{"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/23","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":23,"pages_in_order":108,"rows_per_page":100,"rows":[2201,2300],"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/22","next":"/task/clustering/papers/24","papers":[{"url":"/paper/multiple-partitions-aligned-clustering","slug":"multiple-partitions-aligned-clustering","title":"Multiple Partitions Aligned Clustering","date":"2019-09-13","arxiv_id":"1909.06008","repositories_listed":1,"syntology":null},{"url":"/paper/accelerating-column-generation-via-flexible","slug":"accelerating-column-generation-via-flexible","title":"Accelerating Column Generation via Flexible Dual Optimal Inequalities with Application to Entity Resolution","date":"2019-09-12","arxiv_id":"1909.05460","repositories_listed":1,"syntology":null},{"url":"/paper/end-to-end-neural-speaker-diarization-with-1","slug":"end-to-end-neural-speaker-diarization-with-1","title":"End-to-End Neural Speaker Diarization with Permutation-Free Objectives","date":"2019-09-12","arxiv_id":"1909.05952","repositories_listed":1,"syntology":null},{"url":"/paper/deep-prediction-of-investor-interest-a","slug":"deep-prediction-of-investor-interest-a","title":"Deep Prediction of Investor Interest: a Supervised Clustering Approach","date":"2019-09-11","arxiv_id":"1909.05289","repositories_listed":1,"syntology":null},{"url":"/paper/spike-sorting-using-the-neural-clustering","slug":"spike-sorting-using-the-neural-clustering","title":"Spike Sorting using the Neural Clustering Process","date":"2019-09-11","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/quantum-unsupervised-and-supervised-learning","slug":"quantum-unsupervised-and-supervised-learning","title":"Quantum Unsupervised and Supervised Learning on Superconducting Processors","date":"2019-09-10","arxiv_id":"1909.04226","repositories_listed":1,"syntology":null},{"url":"/paper/a-flexible-framework-for-anomaly-detection","slug":"a-flexible-framework-for-anomaly-detection","title":"A Flexible Framework for Anomaly Detection via Dimensionality Reduction","date":"2019-09-09","arxiv_id":"1909.04060","repositories_listed":1,"syntology":null},{"url":"/paper/crowd-counting-on-images-with-scale-variation","slug":"crowd-counting-on-images-with-scale-variation","title":"Crowd Counting on Images with Scale Variation and Isolated Clusters","date":"2019-09-09","arxiv_id":"1909.03839","repositories_listed":1,"syntology":null},{"url":"/paper/autogmm-automatic-gaussian-mixture-modeling","slug":"autogmm-automatic-gaussian-mixture-modeling","title":"AutoGMM: Automatic and Hierarchical Gaussian Mixture Modeling in Python","date":"2019-09-06","arxiv_id":"1909.02688","repositories_listed":1,"syntology":null},{"url":"/paper/categorical-co-frequency-analysis-clustering","slug":"categorical-co-frequency-analysis-clustering","title":"Categorical Co-Frequency Analysis: Clustering Diagnosis Codes to Predict Hospital Readmissions","date":"2019-09-01","arxiv_id":"1909.00306","repositories_listed":1,"syntology":null},{"url":"/paper/dialog-intent-induction-with-deep-multi-view","slug":"dialog-intent-induction-with-deep-multi-view","title":"Dialog Intent Induction with Deep Multi-View Clustering","date":"2019-08-30","arxiv_id":"1908.11487","repositories_listed":1,"syntology":null},{"url":"/paper/learning-to-discover-novel-visual-categories","slug":"learning-to-discover-novel-visual-categories","title":"Learning to Discover Novel Visual Categories via Deep Transfer Clustering","date":"2019-08-26","arxiv_id":"1908.09884","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"1 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; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/learning-to-discover-novel-visual-categories#ran","syntology_url":"https://syntology.ai/paper/1908.09884","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1908.09884"}},"official":{"repos":["k-han/DTC"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/pareto-optimal-data-compression-for-binary","slug":"pareto-optimal-data-compression-for-binary","title":"Pareto-optimal