{"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/34","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":34,"pages_in_order":129,"rows_per_page":100,"rows":[3301,3400],"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/33","next":"/task/classification-1/papers/35","papers":[{"url":"/paper/deep-short-text-classification-with-knowledge","slug":"deep-short-text-classification-with-knowledge","title":"Deep Short Text Classification with Knowledge Powered Attention","date":"2019-02-21","arxiv_id":"1902.08050","repositories_listed":1,"syntology":null},{"url":"/paper/stable-and-fair-classification","slug":"stable-and-fair-classification","title":"Stable and Fair Classification","date":"2019-02-21","arxiv_id":"1902.07823","repositories_listed":1,"syntology":null},{"url":"/paper/adversarial-augmentation-for-enhancing","slug":"adversarial-augmentation-for-enhancing","title":"Adversarial Augmentation for Enhancing Classification of Mammography Images","date":"2019-02-20","arxiv_id":"1902.07762","repositories_listed":1,"syntology":null},{"url":"/paper/evaluating-model-calibration-in","slug":"evaluating-model-calibration-in","title":"Evaluating model calibration in classification","date":"2019-02-19","arxiv_id":"1902.06977","repositories_listed":1,"syntology":{"n":8,"n_ran":6,"n_constructed":0,"n_ran_checked":6,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":6,"n_pointer_only":0,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 0 honoured, 0 violated, 6 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/evaluating-model-calibration-in#ran","syntology_url":"https://syntology.ai/paper/1902.06977","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1902.06977"}},"official":{"repos":["uu-sml/calibration"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/hybridsn-exploring-3d-2d-cnn-feature","slug":"hybridsn-exploring-3d-2d-cnn-feature","title":"HybridSN: Exploring 3D-2D CNN Feature Hierarchy for Hyperspectral Image Classification","date":"2019-02-18","arxiv_id":"1902.06701","repositories_listed":1,"syntology":null},{"url":"/paper/patchnet-a-tool-for-deep-patch-classification","slug":"patchnet-a-tool-for-deep-patch-classification","title":"PatchNet: A Tool for Deep Patch Classification","date":"2019-02-16","arxiv_id":"1903.02063","repositories_listed":1,"syntology":null},{"url":"/paper/sinkhorn-divergence-of-topological-signature","slug":"sinkhorn-divergence-of-topological-signature","title":"Sinkhorn Divergence of Topological Signature Estimates for Time Series Classification","date":"2019-02-14","arxiv_id":"1902.05326","repositories_listed":1,"syntology":null},{"url":"/paper/semi-supervised-node-classification-via","slug":"semi-supervised-node-classification-via","title":"Hierarchical Graph Convolutional Networks for Semi-supervised Node Classification","date":"2019-02-13","arxiv_id":"1902.06667","repositories_listed":1,"syntology":{"n":13,"n_ran":10,"n_constructed":0,"n_ran_checked":8,"n_instrument":2,"n_unverified":3,"n_honours":3,"n_violates":2,"n_no_contract":3,"n_pointer_only":7,"phrase":"10 ran (of which 0 constructed an object rather than computing a result; 8 with no instrument failure: 3 honoured, 2 violated, 3 with no contract checked; 2 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/semi-supervised-node-classification-via#ran","syntology_url":"https://syntology.ai/paper/1902.06667","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1902.06667"}},"official":{"repos":["CRIPAC-DIG/H-GCN"],"state":"official (archive's flag): 10 ran","n_ran":10,"n_constructed":0,"n_ran_no_instrument_failure":8,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/sparse-feature-selection-in-kernel","slug":"sparse-feature-selection-in-kernel","title":"Sparse Feature Selection in Kernel Discriminant Analysis via Optimal Scoring","date":"2019-02-12","arxiv_id":"1902.04248","repositories_listed":1,"syntology":null},{"url":"/paper/deep-node-ranking-an-algorithm-for-structural","slug":"deep-node-ranking-an-algorithm-for-structural","title":"Deep Node Ranking for Neuro-symbolic Structural Node Embedding and Classification","date":"2019-02-11","arxiv_id":"1902.03964","repositories_listed":1,"syntology":null},{"url":"/paper/efficient-primal-dual-algorithms-for-large","slug":"efficient-primal-dual-algorithms-for-large","title":"Efficient Primal-Dual Algorithms for Large-Scale Multiclass