{"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/3","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":3,"pages_in_order":108,"rows_per_page":100,"rows":[201,300],"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/2","next":"/task/clustering/papers/4","papers":[{"url":"/paper/false-clustering-rate-in-mixture-models","slug":"false-clustering-rate-in-mixture-models","title":"False membership rate control in mixture models","date":"2022-03-04","arxiv_id":"2203.02597","repositories_listed":2,"syntology":null},{"url":"/paper/structure-extraction-in-task-oriented","slug":"structure-extraction-in-task-oriented","title":"Structure Extraction in Task-Oriented Dialogues with Slot Clustering","date":"2022-02-28","arxiv_id":"2203.00073","repositories_listed":2,"syntology":null},{"url":"/paper/sampling-in-dirichlet-process-mixture-models","slug":"sampling-in-dirichlet-process-mixture-models","title":"Sampling in Dirichlet Process Mixture Models for Clustering Streaming Data","date":"2022-02-27","arxiv_id":"2202.13312","repositories_listed":2,"syntology":null},{"url":"/paper/ada-nets-face-clustering-via-adaptive-1","slug":"ada-nets-face-clustering-via-adaptive-1","title":"Ada-NETS: Face Clustering via Adaptive Neighbour Discovery in the Structure Space","date":"2022-02-08","arxiv_id":"2202.03800","repositories_listed":2,"syntology":{"n":15,"n_ran":11,"n_constructed":0,"n_ran_checked":11,"n_instrument":0,"n_unverified":4,"n_honours":0,"n_violates":0,"n_no_contract":11,"n_pointer_only":0,"phrase":"11 ran (of which 0 constructed an object rather than computing a result; 11 with no instrument failure: 0 honoured, 0 violated, 11 with no contract checked; 0 where Syntology's instrument failed) · 4 unverified","sample_list":"/paper/ada-nets-face-clustering-via-adaptive-1#ran","syntology_url":"https://syntology.ai/paper/2202.03800","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2202.03800"}},"official":{"repos":["damo-cv/ada-nets","Thomas-wyh/Ada-NETS"],"state":"official (archive's flag): 11 ran","n_ran":11,"n_constructed":0,"n_ran_no_instrument_failure":11,"n_unverified":4,"ran_from_kinds":["official"]}}},{"url":"/paper/dynamic-persistent-homology-for-brain","slug":"dynamic-persistent-homology-for-brain","title":"Persistent Homological State-Space Estimation of Functional Human Brain Networks at Rest","date":"2022-01-01","arxiv_id":"2201.00087","repositories_listed":2,"syntology":null},{"url":"/paper/deep-graph-clustering-via-dual-correlation","slug":"deep-graph-clustering-via-dual-correlation","title":"Deep Graph Clustering via Dual Correlation Reduction","date":"2021-12-29","arxiv_id":"2112.14772","repositories_listed":2,"syntology":null},{"url":"/paper/on-the-unreasonable-efficiency-of-state-space","slug":"on-the-unreasonable-efficiency-of-state-space","title":"On the Unreasonable Efficiency of State Space Clustering in Personalization Tasks","date":"2021-12-24","arxiv_id":"2112.13141","repositories_listed":2,"syntology":null},{"url":"/paper/anomaly-clustering-grouping-images-into","slug":"anomaly-clustering-grouping-images-into","title":"Anomaly Clustering: Grouping Images into Coherent Clusters of Anomaly Types","date":"2021-12-21","arxiv_id":"2112.11573","repositories_listed":2,"syntology":null},{"url":"/paper/a-proposition-level-clustering-approach-for","slug":"a-proposition-level-clustering-approach-for","title":"Proposition-Level Clustering for Multi-Document Summarization","date":"2021-12-16","arxiv_id":"2112.08770","repositories_listed":2,"syntology":null},{"url":"/paper/active-learning-meets-optimized-item","slug":"active-learning-meets-optimized-item","title":"Active Learning Meets Optimized Item Selection","date":"2021-11-22","arxiv_id":"2112.03105","repositories_listed":2,"syntology":null},{"url":"/paper/deep-embedded-k-means-clustering","slug":"deep-embedded-k-means-clustering","title":"Deep Embedded K-Means Clustering","date":"2021-09-30","arxiv_id":"2109.15149","repositories_listed":2,"syntology":null},{"url":"/paper/clustering-performance-analysis-using-new","slug":"clustering-performance-analysis-using-new","title":"Clustering performance analysis using a new correlation-based cluster validity index","date":"2021-09-23","arxiv_id":"2109.11172","repositories_listed":2,"syntology":null},{"url":"/paper/xci-sketch-extraction-of-color-information","slug":"xci-sketch-extraction-of-color-information","title":"XCI-Sketch: Extraction of Color Information from Images for Generation of Colored Outlines and Sketches","date":"2021-08-26","arxiv_id":"2108.11554","repositories_listed":2,"syntology":null},{"url":"/paper/clustering-acoustic-emission-data-streams","slug":"clustering-acoustic-emission-data-streams","title":"Clustering acoustic emission data streams with sequentially appearing clusters using mixture models","date":"2021-08-25","arxiv_id":"2108.11211","repositories_listed":2,"syntology":null},{"url":"/paper/unsupervised-person-re-identification-with-1","slug":"unsupervised-person-re-identification-with-1","title":"Unsupervised Person Re-identification with Stochastic Training Strategy","date":"2021-08-16","arxiv_id":"2108.06938","repositories_listed":2,"syntology":null},{"url":"/paper/attention-driven-graph-clustering-network","slug":"attention-driven-graph-clustering-network","title":"Attention-driven