{"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/astronomy/papers/4","list_of":"/task/astronomy","task":"Astronomy","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":4,"pages_in_order":4,"rows_per_page":100,"rows":[301,395],"of":395,"counts":{"archive_papers_tagged":395,"with_a_code_link":136,"where_syntology_ran_a_sample":24,"not_listed_spam_title":0,"listed":395,"listed_where_code_ran":24,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":19,"every_run_a_failure_of_syntologys_instrument":5,"listed_with_a_run_with_no_instrument_failure":19,"listed_every_run_a_failure_of_syntologys_instrument":5,"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/astronomy","prev":"/task/astronomy/papers/3","next":null,"papers":[{"url":null,"slug":"first-full-event-reconstruction-from-imaging","title":"First Full-Event Reconstruction from Imaging Atmospheric Cherenkov Telescope Real Data with Deep Learning","date":"2021-05-31","arxiv_id":"2105.14927","repositories_listed":0,"syntology":null},{"url":null,"slug":"segmentation-of-high-dimensional-means-over","title":"Segmentation of high dimensional means over multi-dimensional change points and connections to regression trees","date":"2021-05-20","arxiv_id":"2105.10017","repositories_listed":0,"syntology":null},{"url":null,"slug":"drifting-features-detection-and-evaluation-in","title":"Drifting Features: Detection and evaluation in the context of automatic RRLs identification in VVV","date":"2021-05-04","arxiv_id":"2105.01714","repositories_listed":0,"syntology":null},{"url":null,"slug":"morphological-classification-of-astronomical","title":"Morphological classification of astronomical images with limited labelling","date":"2021-04-27","arxiv_id":"2105.02958","repositories_listed":0,"syntology":null},{"url":null,"slug":"robust-feature-disentanglement-in-imaging","title":"Robust Feature Disentanglement in Imaging Data via Joint Invariant Variational Autoencoders: from Cards to Atoms","date":"2021-04-20","arxiv_id":"2104.10180","repositories_listed":0,"syntology":null},{"url":null,"slug":"passive-inter-photon-imaging","title":"Passive Inter-Photon Imaging","date":"2021-03-31","arxiv_id":"2104.00059","repositories_listed":0,"syntology":null},{"url":null,"slug":"uncovering-dominant-features-in-short-term","title":"Uncovering Dominant Features in Short-term Power Load Forecasting Based on Multi-source Feature","date":"2021-03-23","arxiv_id":"2103.12534","repositories_listed":0,"syntology":null},{"url":null,"slug":"cycle-generative-adversarial-networks","title":"Cycle Generative Adversarial Networks Algorithm With Style Transfer For Image Generation","date":"2021-01-11","arxiv_id":"2101.03921","repositories_listed":0,"syntology":null},{"url":null,"slug":"zcal-machine-learning-methods-for-calibrating","title":"ZCal: Machine learning methods for calibrating radio interferometric data","date":"2021-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"the-vip-gallery-for-video-processing","title":"The VIP Gallery for Video Processing Education","date":"2020-12-29","arxiv_id":"2012.14625","repositories_listed":0,"syntology":null},{"url":null,"slug":"model-optimization-for-deep-space-exploration","title":"Model Optimization for Deep Space Exploration via Simulators and Deep Learning","date":"2020-12-28","arxiv_id":"2012.14092","repositories_listed":0,"syntology":null},{"url":null,"slug":"optical-wavelength-guided-self-supervised","title":"Optical Wavelength Guided Self-Supervised Feature Learning For Galaxy Cluster Richness Estimate","date":"2020-12-04","arxiv_id":"2012.02368","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-survey-on-principles-models-and-methods-for","title":"A Survey on Principles, Models and Methods for Learning from Irregularly Sampled Time