{"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/54","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":54,"pages_in_order":108,"rows_per_page":100,"rows":[5301,5400],"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/53","next":"/task/clustering/papers/55","papers":[{"url":null,"slug":"a-review-of-systematic-selection-of","title":"A review of systematic selection of clustering algorithms and their evaluation","date":"2021-06-24","arxiv_id":"2106.12792","repositories_listed":0,"syntology":null},{"url":null,"slug":"factors-affecting-the-covid-19-risk-in-the-us","title":"Factors affecting the COVID-19 risk in the US counties: an innovative approach by combining unsupervised and supervised learning","date":"2021-06-24","arxiv_id":"2106.12766","repositories_listed":0,"syntology":null},{"url":null,"slug":"regularisation-for-pca-and-svd-type-matrix","title":"Regularisation for PCA- and SVD-type matrix factorisations","date":"2021-06-24","arxiv_id":"2106.12955","repositories_listed":0,"syntology":null},{"url":null,"slug":"closed-form-provable-and-robust-pca-via","title":"Closed-Form, Provable, and Robust PCA via Leverage Statistics and Innovation Search","date":"2021-06-23","arxiv_id":"2106.12190","repositories_listed":0,"syntology":null},{"url":null,"slug":"estimating-the-robustness-of-classification","title":"Estimating the Robustness of Classification Models by the Structure of the Learned Feature-Space","date":"2021-06-23","arxiv_id":"2106.12303","repositories_listed":0,"syntology":null},{"url":null,"slug":"not-all-users-are-the-same-providing","title":"Not all users are the same: Providing personalized explanations for sequential decision making problems","date":"2021-06-23","arxiv_id":"2106.12207","repositories_listed":0,"syntology":null},{"url":null,"slug":"patentnet-a-large-scale-incomplete-multiview","title":"PatentNet: A Large-Scale Incomplete Multiview, Multimodal, Multilabel Industrial Goods Image Database","date":"2021-06-23","arxiv_id":"2106.12139","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-neural-network-based-low-latency-speech","title":"Deep neural network Based Low-latency Speech Separation with Asymmetric analysis-Synthesis Window Pair","date":"2021-06-22","arxiv_id":"2106.11794","repositories_listed":0,"syntology":null},{"url":null,"slug":"machine-learning-for-model-order-selection-in","title":"Machine Learning for Model Order Selection in MIMO OFDM Systems","date":"2021-06-22","arxiv_id":"2106.11633","repositories_listed":0,"syntology":null},{"url":null,"slug":"palmar-towards-adaptive-multi-inhabitant","title":"PALMAR: Towards Adaptive Multi-inhabitant Activity Recognition in Point-Cloud Technology","date":"2021-06-22","arxiv_id":"2106.11902","repositories_listed":0,"syntology":null},{"url":null,"slug":"routine-clustering-of-mobile-sensor-data","title":"Routine Clustering of Mobile Sensor Data Facilitates Psychotic Relapse Prediction in Schizophrenia Patients","date":"2021-06-22","arxiv_id":"2106.11487","repositories_listed":0,"syntology":null},{"url":null,"slug":"universal-domain-adaptation-in-ordinal","title":"Universal Domain Adaptation in Ordinal Regression","date":"2021-06-22","arxiv_id":"2106.11576","repositories_listed":0,"syntology":null},{"url":"/paper/multi-vae-learning-disentangled-view-common","slug":"multi-vae-learning-disentangled-view-common","title":"Multi-VAE: Learning Disentangled View-common and View-peculiar Visual Representations for Multi-view Clustering","date":"2021-06-21","arxiv_id":"2106.11232","repositories_listed":0,"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":1,"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/multi-vae-learning-disentangled-view-common#ran","syntology_url":"https://syntology.ai/paper/2106.11232","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2106.11232"}},"official":null}},{"url":null,"slug":"tensor-learning-based-precoder-codebooks-for","title":"Tensor Learning-based Precoder Codebooks for FD-MIMO