data compression for binary classification tasks","date":"2019-08-23","arxiv_id":"1908.08961","repositories_listed":1,"syntology":null},{"url":"/paper/unsupervised-lemmatization-as-embeddings","slug":"unsupervised-lemmatization-as-embeddings","title":"Unsupervised Lemmatization as Embeddings-Based Word Clustering","date":"2019-08-22","arxiv_id":"1908.08528","repositories_listed":1,"syntology":null},{"url":"/paper/vico-word-embeddings-from-visual-co","slug":"vico-word-embeddings-from-visual-co","title":"ViCo: Word Embeddings from Visual Co-occurrences","date":"2019-08-22","arxiv_id":"1908.08527","repositories_listed":1,"syntology":null},{"url":"/paper/close-spatial-arrangement-of-mutants-favors","slug":"close-spatial-arrangement-of-mutants-favors","title":"Close spatial arrangement of mutants favors and disfavors fixation","date":"2019-08-21","arxiv_id":"1811.08718","repositories_listed":1,"syntology":null},{"url":"/paper/disco-for-the-cia-deep-learning-instance","slug":"disco-for-the-cia-deep-learning-instance","title":"DISCo: Deep learning, Instance Segmentation, and Correlations for cell segmentation in calcium imaging","date":"2019-08-21","arxiv_id":"1908.07957","repositories_listed":1,"syntology":null},{"url":"/paper/consistent-community-detection-in-continuous","slug":"consistent-community-detection-in-continuous","title":"CHIP: A Hawkes Process Model for Continuous-time Networks with Scalable and Consistent Estimation","date":"2019-08-19","arxiv_id":"1908.06940","repositories_listed":1,"syntology":null},{"url":"/paper/correlation-clustering-with-same-cluster","slug":"correlation-clustering-with-same-cluster","title":"Correlation Clustering with Same-Cluster Queries Bounded by Optimal Cost","date":"2019-08-14","arxiv_id":"1908.04976","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/correlation-clustering-with-same-cluster#ran","syntology_url":"https://syntology.ai/paper/1908.04976","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1908.04976"}},"official":{"repos":["sanjayss34/corr-clust-query-esa2019"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/life-after-bootstrap-residual-randomization","slug":"life-after-bootstrap-residual-randomization","title":"Asymptotic Validity and Finite-Sample Properties of Approximate Randomization Tests","date":"2019-08-12","arxiv_id":"1908.04218","repositories_listed":1,"syntology":null},{"url":"/paper/rwr-gae-random-walk-regularization-for-graph","slug":"rwr-gae-random-walk-regularization-for-graph","title":"RWR-GAE: Random Walk Regularization for Graph Auto Encoders","date":"2019-08-12","arxiv_id":"1908.04003","repositories_listed":1,"syntology":{"n":9,"n_ran":7,"n_constructed":0,"n_ran_checked":7,"n_instrument":0,"n_unverified":2,"n_honours":1,"n_violates":1,"n_no_contract":5,"n_pointer_only":0,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 1 honoured, 1 violated, 5 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/rwr-gae-random-walk-regularization-for-graph#ran","syntology_url":"https://syntology.ai/paper/1908.04003","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1908.04003"}},"official":{"repos":["MysteryVaibhav/DW-GAE"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/lightweight-and-scalable-particle-tracking","slug":"lightweight-and-scalable-particle-tracking","title":"Lightweight and Scalable Particle Tracking and Motion Clustering of 3D Cell Trajectories","date":"2019-08-10","arxiv_id":"1908.03775","repositories_listed":1,"syntology":null},{"url":"/paper/video-face-clustering-with-unknown-number-of","slug":"video-face-clustering-with-unknown-number-of","title":"Video Face Clustering with Unknown Number of Clusters","date":"2019-08-09","arxiv_id":"1908.03381","repositories_listed":1,"syntology":null},{"url":"/paper/symmetric-graph-convolutional-autoencoder-for","slug":"symmetric-graph-convolutional-autoencoder-for","title":"Symmetric