{"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/47","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":47,"pages_in_order":108,"rows_per_page":100,"rows":[4601,4700],"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/46","next":"/task/clustering/papers/48","papers":[{"url":null,"slug":"stochastic-parallelizable-eigengap-dilation","title":"Stochastic Parallelizable Eigengap Dilation for Large Graph Clustering","date":"2022-07-29","arxiv_id":"2207.14589","repositories_listed":0,"syntology":null},{"url":null,"slug":"expanding-the-class-of-global-objective","title":"Expanding the class of global objective functions for dissimilarity-based hierarchical clustering","date":"2022-07-28","arxiv_id":"2207.14375","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-clustering-with-features-from-self","title":"Deep Clustering with Features from Self-Supervised Pretraining","date":"2022-07-27","arxiv_id":"2207.13364","repositories_listed":0,"syntology":null},{"url":null,"slug":"clustering-object-centric-event-logs","title":"Clustering Object-Centric Event Logs","date":"2022-07-26","arxiv_id":"2207.12764","repositories_listed":0,"syntology":null},{"url":null,"slug":"orthogonalization-of-data-via-gromov","title":"Orthogonalization of data via Gromov-Wasserstein type feedback for clustering and visualization","date":"2022-07-25","arxiv_id":"2207.12279","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-deep-dive-into-deep-cluster","title":"A Deep Dive into Deep Cluster","date":"2022-07-24","arxiv_id":"2207.11839","repositories_listed":0,"syntology":null},{"url":null,"slug":"exploration-in-linear-bandits-with-rich","title":"Exploration in Linear Bandits with Rich Action Sets and its Implications for Inference","date":"2022-07-23","arxiv_id":"2207.11597","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-dynamical-systems-algorithm-for-clustering","title":"A Dynamical Systems Algorithm for Clustering in Hyperspectral Imagery","date":"2022-07-21","arxiv_id":"2207.10625","repositories_listed":0,"syntology":null},{"url":null,"slug":"an-evolutionary-game-based-secure-clustering","title":"An Evolutionary Game based Secure Clustering Protocol with Fuzzy Trust Evaluation and Outlier Detection for Wireless Sensor Networks","date":"2022-07-21","arxiv_id":"2207.10282","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-secure-clustering-protocol-with-fuzzy-trust","title":"A Secure Clustering Protocol with Fuzzy Trust Evaluation and Outlier Detection for Industrial Wireless Sensor Networks","date":"2022-07-20","arxiv_id":"2207.09936","repositories_listed":0,"syntology":null},{"url":null,"slug":"cancer-subtyping-by-improved-transcriptomic","title":"Cancer Subtyping by Improved Transcriptomic Features Using Vector Quantized Variational Autoencoder","date":"2022-07-20","arxiv_id":"2207.09783","repositories_listed":0,"syntology":null},{"url":null,"slug":"revisiting-data-augmentation-for-subspace","title":"Revisiting data augmentation for subspace clustering","date":"2022-07-20","arxiv_id":"2207.09728","repositories_listed":0,"syntology":null},{"url":null,"slug":"ecdt-event-clustering-for-simultaneous","title":"eCDT: Event Clustering for Simultaneous Feature Detection and Tracking-","date":"2022-07-19","arxiv_id":"2207.09108","repositories_listed":0,"syntology":null},{"url":null,"slug":"over-the-air-federated-edge-learning-with","title":"Over-the-Air Federated Edge Learning with Hierarchical Clustering","date":"2022-07-19","arxiv_id":"2207.09232","repositories_listed":0,"syntology":null},{"url":null,"slug":"semi-supervised-predictive-clustering-trees","title":"Semi-supervised Predictive Clustering Trees for (Hierarchical) Multi-label Classification","date":"2022-07-19","arxiv_id":"2207.09237","repositories_listed":0,"syntology":null},{"url":null,"slug":"efficient-view-clustering-and-selection-for","title":"Efficient View Clustering and