Systems","date":"2021-06-21","arxiv_id":"2106.11374","repositories_listed":0,"syntology":null},{"url":null,"slug":"improving-label-quality-by-jointly-modeling","title":"Improving Label Quality by Jointly Modeling Items and Annotators","date":"2021-06-20","arxiv_id":"2106.10600","repositories_listed":0,"syntology":null},{"url":null,"slug":"cluster-wise-hierarchical-generative-model","title":"Cluster-Wise Hierarchical Generative Model for Deep Amortized Clustering","date":"2021-06-19","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"double-low-rank-representation-with","title":"Double Low-Rank Representation With Projection Distance Penalty for Clustering","date":"2021-06-19","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"defending-adversaries-using-unsupervised","title":"Defending Adversaries Using Unsupervised Feature Clustering VAE","date":"2021-06-18","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"novelty-detection-via-contrastive-learning","title":"Novelty Detection via Contrastive Learning with Negative Data Augmentation","date":"2021-06-18","arxiv_id":"2106.09958","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-a-query-optimal-and-time-efficient","title":"Towards a Query-Optimal and Time-Efficient Algorithm for Clustering with a Faulty Oracle","date":"2021-06-18","arxiv_id":"2106.10374","repositories_listed":0,"syntology":null},{"url":null,"slug":"zero-shot-federated-learning-with-new-classes","title":"Zero-Shot Federated Learning with New Classes for Audio Classification","date":"2021-06-18","arxiv_id":"2106.10019","repositories_listed":0,"syntology":null},{"url":null,"slug":"coane-modeling-context-co-occurrence-for","title":"CoANE: Modeling Context Co-occurrence for Attributed Network Embedding","date":"2021-06-17","arxiv_id":"2106.09241","repositories_listed":0,"syntology":null},{"url":null,"slug":"topological-indoor-mapping-through-wifi","title":"Topological Indoor Mapping through WiFi Signals","date":"2021-06-17","arxiv_id":"2106.09789","repositories_listed":0,"syntology":null},{"url":null,"slug":"clustering-mixture-models-in-almost-linear","title":"Clustering Mixture Models in Almost-Linear Time via List-Decodable Mean Estimation","date":"2021-06-16","arxiv_id":"2106.08537","repositories_listed":0,"syntology":null},{"url":null,"slug":"clustering-of-check-in-sequences-using-the","title":"Clustering of check-in sequences using the mixture Markov chain process","date":"2021-06-16","arxiv_id":"2106.12039","repositories_listed":0,"syntology":null},{"url":null,"slug":"identifiability-guaranteed-simplex-structured","title":"Identifiability-Guaranteed Simplex-Structured Post-Nonlinear Mixture Learning via Autoencoder","date":"2021-06-16","arxiv_id":"2106.09070","repositories_listed":0,"syntology":null},{"url":null,"slug":"mining-interpretable-spatio-temporal-logic","title":"Mining Interpretable Spatio-temporal Logic Properties for Spatially Distributed Systems","date":"2021-06-16","arxiv_id":"2106.08548","repositories_listed":0,"syntology":null},{"url":null,"slug":"patchnet-unsupervised-object-discovery-based","title":"PatchNet: Unsupervised Object Discovery based on Patch Embedding","date":"2021-06-16","arxiv_id":"2106.08599","repositories_listed":0,"syntology":null},{"url":null,"slug":"collaborative-learning-and-personalization-in","title":"Adaptive Clustering and Personalization in Multi-Agent Stochastic Linear Bandits","date":"2021-06-15","arxiv_id":"2106.08902","repositories_listed":0,"syntology":null},{"url":null,"slug":"correlation-clustering-in-constant-many","title":"Correlation Clustering in Constant Many Parallel Rounds","date":"2021-06-15","arxiv_id":"2106.08448","repositories_listed":0,"syntology":null},{"url":null,"slug":"full-bitcoin-blockchain-data-made-easy","title":"Full Bitcoin Blockchain Data Made Easy","date":"2021-06-15","arxiv_id":"2106.08072","repositories_listed":0,"syntology":null},{"url":null,"slug":"multivariate-business-process-representation","title":"Multivariate Business Process Representation Learning utilizing