{"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/classification/papers/112","list_of":"/task/classification","task":"General Classification","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":112,"pages_in_order":146,"rows_per_page":100,"rows":[11101,11200],"of":14581,"counts":{"archive_papers_tagged":14581,"with_a_code_link":3945,"where_syntology_ran_a_sample":713,"not_listed_spam_title":0,"listed":14581,"listed_where_code_ran":713,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":560,"every_run_a_failure_of_syntologys_instrument":153,"listed_with_a_run_with_no_instrument_failure":560,"listed_every_run_a_failure_of_syntologys_instrument":153,"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/classification","prev":"/task/classification/papers/111","next":"/task/classification/papers/113","papers":[{"url":null,"slug":"cross-country-skiing-gears-classification","title":"Cross-Country Skiing Gears Classification using Deep Learning","date":"2017-06-27","arxiv_id":"1706.08924","repositories_listed":0,"syntology":null},{"url":null,"slug":"fast-and-accurate-classification-of","title":"Fast and accurate classification of echocardiograms using deep learning","date":"2017-06-27","arxiv_id":"1706.08658","repositories_listed":0,"syntology":null},{"url":null,"slug":"rate-distortion-classification-for-self","title":"Rate-Distortion Classification for Self-Tuning IoT Networks","date":"2017-06-27","arxiv_id":"1706.08877","repositories_listed":0,"syntology":null},{"url":null,"slug":"an-effective-way-to-improve-youtube-8m","title":"An Effective Way to Improve YouTube-8M Classification Accuracy in Google Cloud Platform","date":"2017-06-26","arxiv_id":"1706.08217","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-semantic-classification-for-3d-lidar","title":"Deep Semantic Classification for 3D LiDAR Data","date":"2017-06-26","arxiv_id":"1706.08355","repositories_listed":0,"syntology":null},{"url":null,"slug":"drvae-drug-response-variational-autoencoder","title":"Dr.VAE: Drug Response Variational Autoencoder","date":"2017-06-26","arxiv_id":"1706.08203","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-local-feature-aggregation-functions","title":"Learning Local Feature Aggregation Functions with Backpropagation","date":"2017-06-26","arxiv_id":"1706.08580","repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-level-svm-based-cad-tool-for","title":"Multi-level SVM Based CAD Tool for Classifying Structural MRIs","date":"2017-06-26","arxiv_id":"1706.08227","repositories_listed":0,"syntology":null},{"url":null,"slug":"robust-sonar-atr-through-bayesian-pose","title":"Robust Sonar ATR Through Bayesian Pose Corrected Sparse Classification","date":"2017-06-26","arxiv_id":"1706.08590","repositories_listed":0,"syntology":null},{"url":null,"slug":"semi-supervised-text-categorization-using","title":"Semi-supervised Text Categorization Using Recursive K-means Clustering","date":"2017-06-24","arxiv_id":"1706.07913","repositories_listed":0,"syntology":null},{"url":null,"slug":"model-selection-with-nonlinear-embedding-for","title":"Model Selection with Nonlinear Embedding for Unsupervised Domain Adaptation","date":"2017-06-23","arxiv_id":"1706.07527","repositories_listed":0,"syntology":null},{"url":null,"slug":"multiresolution-match-kernels-for-gesture","title":"Multiresolution Match Kernels for Gesture Video Classification","date":"2017-06-23","arxiv_id":"1706.07530","repositories_listed":0,"syntology":null},{"url":null,"slug":"comparison-of-time-frequency-representations","title":"Comparison of Time-Frequency Representations for Environmental Sound Classification using Convolutional Neural Networks","date":"2017-06-22","arxiv_id":"1706.07156","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-transfer-learning-a-new-deep-learning","title":"Deep Transfer Learning: A new deep learning glitch classification method for advanced LIGO","date":"2017-06-22","arxiv_id":"1706.07446","repositories_listed":0,"syntology":null},{"url":null,"slug":"fractal-dimension-analysis-for-automatic","title":"Fractal dimension analysis for automatic morphological galaxy classification","date":"2017-06-22","arxiv_id":"1706.07507","repositories_listed":0,"syntology":null},{"url":null,"slug":"scalable-multi-class-gaussian-process","title":"Scalable Multi-Class Gaussian Process Classification using Expectation Propagation","date":"2017-06-22","arxiv_id":"1706.07258","repositories_listed":0,"syntology":null},{"url":null,"slug":"ensembles-of-models-and-metrics-for-robust","title":"Ensembles of phalanxes