Graph Clustering Network","date":"2021-08-12","arxiv_id":"2108.05499","repositories_listed":2,"syntology":null},{"url":"/paper/clustering-with-umap-why-and-how-connectivity","slug":"clustering-with-umap-why-and-how-connectivity","title":"Clustering with UMAP: Why and How Connectivity Matters","date":"2021-08-12","arxiv_id":"2108.05525","repositories_listed":2,"syntology":null},{"url":"/paper/cross-domain-gradient-discrepancy","slug":"cross-domain-gradient-discrepancy","title":"Cross-Domain Gradient Discrepancy Minimization for Unsupervised Domain Adaptation","date":"2021-06-08","arxiv_id":"2106.04151","repositories_listed":2,"syntology":null},{"url":"/paper/unsupervised-representation-learning-for-time-1","slug":"unsupervised-representation-learning-for-time-1","title":"Unsupervised Representation Learning for Time Series with Temporal Neighborhood Coding","date":"2021-06-01","arxiv_id":"2106.00750","repositories_listed":2,"syntology":{"n":5,"n_ran":3,"n_constructed":1,"n_ran_checked":1,"n_instrument":2,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":5,"phrase":"3 ran (of which 1 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 2 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/unsupervised-representation-learning-for-time-1#ran","syntology_url":"https://syntology.ai/paper/2106.00750","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2106.00750"}},"official":{"repos":["sanatonek/TNC_representation_learning"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":1,"n_ran_no_instrument_failure":1,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/xomivae-an-interpretable-deep-learning-model","slug":"xomivae-an-interpretable-deep-learning-model","title":"XOmiVAE: an interpretable deep learning model for cancer classification using high-dimensional omics data","date":"2021-05-26","arxiv_id":"2105.12807","repositories_listed":2,"syntology":{"n":15,"n_ran":7,"n_constructed":0,"n_ran_checked":7,"n_instrument":0,"n_unverified":8,"n_honours":0,"n_violates":0,"n_no_contract":7,"n_pointer_only":0,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 0 honoured, 0 violated, 7 with no contract checked; 0 where Syntology's instrument failed) · 8 unverified","sample_list":"/paper/xomivae-an-interpretable-deep-learning-model#ran","syntology_url":"https://syntology.ai/paper/2105.12807","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2105.12807"}},"official":{"repos":["zhangxiaoyu11/XOmiVAE"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":8,"ran_from_kinds":["official"]}}},{"url":"/paper/distribution-agnostic-symbolic","slug":"distribution-agnostic-symbolic","title":"Distribution Agnostic Symbolic Representations for Time Series Dimensionality Reduction and Online Anomaly Detection","date":"2021-05-20","arxiv_id":"2105.09592","repositories_listed":2,"syntology":null},{"url":"/paper/cross-cluster-weighted-forests","slug":"cross-cluster-weighted-forests","title":"Cross-Cluster Weighted Forests","date":"2021-05-17","arxiv_id":"2105.07610","repositories_listed":2,"syntology":null},{"url":"/paper/skeleton-clustering-dimension-free-density","slug":"skeleton-clustering-dimension-free-density","title":"Skeleton Clustering: Dimension-Free Density-based Clustering","date":"2021-04-21","arxiv_id":"2104.10770","repositories_listed":2,"syntology":null},{"url":"/paper/genesis-v2-inferring-unordered-object","slug":"genesis-v2-inferring-unordered-object","title":"GENESIS-V2: Inferring Unordered Object Representations without Iterative Refinement","date":"2021-04-20","arxiv_id":"2104.09958","repositories_listed":2,"syntology":null},{"url":"/paper/vec2gc-a-graph-based-clustering-method-for","slug":"vec2gc-a-graph-based-clustering-method-for","title":"Vec2GC -- A Graph Based Clustering Method for Text Representations","date":"2021-04-15","arxiv_id":"2104.09439","repositories_listed":2,"syntology":null},{"url":"/paper/pose-id-on-a-novel-framework-for-artwork-pose","slug":"pose-id-on-a-novel-framework-for-artwork-pose","title":"POSE-ID-on—A Novel Framework for Artwork Pose Clustering","date":"2021-04-11","arxiv_id":null,"repositories_listed":2,"syntology":null},{"url":"/paper/adaptive-prototype-learning-and-allocation","slug":"adaptive-prototype-learning-and-allocation","title":"Adaptive Prototype Learning and Allocation for Few-Shot Segmentation","date":"2021-04-05","arxiv_id":"2104.01893","repositories_listed":2,"syntology":null},{"url":"/paper/picie-unsupervised-semantic-segmentation","slug":"picie-unsupervised-semantic-segmentation","title":"PiCIE: Unsupervised Semantic Segmentation using Invariance and Equivariance in Clustering","date":"2021-03-30","arxiv_id":"2103.17070","repositories_listed":2,"syntology":{"n":4,"n_ran":2,"n_constructed":2,"n_ran_checked":2,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":1,"phrase":"2 ran (of which 2 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified; every one of the 2 samples that ran constructed an object rather than computing a