{"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/clustering/papers/19","list_of":"/task/clustering","task":"Clustering","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":19,"pages_in_order":108,"rows_per_page":100,"rows":[1801,1900],"of":10718,"counts":{"archive_papers_tagged":10718,"with_a_code_link":2823,"where_syntology_ran_a_sample":419,"not_listed_spam_title":0,"listed":10718,"listed_where_code_ran":419,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":335,"every_run_a_failure_of_syntologys_instrument":84,"listed_with_a_run_with_no_instrument_failure":335,"listed_every_run_a_failure_of_syntologys_instrument":84,"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/clustering","prev":"/task/clustering/papers/18","next":"/task/clustering/papers/20","papers":[{"url":"/paper/nested-grassmanns-for-dimensionality","slug":"nested-grassmanns-for-dimensionality","title":"Nested Grassmannians for Dimensionality Reduction with Applications","date":"2020-10-27","arxiv_id":"2010.14589","repositories_listed":1,"syntology":null},{"url":"/paper/semi-supervised-segmentation-and-graph-based","slug":"semi-supervised-segmentation-and-graph-based","title":"Semi supervised segmentation and graph-based tracking of 3D nuclei in time-lapse microscopy","date":"2020-10-26","arxiv_id":"2010.13343","repositories_listed":1,"syntology":null},{"url":"/paper/syllabification-of-the-divine-comedy","slug":"syllabification-of-the-divine-comedy","title":"Syllabification of the Divine Comedy","date":"2020-10-26","arxiv_id":"2010.13515","repositories_listed":1,"syntology":null},{"url":"/paper/trajectory-wise-multiple-choice-learning-for","slug":"trajectory-wise-multiple-choice-learning-for","title":"Trajectory-wise Multiple Choice Learning for Dynamics Generalization in Reinforcement Learning","date":"2020-10-26","arxiv_id":"2010.13303","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":0,"n_honours":2,"n_violates":0,"n_no_contract":0,"n_pointer_only":2,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 2 honoured, 0 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/trajectory-wise-multiple-choice-learning-for#ran","syntology_url":"https://syntology.ai/paper/2010.13303","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2010.13303"}},"official":{"repos":["younggyoseo/trajectory_mcl"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/deep-learning-framework-for-measuring-the","slug":"deep-learning-framework-for-measuring-the","title":"Deep Learning Framework for Measuring the Digital Strategy of Companies from Earnings Calls","date":"2020-10-23","arxiv_id":"2010.12418","repositories_listed":1,"syntology":null},{"url":"/paper/kernel-smoothing-mean-shift-and-their","slug":"kernel-smoothing-mean-shift-and-their","title":"Kernel Smoothing, Mean Shift, and Their Learning Theory with Directional Data","date":"2020-10-23","arxiv_id":"2010.13523","repositories_listed":1,"syntology":null},{"url":"/paper/primal-dual-mesh-convolutional-neural","slug":"primal-dual-mesh-convolutional-neural","title":"Primal-Dual Mesh Convolutional Neural Networks","date":"2020-10-23","arxiv_id":"2010.12455","repositories_listed":1,"syntology":{"n":3,"n_ran":0,"n_constructed":0,"n_ran_checked":0,"n_instrument":0,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"0 ran · 3 unverified","sample_list":"/paper/primal-dual-mesh-convolutional-neural#ran","syntology_url":"https://syntology.ai/paper/2010.12455","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2010.12455"}},"official":{"repos":["MIT-SPARK/PD-MeshNet"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":3,"ran_from_kinds":[]}}},{"url":"/paper/geometry-aware-hamiltonian-variational-auto","slug":"geometry-aware-hamiltonian-variational-auto","title":"Geometry-Aware Hamiltonian Variational Auto-Encoder","date":"2020-10-22","arxiv_id":"2010.11518","repositories_listed":1,"syntology":null},{"url":"/paper/on-a-guided-nonnegative-matrix-factorization","slug":"on-a-guided-nonnegative-matrix-factorization","title":"On a Guided Nonnegative Matrix Factorization","date":"2020-10-22","arxiv_id":"2010.11365","repositories_listed":1,"syntology":null},{"url":"/paper/rethinking-pooling-in-graph-neural-networks","slug":"rethinking-pooling-in-graph-neural-networks","title":"Rethinking pooling in graph neural