across assessment metrics for robust ranking of homologous proteins","date":"2017-06-21","arxiv_id":"1706.06971","repositories_listed":0,"syntology":null},{"url":null,"slug":"gm-net-learning-features-with-more-efficiency","title":"GM-Net: Learning Features with More Efficiency","date":"2017-06-21","arxiv_id":"1706.06792","repositories_listed":0,"syntology":null},{"url":null,"slug":"uncertainty-aware-organ-classification-for","title":"Uncertainty-Aware Organ Classification for Surgical Data Science Applications in Laparoscopy","date":"2017-06-21","arxiv_id":"1706.07002","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-hybrid-approach-with-multi-channel-i","title":"A Hybrid Approach with Multi-channel I-Vectors and Convolutional Neural Networks for Acoustic Scene Classification","date":"2017-06-20","arxiv_id":"1706.06525","repositories_listed":0,"syntology":null},{"url":null,"slug":"advanced-steel-microstructural-classification","title":"Advanced Steel Microstructural Classification by Deep Learning Methods","date":"2017-06-20","arxiv_id":"1706.06480","repositories_listed":0,"syntology":null},{"url":null,"slug":"individual-recognition-in-schizophrenia-using","title":"Individual Recognition in Schizophrenia using Deep Learning Methods with Random Forest and Voting Classifiers: Insights from Resting State EEG Streams","date":"2017-06-20","arxiv_id":"1707.03467","repositories_listed":0,"syntology":null},{"url":null,"slug":"most-ligand-based-classification-benchmarks","title":"Most Ligand-Based Classification Benchmarks Reward Memorization Rather than Generalization","date":"2017-06-20","arxiv_id":"1706.06619","repositories_listed":0,"syntology":null},{"url":null,"slug":"passive-classification-of-source-printer","title":"Passive Classification of Source Printer using Text-line-level Geometric Distortion Signatures from Scanned Images of Printed Documents","date":"2017-06-20","arxiv_id":"1706.06651","repositories_listed":0,"syntology":null},{"url":null,"slug":"topic-modeling-for-classification-of-clinical","title":"Topic Modeling for Classification of Clinical Reports","date":"2017-06-19","arxiv_id":"1706.06177","repositories_listed":0,"syntology":null},{"url":null,"slug":"user-intent-classification-using-memory","title":"User Intent Classification using Memory Networks: A Comparative Analysis for a Limited Data Scenario","date":"2017-06-19","arxiv_id":"1706.06160","repositories_listed":0,"syntology":null},{"url":null,"slug":"data-set-operations-to-hide-decision-tree","title":"Data set operations to hide decision tree rules","date":"2017-06-18","arxiv_id":"1706.05733","repositories_listed":0,"syntology":null},{"url":null,"slug":"kernel-two-sample-hypothesis-testing-using","title":"Kernel Two-Sample Hypothesis Testing Using Kernel Set Classification","date":"2017-06-18","arxiv_id":"1706.05612","repositories_listed":0,"syntology":null},{"url":null,"slug":"sample-computation-vs-storage-tradeoffs-for","title":"Sample, computation vs storage tradeoffs for classification using tensor subspace models","date":"2017-06-18","arxiv_id":"1706.05599","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-the-improvement-of-automated","title":"Towards the Improvement of Automated Scientific Document Categorization by Deep Learning","date":"2017-06-18","arxiv_id":"1706.05719","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-large-scale-cnn-ensemble-for-medication","title":"A Large-Scale CNN Ensemble for Medication Safety Analysis","date":"2017-06-17","arxiv_id":"1706.05549","repositories_listed":0,"syntology":null},{"url":null,"slug":"distance-weighted-discrimination-of-face","title":"Distance weighted discrimination of face images for gender classification","date":"2017-06-15","arxiv_id":"1706.05029","repositories_listed":0,"syntology":null},{"url":null,"slug":"effective-sequential-classifier-training-for","title":"Effective Sequential Classifier Training for SVM-based Multitemporal Remote Sensing Image Classification","date":"2017-06-15","arxiv_id":"1706.04719","repositories_listed":0,"syntology":null},{"url":null,"slug":"hierarchical-label-inference-for-video","title":"Hierarchical Label Inference for Video Classification","date":"2017-06-15","arxiv_id":"1706.05028","repositories_listed":0,"syntology":null},{"url":null,"slug":"alignment-distances-on-systems-of-bags","title":"Alignment Distances on Systems of Bags","date":"2017-06-14","arxiv_id":"1706.04388","repositories_listed":0,"syntology":null},{"url":null,"slug":"large-scale-youtube-8m-video-understanding","title":"Large-Scale