{"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/11","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":11,"pages_in_order":146,"rows_per_page":100,"rows":[1001,1100],"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/10","next":"/task/classification/papers/12","papers":[{"url":"/paper/pvanet-deep-but-lightweight-neural-networks","slug":"pvanet-deep-but-lightweight-neural-networks","title":"PVANET: Deep but Lightweight Neural Networks for Real-time Object Detection","date":"2016-08-29","arxiv_id":"1608.08021","repositories_listed":2,"syntology":null},{"url":"/paper/survey-of-resampling-techniques-for-improving","slug":"survey-of-resampling-techniques-for-improving","title":"Survey of resampling techniques for improving classification performance in unbalanced datasets","date":"2016-08-22","arxiv_id":"1608.06048","repositories_listed":2,"syntology":null},{"url":"/paper/reweighting-with-boosted-decision-trees","slug":"reweighting-with-boosted-decision-trees","title":"Reweighting with Boosted Decision Trees","date":"2016-08-20","arxiv_id":"1608.05806","repositories_listed":2,"syntology":null},{"url":"/paper/generative-and-discriminative-voxel-modeling","slug":"generative-and-discriminative-voxel-modeling","title":"Generative and Discriminative Voxel Modeling with Convolutional Neural Networks","date":"2016-08-15","arxiv_id":"1608.04236","repositories_listed":2,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":0,"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) · 0 unverified","sample_list":"/paper/generative-and-discriminative-voxel-modeling#ran","syntology_url":"https://syntology.ai/paper/1608.04236","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1608.04236"}},"official":{"repos":["ajbrock/Generative-and-Discriminative-Voxel-Modeling"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/wikireading-a-novel-large-scale-language","slug":"wikireading-a-novel-large-scale-language","title":"WikiReading: A Novel Large-scale Language Understanding Task over Wikipedia","date":"2016-08-11","arxiv_id":"1608.03542","repositories_listed":2,"syntology":null},{"url":"/paper/unsupervised-feature-learning-based-on-deep","slug":"unsupervised-feature-learning-based-on-deep","title":"Unsupervised Feature Learning Based on Deep Models for Environmental Audio Tagging","date":"2016-07-13","arxiv_id":"1607.03681","repositories_listed":2,"syntology":null},{"url":"/paper/deep-reconstruction-classification-networks","slug":"deep-reconstruction-classification-networks","title":"Deep Reconstruction-Classification Networks for Unsupervised Domain Adaptation","date":"2016-07-12","arxiv_id":"1607.03516","repositories_listed":2,"syntology":null},{"url":"/paper/stock-trend-prediction-using-news-sentiment","slug":"stock-trend-prediction-using-news-sentiment","title":"Stock trend prediction using news sentiment analysis","date":"2016-07-07","arxiv_id":"1607.01958","repositories_listed":2,"syntology":null},{"url":"/paper/adanet-adaptive-structural-learning-of","slug":"adanet-adaptive-structural-learning-of","title":"AdaNet: Adaptive Structural Learning of Artificial Neural Networks","date":"2016-07-05","arxiv_id":"1607.01097","repositories_listed":2,"syntology":null},{"url":"/paper/tagger-deep-unsupervised-perceptual-grouping","slug":"tagger-deep-unsupervised-perceptual-grouping","title":"Tagger: Deep Unsupervised Perceptual Grouping","date":"2016-06-21","arxiv_id":"1606.06724","repositories_listed":2,"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/tagger-deep-unsupervised-perceptual-grouping#ran","syntology_url":"https://syntology.ai/paper/1606.06724","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1606.06724"}},"official":{"repos":["CuriousAI/tagger"],"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/adversarial-deep-averaging-networks-for-cross","slug":"adversarial-deep-averaging-networks-for-cross","title":"Adversarial Deep Averaging Networks for Cross-Lingual Sentiment Classification","date":"2016-06-06","arxiv_id":"1606.01614","repositories_listed":2,"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/adversarial-deep-averaging-networks-for-cross#ran","syntology_url":"https://syntology.ai/paper/1606.01614","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1606.01614"}},"official":{"repos":["ccsasuke/adan"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/recursive-autoconvolution-for-unsupervised","slug":"recursive-autoconvolution-for-unsupervised","title":"Recursive Autoconvolution for Unsupervised Learning of Convolutional Neural Networks","date":"2016-06-02","arxiv_id":"1606.00611","repositories_listed":2,"syntology":null},{"url":"/paper/online-ssvep-based-bci-using-riemannian","slug":"online-ssvep-based-bci-using-riemannian","title":"Online