Selection for City-Scale 3D Reconstruction","date":"2022-07-18","arxiv_id":"2207.08434","repositories_listed":0,"syntology":null},{"url":null,"slug":"open-world-semantic-segmentation-via","title":"Open-world Semantic Segmentation via Contrasting and Clustering Vision-Language Embedding","date":"2022-07-18","arxiv_id":"2207.08455","repositories_listed":0,"syntology":null},{"url":null,"slug":"distributed-finite-time-k-means-clustering","title":"Distributed Finite Time k-means Clustering with Quantized Communucation and Transmission Stopping","date":"2022-07-17","arxiv_id":"2207.08232","repositories_listed":0,"syntology":null},{"url":null,"slug":"lapseg3d-weakly-supervised-semantic","title":"LapSeg3D: Weakly Supervised Semantic Segmentation of Point Clouds Representing Laparoscopic Scenes","date":"2022-07-15","arxiv_id":"2207.07418","repositories_listed":0,"syntology":null},{"url":null,"slug":"seeking-the-truth-beyond-the-data-an","title":"Seeking the Truth Beyond the Data. An Unsupervised Machine Learning Approach","date":"2022-07-14","arxiv_id":"2207.06949","repositories_listed":0,"syntology":null},{"url":null,"slug":"multiple-kernel-clustering-with-dual-noise","title":"Multiple Kernel Clustering with Dual Noise Minimization","date":"2022-07-13","arxiv_id":"2207.06041","repositories_listed":0,"syntology":null},{"url":null,"slug":"online-target-speaker-voice-activity","title":"Online Target Speaker Voice Activity Detection for Speaker Diarization","date":"2022-07-13","arxiv_id":"2207.05920","repositories_listed":0,"syntology":null},{"url":null,"slug":"optimal-clustering-with-noisy-queries-via","title":"Optimal Clustering with Noisy Queries via Multi-Armed Bandit","date":"2022-07-12","arxiv_id":"2207.05376","repositories_listed":0,"syntology":null},{"url":null,"slug":"cluster-based-control-of-transition","title":"Cluster-Based Control of Transition-Independent MDPs","date":"2022-07-11","arxiv_id":"2207.05224","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-squared-euclidean-approximation-to-the","title":"Deep Squared Euclidean Approximation to the Levenshtein Distance for DNA Storage","date":"2022-07-11","arxiv_id":"2207.04684","repositories_listed":0,"syntology":null},{"url":null,"slug":"optimal-clustering-by-lloyd-algorithm-for-low","title":"Optimal Clustering by Lloyd Algorithm for Low-Rank Mixture Model","date":"2022-07-11","arxiv_id":"2207.04600","repositories_listed":0,"syntology":null},{"url":null,"slug":"fuzzy-clustering-by-hyperbolic-smoothing","title":"Fuzzy Clustering by Hyperbolic Smoothing","date":"2022-07-09","arxiv_id":"2207.04261","repositories_listed":0,"syntology":null},{"url":null,"slug":"few-example-clustering-via-contrastive","title":"Few-Example Clustering via Contrastive Learning","date":"2022-07-08","arxiv_id":"2207.04050","repositories_listed":0,"syntology":null},{"url":null,"slug":"adaptive-personlization-in-federated-learning","title":"Adaptive Personlization in Federated Learning for Highly Non-i.i.d. Data","date":"2022-07-07","arxiv_id":"2207.03448","repositories_listed":0,"syntology":null},{"url":null,"slug":"clustering-of-excursion-sets-in-financial","title":"Clustering of Excursion Sets in Financial Market","date":"2022-07-07","arxiv_id":"2207.03221","repositories_listed":0,"syntology":null},{"url":null,"slug":"careful-seeding-for-the-k-medoids-algorithm","title":"Careful Seeding for k-Medois Clustering with Incremental k-Means++ Initialization","date":"2022-07-06","arxiv_id":"2207.02404","repositories_listed":0,"syntology":null},{"url":null,"slug":"eept-early-discovery-of-emerging-entities-in","title":"Early Discovery of Emerging Entities in Persian Twitter with Semantic Similarity","date":"2022-07-06","arxiv_id":"2207.02434","repositories_listed":0,"syntology":null},{"url":null,"slug":"ensemble-feature-selection-with-clustering","title":"Ensemble feature selection with clustering for analysis of