Graph Convolutional Autoencoder for Unsupervised Graph Representation Learning","date":"2019-08-07","arxiv_id":"1908.02441","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":1,"phrase":"1 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; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/symmetric-graph-convolutional-autoencoder-for#ran","syntology_url":"https://syntology.ai/paper/1908.02441","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1908.02441"}},"official":null}},{"url":"/paper/clustering-of-deep-contextualized","slug":"clustering-of-deep-contextualized","title":"Clustering of Deep Contextualized Representations for Summarization of Biomedical Texts","date":"2019-08-06","arxiv_id":"1908.02286","repositories_listed":1,"syntology":null},{"url":"/paper/an-underparametrized-deep-decoder","slug":"an-underparametrized-deep-decoder","title":"An Underparametrized Deep Decoder Architecture for Graph Signals","date":"2019-08-02","arxiv_id":"1908.00878","repositories_listed":1,"syntology":null},{"url":"/paper/linear-dynamics-clustering-without","slug":"linear-dynamics-clustering-without","title":"Linear Dynamics: Clustering without identification","date":"2019-08-02","arxiv_id":"1908.01039","repositories_listed":1,"syntology":null},{"url":"/paper/a-self-training-approach-for-short-text","slug":"a-self-training-approach-for-short-text","title":"A Self-Training Approach for Short Text Clustering","date":"2019-08-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/ensemble-clustering-based-on-evidence","slug":"ensemble-clustering-based-on-evidence","title":"Ensemble clustering based on evidence extracted from the co-association matrix","date":"2019-08-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/measuring-gender-bias-in-word-embeddings","slug":"measuring-gender-bias-in-word-embeddings","title":"Measuring Gender Bias in Word Embeddings across Domains and Discovering New Gender Bias Word Categories","date":"2019-08-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/a-comparative-study-of-general-fuzzy-min-max","slug":"a-comparative-study-of-general-fuzzy-min-max","title":"A comparative study of general fuzzy min-max neural networks for pattern classification problems","date":"2019-07-31","arxiv_id":"1907.13308","repositories_listed":1,"syntology":null},{"url":"/paper/a-novel-multiple-classifier-generation-and","slug":"a-novel-multiple-classifier-generation-and","title":"A Novel Multiple Classifier Generation and Combination Framework Based on Fuzzy Clustering and Individualized Ensemble Construction","date":"2019-07-31","arxiv_id":"1907.13353","repositories_listed":1,"syntology":null},{"url":"/paper/airbnb-price-prediction-using-machine","slug":"airbnb-price-prediction-using-machine","title":"Airbnb Price Prediction Using Machine Learning and Sentiment Analysis","date":"2019-07-29","arxiv_id":"1907.12665","repositories_listed":1,"syntology":null},{"url":"/paper/adaptive-spline-fitting-with-particle-swarm","slug":"adaptive-spline-fitting-with-particle-swarm","title":"Adaptive spline fitting with particle swarm optimization","date":"2019-07-28","arxiv_id":"1907.12160","repositories_listed":1,"syntology":null},{"url":"/paper/constrained-k-means-with-general-pairwise-and","slug":"constrained-k-means-with-general-pairwise-and","title":"Constrained Clustering: General Pairwise and Cardinality Constraints","date":"2019-07-24","arxiv_id":"1907.10410","repositories_listed":1,"syntology":null},{"url":"/paper/dante-deep-affinity-network-for-clustering","slug":"dante-deep-affinity-network-for-clustering","title":"Improving Social Awareness Through DANTE: A Deep Affinity Network for Clustering Conversational Interactants","date":"2019-07-24","arxiv_id":"1907.12910","repositories_listed":1,"syntology":null},{"url":"/paper/lstm-based-similarity-measurement-with","slug":"lstm-based-similarity-measurement-with","title":"LSTM based Similarity Measurement with Spectral Clustering for Speaker