SSVEP-based BCI using Riemannian geometry","date":"2016-05-26","arxiv_id":null,"repositories_listed":2,"syntology":null},{"url":"/paper/rationale-augmented-convolutional-neural","slug":"rationale-augmented-convolutional-neural","title":"Rationale-Augmented Convolutional Neural Networks for Text Classification","date":"2016-05-14","arxiv_id":"1605.04469","repositories_listed":2,"syntology":null},{"url":"/paper/going-deeper-with-contextual-cnn-for","slug":"going-deeper-with-contextual-cnn-for","title":"Going Deeper with Contextual CNN for Hyperspectral Image Classification","date":"2016-04-12","arxiv_id":"1604.03519","repositories_listed":2,"syntology":null},{"url":"/paper/volumetric-and-multi-view-cnns-for-object","slug":"volumetric-and-multi-view-cnns-for-object","title":"Volumetric and Multi-View CNNs for Object Classification on 3D Data","date":"2016-04-12","arxiv_id":"1604.03265","repositories_listed":2,"syntology":null},{"url":"/paper/ntu-rgbd-a-large-scale-dataset-for-3d-human","slug":"ntu-rgbd-a-large-scale-dataset-for-3d-human","title":"NTU RGB+D: A Large Scale Dataset for 3D Human Activity Analysis","date":"2016-04-11","arxiv_id":"1604.02808","repositories_listed":2,"syntology":null},{"url":"/paper/visualization-regularizers-for-neural-network","slug":"visualization-regularizers-for-neural-network","title":"Visualization Regularizers for Neural Network based Image Recognition","date":"2016-04-10","arxiv_id":"1604.02646","repositories_listed":2,"syntology":null},{"url":"/paper/cost-sensitive-label-embedding-for-multi","slug":"cost-sensitive-label-embedding-for-multi","title":"Cost-Sensitive Label Embedding for Multi-Label Classification","date":"2016-03-30","arxiv_id":"1603.09048","repositories_listed":2,"syntology":null},{"url":"/paper/multi-scale-convolutional-neural-networks-for","slug":"multi-scale-convolutional-neural-networks-for","title":"Multi-Scale Convolutional Neural Networks for Time Series Classification","date":"2016-03-22","arxiv_id":"1603.06995","repositories_listed":2,"syntology":null},{"url":"/paper/sequential-short-text-classification-with","slug":"sequential-short-text-classification-with","title":"Sequential Short-Text Classification with Recurrent and Convolutional Neural Networks","date":"2016-03-12","arxiv_id":"1603.03827","repositories_listed":2,"syntology":null},{"url":"/paper/multilingual-twitter-sentiment-classification","slug":"multilingual-twitter-sentiment-classification","title":"Multilingual Twitter Sentiment Classification: The Role of Human Annotators","date":"2016-02-24","arxiv_id":"1602.07563","repositories_listed":2,"syntology":null},{"url":"/paper/learning-a-low-rank-shared-dictionary-for","slug":"learning-a-low-rank-shared-dictionary-for","title":"Learning a low-rank shared dictionary for object classification","date":"2016-01-31","arxiv_id":"1602.00310","repositories_listed":2,"syntology":null},{"url":"/paper/using-filter-banks-in-convolutional-neural","slug":"using-filter-banks-in-convolutional-neural","title":"Using Filter Banks in Convolutional Neural Networks for Texture Classification","date":"2016-01-12","arxiv_id":"1601.02919","repositories_listed":2,"syntology":null},{"url":"/paper/learning-local-image-descriptors-with-deep","slug":"learning-local-image-descriptors-with-deep","title":"Learning Local Image Descriptors with Deep Siamese and Triplet Convolutional Networks by Minimising Global Loss Functions","date":"2015-12-31","arxiv_id":"1512.09272","repositories_listed":2,"syntology":null},{"url":"/paper/video-captioning-with-recurrent-networks","slug":"video-captioning-with-recurrent-networks","title":"Video captioning with recurrent networks based on frame- and video-level features and visual content classification","date":"2015-12-09","arxiv_id":"1512.02949","repositories_listed":2,"syntology":null},{"url":"/paper/mxnet-a-flexible-and-efficient-machine","slug":"mxnet-a-flexible-and-efficient-machine","title":"MXNet: A Flexible and Efficient Machine Learning Library for Heterogeneous Distributed Systems","date":"2015-12-03","arxiv_id":"1512.01274","repositories_listed":2,"syntology":{"n":14,"n_ran":11,"n_constructed":0,"n_ran_checked":11,"n_instrument":0,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":11,"n_pointer_only":0,"phrase":"11 ran (of which 0 constructed an object rather than computing a result; 11 with no instrument failure: 0 honoured, 0 violated, 11 with no contract checked; 0 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/mxnet-a-flexible-and-efficient-machine#ran","syntology_url":"https://syntology.ai/paper/1512.01274","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1512.01274"}},"official":null}},{"url":"/paper/metric-learning-with-adaptive-density","slug":"metric-learning-with-adaptive-density","title":"Metric