high-dimensional, correlated clinical data in the search for Alzheimer's disease biomarkers","date":"2022-07-06","arxiv_id":"2207.02380","repositories_listed":0,"syntology":null},{"url":null,"slug":"clustered-saliency-prediction","title":"Clustered Saliency Prediction","date":"2022-07-05","arxiv_id":"2207.02205","repositories_listed":0,"syntology":null},{"url":null,"slug":"an-improved-probability-propagation-algorithm","title":"An Improved Probability Propagation Algorithm for Density Peak Clustering Based on Natural Nearest Neighborhood","date":"2022-07-04","arxiv_id":"2207.01178","repositories_listed":0,"syntology":null},{"url":null,"slug":"distantly-supervised-aspect-clustering-and","title":"Distantly Supervised Aspect Clustering And Naming For E-Commerce Reviews","date":"2022-07-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"e-clip-large-scale-vision-language","title":"e-CLIP: Large-Scale Vision-Language Representation Learning in E-commerce","date":"2022-07-01","arxiv_id":"2207.00208","repositories_listed":0,"syntology":null},{"url":"/paper/intent-detection-and-discovery-from-user-logs","slug":"intent-detection-and-discovery-from-user-logs","title":"Intent Detection and Discovery from User Logs via Deep Semi-Supervised Contrastive Clustering","date":"2022-07-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"k-arma-models-for-clustering-time-series-data","title":"K-ARMA Models for Clustering Time Series Data","date":"2022-06-30","arxiv_id":"2207.00039","repositories_listed":0,"syntology":null},{"url":null,"slug":"business-cycle-synchronization-in-the-eu-a","title":"Business Cycle Synchronization in the EU: A Regional-Sectoral Look through Soft-Clustering and Wavelet Decomposition","date":"2022-06-28","arxiv_id":"2206.14128","repositories_listed":0,"syntology":null},{"url":null,"slug":"gaussian-latent-dirichlet-allocation-for","title":"Gaussian Latent Dirichlet Allocation for Discrete Human State Discovery","date":"2022-06-28","arxiv_id":"2206.14233","repositories_listed":0,"syntology":null},{"url":null,"slug":"interrelate-training-and-searching-a-unified","title":"Interrelate Training and Searching: A Unified Online Clustering Framework for Speaker Diarization","date":"2022-06-28","arxiv_id":"2206.13760","repositories_listed":0,"syntology":null},{"url":null,"slug":"measuring-and-clustering-network-attackers","title":"Measuring and Clustering Network Attackers using Medium-Interaction Honeypots","date":"2022-06-27","arxiv_id":"2206.13614","repositories_listed":0,"syntology":null},{"url":null,"slug":"k-median-clustering-via-metric-embedding-1","title":"$k$-Median Clustering via Metric Embedding: Towards Better Initialization with Differential Privacy","date":"2022-06-26","arxiv_id":"2206.12895","repositories_listed":0,"syntology":null},{"url":null,"slug":"cascading-failures-in-smart-grids-under","title":"Cascading Failures in Smart Grids under Random, Targeted and Adaptive Attacks","date":"2022-06-25","arxiv_id":"2206.12735","repositories_listed":0,"syntology":null},{"url":null,"slug":"inverted-semantic-index-for-image-retrieval","title":"Inverted Semantic-Index for Image Retrieval","date":"2022-06-25","arxiv_id":"2206.12623","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-embedded-clustering-algorithm-for","title":"Deep embedded clustering algorithm for clustering PACS repositories","date":"2022-06-24","arxiv_id":"2206.12417","repositories_listed":0,"syntology":null},{"url":null,"slug":"constant-factor-approximation-algorithms-for-1","title":"Constant-Factor Approximation Algorithms for Socially Fair $k$-Clustering","date":"2022-06-22","arxiv_id":"2206.11210","repositories_listed":0,"syntology":null},{"url":null,"slug":"noisy-ell-0-sparse-subspace-clustering-on-1","title":"Noisy $\\ell^{0}$-Sparse Subspace Clustering on Dimensionality Reduced