{"about":{"non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","site":"https://codewithpapers.app","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page","syntology":{"site":"https://syntology.ai","developers":"https://syntology.ai/developers","mcp":{"server":"https://syntology.ai/mcp","transport":"streamable-http","server_card":"https://syntology.ai/.well-known/mcp/server-card.json","auth":{"type":"trial token, no account","trial_token":"https://syntology.ai/api/oauth/trial/token","method":"POST","docs":"https://syntology.ai/developers"}},"have":"https://syntology.ai/api/graph/have?x=<method, arXiv id or title> (free, answers coverage only)","paper_base":"https://syntology.ai/paper/","atlas_base":"https://app.syntology.ai/?focus="},"machine_readable":[{"url":"https://codewithpapers.app/llms.txt","what":"the machine catalog: every machine-readable file, counted"},{"url":"https://codewithpapers.app/index/manifest.json","what":"paper-to-code index by arXiv id, with Syntology's counts"},{"url":"https://codewithpapers.app/search/manifest.json","what":"site search index (titles, authors) and its files"},{"url":"https://codewithpapers.app/download","what":"bulk files: Syntology's layer, described there"},{"url":"https://codewithpapers.app/build_manifest.json","what":"the build record: inputs, counts, exclusions, probes"}]},"url":"/task/gpr/papers/3","list_of":"/task/gpr","task":"GPR","archive":{"snapshot":"2025-07-28"},"key_notes":{"n_ran_checked":"legacy name, kept unchanged so existing readers do not break: it counts the samples that ran with no instrument failure (honoured, violated, and ran with no contract checked); it does not mean a contract was checked, and the pages print it as 'K with no instrument failure', not 'K checked'","n_constructed":"a sub-count of the samples that ran, never subtracted from them and never a failure: an executed sample whose run returned an instance of its own class (fixture_out_type equals the entry name): the run built an object and did not compute a result (Syntology's RAN record, counts.constructed)"},"syntology_read_at":"2026-09-28T10:30:06+00:00","order":"archive","order_definition":"repositories listed in the archive (most first), then date (newest first), then slug","page":3,"pages_in_order":3,"rows_per_page":100,"rows":[201,270],"of":270,"counts":{"archive_papers_tagged":270,"with_a_code_link":62,"where_syntology_ran_a_sample":3,"not_listed_spam_title":0,"listed":270,"listed_where_code_ran":3,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":2,"every_run_a_failure_of_syntologys_instrument":1,"listed_with_a_run_with_no_instrument_failure":2,"listed_every_run_a_failure_of_syntologys_instrument":1,"filter":{"states":["a run with no instrument failure","any run, instrument failures included"],"default":"a run with no instrument failure","note":"on the 'only where code ran' pages the default hides, in the browser, the rows where every run was a failure of Syntology's instrument; the second state shows them again. Rows are hidden, never re-ordered; these twins list every row"}},"definition":"distinct papers the archive tags; 'where Syntology ran a sample' counts papers with at least one harvested sample that ran, which is not a correctness claim"},"first_page":"/task/gpr","prev":"/task/gpr/papers/2","next":null,"papers":[{"url":null,"slug":"retrieval-of-coloured-dissolved-organic","title":"Retrieval of Coloured Dissolved Organic Matter with Machine Learning Methods","date":"2021-01-07","arxiv_id":"2101.02505","repositories_listed":0,"syntology":null},{"url":null,"slug":"estimation-of-driver-s-gaze-region-from-head","title":"Estimation of Driver's Gaze Region from Head Position and Orientation using Probabilistic Confidence Regions","date":"2020-12-23","arxiv_id":"2012.12754","repositories_listed":0,"syntology":null},{"url":null,"slug":"emulation-as-an-accurate-alternative-to","title":"Emulation as an Accurate Alternative to Interpolation in Sampling Radiative Transfer Codes","date":"2020-12-07","arxiv_id":"2012.10392","repositories_listed":0,"syntology":null},{"url":null,"slug":"mapping-leaf-area-index-with-a-smartphone-and","title":"Mapping