Learning with Adaptive Density Discrimination","date":"2015-11-18","arxiv_id":"1511.05939","repositories_listed":2,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":0,"n_honours":0,"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; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/metric-learning-with-adaptive-density#ran","syntology_url":"https://syntology.ai/paper/1511.05939","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1511.05939"}},"official":null}},{"url":"/paper/traffic-sign-classification-using-deep","slug":"traffic-sign-classification-using-deep","title":"Traffic Sign Classification Using Deep Inception Based Convolutional Networks","date":"2015-11-10","arxiv_id":"1511.02992","repositories_listed":2,"syntology":null},{"url":"/paper/multiple-instance-dictionary-learning-using","slug":"multiple-instance-dictionary-learning-using","title":"Multiple Instance Dictionary Learning using Functions of Multiple Instances","date":"2015-11-09","arxiv_id":"1511.02825","repositories_listed":2,"syntology":null},{"url":"/paper/the-variational-fair-autoencoder","slug":"the-variational-fair-autoencoder","title":"The Variational Fair Autoencoder","date":"2015-11-03","arxiv_id":"1511.00830","repositories_listed":2,"syntology":null},{"url":"/paper/spiking-deep-networks-with-lif-neurons","slug":"spiking-deep-networks-with-lif-neurons","title":"Spiking Deep Networks with LIF Neurons","date":"2015-10-29","arxiv_id":"1510.08829","repositories_listed":2,"syntology":null},{"url":"/paper/learning-multi-domain-convolutional-neural","slug":"learning-multi-domain-convolutional-neural","title":"Learning Multi-Domain Convolutional Neural Networks for Visual Tracking","date":"2015-10-27","arxiv_id":"1510.07945","repositories_listed":2,"syntology":null},{"url":"/paper/age-and-gender-classification-using","slug":"age-and-gender-classification-using","title":"Age and Gender Classification using Convolutional Neural Networks","date":"2015-10-26","arxiv_id":null,"repositories_listed":2,"syntology":null},{"url":"/paper/grarep-learning-graph-representations-with","slug":"grarep-learning-graph-representations-with","title":"GraRep: Learning Graph Representations with Global Structural Information","date":"2015-10-17","arxiv_id":null,"repositories_listed":2,"syntology":null},{"url":"/paper/neural-networks-with-few-multiplications","slug":"neural-networks-with-few-multiplications","title":"Neural Networks with Few Multiplications","date":"2015-10-11","arxiv_id":"1510.03009","repositories_listed":2,"syntology":null},{"url":"/paper/ranger-a-fast-implementation-of-random","slug":"ranger-a-fast-implementation-of-random","title":"ranger: A Fast Implementation of Random Forests for High Dimensional Data in C++ and R","date":"2015-08-18","arxiv_id":"1508.04409","repositories_listed":2,"syntology":null},{"url":"/paper/molding-cnns-for-text-non-linear-non","slug":"molding-cnns-for-text-non-linear-non","title":"Molding CNNs for text: non-linear, non-consecutive convolutions","date":"2015-08-17","arxiv_id":"1508.04112","repositories_listed":2,"syntology":null},{"url":"/paper/fairness-constraints-mechanisms-for-fair","slug":"fairness-constraints-mechanisms-for-fair","title":"Fairness Constraints: Mechanisms for Fair Classification","date":"2015-07-19","arxiv_id":"1507.05259","repositories_listed":2,"syntology":null},{"url":"/paper/strategic-classification","slug":"strategic-classification","title":"Strategic Classification","date":"2015-06-23","arxiv_id":"1506.06980","repositories_listed":2,"syntology":null},{"url":"/paper/histopathological-image-classification-using","slug":"histopathological-image-classification-using","title":"Histopathological Image Classification using Discriminative Feature-oriented Dictionary Learning","date":"2015-06-16","arxiv_id":"1506.05032","repositories_listed":2,"syntology":null},{"url":"/paper/on-the-job-learning-with-bayesian-decision","slug":"on-the-job-learning-with-bayesian-decision","title":"On-the-Job Learning with Bayesian Decision Theory","date":"2015-06-10","arxiv_id":"1506.03140","repositories_listed":2,"syntology":null},{"url":"/paper/contextual-action-recognition-with-rcnn","slug":"contextual-action-recognition-with-rcnn","title":"Contextual Action Recognition with R*CNN","date":"2015-05-05","arxiv_id":"1505.01197","repositories_listed":2,"syntology":null},{"url":"/paper/empirical-evaluation-of-rectified-activations","slug":"empirical-evaluation-of-rectified-activations","title":"Empirical Evaluation of Rectified