Data","date":"2022-06-22","arxiv_id":"2206.11079","repositories_listed":0,"syntology":null},{"url":null,"slug":"supervision-guided-codebooks-for-masked","title":"Supervision-Guided Codebooks for Masked Prediction in Speech Pre-training","date":"2022-06-21","arxiv_id":"2206.10125","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-distributional-approach-for-soft-clustering","title":"A Distributional Approach for Soft Clustering Comparison and Evaluation","date":"2022-06-20","arxiv_id":"2206.09827","repositories_listed":0,"syntology":null},{"url":null,"slug":"flow-based-clustering-and-spectral-clustering","title":"flow-based clustering and spectral clustering: a comparison","date":"2022-06-20","arxiv_id":"2206.10019","repositories_listed":0,"syntology":null},{"url":null,"slug":"variational-quantum-and-quantum-inspired","title":"Variational Quantum and Quantum-Inspired Clustering","date":"2022-06-20","arxiv_id":"2206.09893","repositories_listed":0,"syntology":null},{"url":null,"slug":"an-analysis-of-the-admissibility-of-the","title":"An Analysis of the Admissibility of the Objective Functions Applied in Evolutionary Multi-objective Clustering","date":"2022-06-19","arxiv_id":"2206.09483","repositories_listed":0,"syntology":null},{"url":null,"slug":"attention-based-dynamic-subspace-learners-for","title":"Attention-based Dynamic Subspace Learners for Medical Image Analysis","date":"2022-06-18","arxiv_id":"2206.09068","repositories_listed":0,"syntology":null},{"url":null,"slug":"federated-learning-with-incremental","title":"Federated learning with incremental clustering for heterogeneous data","date":"2022-06-17","arxiv_id":"2206.08752","repositories_listed":0,"syntology":null},{"url":null,"slug":"asymptotic-soft-cluster-pruning-for-deep","title":"Asymptotic Soft Cluster Pruning for Deep Neural Networks","date":"2022-06-16","arxiv_id":"2206.08186","repositories_listed":0,"syntology":null},{"url":null,"slug":"performance-analysis-of-coreset-selection-for","title":"Performance analysis of coreset selection for quantum implementation of K-Means clustering algorithm","date":"2022-06-16","arxiv_id":"2206.07852","repositories_listed":0,"syntology":null},{"url":null,"slug":"machine-learning-is-abduction-inference","title":"Theory of Machine Learning with Limited Data","date":"2022-06-15","arxiv_id":"2206.07586","repositories_listed":0,"syntology":null},{"url":null,"slug":"sparse-subspace-clustering-in-diverse","title":"Sparse Subspace Clustering in Diverse Multiplex Network Model","date":"2022-06-15","arxiv_id":"2206.07602","repositories_listed":0,"syntology":null},{"url":null,"slug":"sublinear-algorithms-for-hierarchical","title":"Sublinear Algorithms for Hierarchical Clustering","date":"2022-06-15","arxiv_id":"2206.07633","repositories_listed":0,"syntology":null},{"url":null,"slug":"microfounding-garch-models-and-beyond-a-kyle","title":"Microfounding GARCH Models and Beyond: A Kyle-inspired Model with Adaptive Agents","date":"2022-06-14","arxiv_id":"2206.06764","repositories_listed":0,"syntology":null},{"url":null,"slug":"plug-and-play-pseudo-label-correction-network","title":"Plug-and-Play Pseudo Label Correction Network for Unsupervised Person Re-identification","date":"2022-06-14","arxiv_id":"2206.06607","repositories_listed":0,"syntology":null},{"url":null,"slug":"clustering-coefficients-as-measures-of-the","title":"Clustering coefficients as measures of the complex interactions in a directed weighted multilayer network","date":"2022-06-13","arxiv_id":"2206.06309","repositories_listed":0,"syntology":null},{"url":null,"slug":"compressive-clustering-with-an-optical","title":"Compressive Clustering with an Optical Processing Unit","date":"2022-06-13","arxiv_id":"2206.05928","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-new-distance-measurement-and-its","title":"A new distance measurement and its application in K-Means Algorithm","date":"2022-06-10","arxiv_id":"2206.05215","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-multi-view-semi-supervised-clustering","title":"Deep