{"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/multi-view-learning/papers/3","list_of":"/task/multi-view-learning","task":"MULTI-VIEW LEARNING","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":[201,256],"of":256,"counts":{"archive_papers_tagged":256,"with_a_code_link":75,"where_syntology_ran_a_sample":13,"not_listed_spam_title":0,"listed":256,"listed_where_code_ran":13,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":12,"every_run_a_failure_of_syntologys_instrument":1,"listed_with_a_run_with_no_instrument_failure":12,"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/multi-view-learning","prev":"/task/multi-view-learning/papers/2","next":null,"papers":[{"url":null,"slug":"probabilistic-cca-with-implicit-distributions","title":"Probabilistic CCA with Implicit Distributions","date":"2019-07-04","arxiv_id":"1907.02345","repositories_listed":0,"syntology":null},{"url":null,"slug":"canonical-correlation-analysis-cca-based","title":"Canonical Correlation Analysis (CCA) Based Multi-View Learning: An Overview","date":"2019-07-03","arxiv_id":"1907.01693","repositories_listed":0,"syntology":null},{"url":null,"slug":"multimodal-and-multi-view-models-for-emotion","title":"Multimodal and Multi-view Models for Emotion Recognition","date":"2019-06-24","arxiv_id":"1906.10198","repositories_listed":0,"syntology":null},{"url":"/paper/recurrent-neural-network-for-un-supervised-1","slug":"recurrent-neural-network-for-un-supervised-1","title":"Recurrent Neural Network for (Un-)Supervised Learning of Monocular Video Visual Odometry and Depth","date":"2019-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"improving-sentence-representations-with-multi-1","title":"Improving Sentence Representations with Multi-view Frameworks","date":"2019-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"variational-recurrent-models-for","title":"Variational recurrent models for representation learning","date":"2019-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-multi-view-learning-using-neuron-wise","title":"Deep Multi-View Learning using Neuron-Wise Correlation-Maximizing Regularizers","date":"2019-04-25","arxiv_id":"1904.11151","repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-view-intact-space-learning","title":"Multi-View Intact Space Learning","date":"2019-04-04","arxiv_id":"1904.02340","repositories_listed":0,"syntology":null},{"url":null,"slug":"everything-old-is-new-again-a-multi-view","title":"Everything old is new again: A multi-view learning approach to learning using privileged information and distillation","date":"2019-03-08","arxiv_id":"1903.03694","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-nonparametric-multi-view-model-for","title":"A Nonparametric Multi-view Model for Estimating Cell Type-Specific Gene Regulatory Networks","date":"2019-02-21","arxiv_id":"1902.08138","repositories_listed":0,"syntology":null},{"url":null,"slug":"anomaly-detecting-and-ranking-of-the-cloud","title":"Anomaly detecting and ranking of the cloud computing platform by multi-view learning","date":"2019-01-27","arxiv_id":"1901.09294","repositories_listed":0,"syntology":null},{"url":null,"slug":"joint-embedding-learning-and-low-rank","title":"Joint Embedding Learning and Low-Rank Approximation: A Framework for Incomplete Multi-view Learning","date":"2018-12-25","arxiv_id":"1812.10012","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-common-representation-from-rgb-and","title":"Learning Common Representation from RGB and Depth Images","date":"2018-12-17","arxiv_id":"1812.06873","repositories_listed":0,"syntology":null},{"url":null,"slug":"visual-texual-emotion-analysis-with-deep","title":"Visual-Texual Emotion Analysis with Deep Coupled Video and Danmu Neural Networks","date":"2018-11-19","arxiv_id":"1811.07485","repositories_listed":0,"syntology":null},{"url":null,"slug":"stacked-penalized-logistic-regression-for","title":"Stacked Penalized Logistic Regression for Selecting Views in Multi-View Learning","date":"2018-11-06","arxiv_id":"1811.02316","repositories_listed":0,"syntology":null},{"url":null,"slug":"improving-sentence-representations-with-multi-2","title":"Improving Sentence Representations with Multi-view Frameworks","date":"2018-10-22","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"canonical-correlation-analysis-with-implicit","title":"Canonical Correlation Analysis with Implicit