{"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/88","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":88,"pages_in_order":108,"rows_per_page":100,"rows":[8701,8800],"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/87","next":"/task/clustering/papers/89","papers":[{"url":null,"slug":"zoom-in-net-deep-mining-lesions-for-diabetic","title":"Zoom-in-Net: Deep Mining Lesions for Diabetic Retinopathy Detection","date":"2017-06-14","arxiv_id":"1706.04372","repositories_listed":0,"syntology":null},{"url":null,"slug":"von-mises-fisher-mixture-model-based-deep","title":"von Mises-Fisher Mixture Model-based Deep learning: Application to Face Verification","date":"2017-06-13","arxiv_id":"1706.04264","repositories_listed":0,"syntology":null},{"url":null,"slug":"clustering-small-samples-with-quality","title":"Clustering Small Samples with Quality Guarantees: Adaptivity with One2all pps","date":"2017-06-12","arxiv_id":"1706.03607","repositories_listed":0,"syntology":null},{"url":null,"slug":"fast-approximate-spectral-clustering-for","title":"Fast Approximate Spectral Clustering for Dynamic Networks","date":"2017-06-12","arxiv_id":"1706.03591","repositories_listed":0,"syntology":null},{"url":null,"slug":"subspace-clustering-via-optimal-direction","title":"Subspace Clustering via Optimal Direction Search","date":"2017-06-12","arxiv_id":"1706.03860","repositories_listed":0,"syntology":null},{"url":null,"slug":"toeplitz-inverse-covariance-based-clustering","title":"Toeplitz Inverse Covariance-Based Clustering of Multivariate Time Series Data","date":"2017-06-10","arxiv_id":"1706.03161","repositories_listed":0,"syntology":null},{"url":null,"slug":"scalable-kernel-k-means-clustering-with","title":"Scalable Kernel K-Means Clustering with Nystrom Approximation: Relative-Error Bounds","date":"2017-06-09","arxiv_id":"1706.02803","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-balanced-clustering-part-1","title":"Towards balanced clustering - part 1 (preliminaries)","date":"2017-06-09","arxiv_id":"1706.03065","repositories_listed":0,"syntology":null},{"url":null,"slug":"k-means-an-enhancement-over-k-means","title":"K+ Means : An Enhancement Over K-Means Clustering Algorithm","date":"2017-06-08","arxiv_id":"1706.02949","repositories_listed":0,"syntology":null},{"url":null,"slug":"attributed-network-embedding-for-learning-in","title":"Attributed Network Embedding for Learning in a Dynamic Environment","date":"2017-06-06","arxiv_id":"1706.01860","repositories_listed":0,"syntology":null},{"url":null,"slug":"hyperplane-clustering-via-dual-principal","title":"Hyperplane Clustering Via Dual Principal Component Pursuit","date":"2017-06-06","arxiv_id":"1706.01604","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-pairwise-disjoint-simple-languages","title":"Learning Pairwise Disjoint Simple Languages from Positive Examples","date":"2017-06-06","arxiv_id":"1706.01663","repositories_listed":0,"syntology":null},{"url":null,"slug":"deepsf-deep-convolutional-neural-network-for","title":"DeepSF: deep convolutional neural network for mapping protein sequences to folds","date":"2017-06-04","arxiv_id":"1706.01010","repositories_listed":0,"syntology":null},{"url":null,"slug":"hybrid-collaborative-movie-recommender-system","title":"Hybrid Collaborative Movie Recommender System Using Clustering and Bat Optimization","date":"2017-06-04","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"center-of-gravity-pso-for-partitioning","title":"Center of Gravity PSO for Partitioning Clustering","date":"2017-06-03","arxiv_id":"1706.00997","repositories_listed":0,"syntology":null},{"url":null,"slug":"semi-supervised-classification-cluster-and","title":"Semi-supervised Classification: Cluster and Label Approach using Particle Swarm