Gramian Angular Fields and Convolutional Neural Networks","date":"2021-06-15","arxiv_id":"2106.08027","repositories_listed":0,"syntology":null},{"url":null,"slug":"coresets-for-constrained-k-median-and-k-means","title":"Coresets for constrained k-median and k-means clustering in low dimensional Euclidean space","date":"2021-06-14","arxiv_id":"2106.07319","repositories_listed":0,"syntology":null},{"url":null,"slug":"evolutionary-robust-clustering-over-time-for","title":"Evolutionary Robust Clustering Over Time for Temporal Data","date":"2021-06-14","arxiv_id":"2106.07252","repositories_listed":0,"syntology":null},{"url":null,"slug":"fair-clustering-under-a-bounded-cost","title":"Fair Clustering Under a Bounded Cost","date":"2021-06-14","arxiv_id":"2106.07239","repositories_listed":0,"syntology":null},{"url":"/paper/hard-samples-rectification-for-unsupervised","slug":"hard-samples-rectification-for-unsupervised","title":"Hard Samples Rectification for Unsupervised Cross-domain Person Re-identification","date":"2021-06-14","arxiv_id":"2106.07204","repositories_listed":0,"syntology":null},{"url":null,"slug":"self-supervised-metric-learning-in-multi-view","title":"Self-Supervised Metric Learning in Multi-View Data: A Downstream Task Perspective","date":"2021-06-14","arxiv_id":"2106.07138","repositories_listed":0,"syntology":null},{"url":null,"slug":"tangent-space-least-adaptive-clustering","title":"Tangent Space Least Adaptive Clustering","date":"2021-06-13","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"fpt-approximation-for-socially-fair","title":"Tight FPT Approximation for Socially Fair Clustering","date":"2021-06-12","arxiv_id":"2106.06755","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-deep-learning-approach-to-clustering-visual","title":"A deep learning approach to clustering visual arts","date":"2021-06-11","arxiv_id":"2106.06234","repositories_listed":0,"syntology":null},{"url":null,"slug":"differentially-private-algorithms-for-2","title":"Differentially Private Algorithms for Clustering with Stability Assumptions","date":"2021-06-11","arxiv_id":"2106.12959","repositories_listed":0,"syntology":null},{"url":null,"slug":"an-adaptive-origin-destination-flows-cluster","title":"An adaptive Origin-Destination flows cluster-detecting method to identify urban mobility trends","date":"2021-06-10","arxiv_id":"2106.05436","repositories_listed":0,"syntology":null},{"url":null,"slug":"hierarchical-agglomerative-graph-clustering","title":"Hierarchical Agglomerative Graph Clustering in Nearly-Linear Time","date":"2021-06-10","arxiv_id":"2106.05610","repositories_listed":0,"syntology":null},{"url":null,"slug":"multiple-dynamic-pricing-for-demand-response","title":"Multiple Dynamic Pricing for Demand Response with Adaptive Clustering-based Customer Segmentation in Smart Grids","date":"2021-06-10","arxiv_id":"2106.05905","repositories_listed":0,"syntology":null},{"url":null,"slug":"speaker-conversation-factorial-designs-for","title":"Speaker-conversation factorial designs for diarization error analysis","date":"2021-06-10","arxiv_id":"2106.05792","repositories_listed":0,"syntology":null},{"url":null,"slug":"swarm-intelligence-for-self-organized","title":"Swarm Intelligence for Self-Organized Clustering","date":"2021-06-10","arxiv_id":"2106.05521","repositories_listed":0,"syntology":null},{"url":null,"slug":"unsupervised-video-person-re-identification","title":"Unsupervised Video Person Re-identification via Noise and Hard frame Aware Clustering","date":"2021-06-10","arxiv_id":"2106.05441","repositories_listed":0,"syntology":null},{"url":null,"slug":"semi-supervised-training-with-pseudo-labeling","title":"Semi-Supervised Training with Pseudo-Labeling for End-to-End Neural Diarization","date":"2021-06-09","arxiv_id":"2106.04764","repositories_listed":0,"syntology":null},{"url":null,"slug":"very-compact-clusters-with-structural","title":"Separating Boundary Points via Structural Regularization for Very Compact