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54 results for “Gaussian Processes”
Land cover maps: End-to-end Learning for Land Cover Classification using Irregular and Unaligned SITS by Combining Attention-Based Interpolation with Sparse Variational Gaussian Processes
<p>Land cover maps obtained with mTAN-GP, mTAN-MLP, mTAN-LTAE and raw-LTAE models for the year 2018 with Sentinel-2 acquisitions.</p> <p>For further details see the pre-print article "End-to-end Learning for Land Cover Classification using Irregular and Unaligned SITS by Combining Attention-Based Interpolation with Sparse Variational Gaussian Processes ". This article is available : <a href="https://hal.science/hal-04112115">here</a>.</p>
Best learned models : Land Cover Classification with Gaussian Processes using spatio-spectro-temporal features
<p>Best learned models (Gaussian Processes, Random Forest, Multilayer Perceptron and Lightweight Temporal Self-Attention models) for each region based on the classification data set DS-A with seed 0 (see description <a href="https://zenodo.org/deposit/7099785">here</a>). </p><p>Models: GP non spatial, GP spatial (sum), GP spatial (product),RF non spatial, RF spatial, MLP non spatial, MLP spatial, LTAE non spatial, LTAE spatial</p><p>For further details see section VI-C of the pre-print article "Land Cover Classification with Gaussian Processes using spatio-spectro-temporal features ". This article is available <a href="https://hal.archives-ouvertes.fr/hal-03781332">here</a>.</p><p>The implementation of the models is available in the <a href="https://gitlab.cesbio.omp.eu/belletv/land_cover_southfrance_gp">open source repository</a>.</p>
Supplementary data: Predicting grid frequency short-term dynamics with Gaussian processes and sequence modeling
<p>This repository contains data and result files for the paper "Predicting grid frequency short-term dynamics with Gaussian processes and sequence modelling". The code to generate the models and reproduce the results of the comparative study in the above paper is available on this <a href="https://github.com/bolin-liu/sequence-model-and-gaussian-process-for-frequency-prediction">github repository</a></p> <p><strong>Supplementary data</strong>:</p> <p>- The <strong>trained_models</strong> folder contains the results of the trained models.</p> <p>- The folder <strong>data</strong> contains data needed for for the comparative study for the year 2019 in the paper above. This data set (except knn_point_predictions.npy) is generated with the code in this <a href="https://github.com/johkruse/PIML-for-grid-frequency-modelling">github repository</a>. knn_point_predictions.npy is generated with the code in this <a href="https://github.com/bolin-liu/sequence-model-and-gaussian-process-for-frequency-prediction">github repository </a>.</p>
Boundary Data set : Land Cover Classification with Gaussian Processes using spatio-spectro-temporal features
<p>Boundary data set used to evaluate the continuity predictions for different models in boundary zones. It is composed of labelled and unlabelled pixels for a boundary size of 100m and 200m.</p> <p>For further details see section VI-A-1 of the pre-print article "Land Cover Classification with Gaussian Processes using spatio-spectro-temporal features ". This article is available <a href="https://hal.archives-ouvertes.fr/hal-03781332">here</a>.</p> <p>To compute the predictions in the boundary zones with different models (GP, RF, MLP, LTAE), the code is available in the <a href="https://gitlab.cesbio.omp.eu/belletv/land_cover_southfrance_gp">open source repository</a>.</p>
Data for "Quantifying the Oscillatory Evolution of Simulated Boundary-Layer Cloud Fields Using Gaussian Process Regression"
<p>The dataset for the manuscript titled</p> <p>"Quantifying the Oscillatory Evolution of Simulated Boundary-Layer Cloud Fields Using Gaussian Process Regression".</p> <blockquote> <p>Oh, G. and Austin, P. H.: Quantifying the Oscillatory Evolution of Simulated Boundary-Layer Cloud Fields Using Gaussian Process Regression, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2024-352, 2024.</p> </blockquote> <p> </p> <p>The dataset contains input parameters used for the SAM LES model run (Khairoutdinov and Randall, 2003) based on the CGILS S2 parameters (CGILS.zip) and the main raw output used for the manuscript (clusters.zip). The latter contains a list of Parquet files for the 12-hour model run, sampled every minute. The raw output consists of the coordinates of all cloudy cells in the model domain, which is used to construct the cloud size distribution as described in the manuscript.</p>
Reconstructing Blood Flow in Data-Poor Regimes: A Vasculature Network Kernel for Gaussian Process Regression
Open the record for dataset details and reuse information.