Classification","date":"2019-02-11","arxiv_id":"1902.03755","repositories_listed":1,"syntology":null},{"url":"/paper/multi-task-learning-for-target-dependent","slug":"multi-task-learning-for-target-dependent","title":"Multi-task Learning for Target-dependent Sentiment Classification","date":"2019-02-08","arxiv_id":"1902.02930","repositories_listed":1,"syntology":null},{"url":"/paper/graph-classification-with-recurrent","slug":"graph-classification-with-recurrent","title":"Variational Recurrent Neural Networks for Graph Classification","date":"2019-02-07","arxiv_id":"1902.02721","repositories_listed":1,"syntology":null},{"url":"/paper/equal-opportunity-in-online-classification","slug":"equal-opportunity-in-online-classification","title":"Equal Opportunity in Online Classification with Partial Feedback","date":"2019-02-06","arxiv_id":"1902.02242","repositories_listed":1,"syntology":null},{"url":"/paper/machine-learning-for-seizure-type","slug":"machine-learning-for-seizure-type","title":"Seizure Type Classification using EEG signals and Machine Learning: Setting a benchmark","date":"2019-02-04","arxiv_id":"1902.01012","repositories_listed":1,"syntology":{"n":5,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":0,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/machine-learning-for-seizure-type#ran","syntology_url":"https://syntology.ai/paper/1902.01012","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1902.01012"}},"official":null}},{"url":"/paper/colornet-investigating-the-importance-of","slug":"colornet-investigating-the-importance-of","title":"ColorNet: Investigating the importance of color spaces for image classification","date":"2019-02-01","arxiv_id":"1902.00267","repositories_listed":1,"syntology":null},{"url":"/paper/tax2vec-constructing-interpretable-features","slug":"tax2vec-constructing-interpretable-features","title":"tax2vec: Constructing Interpretable Features from Taxonomies for Short Text Classification","date":"2019-02-01","arxiv_id":"1902.00438","repositories_listed":1,"syntology":null},{"url":"/paper/pathologist-level-classification-of","slug":"pathologist-level-classification-of","title":"Pathologist-level classification of histologic patterns on resected lung adenocarcinoma slides with deep neural networks","date":"2019-01-31","arxiv_id":"1901.11489","repositories_listed":1,"syntology":null},{"url":"/paper/noise-tolerant-fair-classification","slug":"noise-tolerant-fair-classification","title":"Noise-tolerant fair classification","date":"2019-01-30","arxiv_id":"1901.10837","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":0,"n_instrument":3,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":3,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 3 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/noise-tolerant-fair-classification#ran","syntology_url":"https://syntology.ai/paper/1901.10837","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1901.10837"}},"official":{"repos":["AIasd/noise_fairlearn"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/on-possibility-and-impossibility-of","slug":"on-possibility-and-impossibility-of","title":"On the Calibration of Multiclass Classification with Rejection","date":"2019-01-30","arxiv_id":"1901.10655","repositories_listed":1,"syntology":null},{"url":"/paper/evaluating-generalization-ability-of","slug":"evaluating-generalization-ability-of","title":"Evaluating Generalization Ability of Convolutional Neural Networks and Capsule Networks for Image Classification via Top-2 Classification","date":"2019-01-29","arxiv_id":"1901.10112","repositories_listed":1,"syntology":null},{"url":"/paper/hyperspherical-prototype-networks","slug":"hyperspherical-prototype-networks","title":"Hyperspherical Prototype Networks","date":"2019-01-29","arxiv_id":"1901.10514","repositories_listed":1,"syntology":null},{"url":"/paper/no-training-required-exploring-random","slug":"no-training-required-exploring-random","title":"No Training Required: Exploring Random Encoders for Sentence Classification","date":"2019-01-29","arxiv_id":"1901.10444","repositories_listed":1,"syntology":null},{"url":"/paper/squeezed-very-deep-convolutional-neural","slug":"squeezed-very-deep-convolutional-neural","title":"Squeezed Very Deep Convolutional Neural Networks for Text Classification","date":"2019-01-28","arxiv_id":"1901.09821","repositories_listed":1,"syntology":null},{"url":"/paper/bayes-metaclassifier-and-soft-confusion","slug":"bayes-metaclassifier-and-soft-confusion","title":"Bayes metaclassifier and