networks","date":"2020-10-22","arxiv_id":"2010.11418","repositories_listed":1,"syntology":null},{"url":"/paper/lcd-line-clustering-and-description-for-place","slug":"lcd-line-clustering-and-description-for-place","title":"LCD -- Line Clustering and Description for Place Recognition","date":"2020-10-21","arxiv_id":"2010.10867","repositories_listed":1,"syntology":null},{"url":"/paper/wasserstein-k-means-for-clustering","slug":"wasserstein-k-means-for-clustering","title":"Wasserstein K-Means for Clustering Tomographic Projections","date":"2020-10-20","arxiv_id":"2010.09989","repositories_listed":1,"syntology":null},{"url":"/paper/multi-view-subspace-clustering-networks-with","slug":"multi-view-subspace-clustering-networks-with","title":"Multi-view Subspace Clustering Networks with Local and Global Graph Information","date":"2020-10-19","arxiv_id":"2010.09323","repositories_listed":1,"syntology":null},{"url":"/paper/active-domain-adaptation-via-clustering","slug":"active-domain-adaptation-via-clustering","title":"Active Domain Adaptation via Clustering Uncertainty-weighted Embeddings","date":"2020-10-16","arxiv_id":"2010.08666","repositories_listed":1,"syntology":{"n":10,"n_ran":7,"n_constructed":0,"n_ran_checked":6,"n_instrument":1,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":6,"n_pointer_only":0,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 0 honoured, 0 violated, 6 with no contract checked; 1 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/active-domain-adaptation-via-clustering#ran","syntology_url":"https://syntology.ai/paper/2010.08666","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2010.08666"}},"official":{"repos":["virajprabhu/clue"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/discovering-hierarchical-processes-using","slug":"discovering-hierarchical-processes-using","title":"Discovering Hierarchical Processes Using Flexible Activity Trees for Event Abstraction","date":"2020-10-16","arxiv_id":"2010.08302","repositories_listed":1,"syntology":null},{"url":"/paper/learning-panoptic-segmentation-from-instance","slug":"learning-panoptic-segmentation-from-instance","title":"Learning Panoptic Segmentation from Instance Contours","date":"2020-10-16","arxiv_id":"2010.11681","repositories_listed":1,"syntology":null},{"url":"/paper/dslib-an-open-source-library-for-the-dominant","slug":"dslib-an-open-source-library-for-the-dominant","title":"DSLib: An open source library for the dominant set clustering method","date":"2020-10-15","arxiv_id":"2010.07906","repositories_listed":1,"syntology":null},{"url":"/paper/fairness-in-streaming-submodular-maximization","slug":"fairness-in-streaming-submodular-maximization","title":"Fairness in Streaming Submodular Maximization: Algorithms and Hardness","date":"2020-10-14","arxiv_id":"2010.07431","repositories_listed":1,"syntology":null},{"url":"/paper/refining-similarity-matrices-to-cluster","slug":"refining-similarity-matrices-to-cluster","title":"Refining Similarity Matrices to Cluster Attributed Networks Accurately","date":"2020-10-14","arxiv_id":"2010.06854","repositories_listed":1,"syntology":null},{"url":"/paper/mixed-data-deep-gaussian-mixture-model-a","slug":"mixed-data-deep-gaussian-mixture-model-a","title":"Mixed data Deep Gaussian Mixture Model: A clustering model for mixed datasets","date":"2020-10-13","arxiv_id":"2010.06661","repositories_listed":1,"syntology":null},{"url":"/paper/comstreamclust-a-communicative-text","slug":"comstreamclust-a-communicative-text","title":"ComStreamClust: a communicative multi-agent approach to text clustering in streaming data","date":"2020-10-11","arxiv_id":"2010.05349","repositories_listed":1,"syntology":null},{"url":"/paper/early-abandoning-pruneddtw-and-its","slug":"early-abandoning-pruneddtw-and-its","title":"Early Abandoning PrunedDTW and its application to similarity search","date":"2020-10-11","arxiv_id":"2010.05371","repositories_listed":1,"syntology":null},{"url":"/paper/smyrf-efficient-attention-using-asymmetric","slug":"smyrf-efficient-attention-using-asymmetric","title":"SMYRF: Efficient Attention using Asymmetric Clustering","date":"2020-10-11","arxiv_id":"2010.05315","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":0,"n_instrument":3,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":3,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 3 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/smyrf-efficient-attention-using-asymmetric#ran","syntology_url":"https://syntology.ai/paper/2010.05315","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2010.05315"}},"official":{"repos":["giannisdaras/smyrf"],"state":"official: no sample here; runs from other or unrecorded repositories","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["unlocated"]}}},{"url":"/paper/dirichlet-graph-variational-autoencoder","slug":"dirichlet-graph-variational-autoencoder","title":"Dirichlet Graph Variational Autoencoder","date":"2020-10-09","arxiv_id":"2010.04408","repositories_listed":1,"syntology":null},{"url":"/paper/learning-binary-trees-via-sparse-relaxation-1","slug":"learning-binary-trees-via-sparse-relaxation-1","title":"Learning Binary Decision Trees by Argmin Differentiation","date":"2020-10-09","arxiv_id":"2010.04627","repositories_listed":1,"syntology":null},{"url":"/paper/weaponizing-unicodes-with-deep-learning","slug":"weaponizing-unicodes-with-deep-learning","title":"Weaponizing Unicodes with Deep Learning -- Identifying Homoglyphs with Weakly Labeled Data","date":"2020-10-09","arxiv_id":"2010.04382","repositories_listed":1,"syntology":null},{"url":"/paper/logan-local-group-bias-detection-by","slug":"logan-local-group-bias-detection-by","title":"LOGAN: Local Group Bias Detection by Clustering","date":"2020-10-06","arxiv_id":"2010.02867","repositories_listed":1,"syntology":null},{"url":"/paper/attention-based-clustering-learning-a-kernel","slug":"attention-based-clustering-learning-a-kernel","title":"Attention-Based Clustering: Learning a Kernel from Context","date":"2020-10-02","arxiv_id":"2010.01040","repositories_listed":1,"syntology":null},{"url":"/paper/an-empirical-investigation-towards-efficient","slug":"an-empirical-investigation-towards-efficient","title":"An Empirical Investigation Towards Efficient Multi-Domain Language Model Pre-training","date":"2020-10-01","arxiv_id":"2010.00784","repositories_listed":1,"syntology":null},{"url":"/paper/self-grouping-convolutional-neural-networks","slug":"self-grouping-convolutional-neural-networks","title":"Self-grouping Convolutional Neural Networks","date":"2020-09-29","arxiv_id":"2009.13803","repositories_listed":1,"syntology":null},{"url":"/paper/statistical-control-for-spatio-temporal-meg","slug":"statistical-control-for-spatio-temporal-meg","title":"Statistical control for spatio-temporal MEG/EEG source imaging with desparsified multi-task Lasso","date":"2020-09-29","arxiv_id":"2009.14310","repositories_listed":1,"syntology":{"n":2,"n_ran":0,"n_constructed":0,"n_ran_checked":0,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"0 ran · 2 unverified","sample_list":"/paper/statistical-control-for-spatio-temporal-meg#ran","syntology_url":"https://syntology.ai/paper/2009.14310","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2009.14310"}},"official":{"repos":["ja-che/hidimstat"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":2,"ran_from_kinds":[]}}},{"url":"/paper/kernel-learning-approaches-for-summarising","slug":"kernel-learning-approaches-for-summarising","title":"Kernel learning approaches for summarising and combining posterior similarity matrices","date":"2020-09-27","arxiv_id":"2009.12852","repositories_listed":1,"syntology":null},{"url":"/paper/learning-self-expression-metrics-for-scalable","slug":"learning-self-expression-metrics-for-scalable","title":"Learning Self-Expression Metrics for Scalable and Inductive Subspace Clustering","date":"2020-09-27","arxiv_id":"2009.12875","repositories_listed":1,"syntology":null},{"url":"/paper/an-unsupervised-sentence-embedding-method","slug":"an-unsupervised-sentence-embedding-method","title":"An Unsupervised Sentence Embedding Method by Mutual Information Maximization","date":"2020-09-25","arxiv_id":"2009.12061","repositories_listed":1,"syntology":null},{"url":"/paper/revealing-the-myth-of-higher-order-inference","slug":"revealing-the-myth-of-higher-order-inference","title":"Revealing the Myth of Higher-Order Inference in Coreference Resolution","date":"2020-09-25","arxiv_id":"2009.12013","repositories_listed":1,"syntology":null},{"url":"/paper/bitenet-bidirectional-temporal-encoder","slug":"bitenet-bidirectional-temporal-encoder","title":"BiteNet: Bidirectional Temporal Encoder Network to Predict Medical Outcomes","date":"2020-09-24","arxiv_id":"2009.13252","repositories_listed":1,"syntology":null},{"url":"/paper/clustering-based-on-graph-of-density-topology","slug":"clustering-based-on-graph-of-density-topology","title":"Clustering