Leaf Area Index with a Smartphone and Gaussian Processes","date":"2020-12-07","arxiv_id":"2012.04596","repositories_listed":0,"syntology":null},{"url":null,"slug":"spectral-band-selection-for-vegetation","title":"Spectral band selection for vegetation properties retrieval using Gaussian processes regression","date":"2020-12-07","arxiv_id":"2012.08640","repositories_listed":0,"syntology":null},{"url":null,"slug":"compact-dual-polarized-vivaldi-antenna-for","title":"Compact Dual-Polarized Vivaldi Antenna for Ground Penetrating Radar (GPR) Application","date":"2020-11-27","arxiv_id":"2011.14918","repositories_listed":0,"syntology":null},{"url":null,"slug":"flight-sensor-data-and-beamforming-based","title":"Flight Sensor Data and Beamforming based Integrated UAV Tracking with Channel Estimation using Gaussian Process Regression","date":"2020-11-20","arxiv_id":"2011.10177","repositories_listed":0,"syntology":null},{"url":null,"slug":"application-of-deep-learning-based","title":"Application of Deep Learning-based Interpolation Methods to Nearshore Bathymetry","date":"2020-11-19","arxiv_id":"2011.09707","repositories_listed":0,"syntology":null},{"url":null,"slug":"gpr-based-model-reconstruction-system-for","title":"GPR-based Model Reconstruction System for Underground Utilities Using GPRNet","date":"2020-11-05","arxiv_id":"2011.02635","repositories_listed":0,"syntology":null},{"url":null,"slug":"polymers-for-extreme-conditions-designed","title":"Polymers for Extreme Conditions Designed Using Syntax-Directed Variational Autoencoders","date":"2020-11-04","arxiv_id":"2011.02551","repositories_listed":0,"syntology":null},{"url":null,"slug":"mimo-ilc-for-precision-sea-robots-using-input","title":"MIMO ILC for Precision SEA robots using Input-weighted Complex-Kernel Regression","date":"2020-10-23","arxiv_id":"2010.04487","repositories_listed":0,"syntology":null},{"url":null,"slug":"taking-a-closer-look-at-synthesis-fine","title":"Taking A Closer Look at Synthesis: Fine-grained Attribute Analysis for Person Re-Identification","date":"2020-10-15","arxiv_id":"2010.08145","repositories_listed":0,"syntology":null},{"url":null,"slug":"physics-informed-gaussian-process-regression","title":"Physics-Informed Gaussian Process Regression for Probabilistic States Estimation and Forecasting in Power Grids","date":"2020-10-09","arxiv_id":"2010.04591","repositories_listed":0,"syntology":null},{"url":null,"slug":"dynamic-mode-decomposition-for-forecasting","title":"Stochastically forced ensemble dynamic mode decomposition for forecasting and analysis of near-periodic systems","date":"2020-10-08","arxiv_id":"2010.04248","repositories_listed":0,"syntology":null},{"url":null,"slug":"short-term-prediction-of-photovoltaic-power","title":"Short-term prediction of photovoltaic power generation using Gaussian process regression","date":"2020-10-05","arxiv_id":"2010.02275","repositories_listed":0,"syntology":null},{"url":null,"slug":"lateral-force-prediction-using-gaussian","title":"Lateral Force Prediction using Gaussian Process Regression for Intelligent Tire Systems","date":"2020-09-25","arxiv_id":"2009.12463","repositories_listed":0,"syntology":null},{"url":null,"slug":"gpr-based-subsurface-object-detection-and","title":"GPR-based Subsurface Object Detection and Reconstruction Using Random Motion and DepthNet","date":"2020-08-20","arxiv_id":"2008.08731","repositories_listed":0,"syntology":null},{"url":null,"slug":"multifidelity-data-fusion-via-gradient","title":"Multifidelity Data Fusion via Gradient-Enhanced Gaussian Process Regression","date":"2020-08-03","arxiv_id":"2008.01066","repositories_listed":0,"syntology":null},{"url":null,"slug":"understanding-the-effect-of-hyperparameter","title":"Understanding the effect of hyperparameter optimization on machine learning models for structure design problems","date":"2020-07-04","arxiv_id":"2007.04431","repositories_listed":0,"syntology":null},{"url":null,"slug":"gaussian-process-regression-with-local","title":"Gaussian