Optimization","date":"2017-06-03","arxiv_id":"1706.00996","repositories_listed":0,"syntology":null},{"url":null,"slug":"streaming-bayesian-inference-theoretical","title":"Streaming Bayesian inference: theoretical limits and mini-batch approximate message-passing","date":"2017-06-02","arxiv_id":"1706.00705","repositories_listed":0,"syntology":null},{"url":null,"slug":"class-specific-feature-selection-for-interval","title":"Class Specific Feature Selection for Interval Valued Data Through Interval K-Means Clustering","date":"2017-05-31","arxiv_id":"1705.10986","repositories_listed":0,"syntology":null},{"url":null,"slug":"does-the-geometry-of-word-embeddings-help-1","title":"Does the Geometry of Word Embeddings Help Document Classification? A Case Study on Persistent Homology Based Representations","date":"2017-05-31","arxiv_id":"1705.10900","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-multi-layer-k-means-approach-for-multi","title":"A Multi-Layer K-means Approach for Multi-Sensor Data Pattern Recognition in Multi-Target Localization","date":"2017-05-30","arxiv_id":"1705.10757","repositories_listed":0,"syntology":null},{"url":null,"slug":"discovering-visual-concept-structure-with","title":"Discovering Visual Concept Structure with Sparse and Incomplete Tags","date":"2017-05-30","arxiv_id":"1705.10659","repositories_listed":0,"syntology":null},{"url":null,"slug":"quantum-low-entropy-based-associative","title":"Quantum Low Entropy based Associative Reasoning or QLEAR Learning","date":"2017-05-30","arxiv_id":"1705.10503","repositories_listed":0,"syntology":null},{"url":null,"slug":"an-automatic-contextual-analysis-and","title":"An Automatic Contextual Analysis and Clustering Classifiers Ensemble approach to Sentiment Analysis","date":"2017-05-29","arxiv_id":"1705.10130","repositories_listed":0,"syntology":null},{"url":null,"slug":"ensemble-of-part-detectors-for-simultaneous","title":"Ensemble of Part Detectors for Simultaneous Classification and Localization","date":"2017-05-29","arxiv_id":"1705.10034","repositories_listed":0,"syntology":null},{"url":null,"slug":"direct-mapping-hidden-excited-state","title":"Direct Mapping Hidden Excited State Interaction Patterns from ab initio Dynamics and Its Implications on Force Field Development","date":"2017-05-28","arxiv_id":"1705.09919","repositories_listed":0,"syntology":null},{"url":null,"slug":"klustree-clustering-answer-trees-from-keyword","title":"KlusTree: Clustering Answer Trees from Keyword Search on Graphs","date":"2017-05-27","arxiv_id":"1705.09808","repositories_listed":0,"syntology":null},{"url":null,"slug":"residual-expansion-algorithm-fast-and","title":"Residual Expansion Algorithm: Fast and Effective Optimization for Nonconvex Least Squares Problems","date":"2017-05-26","arxiv_id":"1705.09549","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-clustering-based-consistency-adaptation","title":"A Clustering-based Consistency Adaptation Strategy for Distributed SDN Controllers","date":"2017-05-25","arxiv_id":"1705.09050","repositories_listed":0,"syntology":null},{"url":null,"slug":"unsupervised-feature-learning-for-writer","title":"Unsupervised Feature Learning for Writer Identification and Writer Retrieval","date":"2017-05-25","arxiv_id":"1705.09369","repositories_listed":0,"syntology":null},{"url":null,"slug":"provable-estimation-of-the-number-of-blocks","title":"Provable Estimation of the Number of Blocks in Block Models","date":"2017-05-24","arxiv_id":"1705.08580","repositories_listed":0,"syntology":null},{"url":null,"slug":"improved-clustering-with-augmented-k-means","title":"Improved Clustering with Augmented k-means","date":"2017-05-22","arxiv_id":"1705.07592","repositories_listed":0,"syntology":null},{"url":null,"slug":"online-factorization-and-partition-of-complex","title":"Online Factorization and Partition of Complex Networks From Random