Diarization","date":"2019-07-23","arxiv_id":"1907.10393","repositories_listed":1,"syntology":null},{"url":"/paper/shared-generative-latent-representation","slug":"shared-generative-latent-representation","title":"Shared Generative Latent Representation Learning for Multi-view Clustering","date":"2019-07-23","arxiv_id":"1907.09747","repositories_listed":1,"syntology":null},{"url":"/paper/unsupervised-segmentation-of-hyperspectral","slug":"unsupervised-segmentation-of-hyperspectral","title":"Unsupervised Segmentation of Hyperspectral Images Using 3D Convolutional Autoencoders","date":"2019-07-20","arxiv_id":"1907.08870","repositories_listed":1,"syntology":null},{"url":"/paper/comparison-of-classical-machine-learning","slug":"comparison-of-classical-machine-learning","title":"Comparison of Classical Machine Learning Approaches on Bangla Textual Emotion Analysis","date":"2019-07-18","arxiv_id":"1907.07826","repositories_listed":1,"syntology":null},{"url":"/paper/a-multivariate-extreme-value-theory-approach","slug":"a-multivariate-extreme-value-theory-approach","title":"A Multivariate Extreme Value Theory Approach to Anomaly Clustering and Visualization","date":"2019-07-17","arxiv_id":"1907.07523","repositories_listed":1,"syntology":null},{"url":"/paper/embedding-based-silhouette-community","slug":"embedding-based-silhouette-community","title":"Embedding-based Silhouette Community Detection","date":"2019-07-17","arxiv_id":"1908.02556","repositories_listed":1,"syntology":null},{"url":"/paper/t-k-means-a-k-means-variant-with-robustness","slug":"t-k-means-a-k-means-variant-with-robustness","title":"$t$-$k$-means: A Robust and Stable $k$-means Variant","date":"2019-07-17","arxiv_id":"1907.07442","repositories_listed":1,"syntology":null},{"url":"/paper/comprehensive-process-drift-detection-with","slug":"comprehensive-process-drift-detection-with","title":"Comprehensive Process Drift Detection with Visual Analytics","date":"2019-07-15","arxiv_id":"1907.06386","repositories_listed":1,"syntology":null},{"url":"/paper/estimation-and-feature-selection-in-mixtures","slug":"estimation-and-feature-selection-in-mixtures","title":"Estimation and Feature Selection in Mixtures of Generalized Linear Experts Models","date":"2019-07-14","arxiv_id":"1907.06994","repositories_listed":1,"syntology":null},{"url":"/paper/alfa-agglomerative-late-fusion-algorithm-for","slug":"alfa-agglomerative-late-fusion-algorithm-for","title":"ALFA: Agglomerative Late Fusion Algorithm for Object Detection","date":"2019-07-13","arxiv_id":"1907.06067","repositories_listed":1,"syntology":null},{"url":"/paper/coarse-graining-of-data-via-inhomogeneous","slug":"coarse-graining-of-data-via-inhomogeneous","title":"Coarse Graining of Data via Inhomogeneous Diffusion Condensation","date":"2019-07-10","arxiv_id":"1907.04463","repositories_listed":1,"syntology":{"n":2,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"1 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; 1 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/coarse-graining-of-data-via-inhomogeneous#ran","syntology_url":"https://syntology.ai/paper/1907.04463","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1907.04463"}},"official":{"repos":["matthew-hirn/condensation"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/a-spectral-approach-to-unsupervised-object","slug":"a-spectral-approach-to-unsupervised-object","title":"A 3D Convolutional Approach to Spectral Object Segmentation in Space and Time","date":"2019-07-05","arxiv_id":"1907.02731","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/a-spectral-approach-to-unsupervised-object#ran","syntology_url":"https://syntology.ai/paper/1907.02731","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1907.02731"}},"official":{"repos":["bit-ml/sfseg"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/feature-based-image-clustering-and","slug":"feature-based-image-clustering-and","title":"Feature-Based Image Clustering and Segmentation Using