result","sample_list":"/paper/picie-unsupervised-semantic-segmentation#ran","syntology_url":"https://syntology.ai/paper/2103.17070","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2103.17070"}},"official":{"repos":["janghyuncho/PiCIE"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":1,"n_ran_no_instrument_failure":1,"n_unverified":2,"ran_from_kinds":["listed","official"]}}},{"url":"/paper/panoptic-polarnet-proposal-free-lidar-point","slug":"panoptic-polarnet-proposal-free-lidar-point","title":"Panoptic-PolarNet: Proposal-free LiDAR Point Cloud Panoptic Segmentation","date":"2021-03-27","arxiv_id":"2103.14962","repositories_listed":2,"syntology":{"n":11,"n_ran":11,"n_constructed":0,"n_ran_checked":9,"n_instrument":2,"n_unverified":0,"n_honours":0,"n_violates":1,"n_no_contract":8,"n_pointer_only":0,"phrase":"11 ran (of which 0 constructed an object rather than computing a result; 9 with no instrument failure: 0 honoured, 1 violated, 8 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/panoptic-polarnet-proposal-free-lidar-point#ran","syntology_url":"https://syntology.ai/paper/2103.14962","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2103.14962"}},"official":{"repos":["edwardzhou130/Panoptic-PolarNet"],"state":"official (archive's flag): 11 ran","n_ran":11,"n_constructed":0,"n_ran_no_instrument_failure":9,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/supporting-clustering-with-contrastive","slug":"supporting-clustering-with-contrastive","title":"Supporting Clustering with Contrastive Learning","date":"2021-03-24","arxiv_id":"2103.12953","repositories_listed":2,"syntology":{"n":1,"n_ran":1,"n_constructed":1,"n_ran_checked":1,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":0,"phrase":"1 ran (of which 1 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified; the one sample that ran constructed an object rather than computing a result","sample_list":"/paper/supporting-clustering-with-contrastive#ran","syntology_url":"https://syntology.ai/paper/2103.12953","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2103.12953"}},"official":{"repos":["amazon-research/sccl"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":1,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/pairwise-adjusted-mutual-information","slug":"pairwise-adjusted-mutual-information","title":"Pairwise Adjusted Mutual Information","date":"2021-03-23","arxiv_id":"2103.12641","repositories_listed":2,"syntology":null},{"url":"/paper/completer-incomplete-multi-view-clustering","slug":"completer-incomplete-multi-view-clustering","title":"COMPLETER: Incomplete Multi-view Clustering via Contrastive Prediction","date":"2021-03-22","arxiv_id":null,"repositories_listed":2,"syntology":null},{"url":"/paper/statistically-robust-clustering-techniques","slug":"statistically-robust-clustering-techniques","title":"Statistically-Robust Clustering Techniques for Mapping Spatial Hotspots: A Survey","date":"2021-03-22","arxiv_id":"2103.12019","repositories_listed":2,"syntology":null},{"url":"/paper/self-supervised-classification-network","slug":"self-supervised-classification-network","title":"Self-Supervised Classification Network","date":"2021-03-19","arxiv_id":"2103.10994","repositories_listed":2,"syntology":null},{"url":"/paper/magface-a-universal-representation-for-face","slug":"magface-a-universal-representation-for-face","title":"MagFace: A Universal Representation for Face Recognition and Quality Assessment","date":"2021-03-11","arxiv_id":"2103.06627","repositories_listed":2,"syntology":{"n":2,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/magface-a-universal-representation-for-face#ran","syntology_url":"https://syntology.ai/paper/2103.06627","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2103.06627"}},"official":{"repos":["IrvingMeng/MagFace"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/multi-pretext-attention-network-for-few-shot","slug":"multi-pretext-attention-network-for-few-shot","title":"Multi-Pretext Attention Network for Few-shot Learning with Self-supervision","date":"2021-03-10","arxiv_id":"2103.05985","repositories_listed":2,"syntology":null},{"url":"/paper/towards-open-world-object-detection","slug":"towards-open-world-object-detection","title":"Towards Open World Object Detection","date":"2021-03-03","arxiv_id":"2103.02603","repositories_listed":2,"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/towards-open-world-object-detection#ran","syntology_url":"https://syntology.ai/paper/2103.02603","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2103.02603"}},"official":{"repos":["JosephKJ/OWOD"],"state":"official: not harvested","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":[]}}},{"url":"/paper/heterogeneity-for-the-win-one-shot-federated","slug":"heterogeneity-for-the-win-one-shot-federated","title":"Heterogeneity for the Win: One-Shot Federated Clustering","date":"2021-03-01","arxiv_id":"2103.00697","repositories_listed":2,"syntology":null},{"url":"/paper/learning-intra-batch-connections-for-deep","slug":"learning-intra-batch-connections-for-deep","title":"Learning Intra-Batch Connections for Deep Metric Learning","date":"2021-02-15","arxiv_id":"2102.07753","repositories_listed":2,"syntology":null},{"url":"/paper/hawks-evolving-challenging-benchmark-sets-for","slug":"hawks-evolving-challenging-benchmark-sets-for","title":"HAWKS: Evolving Challenging Benchmark Sets for Cluster Analysis","date":"2021-02-13","arxiv_id":"2102.06940","repositories_listed":2,"syntology":null},{"url":"/paper/unsupervised-semantic-segmentation-by","slug":"unsupervised-semantic-segmentation-by","title":"Unsupervised Semantic Segmentation by Contrasting Object Mask Proposals","date":"2021-02-11","arxiv_id":"2102.06191","repositories_listed":2,"syntology":{"n":2,"n_ran":2,"n_constructed":2,"n_ran_checked":2,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":2,"phrase":"2 ran (of which 2 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified; every one of the 2 samples that ran constructed an object rather than computing a result","sample_list":"/paper/unsupervised-semantic-segmentation-by#ran","syntology_url":"https://syntology.ai/paper/2102.06191","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2102.06191"}},"official":{"repos":["wvangansbeke/Unsupervised-Semantic-Segmentation"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":1,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["listed","official"]}}},{"url":"/paper/early-abandoning-and-pruning-for-elastic","slug":"early-abandoning-and-pruning-for-elastic","title":"Early Abandoning and Pruning for Elastic Distances including Dynamic Time Warping","date":"2021-02-10","arxiv_id":"2102.05221","repositories_listed":2,"syntology":null},{"url":"/paper/hypergraph-clustering-from-blockmodels-to","slug":"hypergraph-clustering-from-blockmodels-to","title":"Generative hypergraph clustering: from blockmodels to modularity","date":"2021-01-24","arxiv_id":"2101.09611","repositories_listed":2,"syntology":null},{"url":"/paper/multi-view-data-visualisation-via-manifold","slug":"multi-view-data-visualisation-via-manifold","title":"Multi-view Data Visualisation via Manifold Learning","date":"2021-01-17","arxiv_id":"2101.06763","repositories_listed":2,"syntology":null},{"url":"/paper/simple-spectral-graph-convolution","slug":"simple-spectral-graph-convolution","title":"Simple Spectral Graph Convolution","date":"2021-01-01","arxiv_id":null,"repositories_listed":2,"syntology":null},{"url":"/paper/generalized-categorisation-of-digital","slug":"generalized-categorisation-of-digital","title":"Generalized Categorisation of Digital Pathology Whole Image Slides using Unsupervised Learning","date":"2020-12-27","arxiv_id":"2012.13955","repositories_listed":2,"syntology":null},{"url":"/paper/logic-tensor-networks","slug":"logic-tensor-networks","title":"Logic Tensor Networks","date":"2020-12-25","arxiv_id":"2012.13635","repositories_listed":2,"syntology":null},{"url":"/paper/discovering-new-intents-with-deep-aligned","slug":"discovering-new-intents-with-deep-aligned","title":"Discovering New Intents with Deep Aligned Clustering","date":"2020-12-16","arxiv_id":"2012.08987","repositories_listed":2,"syntology":null},{"url":"/paper/interpretable-clustering-on-dynamic-graphs","slug":"interpretable-clustering-on-dynamic-graphs","title":"Interpretable Clustering on Dynamic Graphs with Recurrent Graph Neural Networks","date":"2020-12-16","arxiv_id":"2012.08740","repositories_listed":2,"syntology":null},{"url":"/paper/sequential-estimation-of-nonparametric","slug":"sequential-estimation-of-nonparametric","title":"Sequential estimation of Spearman rank correlation using Hermite series estimators","date":"2020-12-11","arxiv_id":"2012.06287","repositories_listed":2,"syntology":null},{"url":"/paper/clustering-multivariate-functional-data-using","slug":"clustering-multivariate-functional-data-using","title":"Clustering multivariate functional data using unsupervised binary trees","date":"2020-12-10","arxiv_id":"2012.05973","repositories_listed":2,"syntology":null},{"url":"/paper/extractive-opinion-summarization-in-quantized","slug":"extractive-opinion-summarization-in-quantized","title":"Extractive Opinion Summarization in Quantized Transformer Spaces","date":"2020-12-08","arxiv_id":"2012.04443","repositories_listed":2,"syntology":null},{"url":"/paper/towards-uncovering-the-intrinsic-data","slug":"towards-uncovering-the-intrinsic-data","title":"Towards Uncovering the Intrinsic Data Structures for Unsupervised Domain Adaptation using Structurally Regularized Deep Clustering","date":"2020-12-08","arxiv_id":"2012.04280","repositories_listed":2,"syntology":null},{"url":"/paper/maximum-entropy-subspace-clustering-network","slug":"maximum-entropy-subspace-clustering-network","title":"Maximum Entropy Subspace Clustering Network","date":"2020-12-06","arxiv_id":"2012.03176","repositories_listed":2,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":0,"n_instrument":2,"n_unverified":0,"n_honours":0,"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; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/maximum-entropy-subspace-clustering-network#ran","syntology_url":"https://syntology.ai/paper/2012.03176","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2012.03176"}},"official":{"repos":["ZhihaoPENG-CityU/MESC"],"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":["listed","unlocated"]}}},{"url":"/paper/selective-inference-for-hierarchical","slug":"selective-inference-for-hierarchical","title":"Selective