{"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-1/papers/113","list_of":"/task/classification-1","task":"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":113,"pages_in_order":129,"rows_per_page":100,"rows":[11201,11300],"of":12815,"counts":{"archive_papers_tagged":12815,"with_a_code_link":3778,"where_syntology_ran_a_sample":582,"not_listed_spam_title":0,"listed":12815,"listed_where_code_ran":582,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":457,"every_run_a_failure_of_syntologys_instrument":125,"listed_with_a_run_with_no_instrument_failure":457,"listed_every_run_a_failure_of_syntologys_instrument":125,"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-1","prev":"/task/classification-1/papers/112","next":"/task/classification-1/papers/114","papers":[{"url":null,"slug":"ingeotec-at-semeval-2017-task-4-a-b4msa","title":"INGEOTEC at SemEval 2017 Task 4: A B4MSA Ensemble based on Genetic Programming for Twitter Sentiment Analysis","date":"2017-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"initializing-neural-networks-for-hierarchical","title":"Initializing neural networks for hierarchical multi-label text classification","date":"2017-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-local-and-global-contexts-using-a","title":"Learning local and global contexts using a convolutional recurrent network model for relation classification in biomedical text","date":"2017-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"leveraging-eventive-information-for-better","title":"Leveraging Eventive Information for Better Metaphor Detection and Classification","date":"2017-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"lsis-at-semeval-2017-task-4-using-adapted","title":"LSIS at SemEval-2017 Task 4: Using Adapted Sentiment Similarity Seed Words For English and Arabic Tweet Polarity Classification","date":"2017-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"mama-edha-at-semeval-2017-task-8-stance","title":"Mama Edha at SemEval-2017 Task 8: Stance Classification with CNN and Rules","date":"2017-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"mayonlp-at-semeval-2017-task-10-word","title":"MayoNLP at SemEval 2017 Task 10: Word Embedding Distance Pattern for Keyphrase Classification in Scientific Publications","date":"2017-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-class-optimal-margin-distribution","title":"Multi-Class Optimal Margin Distribution Machine","date":"2017-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"multilabel-classification-with-group-testing","title":"Multilabel Classification with Group Testing and Codes","date":"2017-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"self-supervised-learning-for-spinal-mris","title":"Self-Supervised Learning for Spinal MRIs","date":"2017-08-01","arxiv_id":"1708.00367","repositories_listed":0,"syntology":null},{"url":null,"slug":"sinai-at-semeval-2017-task-4-user-based","title":"SINAI at SemEval-2017 Task 4: User based classification","date":"2017-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"target-word-prediction-and-paraphasia","title":"Target word prediction and paraphasia classification in spoken discourse","date":"2017-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"tti-coin-at-semeval-2017-task-10","title":"TTI-COIN at SemEval-2017 Task 10: Investigating Embeddings for End-to-End Relation Extraction from Scientific Papers","date":"2017-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"twina-at-semeval-2017-task-4-twitter","title":"TWINA at SemEval-2017 Task 4: Twitter Sentiment Analysis with Ensemble Gradient Boost Tree Classifier","date":"2017-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"utterance-intent-classification-of-a-spoken","title":"Utterance Intent Classification of a Spoken Dialogue System with Efficiently Untied Recursive Autoencoders","date":"2017-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"work-hard-play-hard-email-classification-on","title":"Work Hard, Play Hard: Email Classification on the Avocado and Enron Corpora","date":"2017-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"convolution-with-logarithmic-filter-groups","title":"Convolution with Logarithmic Filter Groups for Efficient Shallow CNN","date":"2017-07-31","arxiv_id":"1707.09855","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-generalized-multivariate-student-t-mixture","title":"A generalized multivariate Student-t mixture model for Bayesian classification and clustering of radar waveforms","date":"2017-07-29","arxiv_id":"1707.09548","repositories_listed":0,"syntology":null},{"url":"/paper/graph-classification-with-2d-convolutional","slug":"graph-classification-with-2d-convolutional","title":"Graph Classification with 2D Convolutional Neural Networks","date":"2017-07-29","arxiv_id":"1708.02218","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-co-space-sample-mining-across-feature","title":"Deep Co-Space: Sample Mining Across Feature Transformation for Semi-Supervised Learning","date":"2017-07-28","arxiv_id":"1707.09119","repositories_listed":0,"syntology":null},{"url":null,"slug":"spatial-aware-object-embeddings-for-zero-shot","title":"Spatial-Aware Object Embeddings for Zero-Shot Localization and Classification of Actions","date":"2017-07-28","arxiv_id":"1707.09145","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-audio-sequence-representations-for","title":"Learning audio sequence representations for acoustic event