Wavelets","date":"2019-07-05","arxiv_id":"1907.03591","repositories_listed":1,"syntology":null},{"url":"/paper/clustering-of-medical-free-text-records-based","slug":"clustering-of-medical-free-text-records-based","title":"Interpretable Segmentation of Medical Free-Text Records Based on Word Embeddings","date":"2019-07-03","arxiv_id":"1907.04152","repositories_listed":1,"syntology":null},{"url":"/paper/a-semi-supervised-self-organizing-map-for","slug":"a-semi-supervised-self-organizing-map-for","title":"A Semi-Supervised Self-Organizing Map for Clustering and Classification","date":"2019-07-01","arxiv_id":"1907.01070","repositories_listed":1,"syntology":null},{"url":"/paper/a-semi-supervised-self-organizing-map-with","slug":"a-semi-supervised-self-organizing-map-with","title":"A Semi-Supervised Self-Organizing Map with Adaptive Local Thresholds","date":"2019-07-01","arxiv_id":"1907.01086","repositories_listed":1,"syntology":null},{"url":"/paper/nearest-neighbour-induced-isolation","slug":"nearest-neighbour-induced-isolation","title":"Nearest-Neighbour-Induced Isolation Similarity and its Impact on Density-Based Clustering","date":"2019-06-30","arxiv_id":"1907.00378","repositories_listed":1,"syntology":null},{"url":"/paper/loss-switching-fusion-with-similarity-search","slug":"loss-switching-fusion-with-similarity-search","title":"Loss Switching Fusion with Similarity Search for Video Classification","date":"2019-06-27","arxiv_id":"1906.11465","repositories_listed":1,"syntology":{"n":1,"n_ran":0,"n_constructed":0,"n_ran_checked":0,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":1,"phrase":"0 ran · 1 unverified","sample_list":"/paper/loss-switching-fusion-with-similarity-search#ran","syntology_url":"https://syntology.ai/paper/1906.11465","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1906.11465"}},"official":{"repos":["LeiWangR/LSFNet"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":[]}}},{"url":"/paper/business-taxonomy-construction-using-concept","slug":"business-taxonomy-construction-using-concept","title":"Business Taxonomy Construction Using Concept-Level Hierarchical Clustering","date":"2019-06-24","arxiv_id":"1906.09694","repositories_listed":1,"syntology":null},{"url":"/paper/dlime-a-deterministic-local-interpretable","slug":"dlime-a-deterministic-local-interpretable","title":"DLIME: A Deterministic Local Interpretable Model-Agnostic Explanations Approach for Computer-Aided Diagnosis Systems","date":"2019-06-24","arxiv_id":"1906.10263","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/dlime-a-deterministic-local-interpretable#ran","syntology_url":"https://syntology.ai/paper/1906.10263","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1906.10263"}},"official":{"repos":["rehmanzafar/dlime_experiments"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/smaller-text-classifiers-with-discriminative-1","slug":"smaller-text-classifiers-with-discriminative-1","title":"Smaller Text Classifiers with Discriminative Cluster Embeddings","date":"2019-06-23","arxiv_id":"1906.09532","repositories_listed":1,"syntology":null},{"url":"/paper/trade-offs-in-large-scale-distributed","slug":"trade-offs-in-large-scale-distributed","title":"Trade-offs in Large-Scale Distributed Tuplewise Estimation and Learning","date":"2019-06-21","arxiv_id":"1906.09234","repositories_listed":1,"syntology":null},{"url":"/paper/versatile-linkage-a-family-of-space","slug":"versatile-linkage-a-family-of-space","title":"Versatile linkage: a family of space-conserving strategies for agglomerative hierarchical clustering","date":"2019-06-21","arxiv_id":"1906.09222","repositories_listed":1,"syntology":null},{"url":"/paper/coresets-for-clustering-with-fairness","slug":"coresets-for-clustering-with-fairness","title":"Coresets for Clustering with Fairness Constraints","date":"2019-06-20","arxiv_id":"1906.08484","repositories_listed":1,"syntology":null},{"url":"/paper/improving-the-robustness-of-imagenet","slug":"improving-the-robustness-of-imagenet","title":"Improving