Walks","date":"2017-05-22","arxiv_id":"1705.07881","repositories_listed":0,"syntology":null},{"url":null,"slug":"robust-localized-multi-view-subspace","title":"Robust Localized Multi-view Subspace Clustering","date":"2017-05-22","arxiv_id":"1705.07777","repositories_listed":0,"syntology":null},{"url":null,"slug":"size-matters-cardinality-constrained","title":"Size Matters: Cardinality-Constrained Clustering and Outlier Detection via Conic Optimization","date":"2017-05-22","arxiv_id":"1705.07837","repositories_listed":0,"syntology":null},{"url":null,"slug":"clustering-under-local-stability-bridging-the","title":"Clustering under Local Stability: Bridging the Gap between Worst-Case and Beyond Worst-Case Analysis","date":"2017-05-19","arxiv_id":"1705.07157","repositories_listed":0,"syntology":null},{"url":null,"slug":"cnn-based-joint-clustering-and-representation","title":"CNN-Based Joint Clustering and Representation Learning with Feature Drift Compensation for Large-Scale Image Data","date":"2017-05-19","arxiv_id":"1705.07091","repositories_listed":0,"syntology":null},{"url":null,"slug":"segmented-and-non-segmented-stacked-denoising","title":"Segmented and Non-Segmented Stacked Denoising Autoencoder for Hyperspectral Band Reduction","date":"2017-05-19","arxiv_id":"1705.06920","repositories_listed":0,"syntology":null},{"url":null,"slug":"discovering-the-graph-structure-in-the","title":"Discovering the Graph Structure in the Clustering Results","date":"2017-05-18","arxiv_id":"1705.06753","repositories_listed":0,"syntology":null},{"url":null,"slug":"co-clustering-through-optimal-transport","title":"Co-clustering through Optimal Transport","date":"2017-05-17","arxiv_id":"1705.06189","repositories_listed":0,"syntology":null},{"url":null,"slug":"identification-and-off-policy-learning-of","title":"Identification and Off-Policy Learning of Multiple Objectives Using Adaptive Clustering","date":"2017-05-17","arxiv_id":"1705.06342","repositories_listed":0,"syntology":null},{"url":null,"slug":"localized-lrr-on-grassmann-manifolds-an","title":"Localized LRR on Grassmann Manifolds: An Extrinsic View","date":"2017-05-17","arxiv_id":"1705.06599","repositories_listed":0,"syntology":null},{"url":null,"slug":"one-shot-joint-colocalization-and","title":"One Shot Joint Colocalization and Cosegmentation","date":"2017-05-17","arxiv_id":"1705.06000","repositories_listed":0,"syntology":null},{"url":null,"slug":"data-clustering-with-edge-domination-in","title":"Data clustering with edge domination in complex networks","date":"2017-05-16","arxiv_id":"1705.05494","repositories_listed":0,"syntology":null},{"url":null,"slug":"demystifying-relational-latent","title":"Demystifying Relational Latent Representations","date":"2017-05-16","arxiv_id":"1705.05785","repositories_listed":0,"syntology":null},{"url":null,"slug":"kernel-clustering-density-biases-and","title":"Kernel clustering: density biases and solutions","date":"2017-05-16","arxiv_id":"1705.05950","repositories_listed":0,"syntology":null},{"url":null,"slug":"kernel-truncated-regression-representation","title":"Kernel Truncated Regression Representation for Robust Subspace Clustering","date":"2017-05-15","arxiv_id":"1705.05108","repositories_listed":0,"syntology":null},{"url":null,"slug":"k-sets-a-linear-time-clustering-algorithm-for","title":"K-sets+: a Linear-time Clustering Algorithm for Data Points with a Sparse Similarity Measure","date":"2017-05-11","arxiv_id":"1705.04249","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-non-maximum-suppression","title":"Learning non-maximum suppression","date":"2017-05-08","arxiv_id":"1705.02950","repositories_listed":0,"syntology":null},{"url":null,"slug":"finding-bottlenecks-predicting-student","title":"Finding Bottlenecks: Predicting Student Attrition with Unsupervised Classifier","date":"2017-05-07","arxiv_id":"1705.02687","repositories_listed":0,"syntology":null},{"url":null,"slug":"fast-k-means-based-on-knn-graph","title":"Fast