Soft-confusion-matrix classifier in the task of multi-label classification","date":"2019-01-25","arxiv_id":"1901.08827","repositories_listed":1,"syntology":null},{"url":"/paper/bottom-up-broadcast-neural-network-for-music","slug":"bottom-up-broadcast-neural-network-for-music","title":"Bottom-up Broadcast Neural Network For Music Genre Classification","date":"2019-01-24","arxiv_id":"1901.08928","repositories_listed":1,"syntology":null},{"url":"/paper/in-defense-of-the-triplet-loss-for-visual","slug":"in-defense-of-the-triplet-loss-for-visual","title":"Boosting Standard Classification Architectures Through a Ranking Regularizer","date":"2019-01-24","arxiv_id":"1901.08616","repositories_listed":1,"syntology":null},{"url":"/paper/semantic-classification-of-tabular-datasets","slug":"semantic-classification-of-tabular-datasets","title":"Semantic Classification of Tabular Datasets via Character-Level Convolutional Neural Networks","date":"2019-01-24","arxiv_id":"1901.08456","repositories_listed":1,"syntology":null},{"url":"/paper/max-margin-class-imbalanced-learning-with","slug":"max-margin-class-imbalanced-learning-with","title":"Max-margin Class Imbalanced Learning with Gaussian Affinity","date":"2019-01-23","arxiv_id":"1901.07711","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/max-margin-class-imbalanced-learning-with#ran","syntology_url":"https://syntology.ai/paper/1901.07711","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1901.07711"}},"official":null}},{"url":"/paper/stochastic-gradient-trees","slug":"stochastic-gradient-trees","title":"Stochastic Gradient Trees","date":"2019-01-23","arxiv_id":"1901.07777","repositories_listed":1,"syntology":null},{"url":"/paper/self-attention-networks-for-connectionist","slug":"self-attention-networks-for-connectionist","title":"Self-Attention Networks for Connectionist Temporal Classification in Speech Recognition","date":"2019-01-22","arxiv_id":"1901.10055","repositories_listed":1,"syntology":null},{"url":"/paper/hybrid-coarse-fine-classification-for-head","slug":"hybrid-coarse-fine-classification-for-head","title":"Hybrid coarse-fine classification for head pose estimation","date":"2019-01-21","arxiv_id":"1901.06778","repositories_listed":1,"syntology":null},{"url":"/paper/transfer-learning-for-image-based-malware","slug":"transfer-learning-for-image-based-malware","title":"Transfer Learning for Image-Based Malware Classification","date":"2019-01-21","arxiv_id":"1903.11551","repositories_listed":1,"syntology":null},{"url":"/paper/ecgadv-generating-adversarial","slug":"ecgadv-generating-adversarial","title":"ECGadv: Generating Adversarial Electrocardiogram to Misguide Arrhythmia Classification System","date":"2019-01-12","arxiv_id":"1901.03808","repositories_listed":1,"syntology":null},{"url":"/paper/autoencoders-and-generative-adversarial","slug":"autoencoders-and-generative-adversarial","title":"Autoencoders and Generative Adversarial Networks for Imbalanced Sequence Classification","date":"2019-01-08","arxiv_id":"1901.02514","repositories_listed":1,"syntology":null},{"url":"/paper/stance-classification-for-rumour-analysis-in","slug":"stance-classification-for-rumour-analysis-in","title":"Stance Classification for Rumour Analysis in Twitter: Exploiting Affective Information and Conversation Structure","date":"2019-01-07","arxiv_id":"1901.01911","repositories_listed":1,"syntology":null},{"url":"/paper/graph-neural-networks-with-convolutional-arma","slug":"graph-neural-networks-with-convolutional-arma","title":"Graph Neural Networks with convolutional ARMA filters","date":"2019-01-05","arxiv_id":"1901.01343","repositories_listed":1,"syntology":null},{"url":"/paper/multi-class-classification-without-multi","slug":"multi-class-classification-without-multi","title":"Multi-class Classification without Multi-class Labels","date":"2019-01-02","arxiv_id":"1901.00544","repositories_listed":1,"syntology":{"n":13,"n_ran":9,"n_constructed":0,"n_ran_checked":9,"n_instrument":0,"n_unverified":4,"n_honours":0,"n_violates":0,"n_no_contract":9,"n_pointer_only":1,"phrase":"9 ran (of which 0 constructed an object rather than computing a result; 9 with no instrument failure: 0 honoured, 0 violated, 9 with no contract checked; 0 where Syntology's instrument failed) · 4 