Activations in Convolutional Network","date":"2015-05-05","arxiv_id":"1505.00853","repositories_listed":2,"syntology":null},{"url":"/paper/classifying-relations-by-ranking-with","slug":"classifying-relations-by-ranking-with","title":"Classifying Relations by Ranking with Convolutional Neural Networks","date":"2015-04-24","arxiv_id":"1504.06580","repositories_listed":2,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":0,"n_honours":0,"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; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/classifying-relations-by-ranking-with#ran","syntology_url":"https://syntology.ai/paper/1504.06580","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1504.06580"}},"official":null}},{"url":"/paper/label-embedding-for-image-classification","slug":"label-embedding-for-image-classification","title":"Label-Embedding for Image Classification","date":"2015-03-30","arxiv_id":"1503.08677","repositories_listed":2,"syntology":null},{"url":"/paper/rotation-invariant-convolutional-neural","slug":"rotation-invariant-convolutional-neural","title":"Rotation-invariant convolutional neural networks for galaxy morphology prediction","date":"2015-03-24","arxiv_id":"1503.07077","repositories_listed":2,"syntology":null},{"url":"/paper/deep-learning-with-limited-numerical","slug":"deep-learning-with-limited-numerical","title":"Deep Learning with Limited Numerical Precision","date":"2015-02-09","arxiv_id":"1502.02551","repositories_listed":2,"syntology":null},{"url":"/paper/text-understanding-from-scratch","slug":"text-understanding-from-scratch","title":"Text Understanding from Scratch","date":"2015-02-05","arxiv_id":"1502.01710","repositories_listed":2,"syntology":null},{"url":"/paper/naive-bayes-and-text-classification-i","slug":"naive-bayes-and-text-classification-i","title":"Naive Bayes and Text Classification I - Introduction and Theory","date":"2014-10-16","arxiv_id":"1410.5329","repositories_listed":2,"syntology":null},{"url":"/paper/bilbowa-fast-bilingual-distributed","slug":"bilbowa-fast-bilingual-distributed","title":"BilBOWA: Fast Bilingual Distributed Representations without Word Alignments","date":"2014-10-09","arxiv_id":"1410.2455","repositories_listed":2,"syntology":null},{"url":"/paper/evaluation-of-output-embeddings-for-fine","slug":"evaluation-of-output-embeddings-for-fine","title":"Evaluation of Output Embeddings for Fine-Grained Image Classification","date":"2014-09-30","arxiv_id":"1409.8403","repositories_listed":2,"syntology":null},{"url":"/paper/relation-classification-via-convolutional","slug":"relation-classification-via-convolutional","title":"Relation Classification via Convolutional Deep Neural Network","date":"2014-08-01","arxiv_id":null,"repositories_listed":2,"syntology":null},{"url":"/paper/discriminative-unsupervised-feature-learning","slug":"discriminative-unsupervised-feature-learning","title":"Discriminative Unsupervised Feature Learning with Exemplar Convolutional Neural Networks","date":"2014-06-26","arxiv_id":"1406.6909","repositories_listed":2,"syntology":null},{"url":"/paper/caffe-convolutional-architecture-for-fast","slug":"caffe-convolutional-architecture-for-fast","title":"Caffe: Convolutional Architecture for Fast Feature Embedding","date":"2014-06-20","arxiv_id":"1408.5093","repositories_listed":2,"syntology":null},{"url":"/paper/pcanet-a-simple-deep-learning-baseline-for","slug":"pcanet-a-simple-deep-learning-baseline-for","title":"PCANet: A Simple Deep Learning Baseline for Image Classification?","date":"2014-04-14","arxiv_id":"1404.3606","repositories_listed":2,"syntology":{"n":3,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":1,"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) · 1 unverified","sample_list":"/paper/pcanet-a-simple-deep-learning-baseline-for#ran","syntology_url":"https://syntology.ai/paper/1404.3606","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1404.3606"}},"official":null}},{"url":"/paper/densenet-implementing-efficient-convnet","slug":"densenet-implementing-efficient-convnet","title":"DenseNet: Implementing Efficient ConvNet Descriptor Pyramids","date":"2014-04-07","arxiv_id":"1404.1869","repositories_listed":2,"syntology":null},{"url":"/paper/evaluation-measures-for-hierarchical","slug":"evaluation-measures-for-hierarchical","title":"Evaluation Measures for Hierarchical Classification: a unified view and novel approaches","date":"2013-06-28","arxiv_id":"1306.6802","repositories_listed":2,"syntology":{"n":17,"n_ran":16,"n_constructed":0,"n_ran_checked":16,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":16,"n_pointer_only":0,"phrase":"16 ran (of which 0 constructed an object rather than