Multi-scale Gaussian process dataset
<p>A multi-scale hierarchical dataset comprised of Gaussian processes. For more information regarding the dataset and usage see https://github.com/dexgen/backdrop/.</p>
Dataset for the plots in paper: 'A Kronecker product accelerated efficient sparse Gaussian Process (E-SGP) for flow emulation' in 'Journal of Computational Physics'
<p>The .xlsx file contains the data used for the plots Fig. 3, 4, 7, 8 and 9 in the paper 'Kronecker product accelerated efficient sparse Gaussian Process (E-SGP) for flow emulation'.</p>
Inferring GCE Morphology with Gaussian Processes: Figure Data
<p>Figure data for Inferring GCE Morphology with Gaussian Processes.</p> <p>See GitHub repo for details on creating figures.</p>
A Gaussian process emulator for simulating ice sheet-climate interactions on a multi-million year timescale: CLISEMv1.0 (Video supplement)
<p>This video illustrates the ice sheet evolution during a 3 Myr period for three different emulators as described in the manuscript "A Gaussian process emulator for simulating ice sheet - climate interactions on a multi-million year timescale", submitted to Geoscientific Model Development. The ice sheet is forced by declining carbon dioxide concentrations from 980 to 720 ppmv and orbital parameter variations during the late Eocene (between 38 Ma and 35 Ma).</p>
Precise measurements of chromatin diffusion dynamics by modeling using Gaussian processes (Inter-Mito)
<p>Microscopy movies corresponding to interphase and mitotic cells in publication (add DOI when available).</p>
Precise measurements of chromatin diffusion dynamics by modeling using Gaussian processes (code)
<p>GP-FBM and GP-Tool codes and algorithms as in publication day. For updated version: https://github.com/guilmont/GP-Tool</p>
Best Learned Models: End-to-end Learning for Land Cover Classification using Irregular and Unaligned SITS by Combining Attention-Based Interpolation with Sparse Variational Gaussian Processes
<p>Best learned model learned with the dataset available <a href="http://https://doi.org/10.5281/zenodo.8033058">here</a> for the mTAN-GP, mTAN-MLP, mTAN-LTAE, and raw-LTAE.</p> <p>For further details see the pre-print article "End-to-end Learning for Land Cover Classification using Irregular and Unaligned SITS by Combining Attention-Based Interpolation with Sparse Variational Gaussian Processes ". This article is available : <a href="https://hal.science/hal-04112115">here</a>.</p> <p>The implementation of the models is available in the <a href="https://gitlab.cesbio.omp.eu/belletv/land_cover_southfrance_mtan_gp_irregular_sits">open source repository</a>.</p>
Classification Data set: End-to-end Learning for Land Cover Classification using Irregular and Unaligned SITS by Combining Attention-Based Interpolation with Sparse Variational Gaussian Processes
<p>Classification data set (train, validation, test) from the study area based on 27 tiles on the south of the France. This dataset contains irregular and unaligned SITS with their corresponding masks. 9 different random sampling are provided. This data set was used to train mTAN-GP, mTAN-MLP, mTAN-LTAE and raw-LTAE.</p> <p>For further details see the pre-print article "End-to-end Learning for Land Cover Classification using Irregular and Unaligned SITS by Combining Attention-Based Interpolation with Sparse Variational Gaussian Processes ". This article is available : <a href="https://hal.science/hal-04112115">here</a>.</p> <p>The implementation of the models is available in the <a href="https://gitlab.cesbio.omp.eu/belletv/land_cover_southfrance_mtan_gp_irregular_sits">open source repository</a>.</p>
Quasi-periodic Gaussian process rotation period posterior samples for 1132 Kepler objects of interest.
<p>1132 .h5 files containing light curves, hyperparameter priors, period priors and posterior samples for the QP-GP rotation period model in Angus, Morton, Aigrain, Foreman-Mackey & Rajpaul (2017).</p> <p>1132 .png light curve images.</p> <p>To load the samples in python:</p> <p>>>> df = pd.read_hdf("KOI-1.h5", key="samples")</p> <p>>>> ln_period_samples = df.ln_period</p> <p>>>> ln_A_samples = df.ln_A</p> <p>>>> ln_l_samples = df.ln_l</p> <p>>>> ln_G_samples = df.ln_G</p> <p>>>> ln_sigma_samples = df.ln_sigma</p> <p> </p>
Global oceanic PON concentration products derived from Aqua-MODIS through a Gaussian Process Regression model
<p><span>This dataset integrates the monthly PON concentration products from 2002 to 2022 of the global ocean, which were derived from Aqua-MODIS based on our newly developed Gaussian Process Regression models.</span></p>
Precise measurements of chromatin diffusion dynamics by modeling using Gaussian processes (T1T2 Anchor)
<p>Microscopy movies corresponding to T1-T2 anchor cell lines of publication (add DOI when available).</p>
Precise measurements of chromatin diffusion dynamics by modeling using Gaussian processes (T2T3 Anchor)
<p>Microscopy movies corresponding to T2-T3 anchor cell lines of publication (add DOI when available).</p>
A Gaussian Process Regression weights file for the Zundel cation
<p>Trained weight files for a kernel model of the potential energy of the Zundel cation, to be used in an example of the <a href="http://ipi-code.org">i-PI</a> interface of the <a href="https://github.com/lab-cosmo/librascal">librascal</a> ML library</p>
Clustering gene expression time series data using an infinite Gaussian process mixture model
GEO Series GSE104714. Homo sapiens. 24 samples. Type: Expression profiling by high throughput sequencing.
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.