Inference for Hierarchical Clustering","date":"2020-12-05","arxiv_id":"2012.02936","repositories_listed":2,"syntology":null},{"url":"/paper/quick-and-robust-feature-selection-the","slug":"quick-and-robust-feature-selection-the","title":"Quick and Robust Feature Selection: the Strength of Energy-efficient Sparse Training for Autoencoders","date":"2020-12-01","arxiv_id":"2012.00560","repositories_listed":2,"syntology":null},{"url":"/paper/fcm-rdpa-tsk-fuzzy-regression-model","slug":"fcm-rdpa-tsk-fuzzy-regression-model","title":"FCM-RDpA: TSK Fuzzy Regression Model Construction Using Fuzzy C-Means Clustering, Regularization, DropRule, and Powerball AdaBelief","date":"2020-11-30","arxiv_id":"2012.00060","repositories_listed":2,"syntology":null},{"url":"/paper/consistency-aware-and-inconsistency-aware","slug":"consistency-aware-and-inconsistency-aware","title":"Consistency-aware and Inconsistency-aware Graph-based Multi-view Clustering","date":"2020-11-25","arxiv_id":"2011.12532","repositories_listed":2,"syntology":null},{"url":"/paper/the-zero-resource-speech-benchmark-2021","slug":"the-zero-resource-speech-benchmark-2021","title":"The Zero Resource Speech Benchmark 2021: Metrics and baselines for unsupervised spoken language modeling","date":"2020-11-23","arxiv_id":"2011.11588","repositories_listed":2,"syntology":{"n":4,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":4,"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) · 3 unverified","sample_list":"/paper/the-zero-resource-speech-benchmark-2021#ran","syntology_url":"https://syntology.ai/paper/2011.11588","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2011.11588"}},"official":{"repos":["bootphon/zerospeech2021_baseline"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]}}},{"url":"/paper/attentive-clustering-processes","slug":"attentive-clustering-processes","title":"Amortized Probabilistic Detection of Communities in Graphs","date":"2020-10-29","arxiv_id":"2010.15727","repositories_listed":2,"syntology":{"n":9,"n_ran":8,"n_constructed":0,"n_ran_checked":6,"n_instrument":2,"n_unverified":1,"n_honours":0,"n_violates":1,"n_no_contract":5,"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, 1 violated, 5 with no contract checked; 2 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/attentive-clustering-processes#ran","syntology_url":"https://syntology.ai/paper/2010.15727","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2010.15727"}},"official":{"repos":["aripakman/amortized_community_detection","aripakman/attentive_clustering_processes"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/backdoor-attack-against-speaker-verification","slug":"backdoor-attack-against-speaker-verification","title":"Backdoor Attack against Speaker Verification","date":"2020-10-22","arxiv_id":"2010.11607","repositories_listed":2,"syntology":{"n":5,"n_ran":4,"n_constructed":0,"n_ran_checked":4,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":0,"phrase":"4 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; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/backdoor-attack-against-speaker-verification#ran","syntology_url":"https://syntology.ai/paper/2010.11607","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2010.11607"}},"official":{"repos":["zhaitongqing233/Backdoor-attack-against-speaker-verification"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/scalable-bottom-up-hierarchical-clustering","slug":"scalable-bottom-up-hierarchical-clustering","title":"Scalable Hierarchical Agglomerative Clustering","date":"2020-10-22","arxiv_id":"2010.11821","repositories_listed":2,"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/scalable-bottom-up-hierarchical-clustering#ran","syntology_url":"https://syntology.ai/paper/2010.11821","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2010.11821"}},"official":{"repos":["nmonath/scc"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]}}},{"url":"/paper/large-scale-product-graph-construction-for","slug":"large-scale-product-graph-construction-for","title":"Large Scale Product Graph Construction for Recommendation in E-commerce","date":"2020-10-12","arxiv_id":"2010.05525","repositories_listed":2,"syntology":null},{"url":"/paper/rode-learning-roles-to-decompose-multi-agent-1","slug":"rode-learning-roles-to-decompose-multi-agent-1","title":"RODE: Learning Roles to Decompose Multi-Agent Tasks","date":"2020-10-04","arxiv_id":"2010.01523","repositories_listed":2,"syntology":null},{"url":"/paper/semantics-through-time-semi-supervised","slug":"semantics-through-time-semi-supervised","title":"Semantics through Time: Semi-supervised Segmentation of Aerial Videos with Iterative Label Propagation","date":"2020-10-02","arxiv_id":"2010.01910","repositories_listed":2,"syntology":null},{"url":"/paper/sst-bert-at-semeval-2020-task-1-semantic","slug":"sst-bert-at-semeval-2020-task-1-semantic","title":"SST-BERT at SemEval-2020 Task 1: Semantic Shift Tracing by Clustering in BERT-based Embedding Spaces","date":"2020-10-02","arxiv_id":"2010.00857","repositories_listed":2,"syntology":null},{"url":"/paper/from-trees-to-continuous-embeddings-and-back","slug":"from-trees-to-continuous-embeddings-and-back","title":"From