computing a result; 16 with no instrument failure: 0 honoured, 0 violated, 16 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/evaluation-measures-for-hierarchical#ran","syntology_url":"https://syntology.ai/paper/1306.6802","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1306.6802"}},"official":null}},{"url":"/paper/kernel-principal-component-analysis-and-its","slug":"kernel-principal-component-analysis-and-its","title":"Kernel Principal Component Analysis and its Applications in Face Recognition and Active Shape Models","date":"2012-07-15","arxiv_id":"1207.3538","repositories_listed":2,"syntology":null},{"url":"/paper/12043968","slug":"12043968","title":"Convolutional Neural Networks Applied to House Numbers Digit Classification","date":"2012-04-18","arxiv_id":"1204.3968","repositories_listed":2,"syntology":null},{"url":"/paper/11125745","slug":"11125745","title":"Bayesian Active Learning for Classification and Preference Learning","date":"2011-12-24","arxiv_id":"1112.5745","repositories_listed":2,"syntology":null},{"url":"/paper/using-instruction-tuned-large-language-models","slug":"using-instruction-tuned-large-language-models","title":"Using Instruction-Tuned Large Language Models to Identify Indicators of Vulnerability in Police Incident Narratives","date":"2024-12-16","arxiv_id":"2412.11878","repositories_listed":1,"syntology":null},{"url":"/paper/harmonic-nas-hardware-aware-multimodal-neural","slug":"harmonic-nas-hardware-aware-multimodal-neural","title":"Harmonic-NAS: Hardware-Aware Multimodal Neural Architecture Search on Resource-constrained Devices","date":"2023-09-12","arxiv_id":"2309.06612","repositories_listed":1,"syntology":{"n":2,"n_ran":0,"n_constructed":0,"n_ran_checked":0,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":2,"phrase":"0 ran · 2 unverified","sample_list":"/paper/harmonic-nas-hardware-aware-multimodal-neural#ran","syntology_url":"https://syntology.ai/paper/2309.06612","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2309.06612"}},"official":{"repos":["mohamed-imed-eddine/harmonic-nas"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":2,"ran_from_kinds":[]}}},{"url":"/paper/strapper-preference-based-reinforcement","slug":"strapper-preference-based-reinforcement","title":"STRAPPER: Preference-based Reinforcement Learning via Self-training Augmentation and Peer Regularization","date":"2023-07-19","arxiv_id":"2307.09692","repositories_listed":1,"syntology":null},{"url":"/paper/thraws-a-novel-dataset-for-thermal-hotspots","slug":"thraws-a-novel-dataset-for-thermal-hotspots","title":"Unlocking the Use of Raw Multispectral Earth Observation Imagery for Onboard Artificial Intelligence","date":"2023-05-12","arxiv_id":"2305.11891","repositories_listed":1,"syntology":null},{"url":"/paper/vne-an-effective-method-for-improving-deep","slug":"vne-an-effective-method-for-improving-deep","title":"VNE: An Effective Method for Improving Deep Representation by Manipulating Eigenvalue Distribution","date":"2023-04-04","arxiv_id":"2304.01434","repositories_listed":1,"syntology":null},{"url":"/paper/continuous-indeterminate-probability-neural","slug":"continuous-indeterminate-probability-neural","title":"Continuous Indeterminate Probability Neural Network","date":"2023-03-23","arxiv_id":"2303.12964","repositories_listed":1,"syntology":null},{"url":"/paper/fair-and-optimal-classification-via","slug":"fair-and-optimal-classification-via","title":"Fair and Optimal Classification via Post-Processing","date":"2022-11-03","arxiv_id":"2211.01528","repositories_listed":1,"syntology":{"n":1,"n_ran":0,"n_constructed":0,"n_ran_checked":0,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"0 ran · 1 unverified","sample_list":"/paper/fair-and-optimal-classification-via#ran","syntology_url":"https://syntology.ai/paper/2211.01528","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2211.01528"}},"official":{"repos":["rxian/fair-classification"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":[]}}},{"url":"/paper/hierarchical-classification-at-multiple","slug":"hierarchical-classification-at-multiple","title":"Hierarchical classification at multiple operating points","date":"2022-10-19","arxiv_id":"2210.10929","repositories_listed":1,"syntology":{"n":4,"n_ran":2,"n_constructed":0,"n_ran_checked":0,"n_instrument":2,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":4,"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) · 2 unverified","sample_list":"/paper/hierarchical-classification-at-multiple#ran","syntology_url":"https://syntology.ai/paper/2210.10929","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2210.10929"}},"official":{"repos":["jvlmdr/hiercls"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/association-graph-learning-for-multi-task","slug":"association-graph-learning-for-multi-task","title":"Association