unverified","sample_list":"/paper/multi-class-classification-without-multi#ran","syntology_url":"https://syntology.ai/paper/1901.00544","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1901.00544"}},"official":{"repos":["GT-RIPL/L2C"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":0,"n_ran_no_instrument_failure":9,"n_unverified":4,"ran_from_kinds":["official"]}}},{"url":"/paper/weakly-supervised-hierarchical-text","slug":"weakly-supervised-hierarchical-text","title":"Weakly-Supervised Hierarchical Text Classification","date":"2018-12-29","arxiv_id":"1812.11270","repositories_listed":1,"syntology":{"n":5,"n_ran":4,"n_constructed":0,"n_ran_checked":4,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":0,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/weakly-supervised-hierarchical-text#ran","syntology_url":"https://syntology.ai/paper/1812.11270","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1812.11270"}},"official":{"repos":["yumeng5/WeSHClass"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/supervised-sentiment-classification-with-cnns","slug":"supervised-sentiment-classification-with-cnns","title":"Supervised Sentiment Classification with CNNs for Diverse SE Datasets","date":"2018-12-23","arxiv_id":"1812.09653","repositories_listed":1,"syntology":null},{"url":"/paper/a-multi-task-neural-approach-for-emotion","slug":"a-multi-task-neural-approach-for-emotion","title":"A Multi-task Neural Approach for Emotion Attribution, Classification and Summarization","date":"2018-12-21","arxiv_id":"1812.09041","repositories_listed":1,"syntology":null},{"url":"/paper/website-classification-using-word-based","slug":"website-classification-using-word-based","title":"Website Classification Using Word Based Multiple N -Gram Models and Random Search Oriented Feature Parameters","date":"2018-12-21","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/deep-gated-recurrent-and-convolutional","slug":"deep-gated-recurrent-and-convolutional","title":"Deep Gated Recurrent and Convolutional Network Hybrid Model for Univariate Time Series Classification","date":"2018-12-18","arxiv_id":"1812.07683","repositories_listed":1,"syntology":null},{"url":"/paper/deep-transfer-learning-for-static-malware","slug":"deep-transfer-learning-for-static-malware","title":"Deep Transfer Learning for Static Malware Classification","date":"2018-12-18","arxiv_id":"1812.07606","repositories_listed":1,"syntology":null},{"url":"/paper/jointly-learning-convolutional","slug":"jointly-learning-convolutional","title":"Jointly Learning Convolutional Representations to Compress Radiological Images and Classify Thoracic Diseases in the Compressed Domain","date":"2018-12-18","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/classification-using-ensemble-learning-under","slug":"classification-using-ensemble-learning-under","title":"Classification using Ensemble Learning under Weighted Misclassification Loss","date":"2018-12-16","arxiv_id":"1812.06507","repositories_listed":1,"syntology":null},{"url":"/paper/few-shot-classification-in-named-entity","slug":"few-shot-classification-in-named-entity","title":"Few-shot classification in Named Entity Recognition Task","date":"2018-12-14","arxiv_id":"1812.06158","repositories_listed":1,"syntology":null},{"url":"/paper/adversarial-framing-for-image-and-video","slug":"adversarial-framing-for-image-and-video","title":"Adversarial Framing for Image and Video Classification","date":"2018-12-11","arxiv_id":"1812.04599","repositories_listed":1,"syntology":null},{"url":"/paper/classification-reconstruction-learning-for","slug":"classification-reconstruction-learning-for","title":"Classification-Reconstruction Learning for Open-Set Recognition","date":"2018-12-11","arxiv_id":"1812.04246","repositories_listed":1,"syntology":{"n":5,"n_ran":4,"n_constructed":0,"n_ran_checked":4,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":0,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/classification-reconstruction-learning-for#ran","syntology_url":"https://syntology.ai/paper/1812.04246","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1812.04246"}},"official":null}},{"url":"/paper/deep-networks-with-probabilistic-gates","slug":"deep-networks-with-probabilistic-gates","title":"Channel selection using Gumbel Softmax","date":"2018-12-11","arxiv_id":"1812.04180","repositories_listed":1,"syntology":null},{"url":"/paper/licic-less-important-components-for","slug":"licic-less-important-components-for","title":"LICIC: Less Important Components for Imbalanced Multiclass