k-means based on KNN Graph","date":"2017-05-04","arxiv_id":"1705.01813","repositories_listed":0,"syntology":null},{"url":null,"slug":"semi-supervised-model-based-clustering-with","title":"Semi-supervised model-based clustering with controlled clusters leakage","date":"2017-05-04","arxiv_id":"1705.01877","repositories_listed":0,"syntology":null},{"url":null,"slug":"spherical-wards-clustering-and-generalized","title":"Spherical Wards clustering and generalized Voronoi diagrams","date":"2017-05-04","arxiv_id":"1705.02232","repositories_listed":0,"syntology":null},{"url":null,"slug":"semi-supervised-cross-entropy-clustering-with","title":"Semi-supervised cross-entropy clustering with information bottleneck constraint","date":"2017-05-03","arxiv_id":"1705.01601","repositories_listed":0,"syntology":null},{"url":null,"slug":"spectral-clustering-in-the-dynamic-stochastic","title":"Spectral clustering in the dynamic stochastic block model","date":"2017-05-02","arxiv_id":"1705.01204","repositories_listed":0,"syntology":null},{"url":null,"slug":"forced-to-learn-discovering-disentangled","title":"Forced to Learn: Discovering Disentangled Representations Without Exhaustive Labels","date":"2017-05-01","arxiv_id":"1705.00574","repositories_listed":0,"syntology":null},{"url":null,"slug":"twin-learning-for-similarity-and-clustering-a","title":"Twin Learning for Similarity and Clustering: A Unified Kernel Approach","date":"2017-05-01","arxiv_id":"1705.00678","repositories_listed":0,"syntology":null},{"url":null,"slug":"extending-and-improving-wordnet-via","title":"Extending and Improving Wordnet via Unsupervised Word Embeddings","date":"2017-04-29","arxiv_id":"1705.00217","repositories_listed":0,"syntology":null},{"url":null,"slug":"object-discovery-via-cohesion-measurement","title":"Object Discovery via Cohesion Measurement","date":"2017-04-28","arxiv_id":"1704.08944","repositories_listed":0,"syntology":null},{"url":null,"slug":"understanding-people-flow-in-transportation","title":"Understanding People Flow in Transportation Hubs","date":"2017-04-28","arxiv_id":"1705.00027","repositories_listed":0,"syntology":null},{"url":null,"slug":"locality-preserving-projections-for-grassmann","title":"Locality Preserving Projections for Grassmann manifold","date":"2017-04-27","arxiv_id":"1704.08458","repositories_listed":0,"syntology":null},{"url":null,"slug":"automatic-viseme-vocabulary-construction-to","title":"Automatic Viseme Vocabulary Construction to Enhance Continuous Lip-reading","date":"2017-04-26","arxiv_id":"1704.08035","repositories_listed":0,"syntology":null},{"url":null,"slug":"face-identification-and-clustering","title":"Face Identification and Clustering","date":"2017-04-26","arxiv_id":"1704.08328","repositories_listed":0,"syntology":null},{"url":null,"slug":"identifying-similarities-in-epileptic","title":"Identifying Similarities in Epileptic Patients for Drug Resistance Prediction","date":"2017-04-26","arxiv_id":"1704.08361","repositories_listed":0,"syntology":null},{"url":null,"slug":"new-region-force-for-variational-models-in","title":"New region force for variational models in image segmentation and high dimensional data clustering","date":"2017-04-26","arxiv_id":"1704.08218","repositories_listed":0,"syntology":null},{"url":null,"slug":"unsupervised-clustering-and-active-learning","title":"Unsupervised Clustering and Active Learning of Hyperspectral Images with Nonlinear Diffusion","date":"2017-04-26","arxiv_id":"1704.07961","repositories_listed":0,"syntology":null},{"url":null,"slug":"automatic-compositor-attribution-in-the-first","title":"Automatic Compositor Attribution in the First Folio of Shakespeare","date":"2017-04-25","arxiv_id":"1704.07875","repositories_listed":0,"syntology":null},{"url":null,"slug":"joint-layout-estimation-and-global-multi-view","title":"Joint