Series","date":"2020-11-30","arxiv_id":"2012.00168","repositories_listed":0,"syntology":null},{"url":null,"slug":"survey2survey-a-deep-learning-generative","title":"Survey2Survey: A deep learning generative model approach for cross-survey image mapping","date":"2020-11-13","arxiv_id":"2011.07124","repositories_listed":0,"syntology":null},{"url":null,"slug":"domain-adaptation-techniques-for-improved","title":"Domain adaptation techniques for improved cross-domain study of galaxy mergers","date":"2020-11-06","arxiv_id":"2011.03591","repositories_listed":0,"syntology":null},{"url":null,"slug":"comprehensible-counterfactual-interpretation","title":"Comprehensible Counterfactual Explanation on Kolmogorov-Smirnov Test","date":"2020-11-01","arxiv_id":"2011.01223","repositories_listed":0,"syntology":null},{"url":null,"slug":"detection-of-asteroid-trails-in-hubble-space","title":"Detection of asteroid trails in Hubble Space Telescope images using Deep Learning","date":"2020-10-29","arxiv_id":"2010.15425","repositories_listed":0,"syntology":null},{"url":null,"slug":"genetic-bi-objective-optimization-approach-to","title":"Genetic Bi-objective Optimization Approach to Habitability Score","date":"2020-10-12","arxiv_id":"2010.05494","repositories_listed":0,"syntology":null},{"url":null,"slug":"quasar-detection-using-linear-support-vector","title":"Quasar Detection using Linear Support Vector Machine with Learning From Mistakes Methodology","date":"2020-10-01","arxiv_id":"2010.00401","repositories_listed":0,"syntology":null},{"url":null,"slug":"transient-classification-in-low-snr","title":"Transient Classification in low SNR Gravitational Wave data using Deep Learning","date":"2020-09-20","arxiv_id":"2009.12168","repositories_listed":0,"syntology":null},{"url":null,"slug":"joint-deconvolution-and-blind-source","title":"Joint deconvolution and blind source separation on the sphere with an application to radio-astronomy","date":"2020-09-08","arxiv_id":"2009.03606","repositories_listed":0,"syntology":null},{"url":null,"slug":"cyclic-imaging-for-all-sky-interference","title":"Cyclic Imaging for All-Sky Interference Forecasting with Array Radio Telescopes","date":"2020-07-26","arxiv_id":"2007.13035","repositories_listed":0,"syntology":null},{"url":null,"slug":"non-convex-structured-phase-retrieval","title":"Non-Convex Structured Phase Retrieval","date":"2020-06-23","arxiv_id":"2006.13298","repositories_listed":0,"syntology":null},{"url":null,"slug":"mapping-the-long-tail-of-research-funding-a","title":"Mapping the \"long tail\" of research funding: A topic analysis of NSF grant proposals in the Division of Astronomical Sciences","date":"2020-06-18","arxiv_id":"2006.10673","repositories_listed":0,"syntology":null},{"url":null,"slug":"advanced-single-image-resolution-upsurging","title":"Advanced Single Image Resolution Upsurging Using a Generative Adversarial Network","date":"2020-05-30","arxiv_id":"2006.00186","repositories_listed":0,"syntology":null},{"url":null,"slug":"clustering-via-torque-balance-with-mass-and","title":"Clustering via torque balance with mass and distance","date":"2020-04-27","arxiv_id":"2004.13160","repositories_listed":0,"syntology":null},{"url":null,"slug":"self-supervised-learning-for-astronomical","title":"Self-supervised Learning for Astronomical Image Classification","date":"2020-04-23","arxiv_id":"2004.11336","repositories_listed":0,"syntology":null},{"url":null,"slug":"mosaic-super-resolution-via-sequential","title":"Mosaic Super-resolution via Sequential Feature Pyramid Networks","date":"2020-04-15","arxiv_id":"2004.06853","repositories_listed":0,"syntology":null},{"url":"/paper/prediction-of-stellar-age-with-the-help-of","slug":"prediction-of-stellar-age-with-the-help-of","title":"Prediction of Stellar Age with the Help of Extra-Trees Regressor in Machine Learning","date":"2020-04-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"hyperspectral-multispectral-image-fusion-with","title":"Hyperspectral-Multispectral