Distributions","date":"2018-09-27","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"incomplete-multi-view-clustering-via-graph","title":"Incomplete Multi-view Clustering via Graph Regularized Matrix Factorization","date":"2018-09-17","arxiv_id":"1809.05998","repositories_listed":0,"syntology":null},{"url":null,"slug":"ml-locnet-improving-object-localization-with","title":"ML-LocNet: Improving Object Localization with Multi-view Learning Network","date":"2018-09-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"jointly-deep-multi-view-learning-for","title":"Jointly Deep Multi-View Learning for Clustering Analysis","date":"2018-08-19","arxiv_id":"1808.06220","repositories_listed":0,"syntology":null},{"url":null,"slug":"incorporating-deep-visual-features-into","title":"Incorporating Deep Visual Features into Multiobjective based Multi-view Search Results Clustering","date":"2018-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-view-fuzzy-logic-system-with-the","title":"Multi-View Fuzzy Logic System with the Cooperation between Visible and Hidden Views","date":"2018-07-23","arxiv_id":"1807.08595","repositories_listed":0,"syntology":null},{"url":null,"slug":"dynamic-voting-in-multi-view-learning-for","title":"Dynamic voting in multi-view learning for radiomics applications","date":"2018-06-20","arxiv_id":"1806.07686","repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-view-sentence-representation-learning","title":"Multi-view Sentence Representation Learning","date":"2018-05-18","arxiv_id":"1805.07443","repositories_listed":0,"syntology":null},{"url":null,"slug":"improve-the-performance-of-transfer-learning","title":"Improve the performance of transfer learning without fine-tuning using dissimilarity-based multi-view learning for breast cancer histology images","date":"2018-03-29","arxiv_id":"1803.11241","repositories_listed":0,"syntology":null},{"url":null,"slug":"dissimilarity-based-representation-for","title":"Dissimilarity-based representation for radiomics applications","date":"2018-03-12","arxiv_id":"1803.04460","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-multi-view-learning-to-rank","title":"Deep Multi-view Learning to Rank","date":"2018-01-31","arxiv_id":"1801.10402","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-survey-on-multi-view-clustering","title":"A Survey on Multi-View Clustering","date":"2017-12-18","arxiv_id":"1712.06246","repositories_listed":0,"syntology":null},{"url":null,"slug":"joint-adaptive-neighbours-and-metric-learning","title":"Joint Adaptive Neighbours and Metric Learning for Multi-view Subspace Clustering","date":"2017-09-12","arxiv_id":"1709.03656","repositories_listed":0,"syntology":null},{"url":null,"slug":"scalable-and-effective-deep-cca-via-soft","title":"Scalable and Effective Deep CCA via Soft Decorrelation","date":"2017-07-30","arxiv_id":"1707.09669","repositories_listed":0,"syntology":null},{"url":null,"slug":"attentive-convolutional-neural-network-based","title":"Attentive Convolutional Neural Network based Speech Emotion Recognition: A Study on the Impact of Input Features, Signal Length, and Acted Speech","date":"2017-06-02","arxiv_id":"1706.00612","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-deep-multi-view-learning-framework-for-city","title":"A Deep Multi-View Learning Framework for City Event Extraction from Twitter Data Streams","date":"2017-05-28","arxiv_id":"1705.09975","repositories_listed":0,"syntology":null},{"url":null,"slug":"marine-animal-classification-with-correntropy","title":"Marine Animal Classification with Correntropy Loss Based Multi-view Learning","date":"2017-05-03","arxiv_id":"1705.01217","repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-view-unsupervised-feature-selection-by","title":"Multi-view Unsupervised Feature Selection by Cross-diffused Matrix Alignment","date":"2017-05-02","arxiv_id":"1705.00825","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-multi-view-context-aware-approach-to","title":"A Multi-view Context-aware Approach to Android Malware Detection and Malicious Code Localization","date":"2017-04-06","arxiv_id":"1704.01759","repositories_listed":0,"syntology":null},{"url":null,"slug":"one-size-fits-many-column-bundle-for-multi-x","title":"One Size Fits Many: Column Bundle for Multi-X Learning","date":"2017-02-22","arxiv_id":"1702.07021","repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-view-regularized-gaussian-processes","title":"Multi-view