classification","date":"2017-07-27","arxiv_id":"1707.08729","repositories_listed":0,"syntology":null},{"url":null,"slug":"graph-based-classification-of-omnidirectional","title":"Graph-Based Classification of Omnidirectional Images","date":"2017-07-26","arxiv_id":"1707.08301","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-comparison-of-single-trial-eeg","title":"A comparison of single-trial EEG classification and EEG-informed fMRI across three MR compatible EEG recording systems","date":"2017-07-25","arxiv_id":"1707.08077","repositories_listed":0,"syntology":null},{"url":null,"slug":"automatic-breast-cancer-grading-in-lymph","title":"Automatic breast cancer grading in lymph nodes using a deep neural network","date":"2017-07-24","arxiv_id":"1707.07565","repositories_listed":0,"syntology":null},{"url":null,"slug":"generative-openmax-for-multi-class-open-set","title":"Generative OpenMax for Multi-Class Open Set Classification","date":"2017-07-24","arxiv_id":"1707.07418","repositories_listed":0,"syntology":null},{"url":null,"slug":"detecting-and-grouping-identical-objects-for","title":"Detecting and Grouping Identical Objects for Region Proposal and Classification","date":"2017-07-23","arxiv_id":"1707.07255","repositories_listed":0,"syntology":null},{"url":null,"slug":"3dcnn-dqn-rnn-a-deep-reinforcement-learning","title":"3DCNN-DQN-RNN: A Deep Reinforcement Learning Framework for Semantic Parsing of Large-scale 3D Point Clouds","date":"2017-07-21","arxiv_id":"1707.06783","repositories_listed":0,"syntology":null},{"url":null,"slug":"multidimensional-classification-of","title":"Multidimensional classification of hippocampal shape features discriminates Alzheimer's disease and mild cognitive impairment from normal aging","date":"2017-07-19","arxiv_id":"1707.05961","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-novel-deep-learning-architecture-for-testis","title":"A Novel Deep Learning Architecture for Testis Histology Image Classification","date":"2017-07-18","arxiv_id":"1707.05809","repositories_listed":0,"syntology":null},{"url":null,"slug":"domain-adaptation-for-resume-classification","title":"Domain Adaptation for Resume Classification Using Convolutional Neural Networks","date":"2017-07-18","arxiv_id":"1707.05576","repositories_listed":0,"syntology":null},{"url":null,"slug":"order-free-rnn-with-visual-attention-for","title":"Order-Free RNN with Visual Attention for Multi-Label Classification","date":"2017-07-18","arxiv_id":"1707.05495","repositories_listed":0,"syntology":null},{"url":null,"slug":"make-your-bone-great-again-a-study-on","title":"Make Your Bone Great Again : A study on Osteoporosis Classification","date":"2017-07-17","arxiv_id":"1707.05385","repositories_listed":0,"syntology":null},{"url":null,"slug":"be-careful-what-you-backpropagate-a-case-for","title":"Be Careful What You Backpropagate: A Case For Linear Output Activations & Gradient Boosting","date":"2017-07-13","arxiv_id":"1707.04199","repositories_listed":0,"syntology":null},{"url":null,"slug":"do-convolutional-networks-need-to-be-deep-for","title":"Do Convolutional Networks need to be Deep for Text Classification ?","date":"2017-07-13","arxiv_id":"1707.04108","repositories_listed":0,"syntology":null},{"url":null,"slug":"large-scale-video-classification-guided-by","title":"Large-scale Video Classification guided by Batch Normalized LSTM Translator","date":"2017-07-13","arxiv_id":"1707.04045","repositories_listed":0,"syntology":null},{"url":null,"slug":"influence-of-resampling-on-accuracy-of","title":"Influence of Resampling on Accuracy of Imbalanced Classification","date":"2017-07-12","arxiv_id":"1707.03905","repositories_listed":0,"syntology":null},{"url":null,"slug":"model-selection-for-anomaly-detection","title":"Model Selection for Anomaly Detection","date":"2017-07-12","arxiv_id":"1707.03909","repositories_listed":0,"syntology":null},{"url":null,"slug":"underwater-object-classification-using","title":"Underwater object classification using scattering transform of sonar signals","date":"2017-07-11","arxiv_id":"1707.03133","repositories_listed":0,"syntology":null},{"url":null,"slug":"detection-of-bimanual-gestures-everywhere-why","title":"Detection of bimanual gestures everywhere: why it matters, what we need and what is missing","date":"2017-07-09","arxiv_id":"1707.02605","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-spatiotemporal-model-with-visual-attention","title":"A spatiotemporal model with visual attention for video classification","date":"2017-07-07","arxiv_id":"1707.02069","repositories_listed":0,"syntology":null},{"url":null,"slug":"simple-classification-using-binary-data","title":"Simple Classification using Binary Data","date":"2017-07-06","arxiv_id":"1707.01945","repositories_listed":0,"syntology":null},{"url":null,"slug":"zero-shot-deep-domain-adaptation","title":"Zero-Shot Deep Domain Adaptation","date":"2017-07-06","arxiv_id":"1707.01922","repositories_listed":0,"syntology":null},{"url":null,"slug":"multiple-range-restricted-bidirectional-gated","title":"Multiple Range-Restricted Bidirectional Gated Recurrent Units with Attention for Relation Classification","date":"2017-07-05","arxiv_id":"1707.01265","repositories_listed":0,"syntology":null},{"url":null,"slug":"aggregating-frame-level-features-for-large","title":"Aggregating Frame-level Features for Large-Scale Video Classification","date":"2017-07-04","arxiv_id":"1707.00803","repositories_listed":0,"syntology":null},{"url":"/paper/deep-representation-learning-with-part-loss","slug":"deep-representation-learning-with-part-loss","title":"Deep Representation Learning