Image Fusion with Weighted LASSO","date":"2020-03-15","arxiv_id":"2003.06944","repositories_listed":0,"syntology":null},{"url":null,"slug":"characterization-of-hot-stellar-systems-with","title":"Multi-layered characterization of hot stellar systems with confidence","date":"2020-03-12","arxiv_id":"2003.05777","repositories_listed":0,"syntology":null},{"url":null,"slug":"interpreting-galaxy-deblender-gan-from-the","title":"Interpreting Galaxy Deblender GAN from the Discriminator's Perspective","date":"2020-01-17","arxiv_id":"2001.06151","repositories_listed":0,"syntology":null},{"url":null,"slug":"machine-learning-for-total-cloud-cover","title":"Machine learning for total cloud cover prediction","date":"2020-01-16","arxiv_id":"2001.05948","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-with-wasserstein-barycenters-and","title":"On clustering uncertain and structured data with Wasserstein barycenters and a geodesic criterion for the number of clusters","date":"2019-12-26","arxiv_id":"1912.11801","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-variational-semi-supervised-novelty-1","title":"Deep Variational Semi-Supervised Novelty Detection","date":"2019-11-12","arxiv_id":"1911.04971","repositories_listed":0,"syntology":null},{"url":null,"slug":"algorithms-and-statistical-models-for","title":"Algorithms and Statistical Models for Scientific Discovery in the Petabyte Era","date":"2019-11-05","arxiv_id":"1911.02479","repositories_listed":0,"syntology":null},{"url":null,"slug":"unsupervised-star-galaxy-classification-with","title":"Unsupervised Star Galaxy Classification with Cascade Variational Auto-Encoder","date":"2019-10-30","arxiv_id":"1910.14056","repositories_listed":0,"syntology":null},{"url":null,"slug":"new-methods-to-assess-and-improve-ligo","title":"New methods to assess and improve LIGO detector duty cycle","date":"2019-10-26","arxiv_id":"1910.12143","repositories_listed":0,"syntology":null},{"url":null,"slug":"deriving-a-quantitative-relationship-between","title":"Deriving a Quantitative Relationship Between Resolution and Human Classification Error","date":"2019-08-24","arxiv_id":"1908.09183","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-learning-for-energy-estimation-and","title":"Deep Learning for Energy Estimation and Particle Identification in Gamma-ray Astronomy","date":"2019-07-23","arxiv_id":"1907.10480","repositories_listed":0,"syntology":null},{"url":null,"slug":"hierarchical-clustering-supported-by","title":"Hierarchical Clustering Supported by Reciprocal Nearest Neighbors","date":"2019-07-09","arxiv_id":"1907.04915","repositories_listed":0,"syntology":null},{"url":null,"slug":"desaturating-euv-observations-of-solar","title":"Desaturating EUV observations of solar flaring storms","date":"2019-04-08","arxiv_id":"1904.04211","repositories_listed":0,"syntology":null},{"url":null,"slug":"nearby-platform-for-automatic-asteroids","title":"NEARBY Platform for Automatic Asteroids Detection and EURONEAR Surveys","date":"2019-03-08","arxiv_id":"1903.03479","repositories_listed":0,"syntology":null},{"url":null,"slug":"evolutionary-deep-learning-to-identify","title":"Evolutionary Deep Learning to Identify Galaxies in the Zone of Avoidance","date":"2019-03-06","arxiv_id":"1903.07461","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-learning-for-multi-messenger","title":"Deep Learning for Multi-Messenger Astrophysics: A Gateway for Discovery in the Big Data Era","date":"2019-02-01","arxiv_id":"1902.00522","repositories_listed":0,"syntology":null},{"url":null,"slug":"advanced-image-processing-for-astronomical","title":"Advanced Image Processing for Astronomical Images","date":"2018-12-23","arxiv_id":"1812.09702","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-general-approach-to-domain-adaptation-with","title":"A