Based on Graph of Density Topology","date":"2020-09-24","arxiv_id":"2009.11612","repositories_listed":1,"syntology":null},{"url":"/paper/topology-aware-generative-adversarial-network","slug":"topology-aware-generative-adversarial-network","title":"Topology-Aware Generative Adversarial Network for Joint Prediction of Multiple Brain Graphs from a Single Brain Graph","date":"2020-09-23","arxiv_id":"2009.11058","repositories_listed":1,"syntology":null},{"url":"/paper/divik-divisive-intelligent-k-means-for-hands","slug":"divik-divisive-intelligent-k-means-for-hands","title":"DiviK: Divisive intelligent K-Means for hands-free unsupervised clustering in big biological data","date":"2020-09-22","arxiv_id":"2009.10706","repositories_listed":1,"syntology":null},{"url":"/paper/deep-clustering-and-representation-learning","slug":"deep-clustering-and-representation-learning","title":"Generalized Clustering and Multi-Manifold Learning with Geometric Structure Preservation","date":"2020-09-21","arxiv_id":"2009.09590","repositories_listed":1,"syntology":{"n":7,"n_ran":5,"n_constructed":0,"n_ran_checked":5,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":0,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 0 violated, 5 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/deep-clustering-and-representation-learning#ran","syntology_url":"https://syntology.ai/paper/2009.09590","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2009.09590"}},"official":{"repos":["lirongwu/gcml"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/overlapping-community-detection-in-networks-1","slug":"overlapping-community-detection-in-networks-1","title":"Overlapping community detection in networks via sparse spectral decomposition","date":"2020-09-20","arxiv_id":"2009.10641","repositories_listed":1,"syntology":null},{"url":"/paper/few-shot-unsupervised-continual-learning","slug":"few-shot-unsupervised-continual-learning","title":"Few-Shot Unsupervised Continual Learning through Meta-Examples","date":"2020-09-17","arxiv_id":"2009.08107","repositories_listed":1,"syntology":{"n":8,"n_ran":6,"n_constructed":1,"n_ran_checked":5,"n_instrument":1,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":0,"phrase":"6 ran (of which 1 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 0 violated, 5 with no contract checked; 1 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/few-shot-unsupervised-continual-learning#ran","syntology_url":"https://syntology.ai/paper/2009.08107","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2009.08107"}},"official":{"repos":["alessiabertugli/FUSION"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":1,"n_ran_no_instrument_failure":1,"n_unverified":2,"ran_from_kinds":["found_in_text","official"]}}},{"url":"/paper/force2vec-parallel-force-directed-graph","slug":"force2vec-parallel-force-directed-graph","title":"Force2Vec: Parallel force-directed graph embedding","date":"2020-09-17","arxiv_id":"2009.10035","repositories_listed":1,"syntology":null},{"url":"/paper/laat-locally-aligned-ant-technique-for","slug":"laat-locally-aligned-ant-technique-for","title":"LAAT: Locally Aligned Ant Technique for discovering multiple faint low dimensional structures of varying density","date":"2020-09-17","arxiv_id":"2009.08326","repositories_listed":1,"syntology":null},{"url":"/paper/online-semi-supervised-learning-in-contextual","slug":"online-semi-supervised-learning-in-contextual","title":"Online Semi-Supervised Learning in Contextual Bandits with Episodic Reward","date":"2020-09-17","arxiv_id":"2009.08457","repositories_listed":1,"syntology":null},{"url":"/paper/matrix-profile-xxii-exact-discovery-of-time","slug":"matrix-profile-xxii-exact-discovery-of-time","title":"Matrix Profile XXII: Exact Discovery of Time Series Motifs under DTW","date":"2020-09-16","arxiv_id":"2009.07907","repositories_listed":1,"syntology":null},{"url":"/paper/approximate-spectral-clustering-using-both","slug":"approximate-spectral-clustering-using-both","title":"Approximate spectral clustering using both reference vectors and topology of the network generated by growing neural gas","date":"2020-09-15","arxiv_id":"2009.07101","repositories_listed":1,"syntology":null},{"url":"/paper/clustering-of-non-gaussian-data-by","slug":"clustering-of-non-gaussian-data-by","title":"Clustering of non-Gaussian data by variational Bayes for normal inverse Gaussian