Classification","date":"2018-12-09","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/lnemlc-label-network-embeddings-for-multi","slug":"lnemlc-label-network-embeddings-for-multi","title":"LNEMLC: Label Network Embeddings for Multi-Label Classification","date":"2018-12-07","arxiv_id":"1812.02956","repositories_listed":1,"syntology":null},{"url":"/paper/comparative-document-summarisation-via","slug":"comparative-document-summarisation-via","title":"Comparative Document Summarisation via Classification","date":"2018-12-06","arxiv_id":"1812.02171","repositories_listed":1,"syntology":null},{"url":"/paper/squeezefit-label-aware-dimensionality","slug":"squeezefit-label-aware-dimensionality","title":"SqueezeFit: Label-aware dimensionality reduction by semidefinite programming","date":"2018-12-06","arxiv_id":"1812.02768","repositories_listed":1,"syntology":null},{"url":"/paper/mlic-a-maxsat-based-framework-for-learning","slug":"mlic-a-maxsat-based-framework-for-learning","title":"MLIC: A MaxSAT-Based framework for learning interpretable classification rules","date":"2018-12-05","arxiv_id":"1812.01843","repositories_listed":1,"syntology":null},{"url":"/paper/practical-text-classification-with-large-pre","slug":"practical-text-classification-with-large-pre","title":"Practical Text Classification With Large Pre-Trained Language Models","date":"2018-12-04","arxiv_id":"1812.01207","repositories_listed":1,"syntology":null},{"url":"/paper/connectionist-temporal-classification-with","slug":"connectionist-temporal-classification-with","title":"Connectionist Temporal Classification with Maximum Entropy Regularization","date":"2018-12-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/improving-landmark-recognition-using-saliency","slug":"improving-landmark-recognition-using-saliency","title":"Improving Landmark Recognition using Saliency detection and Feature classification","date":"2018-11-30","arxiv_id":"1811.12748","repositories_listed":1,"syntology":null},{"url":"/paper/learning-interpretable-rules-for-multi-label","slug":"learning-interpretable-rules-for-multi-label","title":"Learning Interpretable Rules for Multi-label Classification","date":"2018-11-30","arxiv_id":"1812.00050","repositories_listed":1,"syntology":null},{"url":"/paper/181201711","slug":"181201711","title":"A Graph-CNN for 3D Point Cloud Classification","date":"2018-11-28","arxiv_id":"1812.01711","repositories_listed":1,"syntology":{"n":6,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/181201711#ran","syntology_url":"https://syntology.ai/paper/1812.01711","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1812.01711"}},"official":{"repos":["maggie0106/Graph-CNN-in-3D-Point-Cloud-Classification"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/multi-label-classification-search-space-in","slug":"multi-label-classification-search-space-in","title":"Multi-label classification search space in the MEKA software","date":"2018-11-28","arxiv_id":"1811.11353","repositories_listed":1,"syntology":null},{"url":"/paper/bayesian-graph-convolutional-neural-networks","slug":"bayesian-graph-convolutional-neural-networks","title":"Bayesian graph convolutional neural networks for semi-supervised classification","date":"2018-11-27","arxiv_id":"1811.11103","repositories_listed":1,"syntology":{"n":16,"n_ran":13,"n_constructed":0,"n_ran_checked":12,"n_instrument":1,"n_unverified":3,"n_honours":3,"n_violates":3,"n_no_contract":6,"n_pointer_only":16,"phrase":"13 ran (of which 0 constructed an object rather than computing a result; 12 with no instrument failure: 3 honoured, 3 violated, 6 with no contract checked; 1 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/bayesian-graph-convolutional-neural-networks#ran","syntology_url":"https://syntology.ai/paper/1811.11103","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1811.11103"}},"official":null}},{"url":"/paper/exclusive-autoencoder-xae-for-nucleus","slug":"exclusive-autoencoder-xae-for-nucleus","title":"eXclusive Autoencoder (XAE) for Nucleus Detection and Classification on Hematoxylin and Eosin (H&E) Stained Histopathological Images","date":"2018-11-27","arxiv_id":"1811.11243","repositories_listed":1,"syntology":null},{"url":"/paper/temporal-convolutional-neural-network-for-the","slug":"temporal-convolutional-neural-network-for-the","title":"Temporal Convolutional Neural Network for the Classification of Satellite Image Time Series","date":"2018-11-26","arxiv_id":"1811.10166","repositories_listed":1,"syntology":null},{"url":"/paper/explicit-interaction-model-towards-text","slug":"explicit-interaction-model-towards-text","title":"Explicit