General Approach to Domain Adaptation with Applications in Astronomy","date":"2018-12-20","arxiv_id":"1812.08839","repositories_listed":0,"syntology":null},{"url":null,"slug":"transfer-learning-in-astronomy-a-new-machine","title":"Transfer Learning in Astronomy: A New Machine-Learning Paradigm","date":"2018-12-20","arxiv_id":"1812.10403","repositories_listed":0,"syntology":null},{"url":null,"slug":"particle-identification-in-ground-based-gamma","title":"Particle identification in ground-based gamma-ray astronomy using convolutional neural networks","date":"2018-12-04","arxiv_id":"1812.01551","repositories_listed":0,"syntology":null},{"url":null,"slug":"autowarp-learning-a-warping-distance-from","title":"Autowarp: Learning a Warping Distance from Unlabeled Time Series Using Sequence Autoencoders","date":"2018-10-23","arxiv_id":"1810.10107","repositories_listed":0,"syntology":null},{"url":null,"slug":"efficient-two-dimensional-line-spectrum","title":"Efficient Two-Dimensional Line Spectrum Estimation Based on Decoupled Atomic Norm Minimization","date":"2018-10-11","arxiv_id":"1808.01019","repositories_listed":0,"syntology":null},{"url":null,"slug":"from-fats-to-feets-further-improvements-to-an","title":"From FATS to feets: Further improvements to an astronomical feature extraction tool based on machine learning","date":"2018-09-06","arxiv_id":"1809.02154","repositories_listed":0,"syntology":null},{"url":null,"slug":"stellar-cluster-detection-using-gmm-with-deep","title":"Stellar Cluster Detection using GMM with Deep Variational Autoencoder","date":"2018-09-05","arxiv_id":"1809.01434","repositories_listed":0,"syntology":null},{"url":null,"slug":"digital-compensation-of-the-side-band","title":"Digital compensation of the side-band-rejection ratio in a fully analog 2SB sub-millimeter receiver","date":"2018-06-11","arxiv_id":"1806.04053","repositories_listed":0,"syntology":null},{"url":null,"slug":"machine-learning-in-astronomy-a-case-study-in","title":"Machine Learning in Astronomy: A Case Study in Quasar-Star Classification","date":"2018-04-13","arxiv_id":"1804.05051","repositories_listed":0,"syntology":null},{"url":null,"slug":"image-based-deep-learning-for-classification","title":"Image-based deep learning for classification of noise transients in gravitational wave detectors","date":"2018-03-27","arxiv_id":"1803.09933","repositories_listed":0,"syntology":null},{"url":null,"slug":"compressive-sensing-with-low-precision-data","title":"Compressive Sensing Using Iterative Hard Thresholding with Low Precision Data Representation: Theory and Applications","date":"2018-02-14","arxiv_id":"1802.04907","repositories_listed":0,"syntology":null},{"url":null,"slug":"single-epoch-supernova-classification-with","title":"Single-epoch supernova classification with deep convolutional neural networks","date":"2017-11-30","arxiv_id":"1711.11526","repositories_listed":0,"syntology":null},{"url":null,"slug":"denoising-gravitational-waves-using-deep","title":"Denoising Gravitational Waves using Deep Learning with Recurrent Denoising Autoencoders","date":"2017-11-27","arxiv_id":"1711.09919","repositories_listed":0,"syntology":null},{"url":null,"slug":"pulsar-candidate-identification-with","title":"Pulsar Candidate Identification with Artificial Intelligence Techniques","date":"2017-11-27","arxiv_id":"1711.10339","repositories_listed":0,"syntology":null},{"url":null,"slug":"on-the-runtime-efficacy-trade-off-of-anomaly","title":"On the Runtime-Efficacy Trade-off of Anomaly Detection Techniques for Real-Time Streaming Data","date":"2017-10-12","arxiv_id":"1710.04735","repositories_listed":0,"syntology":null},{"url":null,"slug":"crowdsourcing-predictors-of-residential","title":"Crowdsourcing Predictors of Residential Electric Energy Usage","date":"2017-09-08","arxiv_id":"1709.02739","repositories_listed":0,"syntology":null},{"url":null,"slug":"exploit-imaging-through-opaque-wall-via-deep","title":"Exploit