Trees to Continuous Embeddings and Back: Hyperbolic Hierarchical Clustering","date":"2020-10-01","arxiv_id":"2010.00402","repositories_listed":2,"syntology":null},{"url":"/paper/improving-few-shot-visual-classification-with-1","slug":"improving-few-shot-visual-classification-with-1","title":"Improving Few-Shot Visual Classification with Unlabelled Examples","date":"2020-09-28","arxiv_id":null,"repositories_listed":2,"syntology":null},{"url":"/paper/contrastive-clustering","slug":"contrastive-clustering","title":"Contrastive Clustering","date":"2020-09-21","arxiv_id":"2009.09687","repositories_listed":2,"syntology":{"n":7,"n_ran":7,"n_constructed":2,"n_ran_checked":7,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":7,"n_pointer_only":3,"phrase":"7 ran (of which 2 constructed an object rather than computing a result; 7 with no instrument failure: 0 honoured, 0 violated, 7 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/contrastive-clustering#ran","syntology_url":"https://syntology.ai/paper/2009.09687","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2009.09687"}},"official":{"repos":["Yunfan-Li/Contrastive-Clustering"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":0,"ran_from_kinds":["found_in_text","listed","official"]}}},{"url":"/paper/graph-infoclust-leveraging-cluster-level-node","slug":"graph-infoclust-leveraging-cluster-level-node","title":"Graph InfoClust: Leveraging cluster-level node information for unsupervised graph representation learning","date":"2020-09-15","arxiv_id":"2009.06946","repositories_listed":2,"syntology":{"n":3,"n_ran":2,"n_constructed":0,"n_ran_checked":1,"n_instrument":1,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":1,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 1 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/graph-infoclust-leveraging-cluster-level-node#ran","syntology_url":"https://syntology.ai/paper/2009.06946","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2009.06946"}},"official":null}},{"url":"/paper/consistency-and-regression-with-laplacian","slug":"consistency-and-regression-with-laplacian","title":"Overcoming the curse of dimensionality with Laplacian regularization in semi-supervised learning","date":"2020-09-09","arxiv_id":"2009.04324","repositories_listed":2,"syntology":null},{"url":"/paper/vernal-a-tool-for-mining-fuzzy-network-motifs","slug":"vernal-a-tool-for-mining-fuzzy-network-motifs","title":"VeRNAl: Mining RNA Structures for Fuzzy Base Pairing Network Motifs","date":"2020-09-01","arxiv_id":"2009.00664","repositories_listed":2,"syntology":null},{"url":"/paper/multi-view-graph-learning-by-joint-modeling","slug":"multi-view-graph-learning-by-joint-modeling","title":"Multi-view Graph Learning by Joint Modeling of Consistency and Inconsistency","date":"2020-08-24","arxiv_id":"2008.10208","repositories_listed":2,"syntology":null},{"url":"/paper/model-generalization-in-deep-learning","slug":"model-generalization-in-deep-learning","title":"Model Generalization in Deep Learning Applications for Land Cover Mapping","date":"2020-08-09","arxiv_id":"2008.10351","repositories_listed":2,"syntology":null},{"url":"/paper/scalable-and-flexible-clustering-of-grouped","slug":"scalable-and-flexible-clustering-of-grouped","title":"Scalable and Flexible Clustering of Grouped Data via Parallel and Distributed Sampling in Versatile Hierarchical Dirichlet Processes","date":"2020-08-04","arxiv_id":null,"repositories_listed":2,"syntology":null},{"url":"/paper/scalable-initialization-methods-for-large","slug":"scalable-initialization-methods-for-large","title":"Scalable Initialization Methods for Large-Scale Clustering","date":"2020-07-23","arxiv_id":"2007.11937","repositories_listed":2,"syntology":null},{"url":"/paper/unsupervised-learning-of-image-segmentation","slug":"unsupervised-learning-of-image-segmentation","title":"Unsupervised Learning of Image Segmentation Based on Differentiable Feature Clustering","date":"2020-07-20","arxiv_id":"2007.09990","repositories_listed":2,"syntology":null},{"url":"/paper/featmatch-feature-based-augmentation-for-semi","slug":"featmatch-feature-based-augmentation-for-semi","title":"FeatMatch: Feature-Based Augmentation for Semi-Supervised Learning","date":"2020-07-16","arxiv_id":"2007.08505","repositories_listed":2,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":0,"n_honours":1,"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; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/featmatch-feature-based-augmentation-for-semi#ran","syntology_url":"https://syntology.ai/paper/2007.08505","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2007.08505"}},"official":null}},{"url":"/paper/a-new-basis-for-sparse-pca","slug":"a-new-basis-for-sparse-pca","title":"A New Basis for Sparse Principal Component Analysis","date":"2020-07-01","arxiv_id":"2007.00596","repositories_listed":2,"syntology":null},{"url":"/paper/laplacian-regularized-few-shot-learning","slug":"laplacian-regularized-few-shot-learning","title":"Laplacian Regularized Few-Shot Learning","date":"2020-06-29","arxiv_id":null,"repositories_listed":2,"syntology":null},{"url":"/paper/laplacian-regularized-few-shot-learning-1","slug":"laplacian-regularized-few-shot-learning-1","title":"Laplacian