Regularized Gaussian Processes","date":"2017-01-17","arxiv_id":"1701.04532","repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-view-metric-learning-for-multi-instance","title":"Multi-view metric learning for multi-instance image classification","date":"2016-10-21","arxiv_id":"1610.06671","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-variational-canonical-correlation","title":"Deep Variational Canonical Correlation Analysis","date":"2016-10-11","arxiv_id":"1610.03454","repositories_listed":0,"syntology":null},{"url":"/paper/st-mvl-filling-missing-values-in-geo-sensory","slug":"st-mvl-filling-missing-values-in-geo-sensory","title":"ST-MVL: Filling Missing Values in Geo-Sensory Time Series Data","date":"2016-07-09","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"active-regression-with-adaptive-huber-loss","title":"Active Regression with Adaptive Huber Loss","date":"2016-06-05","arxiv_id":"1606.01568","repositories_listed":0,"syntology":null},{"url":null,"slug":"streaming-view-learning","title":"Streaming View Learning","date":"2016-04-28","arxiv_id":"1604.08291","repositories_listed":0,"syntology":null},{"url":null,"slug":"demand-prediction-and-placement-optimization","title":"Demand Prediction and Placement Optimization for Electric Vehicle Charging Stations","date":"2016-04-19","arxiv_id":"1604.05472","repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-view-learning-as-a-nonparametric","title":"Multi-view Learning as a Nonparametric Nonlinear Inter-Battery Factor Analysis","date":"2016-04-17","arxiv_id":"1604.04939","repositories_listed":0,"syntology":null},{"url":null,"slug":"classification-of-weak-multi-view-signals-by","title":"Classification of weak multi-view signals by sharing factors in a mixture of Bayesian group factor analyzers","date":"2015-12-17","arxiv_id":"1512.05610","repositories_listed":0,"syntology":null},{"url":null,"slug":"semi-supervised-multi-sensor-classification","title":"Semi-supervised Multi-sensor Classification via Consensus-based Multi-View Maximum Entropy Discrimination","date":"2015-07-05","arxiv_id":"1507.01269","repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-view-learning-for-multivariate","title":"Multi-view learning for multivariate performance measures optimization","date":"2015-01-15","arxiv_id":"1501.03786","repositories_listed":0,"syntology":null},{"url":null,"slug":"bayesian-multi-tensor-factorization","title":"Bayesian multi-tensor factorization","date":"2014-12-15","arxiv_id":"1412.4679","repositories_listed":0,"syntology":null},{"url":null,"slug":"pac-bayes-analysis-of-multi-view-learning","title":"PAC-Bayes Analysis of Multi-view Learning","date":"2014-06-21","arxiv_id":"1406.5614","repositories_listed":0,"syntology":null},{"url":null,"slug":"overlapping-trace-norms-in-multi-view","title":"Overlapping Trace Norms in Multi-View Learning","date":"2014-04-24","arxiv_id":"1404.6163","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-unifying-framework-in-vector-valued","title":"A Unifying Framework in Vector-valued Reproducing Kernel Hilbert Spaces for Manifold Regularization and Co-Regularized Multi-view Learning","date":"2014-01-31","arxiv_id":"1401.8066","repositories_listed":0,"syntology":null},{"url":null,"slug":"group-sparse-embeddings-in-collective-matrix","title":"Group-sparse Embeddings in Collective Matrix Factorization","date":"2013-12-20","arxiv_id":"1312.5921","repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-view-learning-for-web-spam-detection","title":"Multi-View Learning for Web Spam Detection","date":"2013-05-16","arxiv_id":"1305.3814","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-survey-on-multi-view-learning","title":"A Survey on Multi-view Learning","date":"2013-04-20","arxiv_id":"1304.5634","repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-view-learning-of-word-embeddings-via","title":"Multi-View Learning of Word Embeddings via CCA","date":"2011-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"factorized-latent-spaces-with-structured","title":"Factorized Latent Spaces with Structured Sparsity","date":"2010-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null}],"record_sha256":"36c871bf18e62cd0b02807c91ea14f62c0a6066fdf9db27a3b96f7cfa0355398","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}