YouTube-8M Video Understanding with Deep Neural Networks","date":"2017-06-14","arxiv_id":"1706.04488","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-and-evaluating-musical-features-with","title":"Learning and Evaluating Musical Features with Deep Autoencoders","date":"2017-06-14","arxiv_id":"1706.04486","repositories_listed":0,"syntology":null},{"url":null,"slug":"modeling-multimodal-clues-in-a-hybrid-deep","title":"Modeling Multimodal Clues in a Hybrid Deep Learning Framework for Video Classification","date":"2017-06-14","arxiv_id":"1706.04508","repositories_listed":0,"syntology":null},{"url":null,"slug":"salprop-salient-object-proposals-via","title":"SalProp: Salient object proposals via aggregated edge cues","date":"2017-06-14","arxiv_id":"1706.04472","repositories_listed":0,"syntology":null},{"url":null,"slug":"shape-color-differential-moment-invariants","title":"Shape-Color Differential Moment Invariants under Affine Transformations","date":"2017-06-14","arxiv_id":"1706.04382","repositories_listed":0,"syntology":null},{"url":"/paper/afif4-deep-gender-classification-based-on","slug":"afif4-deep-gender-classification-based-on","title":"AFIF4: Deep Gender Classification based on AdaBoost-based Fusion of Isolated Facial Features and Foggy Faces","date":"2017-06-13","arxiv_id":"1706.04277","repositories_listed":0,"syntology":null},{"url":null,"slug":"modelling-prosodic-structure-using-artificial","title":"Modelling prosodic structure using Artificial Neural Networks","date":"2017-06-13","arxiv_id":"1706.03952","repositories_listed":0,"syntology":null},{"url":null,"slug":"jctc-a-large-job-posting-corpus-for-text","title":"JCTC: A Large Job posting Corpus for Text Classification","date":"2017-06-12","arxiv_id":"1705.06123","repositories_listed":0,"syntology":null},{"url":null,"slug":"classification-of-questions-and-learning","title":"Classification of Questions and Learning Outcome Statements (LOS) Into Blooms Taxonomy (BT) By Similarity Measurements Towards Extracting Of Learning Outcome from Learning Material","date":"2017-06-10","arxiv_id":"1706.03191","repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-modal-obstacle-detection-in","title":"Multi-Modal Obstacle Detection in Unstructured Environments with Conditional Random Fields","date":"2017-06-09","arxiv_id":"1706.02908","repositories_listed":0,"syntology":null},{"url":null,"slug":"rethinking-skip-thought-a-neighborhood-based","title":"Rethinking Skip-thought: A Neighborhood based Approach","date":"2017-06-09","arxiv_id":"1706.03146","repositories_listed":0,"syntology":null},{"url":null,"slug":"generative-discriminative-variational-model","title":"Generative-Discriminative Variational Model for Visual Recognition","date":"2017-06-07","arxiv_id":"1706.02295","repositories_listed":0,"syntology":null},{"url":null,"slug":"added-value-of-morphological-features-to","title":"Added value of morphological features to breast lesion diagnosis in ultrasound","date":"2017-06-06","arxiv_id":"1706.01855","repositories_listed":0,"syntology":null},{"url":null,"slug":"classifying-documents-within-multiple","title":"Classifying Documents within Multiple Hierarchical Datasets using Multi-Task Learning","date":"2017-06-06","arxiv_id":"1706.01583","repositories_listed":0,"syntology":null},{"url":"/paper/deep-convolutional-decision-jungle-for-image","slug":"deep-convolutional-decision-jungle-for-image","title":"Deep Convolutional Decision Jungle for Image Classification","date":"2017-06-06","arxiv_id":"1706.02003","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-learning-generalization-requires-deep","title":"Deep Learning: Generalization Requires Deep Compositional Feature Space Design","date":"2017-06-06","arxiv_id":"1706.01983","repositories_listed":0,"syntology":null},{"url":null,"slug":"embedding-feature-selection-for-large-scale","title":"Embedding Feature Selection for Large-scale Hierarchical Classification","date":"2017-06-06","arxiv_id":"1706.01581","repositories_listed":0,"syntology":null},{"url":null,"slug":"bayesian-lstms-in-medicine","title":"Bayesian LSTMs in medicine","date":"2017-06-05","arxiv_id":"1706.01242","repositories_listed":0,"syntology":null},{"url":null,"slug":"binary-patterns-encoded-convolutional-neural","title":"Binary Patterns Encoded Convolutional Neural Networks for Texture Recognition and Remote Sensing Scene Classification","date":"2017-06-05","arxiv_id":"1706.01171","repositories_listed":0,"syntology":null},{"url":null,"slug":"inconsistent-node-flattening-for-improving","title":"Inconsistent