Interaction Model towards Text Classification","date":"2018-11-23","arxiv_id":"1811.09386","repositories_listed":1,"syntology":{"n":3,"n_ran":2,"n_constructed":0,"n_ran_checked":0,"n_instrument":2,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":3,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/explicit-interaction-model-towards-text#ran","syntology_url":"https://syntology.ai/paper/1811.09386","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1811.09386"}},"official":{"repos":["NonvolatileMemory/AAAI_2019_EXAM"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/high-dimensional-classification-through-ell_0","slug":"high-dimensional-classification-through-ell_0","title":"High Dimensional Classification through $\\ell_0$-Penalized Empirical Risk Minimization","date":"2018-11-23","arxiv_id":"1811.09540","repositories_listed":1,"syntology":null},{"url":"/paper/spectral-multigraph-networks-for-discovering","slug":"spectral-multigraph-networks-for-discovering","title":"Spectral Multigraph Networks for Discovering and Fusing Relationships in Molecules","date":"2018-11-23","arxiv_id":"1811.09595","repositories_listed":1,"syntology":null},{"url":"/paper/a-baseline-for-multi-label-image","slug":"a-baseline-for-multi-label-image","title":"A Baseline for Multi-Label Image Classification Using An Ensemble of Deep Convolutional Neural Networks","date":"2018-11-20","arxiv_id":"1811.08412","repositories_listed":1,"syntology":null},{"url":"/paper/road-damage-detection-and-classification-in","slug":"road-damage-detection-and-classification-in","title":"Road Damage Detection And Classification In Smartphone Captured Images Using Mask R-CNN","date":"2018-11-12","arxiv_id":"1811.04535","repositories_listed":1,"syntology":null},{"url":"/paper/breast-cancer-classification-from","slug":"breast-cancer-classification-from","title":"Breast Cancer Classification from Histopathological Images with Inception Recurrent Residual Convolutional Neural Network","date":"2018-11-10","arxiv_id":"1811.04241","repositories_listed":1,"syntology":{"n":2,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":0,"phrase":"1 ran (of which 0 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) · 1 unverified","sample_list":"/paper/breast-cancer-classification-from#ran","syntology_url":"https://syntology.ai/paper/1811.04241","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1811.04241"}},"official":null}},{"url":"/paper/amalgamating-knowledge-towards-comprehensive","slug":"amalgamating-knowledge-towards-comprehensive","title":"Amalgamating Knowledge towards Comprehensive Classification","date":"2018-11-07","arxiv_id":"1811.02796","repositories_listed":1,"syntology":null},{"url":"/paper/evolutionary-data-measures-understanding-the","slug":"evolutionary-data-measures-understanding-the","title":"Evolutionary Data Measures: Understanding the Difficulty of Text Classification Tasks","date":"2018-11-05","arxiv_id":"1811.01910","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":0,"n_honours":3,"n_violates":0,"n_no_contract":0,"n_pointer_only":3,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 3 honoured, 0 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/evolutionary-data-measures-understanding-the#ran","syntology_url":"https://syntology.ai/paper/1811.01910","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1811.01910"}},"official":{"repos":["Wluper/edm"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/transfer-learning-for-time-series","slug":"transfer-learning-for-time-series","title":"Transfer learning for time series classification","date":"2018-11-05","arxiv_id":"1811.01533","repositories_listed":1,"syntology":null},{"url":"/paper/towards-sparse-hierarchical-graph-classifiers","slug":"towards-sparse-hierarchical-graph-classifiers","title":"Towards Sparse Hierarchical Graph Classifiers","date":"2018-11-03","arxiv_id":"1811.01287","repositories_listed":1,"syntology":null},{"url":"/paper/minimizing-close-k-aggregate-loss-improves","slug":"minimizing-close-k-aggregate-loss-improves","title":"Minimizing Close-k Aggregate Loss Improves Classification","date":"2018-11-01","arxiv_id":"1811.00521","repositories_listed":1,"syntology":null},{"url":"/paper/shorten-spatial-spectral-rnn-with-parallel","slug":"shorten-spatial-spectral-rnn-with-parallel","title":"Shorten Spatial-spectral RNN with Parallel-GRU