Regularized Few-Shot Learning","date":"2020-06-28","arxiv_id":"2006.15486","repositories_listed":2,"syntology":{"n":12,"n_ran":5,"n_constructed":0,"n_ran_checked":1,"n_instrument":4,"n_unverified":7,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":7,"phrase":"5 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; 4 where Syntology's instrument failed) · 7 unverified","sample_list":"/paper/laplacian-regularized-few-shot-learning-1#ran","syntology_url":"https://syntology.ai/paper/2006.15486","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2006.15486"}},"official":{"repos":["imtiazziko/LaplacianShot"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":4,"ran_from_kinds":["listed","official"]}}},{"url":"/paper/affinity-fusion-graph-based-framework-for","slug":"affinity-fusion-graph-based-framework-for","title":"Affinity Fusion Graph-based Framework for Natural Image Segmentation","date":"2020-06-24","arxiv_id":"2006.13542","repositories_listed":2,"syntology":null},{"url":"/paper/graph-neural-network-based-coarse-grained","slug":"graph-neural-network-based-coarse-grained","title":"Graph Neural Network Based Coarse-Grained Mapping Prediction","date":"2020-06-24","arxiv_id":"2007.04921","repositories_listed":2,"syntology":{"n":8,"n_ran":6,"n_constructed":0,"n_ran_checked":6,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":6,"n_pointer_only":0,"phrase":"6 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; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/graph-neural-network-based-coarse-grained#ran","syntology_url":"https://syntology.ai/paper/2007.04921","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2007.04921"}},"official":{"repos":["rochesterxugroup/DSGPM","rochesterxugroup/HAM_dataset"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/diverse-image-generation-via-self-conditioned-1","slug":"diverse-image-generation-via-self-conditioned-1","title":"Diverse Image Generation via Self-Conditioned GANs","date":"2020-06-18","arxiv_id":"2006.10728","repositories_listed":2,"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":0,"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/diverse-image-generation-via-self-conditioned-1#ran","syntology_url":"https://syntology.ai/paper/2006.10728","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2006.10728"}},"official":{"repos":["stevliu/self-conditioned-gan"],"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/erdos-goes-neural-an-unsupervised-learning","slug":"erdos-goes-neural-an-unsupervised-learning","title":"Erdos Goes Neural: an Unsupervised Learning Framework for Combinatorial Optimization on Graphs","date":"2020-06-18","arxiv_id":"2006.10643","repositories_listed":2,"syntology":null},{"url":"/paper/fair-k-means-clustering","slug":"fair-k-means-clustering","title":"Socially Fair k-Means Clustering","date":"2020-06-17","arxiv_id":"2006.10085","repositories_listed":2,"syntology":null},{"url":"/paper/improving-few-shot-visual-classification-with","slug":"improving-few-shot-visual-classification-with","title":"Enhancing Few-Shot Image Classification with Unlabelled Examples","date":"2020-06-17","arxiv_id":"2006.12245","repositories_listed":2,"syntology":{"n":9,"n_ran":7,"n_constructed":0,"n_ran_checked":4,"n_instrument":3,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":2,"phrase":"7 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; 3 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/improving-few-shot-visual-classification-with#ran","syntology_url":"https://syntology.ai/paper/2006.12245","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2006.12245"}},"official":{"repos":["plai-group/simple-cnaps"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":2,"ran_from_kinds":["listed","official"]}}},{"url":"/paper/dissimilarity-mixture-autoencoder-for-deep","slug":"dissimilarity-mixture-autoencoder-for-deep","title":"Dissimilarity Mixture Autoencoder for Deep Clustering","date":"2020-06-15","arxiv_id":"2006.08177","repositories_listed":2,"syntology":null},{"url":"/paper/learning-diverse-and-discriminative","slug":"learning-diverse-and-discriminative","title":"Learning Diverse and Discriminative Representations via the Principle of Maximal Coding Rate Reduction","date":"2020-06-15","arxiv_id":"2006.08558","repositories_listed":2,"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":2,"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/learning-diverse-and-discriminative#ran","syntology_url":"https://syntology.ai/paper/2006.08558","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2006.08558"}},"official":{"repos":["ryanchankh/mcr2"],"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/information-extraction-of-clinical-trial","slug":"information-extraction-of-clinical-trial","title":"Information Extraction of Clinical Trial Eligibility Criteria","date":"2020-06-12","arxiv_id":"2006.07296","repositories_listed":2,"syntology":null},{"url":"/paper/bandit-pam-almost-linear-time-k-medoids","slug":"bandit-pam-almost-linear-time-k-medoids","title":"BanditPAM: Almost Linear Time $k$-Medoids Clustering via Multi-Armed