with Part Loss for Person Re-Identification","date":"2017-07-04","arxiv_id":"1707.00798","repositories_listed":0,"syntology":null},{"url":null,"slug":"kernel-scaling-for-manifold-learning-and","title":"Kernel Scaling for Manifold Learning and Classification","date":"2017-07-04","arxiv_id":"1707.01093","repositories_listed":0,"syntology":null},{"url":null,"slug":"zero-shot-fine-grained-classification-by-deep","title":"Zero-Shot Fine-Grained Classification by Deep Feature Learning with Semantics","date":"2017-07-04","arxiv_id":"1707.00785","repositories_listed":0,"syntology":null},{"url":null,"slug":"classification-non-supervisee-des-donnees","title":"Classification non supervisée des données hétérogènes à large échelle","date":"2017-07-02","arxiv_id":"1707.00297","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-learning-based-data-page-classification","title":"Deep-learning-based data page classification for holographic memory","date":"2017-07-02","arxiv_id":"1707.00684","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-generative-attentional-neural-network-model","title":"A Generative Attentional Neural Network Model for Dialogue Act Classification","date":"2017-07-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"adaptive-class-preserving-representation-for","title":"Adaptive Class Preserving Representation for Image Classification","date":"2017-07-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"fine-grained-image-classification-via","title":"Fine-Grained Image Classification via Combining Vision and Language","date":"2017-07-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"hierarchical-multimodal-metric-learning-for","title":"Hierarchical Multimodal Metric Learning for Multimodal Classification","date":"2017-07-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":"/paper/lcr-net-localization-classification","slug":"lcr-net-localization-classification","title":"LCR-Net: Localization-Classification-Regression for Human Pose","date":"2017-07-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-deep-match-kernels-for-image-set","title":"Learning Deep Match Kernels for Image-Set Classification","date":"2017-07-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"linguistically-regularized-lstm-for-sentiment","title":"Linguistically Regularized LSTM for Sentiment Classification","date":"2017-07-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-way-multi-level-kernel-modeling-for","title":"Multi-Way Multi-Level Kernel Modeling for Neuroimaging Classification","date":"2017-07-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"polyhedral-conic-classifiers-for-visual","title":"Polyhedral Conic Classifiers for Visual Object Detection and Classification","date":"2017-07-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"twitter-demographic-classification-using-deep","title":"Twitter Demographic Classification Using Deep Multi-modal Multi-task Learning","date":"2017-07-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"variation-autoencoder-based-network","title":"Variation Autoencoder Based Network Representation Learning for Classification","date":"2017-07-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"zero-shot-classification-with-discriminative","title":"Zero-Shot Classification With Discriminative Semantic Representation Learning","date":"2017-07-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"improvement-of-training-set-structure-in","title":"Improvement of training set structure in fusion data cleaning using Time-Domain Global Similarity method","date":"2017-06-30","arxiv_id":"1706.10018","repositories_listed":0,"syntology":null},{"url":null,"slug":"multiple-vlad-encoding-of-cnns-for-image","title":"Multiple VLAD encoding of CNNs for image classification","date":"2017-06-30","arxiv_id":"1707.00058","repositories_listed":0,"syntology":null},{"url":null,"slug":"data-dependent-generalization-bounds-for","title":"Data-dependent Generalization Bounds for Multi-class Classification","date":"2017-06-29","arxiv_id":"1706.09814","repositories_listed":0,"syntology":null},{"url":null,"slug":"image-classification-using-local-tensor","title":"Image classification using local tensor singular value decompositions","date":"2017-06-29","arxiv_id":"1706.09693","repositories_listed":0,"syntology":null},{"url":null,"slug":"real-time-distracted-driver-posture","title":"Real-time Distracted Driver Posture Classification","date":"2017-06-28","arxiv_id":"1706.09498","repositories_listed":0,"syntology":null},{"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":"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":"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":"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":"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":"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":"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":"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":"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":"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":"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":"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":"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":"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":"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":"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}],"record_sha256":"4cbe6b3f385c975b827acf68da0d282ca4d0c8c0d60354e1550477b0d554100a","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}