Layout Estimation and Global Multi-View Registration for Indoor Reconstruction","date":"2017-04-25","arxiv_id":"1704.07632","repositories_listed":0,"syntology":null},{"url":null,"slug":"an-aposteriorical-clusterability-criterion","title":"An Aposteriorical Clusterability Criterion for $k$-Means++ and Simplicity of Clustering","date":"2017-04-24","arxiv_id":"1704.07139","repositories_listed":0,"syntology":null},{"url":null,"slug":"spectral-and-matrix-factorization-methods-for","title":"Spectral and matrix factorization methods for consistent community detection in multi-layer networks","date":"2017-04-24","arxiv_id":"1704.07353","repositories_listed":0,"syntology":null},{"url":null,"slug":"adaptive-estimation-in-structured-factor","title":"Adaptive Estimation in Structured Factor Models with Applications to Overlapping Clustering","date":"2017-04-23","arxiv_id":"1704.06977","repositories_listed":0,"syntology":null},{"url":null,"slug":"deduplication-in-a-massive-clinical-note","title":"Deduplication in a massive clinical note dataset","date":"2017-04-19","arxiv_id":"1704.05617","repositories_listed":0,"syntology":null},{"url":null,"slug":"k-means-as-a-variational-em-approximation-of","title":"$k$-means as a variational EM approximation of Gaussian mixture models","date":"2017-04-16","arxiv_id":"1704.04812","repositories_listed":0,"syntology":null},{"url":null,"slug":"approximating-optimization-problems-using-eas","title":"Approximating Optimization Problems using EAs on Scale-Free Networks","date":"2017-04-12","arxiv_id":"1704.03664","repositories_listed":0,"syntology":null},{"url":null,"slug":"finding-modes-by-probabilistic-hypergraphs","title":"Finding Modes by Probabilistic Hypergraphs Shifting","date":"2017-04-12","arxiv_id":"1704.03612","repositories_listed":0,"syntology":null},{"url":null,"slug":"higher-order-clustering-in-networks","title":"Higher-order clustering in networks","date":"2017-04-12","arxiv_id":"1704.03913","repositories_listed":0,"syntology":null},{"url":null,"slug":"stigmergy-based-modeling-to-discover-urban","title":"Stigmergy-based modeling to discover urban activity patterns from positioning data","date":"2017-04-12","arxiv_id":"1704.03667","repositories_listed":0,"syntology":null},{"url":null,"slug":"efficient-large-scale-clustering-based-on","title":"Efficient Large Scale Clustering based on Data Partitioning","date":"2017-04-11","arxiv_id":"1704.03421","repositories_listed":0,"syntology":null},{"url":null,"slug":"unsupervised-event-abstraction-using-pattern","title":"Unsupervised Event Abstraction using Pattern Abstraction and Local Process Models","date":"2017-04-11","arxiv_id":"1704.03520","repositories_listed":0,"syntology":null},{"url":null,"slug":"r-clustering-for-egocentric-video","title":"R-Clustering for Egocentric Video Segmentation","date":"2017-04-10","arxiv_id":"1704.02809","repositories_listed":0,"syntology":null},{"url":null,"slug":"mlc-toolbox-a-matlaboctave-library-for-multi","title":"MLC Toolbox: A MATLAB/OCTAVE Library for Multi-Label Classification","date":"2017-04-09","arxiv_id":"1704.02592","repositories_listed":0,"syntology":null},{"url":null,"slug":"metric-learning-in-codebook-generation-of-bag","title":"Metric Learning in Codebook Generation of Bag-of-Words for Person Re-identification","date":"2017-04-08","arxiv_id":"1704.02492","repositories_listed":0,"syntology":null},{"url":null,"slug":"seismic-facies-recognition-based-on-prestack","title":"Seismic facies recognition based on prestack data using deep convolutional autoencoder","date":"2017-04-08","arxiv_id":"1704.02446","repositories_listed":0,"syntology":null},{"url":null,"slug":"fast-spectral-clustering-using-autoencoders","title":"Fast Spectral Clustering Using Autoencoders and