{"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/34","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":34,"pages_in_order":146,"rows_per_page":100,"rows":[3301,3400],"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/33","next":"/task/classification/papers/35","papers":[{"url":"/paper/classification-of-point-cloud-scenes-with","slug":"classification-of-point-cloud-scenes-with","title":"Classification of Point Cloud Scenes with Multiscale Voxel Deep Network","date":"2018-04-10","arxiv_id":"1804.03583","repositories_listed":1,"syntology":null},{"url":"/paper/amnet-memorability-estimation-with-attention","slug":"amnet-memorability-estimation-with-attention","title":"AMNet: Memorability Estimation with Attention","date":"2018-04-09","arxiv_id":"1804.03115","repositories_listed":1,"syntology":null},{"url":"/paper/few-shot-text-classification-with-pre-trained","slug":"few-shot-text-classification-with-pre-trained","title":"Few-Shot Text Classification with Pre-Trained Word Embeddings and a Human in the Loop","date":"2018-04-05","arxiv_id":"1804.02063","repositories_listed":1,"syntology":null},{"url":"/paper/staingan-stain-style-transfer-for-digital","slug":"staingan-stain-style-transfer-for-digital","title":"StainGAN: Stain Style Transfer for Digital Histological Images","date":"2018-04-04","arxiv_id":"1804.01601","repositories_listed":1,"syntology":null},{"url":"/paper/unsupervised-semantic-based-aggregation-of","slug":"unsupervised-semantic-based-aggregation-of","title":"Unsupervised Semantic-based Aggregation of Deep Convolutional Features","date":"2018-04-03","arxiv_id":"1804.01422","repositories_listed":1,"syntology":null},{"url":"/paper/weakly-supervised-instance-segmentation-using-1","slug":"weakly-supervised-instance-segmentation-using-1","title":"Weakly Supervised Instance Segmentation using Class Peak Response","date":"2018-04-03","arxiv_id":"1804.00880","repositories_listed":1,"syntology":null},{"url":"/paper/confidence-from-invariance-to-image","slug":"confidence-from-invariance-to-image","title":"Confidence from Invariance to Image Transformations","date":"2018-04-02","arxiv_id":"1804.00657","repositories_listed":1,"syntology":null},{"url":"/paper/learning-latent-opinions-for-aspect-level","slug":"learning-latent-opinions-for-aspect-level","title":"Learning Latent Opinions for Aspect-Level Sentiment Classification","date":"2018-04-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/joint-optimization-framework-for-learning","slug":"joint-optimization-framework-for-learning","title":"Joint Optimization Framework for Learning with Noisy Labels","date":"2018-03-30","arxiv_id":"1803.11364","repositories_listed":1,"syntology":null},{"url":"/paper/learning-to-generate-classifiers","slug":"learning-to-generate-classifiers","title":"Learning to generate classifiers","date":"2018-03-30","arxiv_id":"1803.11373","repositories_listed":1,"syntology":null},{"url":"/paper/parallel-grid-pooling-for-data-augmentation","slug":"parallel-grid-pooling-for-data-augmentation","title":"Parallel Grid Pooling for Data Augmentation","date":"2018-03-30","arxiv_id":"1803.11370","repositories_listed":1,"syntology":null},{"url":"/paper/ai-blue-book-vehicle-price-prediction-using","slug":"ai-blue-book-vehicle-price-prediction-using","title":"AI Blue Book: Vehicle Price Prediction using Visual Features","date":"2018-03-29","arxiv_id":"1803.11227","repositories_listed":1,"syntology":null},{"url":"/paper/mining-on-manifolds-metric-learning-without","slug":"mining-on-manifolds-metric-learning-without","title":"Mining on Manifolds: Metric Learning without Labels","date":"2018-03-29","arxiv_id":"1803.11095","repositories_listed":1,"syntology":null},{"url":"/paper/graphite-iterative-generative-modeling-of","slug":"graphite-iterative-generative-modeling-of","title":"Graphite: Iterative Generative Modeling of Graphs","date":"2018-03-28","arxiv_id":"1803.10459","repositories_listed":1,"syntology":null},{"url":"/paper/learning-deep-representations-with","slug":"learning-deep-representations-with","title":"Learning Deep Representations with Probabilistic Knowledge Transfer","date":"2018-03-28","arxiv_id":"1803.10837","repositories_listed":1,"syntology":null},{"url":"/paper/micronnet-a-highly-compact-deep-convolutional","slug":"micronnet-a-highly-compact-deep-convolutional","title":"MicronNet: A Highly Compact Deep Convolutional Neural Network Architecture for Real-time Embedded Traffic Sign Classification","date":"2018-03-28","arxiv_id":"1804.00497","repositories_listed":1,"syntology":null},{"url":"/paper/deep-faster-detection-of-faint-edges-in-noisy","slug":"deep-faster-detection-of-faint-edges-in-noisy","title":"Multi-scale Processing of Noisy Images using Edge Preservation Losses","date":"2018-03-26","arxiv_id":"1803.09420","repositories_listed":1,"syntology":null},{"url":"/paper/efficient-image-dataset-classification","slug":"efficient-image-dataset-classification","title":"Efficient Image Dataset Classification Difficulty Estimation for Predicting Deep-Learning Accuracy","date":"2018-03-26","arxiv_id":"1803.09588","repositories_listed":1,"syntology":null},{"url":"/paper/low-shot-learning-for-the-semantic","slug":"low-shot-learning-for-the-semantic","title":"Low-Shot Learning for the Semantic Segmentation of Remote Sensing Imagery","date":"2018-03-26","arxiv_id":"1803.09824","repositories_listed":1,"syntology":null},{"url":"/paper/classification-of-simulated-radio-signals","slug":"classification-of-simulated-radio-signals","title":"Classification of simulated radio signals using Wide Residual Networks for use in the search for extra-terrestrial intelligence","date":"2018-03-23","arxiv_id":"1803.08624","repositories_listed":1,"syntology":null},{"url":"/paper/context-is-everything-finding-meaning","slug":"context-is-everything-finding-meaning","title":"Contextual Salience for Fast and Accurate Sentence Vectors","date":"2018-03-22","arxiv_id":"1803.08493","repositories_listed":1,"syntology":null},{"url":"/paper/deep-learning-using-rectified-linear-units","slug":"deep-learning-using-rectified-linear-units","title":"Deep Learning using Rectified Linear Units (ReLU)","date":"2018-03-22","arxiv_id":"1803.08375","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/deep-learning-using-rectified-linear-units#ran","syntology_url":"https://syntology.ai/paper/1803.08375","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1803.08375"}},"official":{"repos":["AFAgarap/relu-classifier"],"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/understanding-measures-of-uncertainty-for","slug":"understanding-measures-of-uncertainty-for","title":"Understanding Measures of Uncertainty for Adversarial Example Detection","date":"2018-03-22","arxiv_id":"1803.08533","repositories_listed":1,"syntology":{"n":8,"n_ran":7,"n_constructed":0,"n_ran_checked":7,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":7,"n_pointer_only":0,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 0 honoured, 0 violated, 7 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/understanding-measures-of-uncertainty-for#ran","syntology_url":"https://syntology.ai/paper/1803.08533","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1803.08533"}},"official":{"repos":["lsgos/uncertainty-adversarial-paper"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/hats-histograms-of-averaged-time-surfaces-for","slug":"hats-histograms-of-averaged-time-surfaces-for","title":"HATS: Histograms of Averaged Time Surfaces for Robust Event-based Object