Process Regression with Local Explanation","date":"2020-07-03","arxiv_id":"2007.01669","repositories_listed":0,"syntology":null},{"url":null,"slug":"l1-gp-l1-adaptive-control-with-bayesian","title":"L1-GP: L1 Adaptive Control with Bayesian Learning","date":"2020-06-08","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"autonomous-materials-discovery-driven-by","title":"Autonomous Materials Discovery Driven by Gaussian Process Regression with Inhomogeneous Measurement Noise and Anisotropic Kernels","date":"2020-06-03","arxiv_id":"2006.02489","repositories_listed":0,"syntology":null},{"url":null,"slug":"an-innovative-approach-to-determine-rebar","title":"An Innovative Approach to Determine Rebar Depth and Size by Comparing GPR Data with a Theoretical Database","date":"2020-05-19","arxiv_id":"2005.09643","repositories_listed":0,"syntology":null},{"url":null,"slug":"estimating-blood-pressure-from","title":"Estimating Blood Pressure from Photoplethysmogram Signal and Demographic Features using Machine Learning Techniques","date":"2020-05-07","arxiv_id":"2005.03357","repositories_listed":0,"syntology":null},{"url":null,"slug":"mathcal-l-1-mathcal-gp-mathcal-l-1-adaptive","title":"$\\mathcal{L}_1$-$\\mathcal{GP}$: $\\mathcal{L}_1$ Adaptive Control with Bayesian Learning","date":"2020-04-30","arxiv_id":"2004.14594","repositories_listed":0,"syntology":null},{"url":null,"slug":"defect-segmentation-mapping-tunnel-lining","title":"Defect segmentation: Mapping tunnel lining internal defects with ground penetrating radar data using a convolutional neural network","date":"2020-03-29","arxiv_id":"2003.13120","repositories_listed":0,"syntology":null},{"url":null,"slug":"analysis-of-greenhouse-gases","title":"Analysis of Greenhouse Gases","date":"2020-03-21","arxiv_id":"2003.11916","repositories_listed":0,"syntology":null},{"url":null,"slug":"gaze-sensing-leds-for-head-mounted-displays","title":"Gaze-Sensing LEDs for Head Mounted Displays","date":"2020-03-18","arxiv_id":"2003.08499","repositories_listed":0,"syntology":null},{"url":null,"slug":"model-based-reinforcement-learning-for-2","title":"Model-Based Reinforcement Learning for Physical Systems Without Velocity and Acceleration Measurements","date":"2020-02-25","arxiv_id":"2002.10621","repositories_listed":0,"syntology":null},{"url":null,"slug":"projection-based-active-gaussian-process","title":"Projection based Active Gaussian Process Regression for Pareto Front Modeling","date":"2020-01-20","arxiv_id":"2001.07072","repositories_listed":0,"syntology":null},{"url":null,"slug":"simulation-of-turbulent-flow-around-a-generic","title":"Simulation of Turbulent Flow around a Generic High-Speed Train using Hybrid Models of RANS Numerical Method with Machine Learning","date":"2019-12-25","arxiv_id":"2001.01569","repositories_listed":0,"syntology":null},{"url":null,"slug":"tensor-basis-gaussian-process-models-of","title":"Tensor Basis Gaussian Process Models of Hyperelastic Materials","date":"2019-12-23","arxiv_id":"1912.10872","repositories_listed":0,"syntology":null},{"url":null,"slug":"gprinvnet-deep-learning-based-ground","title":"GPRInvNet: Deep Learning-Based Ground Penetrating Radar Data Inversion for Tunnel Lining","date":"2019-12-12","arxiv_id":"1912.05759","repositories_listed":0,"syntology":null},{"url":null,"slug":"domain-adaptive-crowd-counting-via-inter","title":"Domain-adaptive Crowd Counting via High-quality Image Translation and Density Reconstruction","date":"2019-12-08","arxiv_id":"1912.03677","repositories_listed":0,"syntology":null},{"url":null,"slug":"ultra-reliable-and-low-latency-vehicular","title":"Ultra-Reliable and Low-Latency Vehicular Communication: An Active Learning Approach","date":"2019-11-27","arxiv_id":"1912.03359","repositories_listed":0,"syntology":null},{"url":null,"slug":"electric-load-and-power-forecasting-using","title":"Electric Load and Power Forecasting Using Ensemble Gaussian Process Regression","date":"2019-10-09","arxiv_id":"1910.03783","repositories_listed":0,"syntology":null},{"url":null,"slug":"dnanet-de-normalized-attention-based-multi","title":"Learning