Graph Learning for Multi-Task Classification with Category Shifts","date":"2022-10-10","arxiv_id":"2210.04637","repositories_listed":1,"syntology":null},{"url":"/paper/svl-adapter-self-supervised-adapter-for","slug":"svl-adapter-self-supervised-adapter-for","title":"SVL-Adapter: Self-Supervised Adapter for Vision-Language Pretrained Models","date":"2022-10-07","arxiv_id":"2210.03794","repositories_listed":1,"syntology":null},{"url":"/paper/explainable-abuse-detection-as-intent","slug":"explainable-abuse-detection-as-intent","title":"Explainable Abuse Detection as Intent Classification and Slot Filling","date":"2022-10-06","arxiv_id":"2210.02659","repositories_listed":1,"syntology":null},{"url":"/paper/a-kernel-based-quantum-random-forest-for","slug":"a-kernel-based-quantum-random-forest-for","title":"A kernel-based quantum random forest for improved classification","date":"2022-10-05","arxiv_id":"2210.02355","repositories_listed":1,"syntology":null},{"url":"/paper/learning-the-spectrogram-temporal-resolution","slug":"learning-the-spectrogram-temporal-resolution","title":"Learning Temporal Resolution in Spectrogram for Audio Classification","date":"2022-10-04","arxiv_id":"2210.01719","repositories_listed":1,"syntology":null},{"url":"/paper/domain-adaptation-for-question-answering-via","slug":"domain-adaptation-for-question-answering-via","title":"Domain Adaptation for Question Answering via Question Classification","date":"2022-09-12","arxiv_id":"2209.04998","repositories_listed":1,"syntology":null},{"url":"/paper/supervised-dimensionality-reduction-and-1","slug":"supervised-dimensionality-reduction-and-1","title":"Supervised Dimensionality Reduction and Image Classification Utilizing Convolutional Autoencoders","date":"2022-08-25","arxiv_id":"2208.12152","repositories_listed":1,"syntology":null},{"url":"/paper/semi-supervised-classification-using-a","slug":"semi-supervised-classification-using-a","title":"Semi-supervised classification using a supervised autoencoder for biomedical applications","date":"2022-08-22","arxiv_id":"2208.10315","repositories_listed":1,"syntology":null},{"url":"/paper/stop-hop-early-classification-of-irregular","slug":"stop-hop-early-classification-of-irregular","title":"Stop&Hop: Early Classification of Irregular Time Series","date":"2022-08-21","arxiv_id":"2208.09795","repositories_listed":1,"syntology":null},{"url":"/paper/autism-spectrum-disorder-classification-based","slug":"autism-spectrum-disorder-classification-based","title":"Autism spectrum disorder classification based on interpersonal neural synchrony: Can classification be improved by dyadic neural biomarkers using unsupervised graph representation learning?","date":"2022-08-17","arxiv_id":"2208.08902","repositories_listed":1,"syntology":null},{"url":"/paper/towards-interpretable-sleep-stage","slug":"towards-interpretable-sleep-stage","title":"Toward Interpretable Sleep Stage Classification Using Cross-Modal Transformers","date":"2022-08-15","arxiv_id":"2208.06991","repositories_listed":1,"syntology":null},{"url":"/paper/visual-localization-via-few-shot-scene-region","slug":"visual-localization-via-few-shot-scene-region","title":"Visual Localization via Few-Shot Scene Region Classification","date":"2022-08-14","arxiv_id":"2208.06933","repositories_listed":1,"syntology":null},{"url":"/paper/on-the-limitations-of-continual-learning-for","slug":"on-the-limitations-of-continual-learning-for","title":"On the Limitations of Continual Learning for Malware Classification","date":"2022-08-13","arxiv_id":"2208.06568","repositories_listed":1,"syntology":null},{"url":"/paper/scalable-quantum-neural-networks-for","slug":"scalable-quantum-neural-networks-for","title":"Scalable Quantum Neural Networks for Classification","date":"2022-08-04","arxiv_id":"2208.07719","repositories_listed":1,"syntology":null},{"url":"/paper/inductive-and-transductive-few-shot-video","slug":"inductive-and-transductive-few-shot-video","title":"Inductive and Transductive Few-Shot Video Classification via Appearance and Temporal Alignments","date":"2022-07-21","arxiv_id":"2207.10785","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":1,"n_ran_checked":1,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":0,"phrase":"1 ran (of which 1 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified; the one sample that ran constructed an object rather than computing a