Classification","date":"2018-03-21","arxiv_id":"1803.07913","repositories_listed":1,"syntology":null},{"url":"/paper/seglearn-a-python-package-for-learning","slug":"seglearn-a-python-package-for-learning","title":"Seglearn: A Python Package for Learning Sequences and Time Series","date":"2018-03-21","arxiv_id":"1803.08118","repositories_listed":1,"syntology":null},{"url":"/paper/gaan-gated-attention-networks-for-learning-on","slug":"gaan-gated-attention-networks-for-learning-on","title":"GaAN: Gated Attention Networks for Learning on Large and Spatiotemporal Graphs","date":"2018-03-20","arxiv_id":"1803.07294","repositories_listed":1,"syntology":null},{"url":"/paper/mltuner-system-support-for-automatic-machine","slug":"mltuner-system-support-for-automatic-machine","title":"MLtuner: System Support for Automatic Machine Learning Tuning","date":"2018-03-20","arxiv_id":"1803.07445","repositories_listed":1,"syntology":null},{"url":"/paper/graph-partition-neural-networks-for-semi","slug":"graph-partition-neural-networks-for-semi","title":"Graph Partition Neural Networks for Semi-Supervised Classification","date":"2018-03-16","arxiv_id":"1803.06272","repositories_listed":1,"syntology":{"n":8,"n_ran":5,"n_constructed":0,"n_ran_checked":5,"n_instrument":0,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":0,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 0 violated, 5 with no contract checked; 0 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/graph-partition-neural-networks-for-semi#ran","syntology_url":"https://syntology.ai/paper/1803.06272","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1803.06272"}},"official":{"repos":["Microsoft/graph-partition-neural-network-samples"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/towards-image-understanding-from-deep","slug":"towards-image-understanding-from-deep","title":"Towards Image Understanding from Deep Compression without Decoding","date":"2018-03-16","arxiv_id":"1803.06131","repositories_listed":1,"syntology":null},{"url":"/paper/triplet-center-loss-for-multi-view-3d-object","slug":"triplet-center-loss-for-multi-view-3d-object","title":"Triplet-Center Loss for Multi-View 3D Object Retrieval","date":"2018-03-16","arxiv_id":"1803.06189","repositories_listed":1,"syntology":null},{"url":"/paper/neural-network-quine","slug":"neural-network-quine","title":"Neural Network Quine","date":"2018-03-15","arxiv_id":"1803.05859","repositories_listed":1,"syntology":null},{"url":"/paper/expert-identification-of-visual-primitives","slug":"expert-identification-of-visual-primitives","title":"Expert identification of visual primitives used by CNNs during mammogram classification","date":"2018-03-13","arxiv_id":"1803.04858","repositories_listed":1,"syntology":null},{"url":"/paper/classifying-online-dating-profiles-on-tinder","slug":"classifying-online-dating-profiles-on-tinder","title":"Classifying Online Dating Profiles on Tinder using FaceNet Facial Embeddings","date":"2018-03-12","arxiv_id":"1803.04347","repositories_listed":1,"syntology":null},{"url":"/paper/feta-a-dca-pruning-algorithm-with","slug":"feta-a-dca-pruning-algorithm-with","title":"FeTa: A DCA Pruning Algorithm with Generalization Error Guarantees","date":"2018-03-12","arxiv_id":"1803.04239","repositories_listed":1,"syntology":null},{"url":"/paper/a-pathway-based-kernel-boosting-method-for","slug":"a-pathway-based-kernel-boosting-method-for","title":"A pathway-based kernel boosting method for sample classification using genomic data","date":"2018-03-11","arxiv_id":"1803.03910","repositories_listed":1,"syntology":null},{"url":"/paper/two-stage-convolutional-neural-network-for","slug":"two-stage-convolutional-neural-network-for","title":"Two-Stage Convolutional Neural Network for Breast Cancer Histology Image