affect sensing in small collaborative groups, particularly when labeled data is scarce. Using the GroupAffect-4 dataset, which includes physiol…
-
New RS-MFBO framework optimizes industrial simulations with reduced fidelity evaluations
Researchers have developed a new framework called Reduced-Space Multi-Fidelity Bayesian Optimization (RS-MFBO) to tackle the computational challenges of optimizing industrial process simulations. This method combines Gl…
-
New Bayesian Optimization Algorithm Uses Product-of-Experts GP Models
Researchers have introduced BO-pro-c, a novel Bayesian optimization algorithm that utilizes a product-of-experts Gaussian process (GP) model. This approach addresses the computational limitations of traditional GP model…
-
New Bayesian optimization method enhances source localization and acoustic inversion
Researchers have developed a novel Bayesian optimization technique using kernel ensembles and a disagreement-based acquisition function to improve source localization and acoustic inversion. This method combines multipl…
-
New VBLL method enhances online node classification on evolving graphs
Researchers have developed a new method called variational Bayesian last-layer (VBLL) for online node classification on evolving graphs. This approach addresses the challenges of inductive generalization and calibrated …
-
New nonlinear dimensionality reduction techniques enhance Bayesian optimization
Researchers have developed new nonlinear dimensionality reduction techniques for Bayesian optimization, a method used for efficient global optimization of expensive black-box functions. The proposed approach, SDR-LSBO, …
-
Language models dynamically learn chemical reaction representations for optimization
Researchers have developed a novel method for optimizing chemical reactions by dynamically learning representations from text using fine-tuned language models. This approach, integrated with Gaussian processes and Bayes…
-
New MPC framework uses Gaussian Processes for robust control
Researchers have developed a new model predictive control (MPC) framework designed for uncertain nonlinear systems. This framework utilizes Gaussian Processes (GPs) to learn system dynamics from noisy measurements, inco…
-
Gaussian Process Optimization Automates Hyphenation Pattern Generation
Researchers have developed a method using Gaussian Process Bayesian optimization to automatically generate hyphenation patterns, a crucial component for text processing systems. This approach formulates the selection of…
-
New Gaussian Linear Functional Manifold method reconstructs terrain from LiDAR data
Researchers have developed a new statistical framework called the Gaussian Linear Functional Manifold (GLFM) to reconstruct continuous terrain from massive airborne LiDAR point clouds. This method uses deterministic lin…
-
New framework learns kernels by alignment for multiclass Bayes classification
Researchers have developed a new framework for multiclass Bayes classification that learns kernels through alignment, moving beyond the traditional approach of pre-selecting kernels. This method, termed Collaborative Le…
-
New method speeds up training for Time Series Foundation Models
Researchers have introduced Synthetic Data Distillation (SDD), a novel training objective for Time Series Foundation Models (TSFMs). SDD enhances pre-training by comparing TSFM outputs to the conditional forecast distri…
-
New Gaussian Process Model Enhances Chemical Hazard Classification
Researchers have developed a new Gaussian process model designed for chemoinformatics, specifically to classify the hazard level of organic solvents. This model utilizes the Tanimoto distance to measure chemical similar…
-
AI framework enables efficient partial inverse design for high-performance concrete
Researchers have developed a novel cooperative neural network (CoNN) framework to address the complex challenge of partial inverse design for high-performance concrete (HPC). This AI-driven approach integrates an imputa…
-
New SMOTE-VAR method improves AI prediction of depression remission
Researchers have developed a new oversampling method called SMOTE-VAR to improve the accuracy of machine learning models predicting depression remission in university students. Traditional methods like SMOTE can generat…
-
New Gaussian Process Model Simplifies Multiclass Classification
Researchers have developed a new Gaussian process (GP) model for multiclass classification that leverages the geometry of the probability simplex. This approach maps simplex-valued class probabilities to a Euclidean spa…
-
New research explores adaptable and domain-independent neural operators · 4 sources tracked
Researchers are exploring new methods for neural operators, which are used to approximate physical simulations. One approach, LatentDDM, focuses on pretraining operators on smaller subdomains and then using a lightweigh…
-
New SMOTE-VAR method improves AI prediction of depression remission in students
Researchers have developed a new oversampling method called SMOTE-VAR to improve the accuracy of machine learning models predicting depression remission in university students. This novel approach uses a Gaussian proces…
-
New system tracks tiny, low-power devices like bees with high accuracy
Researchers have developed a novel system for tracking small, low-power devices, such as bees, across large landscapes. This system utilizes a minimal number of Received Signal Strength (RSS) measurements from rotating …