result","sample_list":"/paper/inductive-and-transductive-few-shot-video#ran","syntology_url":"https://syntology.ai/paper/2207.10785","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2207.10785"}},"official":{"repos":["vinairesearch/fsvc-ata"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":1,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/large-scale-radio-frequency-signal","slug":"large-scale-radio-frequency-signal","title":"Large Scale Radio Frequency Signal Classification","date":"2022-07-20","arxiv_id":"2207.09918","repositories_listed":1,"syntology":null},{"url":"/paper/visual-knowledge-tracing","slug":"visual-knowledge-tracing","title":"Visual Knowledge Tracing","date":"2022-07-20","arxiv_id":"2207.10157","repositories_listed":1,"syntology":{"n":1,"n_ran":0,"n_constructed":0,"n_ran_checked":0,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":1,"phrase":"0 ran · 1 unverified","sample_list":"/paper/visual-knowledge-tracing#ran","syntology_url":"https://syntology.ai/paper/2207.10157","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2207.10157"}},"official":{"repos":["nkondapa/visualknowledgetracing"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":[]}}},{"url":"/paper/contributions-of-shape-texture-and-color-in","slug":"contributions-of-shape-texture-and-color-in","title":"Contributions of Shape, Texture, and Color in Visual Recognition","date":"2022-07-19","arxiv_id":"2207.09510","repositories_listed":1,"syntology":{"n":8,"n_ran":5,"n_constructed":0,"n_ran_checked":3,"n_instrument":2,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":0,"phrase":"5 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; 2 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/contributions-of-shape-texture-and-color-in#ran","syntology_url":"https://syntology.ai/paper/2207.09510","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2207.09510"}},"official":{"repos":["gyhandy/humanoid-vision-engine"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/semi-supervised-temporal-action-detection","slug":"semi-supervised-temporal-action-detection","title":"Semi-Supervised Temporal Action Detection with Proposal-Free Masking","date":"2022-07-14","arxiv_id":"2207.07059","repositories_listed":1,"syntology":{"n":4,"n_ran":3,"n_constructed":1,"n_ran_checked":3,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":4,"phrase":"3 ran (of which 1 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/semi-supervised-temporal-action-detection#ran","syntology_url":"https://syntology.ai/paper/2207.07059","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2207.07059"}},"official":{"repos":["sauradip/spot"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":1,"n_ran_no_instrument_failure":3,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/a-data-efficient-deep-learning-framework-for","slug":"a-data-efficient-deep-learning-framework-for","title":"A Data-Efficient Deep Learning Framework for Segmentation and Classification of Histopathology Images","date":"2022-07-13","arxiv_id":"2207.06489","repositories_listed":1,"syntology":null},{"url":"/paper/revbifpn-the-fully-reversible-bidirectional","slug":"revbifpn-the-fully-reversible-bidirectional","title":"RevBiFPN: The Fully Reversible Bidirectional Feature Pyramid Network","date":"2022-06-28","arxiv_id":"2206.14098","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":2,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":2,"n_no_contract":0,"n_pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 2 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/revbifpn-the-fully-reversible-bidirectional#ran","syntology_url":"https://syntology.ai/paper/2206.14098","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2206.14098"}},"official":{"repos":["cerebrasresearch/revbifpn"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/truly-unordered-probabilistic-rule-sets-for","slug":"truly-unordered-probabilistic-rule-sets-for","title":"Truly Unordered Probabilistic Rule Sets for Multi-class Classification","date":"2022-06-17","arxiv_id":"2206.08804","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":2,"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/truly-unordered-probabilistic-rule-sets-for#ran","syntology_url":"https://syntology.ai/paper/2206.08804","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2206.08804"}},"official":{"repos":["ylincen/turs"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/differentiable-top-k-classification-learning-1","slug":"differentiable-top-k-classification-learning-1","title":"Differentiable Top-k Classification Learning","date":"2022-06-15","arxiv_id":"2206.07290","repositories_listed":1,"syntology":{"n":12,"n_ran":9,"n_constructed":0,"n_ran_checked":7,"n_instrument":2,"n_unverified":3,"n_honours":1,"n_violates":0,"n_no_contract":6,"n_pointer_only":0,"phrase":"9 