for Hyperspectral Image Classification","date":"2018-10-30","arxiv_id":"1810.12563","repositories_listed":1,"syntology":null},{"url":"/paper/semi-unsupervised-learning-of-human-activity","slug":"semi-unsupervised-learning-of-human-activity","title":"Semi-unsupervised Learning of Human Activity using Deep Generative Models","date":"2018-10-29","arxiv_id":"1810.12176","repositories_listed":1,"syntology":null},{"url":"/paper/convolutional-lstms-for-cloud-robust","slug":"convolutional-lstms-for-cloud-robust","title":"Convolutional LSTMs for Cloud-Robust Segmentation of Remote Sensing Imagery","date":"2018-10-28","arxiv_id":"1811.02471","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/convolutional-lstms-for-cloud-robust#ran","syntology_url":"https://syntology.ai/paper/1811.02471","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1811.02471"}},"official":{"repos":["TUM-LMF/MTLCC"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/fewrel-a-large-scale-supervised-few-shot","slug":"fewrel-a-large-scale-supervised-few-shot","title":"FewRel: A Large-Scale Supervised Few-Shot Relation Classification Dataset with State-of-the-Art Evaluation","date":"2018-10-24","arxiv_id":"1810.10147","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":1,"n_pointer_only":1,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 1 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/fewrel-a-large-scale-supervised-few-shot#ran","syntology_url":"https://syntology.ai/paper/1810.10147","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1810.10147"}},"official":null}},{"url":"/paper/domain-adaptive-segmentation-in-volume","slug":"domain-adaptive-segmentation-in-volume","title":"Domain Adaptive Segmentation in Volume Electron Microscopy Imaging","date":"2018-10-23","arxiv_id":"1810.09734","repositories_listed":1,"syntology":null},{"url":"/paper/compositional-coding-capsule-network-with-k","slug":"compositional-coding-capsule-network-with-k","title":"Compositional Coding Capsule Network with K-Means Routing for Text Classification","date":"2018-10-22","arxiv_id":"1810.09177","repositories_listed":1,"syntology":null},{"url":"/paper/varifocal-net-a-chromosome-classification","slug":"varifocal-net-a-chromosome-classification","title":"Varifocal-Net: A Chromosome Classification Approach using Deep Convolutional Networks","date":"2018-10-13","arxiv_id":"1810.05943","repositories_listed":1,"syntology":null},{"url":"/paper/applications-of-pagerank-to-function","slug":"applications-of-pagerank-to-function","title":"Applications of Graph Integration to Function Comparison and Malware Classification","date":"2018-10-11","arxiv_id":"1810.04789","repositories_listed":1,"syntology":null},{"url":"/paper/classification-using-margin-pursuit","slug":"classification-using-margin-pursuit","title":"Classification using margin pursuit","date":"2018-10-11","arxiv_id":"1810.04863","repositories_listed":1,"syntology":{"n":12,"n_ran":9,"n_constructed":0,"n_ran_checked":9,"n_instrument":0,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":9,"n_pointer_only":0,"phrase":"9 ran (of which 0 constructed an object rather than computing a result; 9 with no instrument failure: 0 honoured, 0 violated, 9 with no contract checked; 0 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/classification-using-margin-pursuit#ran","syntology_url":"https://syntology.ai/paper/1810.04863","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1810.04863"}},"official":{"repos":["feedbackward/catcube"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":0,"n_ran_no_instrument_failure":9,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/bird-species-classification-using-transfer","slug":"bird-species-classification-using-transfer","title":"Bird Species Classification using Transfer Learning with Multistage Training","date":"2018-10-09","arxiv_id":"1810.04250","repositories_listed":1,"syntology":null},{"url":"/paper/deepweeds-a-multiclass-weed-species-image","slug":"deepweeds-a-multiclass-weed-species-image","title":"DeepWeeds: A Multiclass Weed Species Image Dataset for Deep Learning","date":"2018-10-09","arxiv_id":"1810.05726","repositories_listed":1,"syntology":{"n":2,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":1,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/deepweeds-a-multiclass-weed-species-image#ran","syntology_url":"https://syntology.ai/paper/1810.05726","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1810.05726"}},"official":{"repos":["AlexOlsen/DeepWeeds"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/deep-convolutional-gaussian-processes","slug":"deep-convolutional-gaussian-processes","title":"Deep