the robustness of ImageNet classifiers using elements of human visual cognition","date":"2019-06-20","arxiv_id":"1906.08416","repositories_listed":1,"syntology":null},{"url":"/paper/constrained-bilinear-factorization-multi-view","slug":"constrained-bilinear-factorization-multi-view","title":"Constrained Bilinear Factorization Multi-view Subspace Clustering","date":"2019-06-19","arxiv_id":"1906.08107","repositories_listed":1,"syntology":null},{"url":"/paper/supervised-hierarchical-clustering-with","slug":"supervised-hierarchical-clustering-with","title":"Supervised Hierarchical Clustering with Exponential Linkage","date":"2019-06-19","arxiv_id":"1906.07859","repositories_listed":1,"syntology":null},{"url":"/paper/3d-geometric-salient-patterns-analysis-on-3d","slug":"3d-geometric-salient-patterns-analysis-on-3d","title":"3D Geometric salient patterns analysis on 3D meshes","date":"2019-06-18","arxiv_id":"1906.07645","repositories_listed":1,"syntology":null},{"url":"/paper/a-weakly-supervised-learning-based-clustering","slug":"a-weakly-supervised-learning-based-clustering","title":"Weakly Supervised Clustering by Exploiting Unique Class Count","date":"2019-06-18","arxiv_id":"1906.07647","repositories_listed":1,"syntology":null},{"url":"/paper/uncovering-why-deep-neural-networks-lack","slug":"uncovering-why-deep-neural-networks-lack","title":"Representation Quality Of Neural Networks Links To Adversarial Attacks and Defences","date":"2019-06-15","arxiv_id":"1906.06627","repositories_listed":1,"syntology":null},{"url":"/paper/divide-and-conquer-the-embedding-space-for-1","slug":"divide-and-conquer-the-embedding-space-for-1","title":"Divide and Conquer the Embedding Space for Metric Learning","date":"2019-06-14","arxiv_id":"1906.05990","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":1,"phrase":"1 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; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/divide-and-conquer-the-embedding-space-for-1#ran","syntology_url":"https://syntology.ai/paper/1906.05990","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1906.05990"}},"official":null}},{"url":"/paper/correlating-twitter-language-with-community","slug":"correlating-twitter-language-with-community","title":"Correlating Twitter Language with Community-Level Health Outcomes","date":"2019-06-13","arxiv_id":"1906.06465","repositories_listed":1,"syntology":null},{"url":"/paper/real-time-attention-based-look-alike-model","slug":"real-time-attention-based-look-alike-model","title":"Real-time Attention Based Look-alike Model for Recommender System","date":"2019-06-12","arxiv_id":"1906.05022","repositories_listed":1,"syntology":null},{"url":"/paper/datalearner-a-data-mining-and-knowledge","slug":"datalearner-a-data-mining-and-knowledge","title":"DataLearner: A Data Mining and Knowledge Discovery Tool for Android Smartphones and Tablets","date":"2019-06-10","arxiv_id":"1906.03773","repositories_listed":1,"syntology":null},{"url":"/paper/fine-grained-event-categorization-with","slug":"fine-grained-event-categorization-with","title":"Fine-grained Event Categorization with Heterogeneous Graph Convolutional Networks","date":"2019-06-09","arxiv_id":"1906.04580","repositories_listed":1,"syntology":null},{"url":"/paper/global-semantic-description-of-objects-based","slug":"global-semantic-description-of-objects-based","title":"Global Semantic Description of Objects based on Prototype Theory","date":"2019-06-08","arxiv_id":"1906.03365","repositories_listed":1,"syntology":null},{"url":"/paper/benchmarking-minimax-linkage","slug":"benchmarking-minimax-linkage","title":"Benchmarking Minimax Linkage","date":"2019-06-07","arxiv_id":"1906.03336","repositories_listed":1,"syntology":null},{"url":"/paper/a-numerical-measure-of-the-instability-of","slug":"a-numerical-measure-of-the-instability-of","title":"A numerical measure of the instability of Mapper-type