mixture models","date":"2020-09-13","arxiv_id":"2009.06002","repositories_listed":1,"syntology":null},{"url":"/paper/multi-way-spectral-clustering-of-augmented","slug":"multi-way-spectral-clustering-of-augmented","title":"Multi-way Spectral Clustering of Augmented Multi-view Data through Deep Collective Matrix Tri-factorization","date":"2020-09-12","arxiv_id":"2009.05805","repositories_listed":1,"syntology":null},{"url":"/paper/a-black-box-adversarial-attack-for-poisoning","slug":"a-black-box-adversarial-attack-for-poisoning","title":"A black-box adversarial attack for poisoning clustering","date":"2020-09-09","arxiv_id":"2009.05474","repositories_listed":1,"syntology":null},{"url":"/paper/deep-metric-learning-meets-deep-clustering-an","slug":"deep-metric-learning-meets-deep-clustering-an","title":"Deep Metric Learning Meets Deep Clustering: An Novel Unsupervised Approach for Feature Embedding","date":"2020-09-09","arxiv_id":"2009.04091","repositories_listed":1,"syntology":null},{"url":"/paper/a-distance-preserving-matrix-sketch","slug":"a-distance-preserving-matrix-sketch","title":"A Distance-preserving Matrix Sketch","date":"2020-09-08","arxiv_id":"2009.03979","repositories_listed":1,"syntology":null},{"url":"/paper/gpu-based-self-organizing-maps-for-post","slug":"gpu-based-self-organizing-maps-for-post","title":"GPU-based Self-Organizing Maps for Post-Labeled Few-Shot Unsupervised Learning","date":"2020-09-04","arxiv_id":"2009.03665","repositories_listed":1,"syntology":null},{"url":"/paper/the-area-under-the-roc-curve-as-a-measure-of","slug":"the-area-under-the-roc-curve-as-a-measure-of","title":"The Area Under the ROC Curve as a Measure of Clustering Quality","date":"2020-09-04","arxiv_id":"2009.02400","repositories_listed":1,"syntology":null},{"url":"/paper/an-internal-cluster-validity-index-based-on","slug":"an-internal-cluster-validity-index-based-on","title":"An Internal Cluster Validity Index Using a Distance-based Separability Measure","date":"2020-09-02","arxiv_id":"2009.01328","repositories_listed":1,"syntology":null},{"url":"/paper/superpal-supervised-proposition-alignment-for","slug":"superpal-supervised-proposition-alignment-for","title":"Summary-Source Proposition-level Alignment: Task, Datasets and Supervised Baseline","date":"2020-09-01","arxiv_id":"2009.00590","repositories_listed":1,"syntology":null},{"url":"/paper/learning-adaptive-embedding-considering","slug":"learning-adaptive-embedding-considering","title":"Learning Adaptive Embedding Considering Incremental Class","date":"2020-08-31","arxiv_id":"2008.13351","repositories_listed":1,"syntology":null},{"url":"/paper/an-objective-for-hierarchical-clustering-in","slug":"an-objective-for-hierarchical-clustering-in","title":"An Objective for Hierarchical Clustering in Euclidean Space and its Connection to Bisecting K-means","date":"2020-08-30","arxiv_id":"2008.13235","repositories_listed":1,"syntology":null},{"url":"/paper/reval-a-python-package-to-determine-the-best","slug":"reval-a-python-package-to-determine-the-best","title":"reval: a Python package to determine best clustering solutions with stability-based relative clustering validation","date":"2020-08-27","arxiv_id":"2009.01077","repositories_listed":1,"syntology":null},{"url":"/paper/query-understanding-via-intent-description","slug":"query-understanding-via-intent-description","title":"Query Understanding via Intent Description Generation","date":"2020-08-25","arxiv_id":"2008.10889","repositories_listed":1,"syntology":null},{"url":"/paper/3rd-place-solution-to-google-landmark","slug":"3rd-place-solution-to-google-landmark","title":"3rd Place Solution to \"Google Landmark Retrieval 2020\"","date":"2020-08-24","arxiv_id":"2008.10480","repositories_listed":1,"syntology":null},{"url":"/paper/self-supervised-learning-for-large-scale","slug":"self-supervised-learning-for-large-scale","title":"Self-Supervised Learning for Large-Scale Unsupervised Image Clustering","date":"2020-08-24","arxiv_id":"2008.10312","repositories_listed":1,"syntology":{"n":11,"n_ran":8,"n_constructed":0,"n_ran_checked":6,"n_instrument":2,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":6,"n_pointer_only":0,"phrase":"8 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 0 honoured, 0 violated, 6 with no contract checked; 2 