Multi-View Semi-Supervised Clustering with Sample Pairwise Constraints","date":"2022-06-10","arxiv_id":"2206.04949","repositories_listed":0,"syntology":null},{"url":null,"slug":"federated-momentum-contrastive-clustering","title":"Federated Momentum Contrastive Clustering","date":"2022-06-10","arxiv_id":"2206.05093","repositories_listed":0,"syntology":null},{"url":null,"slug":"hierarchical-mixtures-of-gaussians-for","title":"Hierarchical mixtures of Gaussians for combined dimensionality reduction and clustering","date":"2022-06-10","arxiv_id":"2206.04841","repositories_listed":0,"syntology":null},{"url":null,"slug":"self-supervised-deep-subspace-clustering-with","title":"Self-Supervised Deep Subspace Clustering with Entropy-norm","date":"2022-06-10","arxiv_id":"2206.04958","repositories_listed":0,"syntology":null},{"url":null,"slug":"analyzing-folktales-of-different-regions","title":"Analyzing Folktales of Different Regions Using Topic Modeling and Clustering","date":"2022-06-09","arxiv_id":"2206.04221","repositories_listed":0,"syntology":null},{"url":null,"slug":"applying-separative-non-negative-matrix","title":"Applying separative non-negative matrix factorization to extra-financial data","date":"2022-06-09","arxiv_id":"2206.04350","repositories_listed":0,"syntology":null},{"url":null,"slug":"clustering-with-queries-under-semi-random","title":"Clustering with Queries under Semi-Random Noise","date":"2022-06-09","arxiv_id":"2206.04583","repositories_listed":0,"syntology":null},{"url":null,"slug":"exploring-predictive-states-via-cantor","title":"Exploring Predictive States via Cantor Embeddings and Wasserstein Distance","date":"2022-06-09","arxiv_id":"2206.04198","repositories_listed":0,"syntology":null},{"url":null,"slug":"unsupervised-deep-discriminant-analysis-based","title":"Unsupervised Deep Discriminant Analysis Based Clustering","date":"2022-06-09","arxiv_id":"2206.04686","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-generative-recommender-system-with-gmm","title":"A generative recommender system with GMM prior for cancer drug generation and sensitivity prediction","date":"2022-06-07","arxiv_id":"2206.03555","repositories_listed":0,"syntology":null},{"url":null,"slug":"online-deep-clustering-with-video-track","title":"Online Deep Clustering with Video Track Consistency","date":"2022-06-07","arxiv_id":"2206.03086","repositories_listed":0,"syntology":null},{"url":null,"slug":"global-mixup-eliminating-ambiguity-with-1","title":"Global Mixup: Eliminating Ambiguity with Clustering","date":"2022-06-06","arxiv_id":"2206.02734","repositories_listed":0,"syntology":null},{"url":"/paper/restructuring-graph-for-higher-homophily-via","slug":"restructuring-graph-for-higher-homophily-via","title":"Restructuring Graph for Higher Homophily via Adaptive Spectral Clustering","date":"2022-06-06","arxiv_id":"2206.02386","repositories_listed":0,"syntology":null},{"url":null,"slug":"stacked-unsupervised-learning-with-a-network","title":"Stacked unsupervised learning with a network architecture found by supervised meta-learning","date":"2022-06-06","arxiv_id":"2206.02716","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-responsible-ai-for-financial","title":"Towards Responsible AI for Financial Transactions","date":"2022-06-06","arxiv_id":"2206.02419","repositories_listed":0,"syntology":null},{"url":null,"slug":"using-connectome-features-to-constrain-echo","title":"Using Connectome Features to Constrain Echo State Networks","date":"2022-06-05","arxiv_id":"2206.02094","repositories_listed":0,"syntology":null},{"url":null,"slug":"occlusion-resistant-instance-segmentation-of","title":"Occlusion-Resistant Instance Segmentation of Piglets in Farrowing Pens Using Center Clustering