Node Flattening for Improving Top-down Hierarchical Classification","date":"2017-06-05","arxiv_id":"1706.01214","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-structured-semantic-embeddings-for","title":"Learning Structured Semantic Embeddings for Visual Recognition","date":"2017-06-05","arxiv_id":"1706.01237","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-random-fern-based-feature-approach-for","title":"A Random-Fern based Feature Approach for Image Matching","date":"2017-06-04","arxiv_id":"1706.01115","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":"evolving-imputation-strategies-for-missing","title":"Evolving imputation strategies for missing data in classification problems with TPOT","date":"2017-06-04","arxiv_id":"1706.01120","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":"swarm-intelligence-in-semi-supervised","title":"Swarm Intelligence in Semi-supervised Classification","date":"2017-06-03","arxiv_id":"1706.00998","repositories_listed":0,"syntology":null},{"url":null,"slug":"detection-recognition-and-tracking-of-moving","title":"Detection, Recognition and Tracking of Moving Objects from Real-time Video via Visual Vocabulary Model and Species Inspired PSO","date":"2017-06-02","arxiv_id":"1707.05224","repositories_listed":0,"syntology":null},{"url":null,"slug":"facies-classification-from-well-logs-using-an","title":"Facies classification from well logs using an inception convolutional network","date":"2017-06-02","arxiv_id":"1706.00613","repositories_listed":0,"syntology":null},{"url":null,"slug":"multiple-kernel-learning-and-automatic","title":"Multiple Kernel Learning and Automatic Subspace Relevance Determination for High-dimensional Neuroimaging Data","date":"2017-06-02","arxiv_id":"1706.00856","repositories_listed":0,"syntology":null},{"url":null,"slug":"d-eterminants-et-quantificateurs-g-en-eralis","title":"D\\'eterminants et quantificateurs g\\'en\\'eralis\\'es dynamiques (Determiners and dynamic generalised quantifiers)","date":"2017-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":"/paper/discriminative-k-shot-learning-using","slug":"discriminative-k-shot-learning-using","title":"Discriminative k-shot learning using probabilistic models","date":"2017-06-01","arxiv_id":"1706.00326","repositories_listed":0,"syntology":null},{"url":null,"slug":"line-profile-based-segmentation-algorithm-for","title":"Line Profile Based Segmentation Algorithm for Touching Corn Kernels","date":"2017-06-01","arxiv_id":"1706.00396","repositories_listed":0,"syntology":null},{"url":null,"slug":"tri-automatique-de-la-litt-erature-pour-les","title":"Tri Automatique de la Litt\\'erature pour les Revues Syst\\'ematiques (Automatically Ranking the Literature in Support of Systematic Reviews)","date":"2017-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"brain-tumor-detection-and-classification-with","title":"Brain Tumor Detection and Classification with Feed Forward Back-Prop Neural Network","date":"2017-05-31","arxiv_id":"1706.06411","repositories_listed":0,"syntology":null},{"url":null,"slug":"controllable-invariance-through-adversarial","title":"Controllable Invariance through Adversarial Feature Learning","date":"2017-05-31","arxiv_id":"1705.11122","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-supervised-discrete-hashing","title":"Deep Supervised Discrete Hashing","date":"2017-05-31","arxiv_id":"1705.10999","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":"hinet-hierarchical-classification-with-neural","title":"HiNet: Hierarchical Classification with Neural Network","date":"2017-05-31","arxiv_id":"1705.11105","repositories_listed":0,"syntology":null},{"url":null,"slug":"dynamics-based-features-for-graph","title":"Dynamics Based Features For Graph Classification","date":"2017-05-30","arxiv_id":"1705.10817","repositories_listed":0,"syntology":null},{"url":null,"slug":"generic-tubelet-proposals-for-action","title":"Generic Tubelet Proposals for Action Localization","date":"2017-05-30","arxiv_id":"1705.10861","repositories_listed":0,"syntology":null},{"url":null,"slug":"morphological-error-detection-in-3d","title":"Morphological Error Detection in 3D Segmentations","date":"2017-05-30","arxiv_id":"1705.10882","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":"robust-tracking-using-region-proposal","title":"Robust Tracking Using Region Proposal Networks","date":"2017-05-30","arxiv_id":"1705.10447","repositories_listed":0,"syntology":null},{"url":null,"slug":"character-based-text-classification-using-top","title":"Character-Based