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/self-supervised-learning-for-large-scale#ran","syntology_url":"https://syntology.ai/paper/2008.10312","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2008.10312"}},"official":{"repos":["Randl/kmeans_selfsuper"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/multiverse-a-multiplex-and-multiplex","slug":"multiverse-a-multiplex-and-multiplex","title":"MultiVERSE: a multiplex and multiplex-heterogeneous network embedding approach","date":"2020-08-23","arxiv_id":"2008.10085","repositories_listed":1,"syntology":null},{"url":"/paper/icvi-artmap-accelerating-and-improving","slug":"icvi-artmap-accelerating-and-improving","title":"iCVI-ARTMAP: Accelerating and improving clustering using adaptive resonance theory predictive mapping and incremental cluster validity indices","date":"2020-08-22","arxiv_id":"2008.09903","repositories_listed":1,"syntology":null},{"url":"/paper/mpcc-matching-priors-and-conditionals-for","slug":"mpcc-matching-priors-and-conditionals-for","title":"MPCC: Matching Priors and Conditionals for Clustering","date":"2020-08-21","arxiv_id":"2008.09641","repositories_listed":1,"syntology":null},{"url":"/paper/topological-gradient-based-competitive","slug":"topological-gradient-based-competitive","title":"Topological Gradient-based Competitive Learning","date":"2020-08-21","arxiv_id":"2008.09477","repositories_listed":1,"syntology":null},{"url":"/paper/towards-lightweight-lane-detection-by","slug":"towards-lightweight-lane-detection-by","title":"Towards Lightweight Lane Detection by Optimizing Spatial Embedding","date":"2020-08-19","arxiv_id":"2008.08311","repositories_listed":1,"syntology":null},{"url":"/paper/robust-autoencoder-gan-for-cryo-em-image","slug":"robust-autoencoder-gan-for-cryo-em-image","title":"Generative Adversarial Networks for Robust Cryo-EM Image Denoising","date":"2020-08-17","arxiv_id":"2008.07307","repositories_listed":1,"syntology":null},{"url":"/paper/consistent-k-median-simpler-better-and-robust","slug":"consistent-k-median-simpler-better-and-robust","title":"Consistent $k$-Median: Simpler, Better and Robust","date":"2020-08-13","arxiv_id":"2008.06101","repositories_listed":1,"syntology":null},{"url":"/paper/few-shot-clustering-for-indoor-occupancy","slug":"few-shot-clustering-for-indoor-occupancy","title":"Few shot clustering for indoor occupancy detection with extremely low-quality images from battery free cameras","date":"2020-08-13","arxiv_id":"2008.05654","repositories_listed":1,"syntology":null},{"url":"/paper/automatic-assembly-of-aero-engine-low","slug":"automatic-assembly-of-aero-engine-low","title":"Automatic assembly of aero engine low pressure turbine shaft based on 3D vision measurement","date":"2020-08-12","arxiv_id":"2008.04903","repositories_listed":1,"syntology":null},{"url":"/paper/investigation-of-end-to-end-speaker","slug":"investigation-of-end-to-end-speaker","title":"Investigation of End-To-End Speaker-Attributed ASR for Continuous Multi-Talker Recordings","date":"2020-08-11","arxiv_id":"2008.04546","repositories_listed":1,"syntology":null},{"url":"/paper/learning-to-cluster-under-domain-shift","slug":"learning-to-cluster-under-domain-shift","title":"Learning to Cluster under Domain Shift","date":"2020-08-11","arxiv_id":"2008.04646","repositories_listed":1,"syntology":null},{"url":"/paper/k-means-on-a-log-cholesky-manifold-with","slug":"k-means-on-a-log-cholesky-manifold-with","title":"$k$-means on Positive Definite Matrices, and an Application to Clustering in Radar Image Sequences","date":"2020-08-08","arxiv_id":"2008.03454","repositories_listed":1,"syntology":null},{"url":"/paper/deep-robust-clustering-by-contrastive","slug":"deep-robust-clustering-by-contrastive","title":"Deep Robust Clustering by Contrastive Learning","date":"2020-08-07","arxiv_id":"2008.03030","repositories_listed":1,"syntology":null},{"url":"/paper/qubo-formulations-for-training-machine","slug":"qubo-formulations-for-training-machine","title":"QUBO Formulations for Training Machine Learning Models","date":"2020-08-05","arxiv_id":"2008.02369","repositories_listed":1,"syntology":null},{"url":"/paper/addressing-the-cold-start-problem-in-outfit","slug":"addressing-the-cold-start-problem-in-outfit","title":"Addressing the Cold-Start Problem in Outfit Recommendation Using Visual Preference Modelling","date":"2020-08-04","arxiv_id":"2008.01437","repositories_listed":1,"syntology":null},{"url":"/paper/the-exact-asymptotic-form-of-bayesian","slug":"the-exact-asymptotic-form-of-bayesian","title":"The