Clusters","date":"2021-06-09","arxiv_id":"2106.05430","repositories_listed":0,"syntology":null},{"url":null,"slug":"efficient-online-learning-for-dynamic-k","title":"Efficient Online Learning for Dynamic k-Clustering","date":"2021-06-08","arxiv_id":"2106.04336","repositories_listed":0,"syntology":null},{"url":null,"slug":"end-to-end-speaker-diarization-conditioned-on","title":"End-to-End Speaker Diarization Conditioned on Speech Activity and Overlap Detection","date":"2021-06-08","arxiv_id":"2106.04078","repositories_listed":0,"syntology":null},{"url":null,"slug":"inference-for-network-regression-models-with","title":"Inference for Network Regression Models with Community Structure","date":"2021-06-08","arxiv_id":"2106.04271","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-distance-covariance-based-kernel-for","title":"A Distance Covariance-based Kernel for Nonlinear Causal Clustering in Heterogeneous Populations","date":"2021-06-07","arxiv_id":"2106.03480","repositories_listed":0,"syntology":null},{"url":null,"slug":"local-algorithms-for-estimating-effective","title":"Local Algorithms for Estimating Effective Resistance","date":"2021-06-07","arxiv_id":"2106.03476","repositories_listed":0,"syntology":null},{"url":null,"slug":"collaborative-causal-discovery-with-atomic","title":"Collaborative Causal Discovery with Atomic Interventions","date":"2021-06-06","arxiv_id":"2106.03028","repositories_listed":0,"syntology":null},{"url":null,"slug":"integrating-auxiliary-information-in-self","title":"Integrating Auxiliary Information in Self-supervised Learning","date":"2021-06-05","arxiv_id":"2106.02869","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-novel-semi-supervised-framework-for-call","title":"A Novel Semi-supervised Framework for Call Center Agent Malpractice Detection via Neural Feature Learning","date":"2021-06-04","arxiv_id":"2106.02433","repositories_listed":0,"syntology":null},{"url":null,"slug":"manifold-aware-deep-clustering-maximizing","title":"Manifold-Aware Deep Clustering: Maximizing Angles between Embedding Vectors Based on Regular Simplex","date":"2021-06-04","arxiv_id":"2106.02331","repositories_listed":0,"syntology":null},{"url":null,"slug":"laplacian-based-dimensionality-reduction","title":"Laplacian-Based Dimensionality Reduction Including Spectral Clustering, Laplacian Eigenmap, Locality Preserving Projection, Graph Embedding, and Diffusion Map: Tutorial and Survey","date":"2021-06-03","arxiv_id":"2106.02154","repositories_listed":0,"syntology":null},{"url":null,"slug":"limiirl-lightweight-multiple-intent-inverse","title":"LiMIIRL: Lightweight Multiple-Intent Inverse Reinforcement Learning","date":"2021-06-03","arxiv_id":"2106.01777","repositories_listed":0,"syntology":null},{"url":null,"slug":"you-never-cluster-alone","title":"You Never Cluster Alone","date":"2021-06-03","arxiv_id":"2106.01908","repositories_listed":0,"syntology":null},{"url":null,"slug":"decision-making-oriented-clustering","title":"Decision-making Oriented Clustering: Application to Pricing and Power Consumption Scheduling","date":"2021-06-02","arxiv_id":"2106.01021","repositories_listed":0,"syntology":null},{"url":null,"slug":"general-rough-modeling-of-cluster-analysis","title":"General Rough Modeling of Cluster Analysis","date":"2021-06-02","arxiv_id":"2106.04683","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-clustering-activation-maps-for-emphysema","title":"Deep Clustering Activation Maps for Emphysema Subtyping","date":"2021-06-01","arxiv_id":"2106.01351","repositories_listed":0,"syntology":null},{"url":null,"slug":"dreca-a-general-task-augmentation-strategy","title":"DReCa: A General Task Augmentation Strategy for Few-Shot Natural Language Inference","date":"2021-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"fair-clustering-using-antidote-data","title":"Fair Clustering Using Antidote Data","date":"2021-06-01","arxiv_id":"2106.00600","repositories_listed":0,"syntology":null},{"url":null,"slug":"leveraging-english-word-embeddings-for-semi","title":"Leveraging