convolutional Gaussian processes","date":"2018-10-06","arxiv_id":"1810.03052","repositories_listed":1,"syntology":{"n":4,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/deep-convolutional-gaussian-processes#ran","syntology_url":"https://syntology.ai/paper/1810.03052","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1810.03052"}},"official":{"repos":["kekeblom/DeepCGP"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/italian-event-detection-goes-deep-learning","slug":"italian-event-detection-goes-deep-learning","title":"Italian Event Detection Goes Deep Learning","date":"2018-10-04","arxiv_id":"1810.02229","repositories_listed":1,"syntology":null},{"url":"/paper/image-and-encoded-text-fusion-for-multi-modal","slug":"image-and-encoded-text-fusion-for-multi-modal","title":"Image and Encoded Text Fusion for Multi-Modal Classification","date":"2018-10-03","arxiv_id":"1810.02001","repositories_listed":1,"syntology":null},{"url":"/paper/a-hierarchical-neural-attention-based-text","slug":"a-hierarchical-neural-attention-based-text","title":"A Hierarchical Neural Attention-based Text Classifier","date":"2018-10-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/aggression-detection-on-social-media-text","slug":"aggression-detection-on-social-media-text","title":"Aggression Detection on Social Media Text Using Deep Neural Networks","date":"2018-10-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/classification-from-positive-unlabeled-and","slug":"classification-from-positive-unlabeled-and","title":"Classification from Positive, Unlabeled and Biased Negative Data","date":"2018-10-01","arxiv_id":"1810.00846","repositories_listed":1,"syntology":{"n":3,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":0,"phrase":"1 ran (of which 0 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) · 2 unverified","sample_list":"/paper/classification-from-positive-unlabeled-and#ran","syntology_url":"https://syntology.ai/paper/1810.00846","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1810.00846"}},"official":null}},{"url":"/paper/classification-using-link-prediction","slug":"classification-using-link-prediction","title":"Classification Using Link Prediction","date":"2018-10-01","arxiv_id":"1810.00717","repositories_listed":1,"syntology":null},{"url":"/paper/datasearch-at-iest-2018-multiple-word","slug":"datasearch-at-iest-2018-multiple-word","title":"DataSEARCH at IEST 2018: Multiple Word Embedding based Models for Implicit Emotion Classification of Tweets with Deep Learning","date":"2018-10-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/emotiklue-at-iest-2018-topic-informed","slug":"emotiklue-at-iest-2018-topic-informed","title":"EmotiKLUE at IEST 2018: Topic-Informed Classification of Implicit Emotions","date":"2018-10-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/increasing-in-class-similarity-by","slug":"increasing-in-class-similarity-by","title":"Increasing In-Class Similarity by Retrofitting Embeddings with Demographic Information","date":"2018-10-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/learnable-pooling-methods-for-video","slug":"learnable-pooling-methods-for-video","title":"Learnable Pooling Methods for Video Classification","date":"2018-10-01","arxiv_id":"1810.00530","repositories_listed":1,"syntology":null},{"url":"/paper/multi-grained-attention-network-for-aspect","slug":"multi-grained-attention-network-for-aspect","title":"Multi-grained Attention Network for Aspect-Level Sentiment Classification","date":"2018-10-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/revisiting-neural-relation-classification-in","slug":"revisiting-neural-relation-classification-in","title":"Revisiting neural relation classification in clinical notes with external information","date":"2018-10-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/self-governing-neural-networks-for-on-device","slug":"self-governing-neural-networks-for-on-device","title":"Self-Governing Neural Networks for On-Device Short Text Classification","date":"2018-10-01","arxiv_id":null,"repositories_listed":1,"syntology":null}],"record_sha256":"435a81f10197b8fa3dd001cf9f4cd5c9cf68fdccf3d183d1eeff49e35defb37e","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}