algorithms","date":"2019-06-04","arxiv_id":"1906.01507","repositories_listed":1,"syntology":null},{"url":"/paper/attributed-graph-clustering-via-adaptive","slug":"attributed-graph-clustering-via-adaptive","title":"Attributed Graph Clustering via Adaptive Graph Convolution","date":"2019-06-04","arxiv_id":"1906.01210","repositories_listed":1,"syntology":null},{"url":"/paper/dominant-set-clustering-and-pooling-for-multi","slug":"dominant-set-clustering-and-pooling-for-multi","title":"Dominant Set Clustering and Pooling for Multi-View 3D Object Recognition","date":"2019-06-04","arxiv_id":"1906.01592","repositories_listed":1,"syntology":null},{"url":"/paper/learning-object-bounding-boxes-for-3d","slug":"learning-object-bounding-boxes-for-3d","title":"Learning Object Bounding Boxes for 3D Instance Segmentation on Point Clouds","date":"2019-06-04","arxiv_id":"1906.01140","repositories_listed":1,"syntology":{"n":4,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":1,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/learning-object-bounding-boxes-for-3d#ran","syntology_url":"https://syntology.ai/paper/1906.01140","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1906.01140"}},"official":{"repos":["Yang7879/3D-BoNet"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/towards-better-validity-dispersion-based","slug":"towards-better-validity-dispersion-based","title":"Towards better Validity: Dispersion based Clustering for Unsupervised Person Re-identification","date":"2019-06-04","arxiv_id":"1906.01308","repositories_listed":1,"syntology":null},{"url":"/paper/190600570","slug":"190600570","title":"Clustering by Orthogonal NMF Model and Non-Convex Penalty Optimization","date":"2019-06-03","arxiv_id":"1906.00570","repositories_listed":1,"syntology":null},{"url":"/paper/190600891","slug":"190600891","title":"Automated Steel Bar Counting and Center Localization with Convolutional Neural Networks","date":"2019-06-03","arxiv_id":"1906.00891","repositories_listed":1,"syntology":null},{"url":"/paper/190600938","slug":"190600938","title":"Big-Data Clustering: K-Means or K-Indicators?","date":"2019-06-03","arxiv_id":"1906.00938","repositories_listed":1,"syntology":null},{"url":"/paper/clustering-based-article-identification-in","slug":"clustering-based-article-identification-in","title":"Clustering-Based Article Identification in Historical Newspapers","date":"2019-06-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/efficient-parameter-free-clustering-using-1","slug":"efficient-parameter-free-clustering-using-1","title":"Efficient Parameter-Free Clustering Using First Neighbor Relations","date":"2019-06-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/fast-concept-mention-grouping-for-concept-map","slug":"fast-concept-mention-grouping-for-concept-map","title":"Fast Concept Mention Grouping for Concept Map-based Multi-Document Summarization","date":"2019-06-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/sign-clustering-and-topic-extraction-in-proto","slug":"sign-clustering-and-topic-extraction-in-proto","title":"Sign Clustering and Topic Extraction in Proto-Elamite","date":"2019-06-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/reinforcement-learning-and-adaptive-sampling","slug":"reinforcement-learning-and-adaptive-sampling","title":"Reinforcement Learning and Adaptive Sampling for Optimized DNN Compilation","date":"2019-05-30","arxiv_id":"1905.12799","repositories_listed":1,"syntology":null},{"url":"/paper/sequential-no-substitution-k-median","slug":"sequential-no-substitution-k-median","title":"Sequential no-Substitution k-Median-Clustering","date":"2019-05-30","arxiv_id":"1905.12925","repositories_listed":1,"syntology":null},{"url":"/paper/accelerating-extreme-classification-via","slug":"accelerating-extreme-classification-via","title":"Accelerating Extreme Classification via Adaptive Feature Agglomeration","date":"2019-05-28","arxiv_id":"1905.11769","repositories_listed":1,"syntology":null},{"url":"/paper/correlation-clustering-with-adaptive","slug":"correlation-clustering-with-adaptive","title":"Correlation