imaging through opaque wall via deep learning","date":"2017-08-09","arxiv_id":"1708.07881","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-unified-method-for-super-resolution","title":"A unified method for super-resolution recovery and real exponential-sum separation","date":"2017-07-26","arxiv_id":"1707.09428","repositories_listed":0,"syntology":null},{"url":null,"slug":"exhaustive-search-for-sparse-variable","title":"Exhaustive search for sparse variable selection in linear regression","date":"2017-07-07","arxiv_id":"1707.02050","repositories_listed":0,"syntology":null},{"url":null,"slug":"big-universe-big-data-machine-learning-and","title":"Big Universe, Big Data: Machine Learning and Image Analysis for Astronomy","date":"2017-04-15","arxiv_id":"1704.04650","repositories_listed":0,"syntology":null},{"url":null,"slug":"restoration-of-images-with-wavefront","title":"Restoration of Images with Wavefront Aberrations","date":"2017-04-02","arxiv_id":"1704.00331","repositories_listed":0,"syntology":null},{"url":null,"slug":"how-brains-are-built-principles-of","title":"How brains are built: Principles of computational neuroscience","date":"2017-03-29","arxiv_id":"1704.03855","repositories_listed":0,"syntology":null},{"url":null,"slug":"psf-field-learning-based-on-optimal-transport","title":"PSF field learning based on Optimal Transport Distances","date":"2017-03-17","arxiv_id":"1703.06066","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-neural-networks-to-enable-real-time","title":"Deep Neural Networks to Enable Real-time Multimessenger Astrophysics","date":"2016-12-30","arxiv_id":"1701.00008","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-general-framework-for-density-based-time","title":"A General Framework for Density Based Time Series Clustering Exploiting a Novel Admissible Pruning Strategy","date":"2016-12-02","arxiv_id":"1612.00637","repositories_listed":0,"syntology":null},{"url":null,"slug":"selective-de-noising-of-sparse-coloured","title":"Selective De-noising of Sparse-Coloured Images","date":"2016-10-29","arxiv_id":"1610.09455","repositories_listed":0,"syntology":null},{"url":null,"slug":"technical-report-graph-structured-sparse","title":"Technical Report: Graph-Structured Sparse Optimization for Connected Subgraph Detection","date":"2016-09-30","arxiv_id":"1609.09864","repositories_listed":0,"syntology":null},{"url":null,"slug":"efficient-feature-selection-with-large-and","title":"Balancing Statistical and Computational Precision: A General Theory and Applications to Sparse Regression","date":"2016-09-23","arxiv_id":"1609.07195","repositories_listed":0,"syntology":null},{"url":null,"slug":"proceedings-of-the-third-international","title":"Proceedings of the third \"international Traveling Workshop on Interactions between Sparse models and Technology\" (iTWIST'16)","date":"2016-09-14","arxiv_id":"1609.04167","repositories_listed":0,"syntology":null},{"url":null,"slug":"fast-algorithms-for-demixing-sparse-signals","title":"Fast Algorithms for Demixing Sparse Signals from Nonlinear Observations","date":"2016-08-03","arxiv_id":"1608.01234","repositories_listed":0,"syntology":null},{"url":null,"slug":"clustering-with-phylogenetic-tools-in","title":"Clustering with phylogenetic tools in astrophysics","date":"2016-06-01","arxiv_id":"1606.00235","repositories_listed":0,"syntology":null},{"url":null,"slug":"generation-of-a-supervised-classification","title":"Generation of a Supervised Classification Algorithm for Time-Series Variable Stars with an Application to the LINEAR Dataset","date":"2016-01-14","arxiv_id":"1601.03769","repositories_listed":0,"syntology":null},{"url":null,"slug":"online-principal-component-analysis-in-high","title":"Online Principal Component Analysis in High Dimension: Which Algorithm to Choose?","date":"2015-11-11","arxiv_id":"1511.03688","repositories_listed":0,"syntology":null},{"url":null,"slug":"quantifying-the-cognitive-extent-of-science","title":"Quantifying the Cognitive Extent of Science","date":"2015-10-30","arxiv_id":"1511.00040","repositories_listed":0,"syntology":null},{"url":null,"slug":"super-resolution-off-the-grid","title":"Super-Resolution Off the Grid","date":"2015-09-26","arxiv_id":"1509.07943","repositories_listed":0,"syntology":null},{"url":null,"slug":"computational-intelligence-challenges-and","title":"Computational Intelligence Challenges and Applications on Large-Scale Astronomical Time Series Databases","date":"2015-09-25","arxiv_id":"1509.07823","repositories_listed":0,"syntology":null},{"url":null,"slug":"distributed-image-reconstruction-for-very","title":"Distributed image reconstruction for very large arrays in radio astronomy","date":"2015-07-02","arxiv_id":"1507.00501","repositories_listed":0,"syntology":null},{"url":null,"slug":"astromlskit-a-new-statistical-machine","title":"ASTROMLSKIT: A New Statistical Machine Learning Toolkit: A Platform for Data Analytics in Astronomy","date":"2015-04-29","arxiv_id":"1504.07865","repositories_listed":0,"syntology":null},{"url":null,"slug":"contextualization-of-topics-browsing-through","title":"Contextualization of topics - browsing through terms, authors, journals and cluster allocations","date":"2015-04-16","arxiv_id":"1504.04208","repositories_listed":0,"syntology":null},{"url":null,"slug":"fast-and-optimal-nonparametric-sequential","title":"Fast and optimal nonparametric sequential design for astronomical observations","date":"2015-01-11","arxiv_id":"1501.02467","repositories_listed":0,"syntology":null},{"url":null,"slug":"an-effective-semi-supervised-divisive","title":"An Effective Semi-supervised Divisive Clustering Algorithm","date":"2014-12-24","arxiv_id":"1412.7625","repositories_listed":0,"syntology":null},{"url":null,"slug":"variational-inference-for-gaussian-process","title":"Variational Inference for Gaussian Process Modulated Poisson Processes","date":"2014-11-02","arxiv_id":"1411.0254","repositories_listed":0,"syntology":null},{"url":null,"slug":"the-continuity-of-images-by-transmission","title":"The Continuity of Images by Transmission Imaging Revisited","date":"2014-01-08","arxiv_id":"1401.1558","repositories_listed":0,"syntology":null},{"url":null,"slug":"feature-selection-strategies-for-classifying","title":"Feature Selection Strategies for Classifying High Dimensional Astronomical Data Sets","date":"2013-10-08","arxiv_id":"1310.1976","repositories_listed":0,"syntology":null},{"url":null,"slug":"skynet-an-efficient-and-robust-neural-network","title":"SKYNET: an efficient and robust neural network training tool for machine learning in astronomy","date":"2013-09-03","arxiv_id":"1309.0790","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-study-on-classification-in-imbalanced-and","title":"A Study on Classification in Imbalanced and Partially-Labelled Data Streams","date":"2013-07-30","arxiv_id":"1307.8012","repositories_listed":0,"syntology":null},{"url":null,"slug":"spectral-classification-using-restricted","title":"Spectral Classification Using Restricted Boltzmann Machine","date":"2013-05-03","arxiv_id":"1305.0665","repositories_listed":0,"syntology":null},{"url":null,"slug":"active-learning-of-model-evidence-using","title":"Active Learning of Model Evidence Using Bayesian Quadrature","date":"2012-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"poisson-noise-reduction-with-non-local-pca","title":"Poisson noise reduction with non-local PCA","date":"2012-06-02","arxiv_id":"1206.0338","repositories_listed":0,"syntology":null}],"record_sha256":"7a8289f2cd34dc790f07e96802cd62da467536f27066e1ca0c5905895dbb87cf","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}