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 1 honoured, 0 violated, 6 with no contract checked; 2 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/differentiable-top-k-classification-learning-1#ran","syntology_url":"https://syntology.ai/paper/2206.07290","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2206.07290"}},"official":{"repos":["felix-petersen/difftopk"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/evaluating-histopathology-transfer-learning","slug":"evaluating-histopathology-transfer-learning","title":"Evaluating histopathology transfer learning with ChampKit","date":"2022-06-14","arxiv_id":"2206.06862","repositories_listed":1,"syntology":{"n":1,"n_ran":0,"n_constructed":0,"n_ran_checked":0,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"0 ran · 1 unverified","sample_list":"/paper/evaluating-histopathology-transfer-learning#ran","syntology_url":"https://syntology.ai/paper/2206.06862","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2206.06862"}},"official":{"repos":["kaczmarj/champkit"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":[]}}},{"url":"/paper/lift-language-interfaced-fine-tuning-for-non","slug":"lift-language-interfaced-fine-tuning-for-non","title":"LIFT: Language-Interfaced Fine-Tuning for Non-Language Machine Learning Tasks","date":"2022-06-14","arxiv_id":"2206.06565","repositories_listed":1,"syntology":{"n":5,"n_ran":5,"n_constructed":0,"n_ran_checked":4,"n_instrument":1,"n_unverified":0,"n_honours":2,"n_violates":2,"n_no_contract":0,"n_pointer_only":0,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 2 honoured, 2 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/lift-language-interfaced-fine-tuning-for-non#ran","syntology_url":"https://syntology.ai/paper/2206.06565","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2206.06565"}},"official":{"repos":["uw-madison-lee-lab/languageinterfacedfinetuning"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/localizing-semantic-patches-for-accelerating","slug":"localizing-semantic-patches-for-accelerating","title":"Localizing Semantic Patches for Accelerating Image Classification","date":"2022-06-07","arxiv_id":"2206.03367","repositories_listed":1,"syntology":null},{"url":"/paper/hopular-modern-hopfield-networks-for-tabular-1","slug":"hopular-modern-hopfield-networks-for-tabular-1","title":"Hopular: Modern Hopfield Networks for Tabular Data","date":"2022-06-01","arxiv_id":"2206.00664","repositories_listed":1,"syntology":{"n":1,"n_ran":0,"n_constructed":0,"n_ran_checked":0,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"0 ran · 1 unverified","sample_list":"/paper/hopular-modern-hopfield-networks-for-tabular-1#ran","syntology_url":"https://syntology.ai/paper/2206.00664","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2206.00664"}},"official":{"repos":["ml-jku/hopular"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":[]}}},{"url":"/paper/failure-detection-in-medical-image","slug":"failure-detection-in-medical-image","title":"Failure Detection in Medical Image Classification: A Reality Check and Benchmarking Testbed","date":"2022-05-27","arxiv_id":"2205.14094","repositories_listed":1,"syntology":null},{"url":"/paper/progressive-class-semantic-matching-for-semi-1","slug":"progressive-class-semantic-matching-for-semi-1","title":"Progressive Class Semantic Matching for Semi-supervised Text Classification","date":"2022-05-20","arxiv_id":"2205.10189","repositories_listed":1,"syntology":null},{"url":"/paper/a-classification-of-g-invariant-shallow","slug":"a-classification-of-g-invariant-shallow","title":"A Classification of $G$-invariant Shallow Neural Networks","date":"2022-05-18","arxiv_id":"2205.09219","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":0,"n_instrument":2,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":2,"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) · 0 unverified","sample_list":"/paper/a-classification-of-g-invariant-shallow#ran","syntology_url":"https://syntology.ai/paper/2205.09219","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2205.09219"}},"official":{"repos":["dagrawa2/gsnn_classification_code"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/few-shot-image-classification-benchmarks-are","slug":"few-shot-image-classification-benchmarks-are","title":"Few-Shot Image Classification Benchmarks are Too Far From Reality: Build Back Better with Semantic Task Sampling","date":"2022-05-10","arxiv_id":"2205.05155","repositories_listed":1,"syntology":null}],"record_sha256":"fb32b4e4f7d772d2179c76f11a8513310d1a14a57cccd83330661ab7dbd36fa9","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}