Clustering with Adaptive Similarity Queries","date":"2019-05-28","arxiv_id":"1905.11902","repositories_listed":1,"syntology":null},{"url":"/paper/quantization-based-regularization-for","slug":"quantization-based-regularization-for","title":"Quantization-Based Regularization for Autoencoders","date":"2019-05-27","arxiv_id":"1905.11062","repositories_listed":1,"syntology":null},{"url":"/paper/toward-self-supervised-object-detection-in","slug":"toward-self-supervised-object-detection-in","title":"Learning to Detect and Retrieve Objects from Unlabeled Videos","date":"2019-05-27","arxiv_id":"1905.11137","repositories_listed":1,"syntology":null},{"url":"/paper/ultrametric-fitting-by-gradient-descent","slug":"ultrametric-fitting-by-gradient-descent","title":"Ultrametric Fitting by Gradient Descent","date":"2019-05-25","arxiv_id":"1905.10566","repositories_listed":1,"syntology":null},{"url":"/paper/190510350","slug":"190510350","title":"Unsupervised Community Detection with Modularity-Based Attention Model","date":"2019-05-20","arxiv_id":"1905.10350","repositories_listed":1,"syntology":{"n":3,"n_ran":2,"n_constructed":0,"n_ran_checked":1,"n_instrument":1,"n_unverified":1,"n_honours":1,"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; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/190510350#ran","syntology_url":"https://syntology.ai/paper/1905.10350","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1905.10350"}},"official":{"repos":["Ivanopolo/modnet"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/scalable-gromov-wasserstein-learning-for","slug":"scalable-gromov-wasserstein-learning-for","title":"Scalable Gromov-Wasserstein Learning for Graph Partitioning and Matching","date":"2019-05-18","arxiv_id":"1905.07645","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":1,"n_instrument":2,"n_unverified":0,"n_honours":1,"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; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/scalable-gromov-wasserstein-learning-for#ran","syntology_url":"https://syntology.ai/paper/1905.07645","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1905.07645"}},"official":{"repos":["HongtengXu/s-gwl"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/spectral-metric-for-dataset-complexity","slug":"spectral-metric-for-dataset-complexity","title":"Spectral Metric for Dataset Complexity Assessment","date":"2019-05-17","arxiv_id":"1905.07299","repositories_listed":1,"syntology":null},{"url":"/paper/hierarchically-structured-meta-learning","slug":"hierarchically-structured-meta-learning","title":"Hierarchically Structured Meta-learning","date":"2019-05-13","arxiv_id":"1905.05301","repositories_listed":1,"syntology":null},{"url":"/paper/hhmm-at-semeval-2019-task-2-unsupervised","slug":"hhmm-at-semeval-2019-task-2-unsupervised","title":"HHMM at SemEval-2019 Task 2: Unsupervised Frame Induction using Contextualized Word Embeddings","date":"2019-05-05","arxiv_id":"1905.01739","repositories_listed":1,"syntology":null},{"url":"/paper/a-similarity-measure-for-material-appearance","slug":"a-similarity-measure-for-material-appearance","title":"A Similarity Measure for Material Appearance","date":"2019-05-04","arxiv_id":"1905.01562","repositories_listed":1,"syntology":null},{"url":"/paper/temporal-ordered-clustering-in-dynamic","slug":"temporal-ordered-clustering-in-dynamic","title":"Temporal Ordered Clustering in Dynamic Networks: Unsupervised and Semi-supervised Learning Algorithms","date":"2019-05-02","arxiv_id":"1905.00672","repositories_listed":1,"syntology":null},{"url":"/paper/190500531","slug":"190500531","title":"Recombinator-k-means: An evolutionary algorithm that exploits k-means++ for recombination","date":"2019-05-01","arxiv_id":"1905.00531","repositories_listed":1,"syntology":null}],"record_sha256":"3186a9cf87a7d08a418245b12a27ec54546867070fe20ab2af11fb00d5db1c60","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}