Exact Asymptotic Form of Bayesian Generalization Error in Latent Dirichlet Allocation","date":"2020-08-04","arxiv_id":"2008.01304","repositories_listed":1,"syntology":null},{"url":"/paper/conditional-latent-block-model-a-multivariate","slug":"conditional-latent-block-model-a-multivariate","title":"Conditional Latent Block Model: a Multivariate Time Series Clustering Approach for Autonomous Driving Validation","date":"2020-08-03","arxiv_id":"2008.00946","repositories_listed":1,"syntology":null},{"url":"/paper/weakly-supervised-semantic-segmentation-via-1","slug":"weakly-supervised-semantic-segmentation-via-1","title":"Weakly-Supervised Semantic Segmentation via Sub-category Exploration","date":"2020-08-03","arxiv_id":"2008.01183","repositories_listed":1,"syntology":{"n":16,"n_ran":11,"n_constructed":0,"n_ran_checked":10,"n_instrument":1,"n_unverified":5,"n_honours":0,"n_violates":0,"n_no_contract":10,"n_pointer_only":0,"phrase":"11 ran (of which 0 constructed an object rather than computing a result; 10 with no instrument failure: 0 honoured, 0 violated, 10 with no contract checked; 1 where Syntology's instrument failed) · 5 unverified","sample_list":"/paper/weakly-supervised-semantic-segmentation-via-1#ran","syntology_url":"https://syntology.ai/paper/2008.01183","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2008.01183"}},"official":{"repos":["Juliachang/SC-CAM"],"state":"official (archive's flag): 11 ran","n_ran":11,"n_constructed":0,"n_ran_no_instrument_failure":10,"n_unverified":5,"ran_from_kinds":["official"]}}},{"url":"/paper/stochastic-bundle-adjustment-for-efficient","slug":"stochastic-bundle-adjustment-for-efficient","title":"Stochastic Bundle Adjustment for Efficient and Scalable 3D Reconstruction","date":"2020-08-02","arxiv_id":"2008.00446","repositories_listed":1,"syntology":null},{"url":"/paper/on-the-usage-of-the-trifocal-tensor-in-motion","slug":"on-the-usage-of-the-trifocal-tensor-in-motion","title":"On the Usage of the Trifocal Tensor in Motion Segmentation","date":"2020-08-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/denoising-individual-bias-for-a-fairer-binary","slug":"denoising-individual-bias-for-a-fairer-binary","title":"Denoising individual bias for a fairer binary submatrix detection","date":"2020-07-31","arxiv_id":"2007.15816","repositories_listed":1,"syntology":null},{"url":"/paper/learning-rgb-d-feature-embeddings-for-unseen","slug":"learning-rgb-d-feature-embeddings-for-unseen","title":"Learning RGB-D Feature Embeddings for Unseen Object Instance Segmentation","date":"2020-07-30","arxiv_id":"2007.15157","repositories_listed":1,"syntology":null},{"url":"/paper/almost-exact-recovery-in-noisy-semi","slug":"almost-exact-recovery-in-noisy-semi","title":"Almost exact recovery in noisy semi-supervised learning","date":"2020-07-29","arxiv_id":"2007.14717","repositories_listed":1,"syntology":null},{"url":"/paper/dynamic-character-graph-via-online-face","slug":"dynamic-character-graph-via-online-face","title":"Dynamic Character Graph via Online Face Clustering for Movie Analysis","date":"2020-07-29","arxiv_id":"2007.14913","repositories_listed":1,"syntology":null},{"url":"/paper/music-fadernets-controllable-music-generation","slug":"music-fadernets-controllable-music-generation","title":"Music FaderNets: Controllable Music Generation Based On High-Level Features via Low-Level Feature Modelling","date":"2020-07-29","arxiv_id":"2007.15474","repositories_listed":1,"syntology":{"n":3,"n_ran":2,"n_constructed":0,"n_ran_checked":0,"n_instrument":2,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":3,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/music-fadernets-controllable-music-generation#ran","syntology_url":"https://syntology.ai/paper/2007.15474","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2007.15474"}},"official":{"repos":["gudgud96/music-fader-nets"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/faster-mean-shift-gpu-accelerated-embedding","slug":"faster-mean-shift-gpu-accelerated-embedding","title":"Faster Mean-shift: GPU-accelerated clustering for cosine embedding-based cell segmentation and tracking","date":"2020-07-28","arxiv_id":"2007.14283","repositories_listed":1,"syntology":null},{"url":"/paper/hierarchical-sparse-subspace-clustering-hessc","slug":"hierarchical-sparse-subspace-clustering-hessc","title":"Hierarchical Sparse