Network","date":"2022-06-04","arxiv_id":"2206.01942","repositories_listed":0,"syntology":null},{"url":null,"slug":"contextual-unsupervised-clustering-of-signs","title":"Contextual Unsupervised Clustering of Signs for Ancient Writing Systems","date":"2022-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"deepclue-enhanced-image-clustering-via-multi","title":"DeepCluE: Enhanced Image Clustering via Multi-layer Ensembles in Deep Neural Networks","date":"2022-06-01","arxiv_id":"2206.00359","repositories_listed":0,"syntology":null},{"url":null,"slug":"financial-narrative-summarisation-using-a","title":"Financial Narrative Summarisation Using a Hybrid TF-IDF and Clustering Summariser: AO-Lancs System at FNS 2022","date":"2022-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":"/paper/maked-multi-lingual-automatic-keyword","slug":"maked-multi-lingual-automatic-keyword","title":"MAKED: Multi-lingual Automatic Keyword Extraction Dataset","date":"2022-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"my-case-for-an-adposition-lexical-polysemy-of","title":"My Case, For an Adposition: Lexical Polysemy of Adpositions and Case Markers in Finnish and Latin","date":"2022-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"niksss-at-quran-qa-2022-a-heavily-optimized","title":"niksss at Qur’an QA 2022: A Heavily Optimized BERT Based Model for Answering Questions from the Holy Qu’ran","date":"2022-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-speaker-verification-for-crowdsourced","title":"Towards Speaker Verification for Crowdsourced Speech Collections","date":"2022-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"improving-ads-profitability-using-traffic","title":"Improving Ads-Profitability Using Traffic-Fingerprints","date":"2022-05-31","arxiv_id":"2206.02630","repositories_listed":0,"syntology":null},{"url":null,"slug":"secure-federated-clustering","title":"Secure Federated Clustering","date":"2022-05-31","arxiv_id":"2205.15564","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-review-and-evaluation-of-elastic-distance","title":"A Review and Evaluation of Elastic Distance Functions for Time Series Clustering","date":"2022-05-30","arxiv_id":"2205.15181","repositories_listed":0,"syntology":null},{"url":null,"slug":"leave-one-out-singular-subspace-perturbation","title":"Leave-one-out Singular Subspace Perturbation Analysis for Spectral Clustering","date":"2022-05-30","arxiv_id":"2205.14855","repositories_listed":0,"syntology":null},{"url":null,"slug":"an-adaptive-granularity-clustering-method","title":"GBC: An Efficient and Adaptive Clustering Algorithm Based on Granular-Ball","date":"2022-05-29","arxiv_id":"2205.14592","repositories_listed":0,"syntology":null},{"url":null,"slug":"fair-labeled-clustering","title":"Fair Labeled Clustering","date":"2022-05-28","arxiv_id":"2205.14358","repositories_listed":0,"syntology":null},{"url":null,"slug":"data-driven-clustering-and-bernoulli-merging","title":"Data-driven clustering and Bernoulli merging for the Poisson multi-Bernoulli mixture filter","date":"2022-05-27","arxiv_id":"2205.14021","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-face-recognition-with-clustering-based","title":"Deep face recognition with clustering based domain adaptation","date":"2022-05-27","arxiv_id":"2205.13937","repositories_listed":0,"syntology":null},{"url":null,"slug":"efficient-forecasting-of-large-scale","title":"Efficient Forecasting of Large Scale Hierarchical Time Series via Multilevel Clustering","date":"2022-05-27","arxiv_id":"2205.14104","repositories_listed":0,"syntology":null},{"url":null,"slug":"exploring-techniques-for-the-analysis-of","title":"Exploring Techniques for the Analysis of Spontaneous Asynchronicity in MPI-Parallel Applications","date":"2022-05-27","arxiv_id":"2205.13963","repositories_listed":0,"syntology":null}],"record_sha256":"4c4c381b5ea61383f728dbf2ee8cdea94fb8996ee99cb956be3ddc47b4538ec1","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}