English Word Embeddings for Semi-Automatic Semantic Classification in Nêhiyawêwin (Plains Cree)","date":"2021-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"locally-private-k-means-clustering-with","title":"Locally Private $k$-Means Clustering with Constant Multiplicative Approximation and Near-Optimal Additive Error","date":"2021-05-31","arxiv_id":"2105.15007","repositories_listed":0,"syntology":null},{"url":null,"slug":"the-cold-start-problem-minimal-users-activity","title":"The Cold-start Problem: Minimal Users' Activity Estimation","date":"2021-05-31","arxiv_id":"2106.00102","repositories_listed":0,"syntology":null},{"url":null,"slug":"ell-2-norm-flow-diffusion-in-near-linear-time","title":"$\\ell_2$-norm Flow Diffusion in Near-Linear Time","date":"2021-05-30","arxiv_id":"2105.14629","repositories_listed":0,"syntology":null},{"url":null,"slug":"automatic-ct-segmentation-from-bounding-box","title":"Automatic CT Segmentation from Bounding Box Annotations using Convolutional Neural Networks","date":"2021-05-29","arxiv_id":"2105.14314","repositories_listed":0,"syntology":null},{"url":null,"slug":"slgcn-structure-learning-graph-convolutional","title":"GCN-SL: Graph Convolutional Networks with Structure Learning for Graphs under Heterophily","date":"2021-05-28","arxiv_id":"2105.13795","repositories_listed":0,"syntology":null},{"url":null,"slug":"volatility-modeling-of-stocks-from-selected","title":"Volatility Modeling of Stocks from Selected Sectors of the Indian Economy Using GARCH","date":"2021-05-28","arxiv_id":"2105.13898","repositories_listed":0,"syntology":null},{"url":null,"slug":"an-impossibility-theorem-for-node-embedding","title":"An Impossibility Theorem for Node Embedding","date":"2021-05-27","arxiv_id":"2105.13251","repositories_listed":0,"syntology":null},{"url":null,"slug":"robotic-brain-storm-optimization-a-multi","title":"Robotic Brain Storm Optimization: A Multi-target Collaborative Searching Paradigm for Swarm Robotics","date":"2021-05-27","arxiv_id":"2105.13108","repositories_listed":0,"syntology":null},{"url":null,"slug":"semantic-frame-induction-using-masked-word","title":"Semantic Frame Induction using Masked Word Embeddings and Two-Step Clustering","date":"2021-05-27","arxiv_id":"2105.13466","repositories_listed":0,"syntology":null},{"url":null,"slug":"verb-sense-clustering-using-contextualized","title":"Verb Sense Clustering using Contextualized Word Representations for Semantic Frame Induction","date":"2021-05-27","arxiv_id":"2105.13465","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-comprehensive-survey-on-community-detection","title":"A Comprehensive Survey on Community Detection with Deep Learning","date":"2021-05-26","arxiv_id":"2105.12584","repositories_listed":0,"syntology":null},{"url":null,"slug":"exploring-dual-information-in-distance-metric","title":"Exploring dual information in distance metric learning for clustering","date":"2021-05-26","arxiv_id":"2105.12703","repositories_listed":0,"syntology":null},{"url":null,"slug":"hybrid-movie-recommender-system-based-on","title":"Hybrid Movie Recommender System based on Resource Allocation","date":"2021-05-25","arxiv_id":"2105.11678","repositories_listed":0,"syntology":null},{"url":null,"slug":"investigating-manifold-neighborhood-size-for","title":"Investigating Manifold Neighborhood size for Nonlinear Analysis of LIBS Amino Acid Spectra","date":"2021-05-25","arxiv_id":"2105.12089","repositories_listed":0,"syntology":null},{"url":null,"slug":"providing-meaningful-data-summarizations","title":"Providing Meaningful Data Summarizations Using Exemplar-based Clustering in Industry 4.0","date":"2021-05-25","arxiv_id":"2105.12026","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-descriptive-clustering","title":"Deep Descriptive Clustering","date":"2021-05-24","arxiv_id":"2105.11549","repositories_listed":0,"syntology":null},{"url":null,"slug":"analysis-of-contractions-in-system-graphs","title":"Analysis of Contractions in System Graphs: Application to State Estimation","date":"2021-05-22","arxiv_id":"2105.10641","repositories_listed":0,"syntology":null},{"url":null,"slug":"pal-intelligence-augmentation-using","title":"PAL: Intelligence Augmentation using Egocentric Visual Context Detection","date":"2021-05-22","arxiv_id":"2105.10735","repositories_listed":0,"syntology":null},{"url":null,"slug":"v2v-spatiotemporal-interactive-pattern","title":"V2V Spatiotemporal Interactive Pattern Recognition and Risk Analysis in Lane Changes","date":"2021-05-22","arxiv_id":"2105.10688","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-distance-correlation-based-kernel-for","title":"A Distance Correlation-based Kernel for Nonlinear Causal Clustering in Heterogeneous Populations","date":"2021-05-21","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"gssf-a-generative-sequence-similarity","title":"GSSF: A Generative Sequence Similarity Function based on a Seq2Seq model for clustering online handwritten mathematical answers","date":"2021-05-21","arxiv_id":"2105.10159","repositories_listed":0,"syntology":null},{"url":null,"slug":"local-k-means-an-efficient-optimization","title":"Local $K$-means: An Efficient Optimization Algorithm And Its Generalization","date":"2021-05-21","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"rfid-based-article-to-fixture-predictions-in","title":"RFID-based Article-to-Fixture Predictions in Real-World Fashion Stores","date":"2021-05-21","arxiv_id":"2105.10216","repositories_listed":0,"syntology":null},{"url":null,"slug":"safety-metrics-for-semantic-segmentation-in","title":"Safety Metrics for Semantic Segmentation in Autonomous Driving","date":"2021-05-21","arxiv_id":"2105.10142","repositories_listed":0,"syntology":null},{"url":"/paper/face-body-voice-video-person-clustering-with","slug":"face-body-voice-video-person-clustering-with","title":"Face, Body, Voice: Video Person-Clustering with Multiple Modalities","date":"2021-05-20","arxiv_id":"2105.09939","repositories_listed":0,"syntology":null},{"url":null,"slug":"graph-sanitation-with-application-to-node","title":"Graph Sanitation with Application to Node Classification","date":"2021-05-19","arxiv_id":"2105.09384","repositories_listed":0,"syntology":null},{"url":null,"slug":"on-convex-clustering-solutions","title":"On Convex Clustering Solutions","date":"2021-05-18","arxiv_id":"2105.08348","repositories_listed":0,"syntology":null},{"url":null,"slug":"shape-analysis-of-functional-data-with","title":"Shape Analysis of Functional Data with Elastic Partial Matching","date":"2021-05-18","arxiv_id":"2105.08604","repositories_listed":0,"syntology":null},{"url":null,"slug":"unsupervised-identification-of-surgical","title":"Unsupervised identification of surgical robotic actions from small non homogeneous datasets","date":"2021-05-18","arxiv_id":"2105.08488","repositories_listed":0,"syntology":null},{"url":null,"slug":"class-incremental-few-shot-object-detection","title":"Class-Incremental Few-Shot Object Detection","date":"2021-05-17","arxiv_id":"2105.07637","repositories_listed":0,"syntology":null},{"url":"/paper/divide-and-contrast-self-supervised-learning","slug":"divide-and-contrast-self-supervised-learning","title":"Divide and Contrast: Self-supervised Learning from Uncurated Data","date":"2021-05-17","arxiv_id":"2105.08054","repositories_listed":0,"syntology":null},{"url":null,"slug":"leveraging-efficientnet-and-contrastive","title":"Leveraging EfficientNet and Contrastive Learning for Accurate Global-scale Location Estimation","date":"2021-05-17","arxiv_id":"2105.07645","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-unsupervised-domain-adaptation-for","title":"Towards Unsupervised Domain Adaptation for Deep Face Recognition under Privacy Constraints via Federated Learning","date":"2021-05-17","arxiv_id":"2105.07606","repositories_listed":0,"syntology":null}],"record_sha256":"1389878a5d75a89f0c236de5235f1861693640318666b42f11f719d4fa6edef8","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}