Subspace Clustering (HESSC): An Automatic Approach for Hyperspectral Image Analysis","date":"2020-07-28","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/lifelong-incremental-reinforcement-learning","slug":"lifelong-incremental-reinforcement-learning","title":"Lifelong Incremental Reinforcement Learning with Online Bayesian Inference","date":"2020-07-28","arxiv_id":"2007.14196","repositories_listed":1,"syntology":null},{"url":"/paper/identity-guided-human-semantic-parsing-for","slug":"identity-guided-human-semantic-parsing-for","title":"Identity-Guided Human Semantic Parsing for Person Re-Identification","date":"2020-07-27","arxiv_id":"2007.13467","repositories_listed":1,"syntology":{"n":7,"n_ran":6,"n_constructed":0,"n_ran_checked":4,"n_instrument":2,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":1,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 2 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/identity-guided-human-semantic-parsing-for#ran","syntology_url":"https://syntology.ai/paper/2007.13467","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2007.13467"}},"official":{"repos":["CASIA-IVA-Lab/ISP-reID"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/oblique-predictive-clustering-trees","slug":"oblique-predictive-clustering-trees","title":"Oblique Predictive Clustering Trees","date":"2020-07-27","arxiv_id":"2007.13617","repositories_listed":1,"syntology":null},{"url":"/paper/deep-embedded-multi-view-clustering-with","slug":"deep-embedded-multi-view-clustering-with","title":"Deep Embedded Multi-view Clustering with Collaborative Training","date":"2020-07-26","arxiv_id":"2007.13067","repositories_listed":1,"syntology":null},{"url":"/paper/mixture-of-experts-category-hierarchy-soft","slug":"mixture-of-experts-category-hierarchy-soft","title":"Adversarial Mixture Of Experts with Category Hierarchy Soft Constraint","date":"2020-07-24","arxiv_id":"2007.12349","repositories_listed":1,"syntology":null},{"url":"/paper/clustering-of-social-media-messages-for","slug":"clustering-of-social-media-messages-for","title":"Clustering of Social Media Messages for Humanitarian Aid Response during Crisis","date":"2020-07-23","arxiv_id":"2007.11756","repositories_listed":1,"syntology":null},{"url":"/paper/deep-image-clustering-with-category-style","slug":"deep-image-clustering-with-category-style","title":"Deep Image Clustering with Category-Style Representation","date":"2020-07-20","arxiv_id":"2007.10004","repositories_listed":1,"syntology":null},{"url":"/paper/summpip-unsupervised-multi-document","slug":"summpip-unsupervised-multi-document","title":"SummPip: Unsupervised Multi-Document Summarization with Sentence Graph Compression","date":"2020-07-17","arxiv_id":"2007.08954","repositories_listed":1,"syntology":null},{"url":"/paper/autoregressive-unsupervised-image","slug":"autoregressive-unsupervised-image","title":"Autoregressive Unsupervised Image Segmentation","date":"2020-07-16","arxiv_id":"2007.08247","repositories_listed":1,"syntology":null},{"url":"/paper/graph-topology-inference-benchmarks-for","slug":"graph-topology-inference-benchmarks-for","title":"Graph topology inference benchmarks for machine learning","date":"2020-07-16","arxiv_id":"2007.08216","repositories_listed":1,"syntology":{"n":13,"n_ran":10,"n_constructed":0,"n_ran_checked":10,"n_instrument":0,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":10,"n_pointer_only":0,"phrase":"10 ran (of which 0 constructed an object rather than computing a result; 10 with no instrument failure: 0 honoured, 0 violated, 10 with no contract checked; 0 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/graph-topology-inference-benchmarks-for#ran","syntology_url":"https://syntology.ai/paper/2007.08216","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2007.08216"}},"official":{"repos":["cadurosar/benchmark_graphinference"],"state":"official (archive's flag): 10 ran","n_ran":10,"n_constructed":0,"n_ran_no_instrument_failure":10,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/in-search-of-the-weirdest-galaxies-in-the","slug":"in-search-of-the-weirdest-galaxies-in-the","title":"In search of the weirdest galaxies in the Universe","date":"2020-07-16","arxiv_id":"2007.08530","repositories_listed":1,"syntology":null}],"record_sha256":"09e594b987689493180e0d65fb35aed4c19050a06a5cda5f71edc527fab4630b","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}