Enhanced Resolution-wise features for Human Pose Estimation","date":"2019-09-11","arxiv_id":"1909.05090","repositories_listed":0,"syntology":null},{"url":null,"slug":"regression-clustering-for-improved-accuracy","title":"Regression-clustering for Improved Accuracy and Training Cost with Molecular-Orbital-Based Machine Learning","date":"2019-09-04","arxiv_id":"1909.02041","repositories_listed":0,"syntology":null},{"url":null,"slug":"modeling-daily-pan-evaporation-in-humid","title":"Modeling Daily Pan Evaporation in Humid Climates Using Gaussian Process Regression","date":"2019-08-01","arxiv_id":"1908.04267","repositories_listed":0,"syntology":null},{"url":null,"slug":"an-improved-convolutional-neural-network","title":"An Improved Convolutional Neural Network System for Automatically Detecting Rebar in GPR Data","date":"2019-07-23","arxiv_id":"1907.09997","repositories_listed":0,"syntology":null},{"url":null,"slug":"gaussian-process-regression-for-pricing","title":"Gaussian Process Regression for Pricing Variable Annuities with Stochastic Volatility and Interest Rate","date":"2019-07-22","arxiv_id":"1903.00369","repositories_listed":0,"syntology":null},{"url":null,"slug":"applying-generative-adversarial-networks-to","title":"Applying Generative Adversarial Networks to Intelligent Subsurface Imaging and Identification","date":"2019-05-30","arxiv_id":"1905.13321","repositories_listed":0,"syntology":null},{"url":null,"slug":"optimizing-generalized-pagerank-methods-for","title":"Optimizing Generalized PageRank Methods for Seed-Expansion Community Detection","date":"2019-05-26","arxiv_id":"1905.10881","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-bulirsch-stoer-algorithm-using-gaussian","title":"A Bulirsch-Stoer algorithm using Gaussian processes","date":"2019-05-23","arxiv_id":"1905.09892","repositories_listed":0,"syntology":null},{"url":null,"slug":"machine-learning-for-pricing-american-options","title":"Machine Learning for Pricing American Options in High-Dimensional Markovian and non-Markovian models","date":"2019-05-22","arxiv_id":"1905.09474","repositories_listed":0,"syntology":null},{"url":null,"slug":"local-bandwidth-estimation-via-mixture-of","title":"Local Function Complexity for Active Learning via Mixture of Gaussian Processes","date":"2019-02-27","arxiv_id":"1902.10664","repositories_listed":0,"syntology":null},{"url":null,"slug":"physics-informed-cokriging-a-gaussian-process","title":"Physics-Informed CoKriging: A Gaussian-Process-Regression-Based Multifidelity Method for Data-Model Convergence","date":"2018-11-24","arxiv_id":"1811.09757","repositories_listed":0,"syntology":null},{"url":null,"slug":"gpr-based-detection-of-voids-and-evaluation","title":"GPR-based Detection of Voids and Evaluation of Grouting Under Semi-rigid Basement","date":"2018-11-21","arxiv_id":"1812.07495","repositories_listed":0,"syntology":null},{"url":null,"slug":"physics-informed-kriging-a-physics-informed","title":"Physics-Information-Aided Kriging: Constructing Covariance Functions using Stochastic Simulation Models","date":"2018-09-10","arxiv_id":"1809.03461","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-learning-for-epidemiological-predictions","title":"Deep Learning for Epidemiological Predictions","date":"2018-07-21","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"spatiotemporal-prediction-of-ambulance-demand","title":"Spatiotemporal Prediction of Ambulance Demand using Gaussian Process Regression","date":"2018-06-28","arxiv_id":"1806.10873","repositories_listed":0,"syntology":null},{"url":null,"slug":"gprhog-and-the-popularity-of-histogram-of","title":"gprHOG and the popularity of Histogram of Oriented Gradients (HOG) for Buried Threat Detection in Ground-Penetrating Radar","date":"2018-06-04","arxiv_id":"1806.01349","repositories_listed":0,"syntology":null},{"url":null,"slug":"dictionary-learning-for-adaptive-gpr-target","title":"Dictionary Learning for Adaptive GPR Landmine Classification","date":"2018-05-24","arxiv_id":"1806.04599","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-manifolds-from-non-stationary","title":"Learning