Text Classification using Top Down Semantic Model for Sentence Representation","date":"2017-05-29","arxiv_id":"1705.10586","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":"kernel-implicit-variational-inference","title":"Kernel Implicit Variational Inference","date":"2017-05-29","arxiv_id":"1705.10119","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-learning-for-user-comment-moderation","title":"Deep Learning for User Comment Moderation","date":"2017-05-28","arxiv_id":"1705.09993","repositories_listed":0,"syntology":null},{"url":null,"slug":"subject-specific-stream-classification","title":"Subject Specific Stream Classification Preprocessing Algorithm for Twitter Data Stream","date":"2017-05-28","arxiv_id":"1705.09995","repositories_listed":0,"syntology":null},{"url":null,"slug":"abnormality-detection-and-localization-in","title":"Abnormality Detection and Localization in Chest X-Rays using Deep Convolutional Neural Networks","date":"2017-05-27","arxiv_id":"1705.09850","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-learning-for-spatio-temporal-modeling","title":"Deep Learning for Spatio-Temporal Modeling: Dynamic Traffic Flows and High Frequency Trading","date":"2017-05-27","arxiv_id":"1705.09851","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-metric-learning-and-image-classification","title":"Deep Metric Learning and Image Classification with Nearest Neighbour Gaussian Kernels","date":"2017-05-27","arxiv_id":"1705.09780","repositories_listed":0,"syntology":null},{"url":null,"slug":"dimensionality-reduction-for-acoustic-vehicle","title":"Dimensionality reduction for acoustic vehicle classification with spectral embedding","date":"2017-05-27","arxiv_id":"1705.09869","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-wl-sppim-semantic-model-for-document","title":"A WL-SPPIM Semantic Model for Document Classification","date":"2017-05-26","arxiv_id":"1706.01758","repositories_listed":0,"syntology":null},{"url":null,"slug":"adaptive-classification-for-prediction-under","title":"Adaptive Classification for Prediction Under a Budget","date":"2017-05-26","arxiv_id":"1705.10194","repositories_listed":0,"syntology":null},{"url":null,"slug":"algorithmic-clothing-hybrid-recommendation","title":"Algorithmic clothing: hybrid recommendation, from street-style-to-shop","date":"2017-05-26","arxiv_id":"1705.09451","repositories_listed":0,"syntology":null},{"url":null,"slug":"an-efficient-algorithm-for-bayesian-nearest","title":"An Efficient Algorithm for Bayesian Nearest Neighbours","date":"2017-05-26","arxiv_id":"1705.09407","repositories_listed":0,"syntology":null},{"url":null,"slug":"classification-of-major-depressive-disorder","title":"Classification of Major Depressive Disorder via Multi-Site Weighted LASSO Model","date":"2017-05-26","arxiv_id":"1705.10312","repositories_listed":0,"syntology":null},{"url":null,"slug":"classification-regions-of-deep-neural","title":"Classification regions of deep neural networks","date":"2017-05-26","arxiv_id":"1705.09552","repositories_listed":0,"syntology":null},{"url":null,"slug":"classification-of-quantitative-light-induced","title":"Classification of Quantitative Light-Induced Fluorescence Images Using Convolutional Neural Network","date":"2017-05-25","arxiv_id":"1705.09193","repositories_listed":0,"syntology":null},{"url":null,"slug":"extraction-and-classification-of-diving-clips","title":"Extraction and Classification of Diving Clips from Continuous Video Footage","date":"2017-05-25","arxiv_id":"1705.09003","repositories_listed":0,"syntology":null},{"url":null,"slug":"the-cost-of-fairness-in-classification","title":"The cost of fairness in classification","date":"2017-05-25","arxiv_id":"1705.09055","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":"an-experimental-study-of-graph-based-semi","title":"An experimental study of graph-based semi-supervised classification with additional node information","date":"2017-05-24","arxiv_id":"1705.08716","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-with-average-top-k-loss","title":"Learning with Average Top-k Loss","date":"2017-05-24","arxiv_id":"1705.08826","repositories_listed":0,"syntology":null},{"url":null,"slug":"open-category-classification-by-adversarial","title":"Open-Category Classification by Adversarial Sample Generation","date":"2017-05-24","arxiv_id":"1705.08722","repositories_listed":0,"syntology":null}],"record_sha256":"5fc9c0251e1e44b13edf03ebcd7c37e4e17cd01a390faf094d25cb20ebdc6015","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}