Landmarks","date":"2017-04-07","arxiv_id":"1704.02345","repositories_listed":0,"syntology":null},{"url":null,"slug":"hierarchical-clustering-objective-functions","title":"Hierarchical Clustering: Objective Functions and Algorithms","date":"2017-04-07","arxiv_id":"1704.02147","repositories_listed":0,"syntology":null},{"url":null,"slug":"restricted-isometry-property-of-gaussian","title":"Restricted Isometry Property of Gaussian Random Projection for Finite Set of Subspaces","date":"2017-04-07","arxiv_id":"1704.02109","repositories_listed":0,"syntology":null},{"url":null,"slug":"uncovering-group-level-insights-with","title":"Uncovering Group Level Insights with Accordant Clustering","date":"2017-04-07","arxiv_id":"1704.02378","repositories_listed":0,"syntology":null},{"url":null,"slug":"variance-based-moving-k-means-algorithm","title":"Variance Based Moving K-Means Algorithm","date":"2017-04-07","arxiv_id":"1704.02197","repositories_listed":0,"syntology":null},{"url":null,"slug":"dimm-sc-a-dirichlet-mixture-model-for","title":"DIMM-SC: A Dirichlet mixture model for clustering droplet-based single cell transcriptomic data","date":"2017-04-06","arxiv_id":"1704.02007","repositories_listed":0,"syntology":null},{"url":null,"slug":"massive-data-clustering-in-moderate","title":"Massive Data Clustering in Moderate Dimensions from the Dual Spaces of Observation and Attribute Data Clouds","date":"2017-04-06","arxiv_id":"1704.01871","repositories_listed":0,"syntology":null},{"url":null,"slug":"greedy-sampling-of-graph-signals","title":"Greedy Sampling of Graph Signals","date":"2017-04-05","arxiv_id":"1704.01223","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-a-collaborative-multiscale","title":"Learning a collaborative multiscale dictionary based on robust empirical mode decomposition","date":"2017-04-04","arxiv_id":"1704.04422","repositories_listed":0,"syntology":null},{"url":null,"slug":"clustering-in-hilbert-simplex-geometry","title":"Clustering in Hilbert simplex geometry","date":"2017-04-03","arxiv_id":"1704.00454","repositories_listed":0,"syntology":null},{"url":null,"slug":"semi-supervised-generation-with-cluster-aware","title":"Semi-Supervised Generation with Cluster-aware Generative Models","date":"2017-04-03","arxiv_id":"1704.00637","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-tool-for-extracting-sense-disambiguated","title":"A tool for extracting sense-disambiguated example sentences through user feedback","date":"2017-04-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"adocs-automatic-designer-of-conference","title":"ADoCS: Automatic Designer of Conference Schedules","date":"2017-04-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"an-unsupervised-speaker-clustering-technique","title":"An Unsupervised Speaker Clustering Technique based on SOM and I-vectors for Speech Recognition Systems","date":"2017-04-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"babeldomains-large-scale-domain-labeling-of","title":"BabelDomains: Large-Scale Domain Labeling of Lexical Resources","date":"2017-04-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"clustering-based-source-aware-assessment-of","title":"Clustering-based Source-aware Assessment of True Robustness for Learning Models","date":"2017-04-01","arxiv_id":"1704.00158","repositories_listed":0,"syntology":null},{"url":null,"slug":"clustering-of-russian-adjective-noun","title":"Clustering of Russian Adjective-Noun Constructions using Word Embeddings","date":"2017-04-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"distributed-document-and-phrase-co-embeddings","title":"Distributed Document and Phrase Co-embeddings for Descriptive Clustering","date":"2017-04-01","arxiv_id":null,"repositories_listed":0,"syntology":null}],"record_sha256":"61d110d0cefb1f1736c3a33f2b195730b584c8d9fc2e771f58298e41cea134ee","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}