Manifolds from Non-stationary Streaming Data","date":"2018-04-24","arxiv_id":"1804.08833","repositories_listed":0,"syntology":null},{"url":null,"slug":"buried-object-detection-from-b-scan-ground","title":"Buried object detection from B-scan ground penetrating radar data using Faster-RCNN","date":"2018-03-22","arxiv_id":"1803.08414","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-large-scale-multi-institutional-evaluation","title":"A Large-Scale Multi-Institutional Evaluation of Advanced Discrimination Algorithms for Buried Threat Detection in Ground Penetrating Radar","date":"2018-03-10","arxiv_id":"1803.03729","repositories_listed":0,"syntology":null},{"url":null,"slug":"network-scale-traffic-modeling-and","title":"Network-Scale Traffic Modeling and Forecasting with Graphical Lasso and Neural Networks","date":"2017-12-25","arxiv_id":"1801.00711","repositories_listed":0,"syntology":null},{"url":null,"slug":"stochastic-variational-inference-for-fully","title":"Stochastic Variational Inference for Bayesian Sparse Gaussian Process Regression","date":"2017-11-01","arxiv_id":"1711.00221","repositories_listed":0,"syntology":null},{"url":"/paper/personality-traits-and-job-candidate","slug":"personality-traits-and-job-candidate","title":"Personality Traits and Job Candidate Screening via Analyzing Facial Videos","date":"2017-07-21","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"overlapping-cover-local-regression-machines","title":"Overlapping Cover Local Regression Machines","date":"2017-01-05","arxiv_id":"1701.01218","repositories_listed":0,"syntology":null},{"url":null,"slug":"on-choosing-training-and-testing-data-for","title":"On Choosing Training and Testing Data for Supervised Algorithms in Ground Penetrating Radar Data for Buried Threat Detection","date":"2016-12-11","arxiv_id":"1612.03477","repositories_listed":0,"syntology":null},{"url":null,"slug":"incremental-variational-sparse-gaussian","title":"Incremental Variational Sparse Gaussian Process Regression","date":"2016-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"how-priors-of-initial-hyperparameters-affect","title":"How priors of initial hyperparameters affect Gaussian process regression models","date":"2016-05-25","arxiv_id":"1605.07906","repositories_listed":0,"syntology":null},{"url":null,"slug":"automatic-classification-of-irregularly","title":"Automatic Classification of Irregularly Sampled Time Series with Unequal Lengths: A Case Study on Estimated Glomerular Filtration Rate","date":"2016-05-17","arxiv_id":"1605.05142","repositories_listed":0,"syntology":null},{"url":null,"slug":"fast-methods-for-training-gaussian-processes","title":"Fast methods for training Gaussian processes on large data sets","date":"2016-04-05","arxiv_id":"1604.01250","repositories_listed":0,"syntology":null},{"url":null,"slug":"quantum-assisted-gaussian-process-regression","title":"Quantum assisted Gaussian process regression","date":"2015-12-12","arxiv_id":"1512.03929","repositories_listed":0,"syntology":null},{"url":null,"slug":"kernel-methods-for-accurate-uwb-based-ranging","title":"Kernel Methods for Accurate UWB-Based Ranging with Reduced Complexity","date":"2015-11-10","arxiv_id":"1511.04045","repositories_listed":0,"syntology":null},{"url":null,"slug":"kernel-based-information-criterion","title":"Kernel-based Information Criterion","date":"2014-08-25","arxiv_id":"1408.5810","repositories_listed":0,"syntology":null},{"url":null,"slug":"anisotropic-mesh-adaptation-for-image","title":"Anisotropic Mesh Adaptation for Image Representation","date":"2014-02-20","arxiv_id":"1402.4893","repositories_listed":0,"syntology":null},{"url":null,"slug":"local-gaussian-process-regression-for-real","title":"Local Gaussian Process Regression for Real Time Online Model Learning","date":"2008-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null}],"record_sha256":"7bdd46da6a2c302250805cd93d79c0a2ed968973ddec71643e0e02848670d7a0","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}