Bandits","date":"2020-06-11","arxiv_id":"2006.06856","repositories_listed":2,"syntology":{"n":5,"n_ran":0,"n_constructed":0,"n_ran_checked":0,"n_instrument":0,"n_unverified":5,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":5,"phrase":"0 ran · 5 unverified","sample_list":"/paper/bandit-pam-almost-linear-time-k-medoids#ran","syntology_url":"https://syntology.ai/paper/2006.06856","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2006.06856"}},"official":{"repos":["motiwari/BanditPAM-python"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":5,"ran_from_kinds":[]}}},{"url":"/paper/slic-uav-a-method-for-monitoring-recovery-in","slug":"slic-uav-a-method-for-monitoring-recovery-in","title":"SLIC-UAV: A Method for monitoring recovery in tropical restoration projects through identification of signature species using UAVs","date":"2020-06-11","arxiv_id":"2006.06624","repositories_listed":2,"syntology":null},{"url":"/paper/embed2detect-temporally-clustered-embedded","slug":"embed2detect-temporally-clustered-embedded","title":"Embed2Detect: Temporally Clustered Embedded Words for Event Detection in Social Media","date":"2020-06-10","arxiv_id":"2006.05908","repositories_listed":2,"syntology":null},{"url":"/paper/declutr-deep-contrastive-learning-for","slug":"declutr-deep-contrastive-learning-for","title":"DeCLUTR: Deep Contrastive Learning for Unsupervised Textual Representations","date":"2020-06-05","arxiv_id":"2006.03659","repositories_listed":2,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/declutr-deep-contrastive-learning-for#ran","syntology_url":"https://syntology.ai/paper/2006.03659","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2006.03659"}},"official":{"repos":["JohnGiorgi/DeCLUTR"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/exkmc-expanding-explainable-k-means","slug":"exkmc-expanding-explainable-k-means","title":"ExKMC: Expanding Explainable $k$-Means Clustering","date":"2020-06-03","arxiv_id":"2006.02399","repositories_listed":2,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":1,"n_no_contract":0,"n_pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 1 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/exkmc-expanding-explainable-k-means#ran","syntology_url":"https://syntology.ai/paper/2006.02399","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2006.02399"}},"official":{"repos":["navefr/ExKMC"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/what-are-people-asking-about-covid-19-a","slug":"what-are-people-asking-about-covid-19-a","title":"What Are People Asking About COVID-19? A Question Classification Dataset","date":"2020-05-26","arxiv_id":"2005.12522","repositories_listed":2,"syntology":null},{"url":"/paper/learning-to-classify-images-without-labels","slug":"learning-to-classify-images-without-labels","title":"SCAN: Learning to Classify Images without Labels","date":"2020-05-25","arxiv_id":"2005.12320","repositories_listed":2,"syntology":null},{"url":"/paper/p-norm-flow-diffusion-for-local-graph","slug":"p-norm-flow-diffusion-for-local-graph","title":"$p$-Norm Flow Diffusion for Local Graph Clustering","date":"2020-05-20","arxiv_id":"2005.09810","repositories_listed":2,"syntology":{"n":12,"n_ran":5,"n_constructed":0,"n_ran_checked":5,"n_instrument":0,"n_unverified":7,"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) · 7 unverified","sample_list":"/paper/p-norm-flow-diffusion-for-local-graph#ran","syntology_url":"https://syntology.ai/paper/2005.09810","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2005.09810"}},"official":{"repos":["s-h-yang/pNormFlowDiffusion"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]}}},{"url":"/paper/simple-scalable-and-stable-variational-deep","slug":"simple-scalable-and-stable-variational-deep","title":"Simple, Scalable, and Stable Variational Deep Clustering","date":"2020-05-16","arxiv_id":"2005.08047","repositories_listed":2,"syntology":{"n":4,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":0,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/simple-scalable-and-stable-variational-deep#ran","syntology_url":"https://syntology.ai/paper/2005.08047","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2005.08047"}},"official":{"repos":["king/s3vdc"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/prototypical-contrastive-learning-of","slug":"prototypical-contrastive-learning-of","title":"Prototypical Contrastive Learning of Unsupervised Representations","date":"2020-05-11","arxiv_id":"2005.04966","repositories_listed":2,"syntology":{"n":5,"n_ran":2,"n_constructed":0,"n_ran_checked":1,"n_instrument":1,"n_unverified":3,"n_honours":0,"n_violates":1,"n_no_contract":0,"n_pointer_only":1,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 1 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/prototypical-contrastive-learning-of#ran","syntology_url":"https://syntology.ai/paper/2005.04966","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2005.04966"}},"official":{"repos":["salesforce/PCL"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":2,"ran_from_kinds":["official"]}}}],"record_sha256":"5920386be9d20b042233feb3aebb8884224551bc4b93f1c6dbf5b5b7670c8b0a","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}