Classification","date":"2018-03-11","arxiv_id":"1803.04054","repositories_listed":1,"syntology":null},{"url":"/paper/fusing-hierarchical-convolutional-features","slug":"fusing-hierarchical-convolutional-features","title":"Fusing Hierarchical Convolutional Features for Human Body Segmentation and Clothing Fashion Classification","date":"2018-03-09","arxiv_id":"1803.03415","repositories_listed":1,"syntology":null},{"url":"/paper/on-generation-of-adversarial-examples-using","slug":"on-generation-of-adversarial-examples-using","title":"On Generation of Adversarial Examples using Convex Programming","date":"2018-03-09","arxiv_id":"1803.03607","repositories_listed":1,"syntology":null},{"url":"/paper/efficient-loss-based-decoding-on-graphs-for","slug":"efficient-loss-based-decoding-on-graphs-for","title":"Efficient Loss-Based Decoding on Graphs For Extreme Classification","date":"2018-03-08","arxiv_id":"1803.03319","repositories_listed":1,"syntology":{"n":4,"n_ran":4,"n_constructed":0,"n_ran_checked":4,"n_instrument":0,"n_unverified":0,"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) · 0 unverified","sample_list":"/paper/efficient-loss-based-decoding-on-graphs-for#ran","syntology_url":"https://syntology.ai/paper/1803.03319","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1803.03319"}},"official":{"repos":["ievron/wltls"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/rethinking-feature-distribution-for-loss","slug":"rethinking-feature-distribution-for-loss","title":"Rethinking Feature Distribution for Loss Functions in Image Classification","date":"2018-03-08","arxiv_id":"1803.02988","repositories_listed":1,"syntology":null},{"url":"/paper/a-bag-to-class-divergence-approach-to","slug":"a-bag-to-class-divergence-approach-to","title":"A bag-to-class divergence approach to multiple-instance learning","date":"2018-03-07","arxiv_id":"1803.02782","repositories_listed":1,"syntology":null},{"url":"/paper/gpsp-graph-partition-and-space-projection","slug":"gpsp-graph-partition-and-space-projection","title":"GPSP: Graph Partition and Space Projection based Approach for Heterogeneous Network Embedding","date":"2018-03-07","arxiv_id":"1803.02590","repositories_listed":1,"syntology":null},{"url":"/paper/visual-explanations-from-deep-3d","slug":"visual-explanations-from-deep-3d","title":"Visual Explanations From Deep 3D Convolutional Neural Networks for Alzheimer's Disease Classification","date":"2018-03-07","arxiv_id":"1803.02544","repositories_listed":1,"syntology":null},{"url":"/paper/deep-super-learner-a-deep-ensemble-for","slug":"deep-super-learner-a-deep-ensemble-for","title":"Deep Super Learner: A Deep Ensemble for Classification Problems","date":"2018-03-06","arxiv_id":"1803.02323","repositories_listed":1,"syntology":null},{"url":"/paper/masked-conditional-neural-networks-for-audio","slug":"masked-conditional-neural-networks-for-audio","title":"Masked Conditional Neural Networks for Audio Classification","date":"2018-03-06","arxiv_id":"1803.02421","repositories_listed":1,"syntology":null},{"url":"/paper/adversarial-extreme-multi-label","slug":"adversarial-extreme-multi-label","title":"Adversarial Extreme Multi-label Classification","date":"2018-03-05","arxiv_id":"1803.01570","repositories_listed":1,"syntology":null},{"url":"/paper/segmentation-of-drosophila-heart-in-optical","slug":"segmentation-of-drosophila-heart-in-optical","title":"Segmentation of Drosophila Heart in Optical Coherence Microscopy Images Using Convolutional Neural Networks","date":"2018-03-05","arxiv_id":"1803.01947","repositories_listed":1,"syntology":null},{"url":"/paper/a-multi-instance-deep-neural-network","slug":"a-multi-instance-deep-neural-network","title":"A multi-instance deep neural network classifier: application to Higgs boson CP measurement","date":"2018-03-02","arxiv_id":"1803.00838","repositories_listed":1,"syntology":null},{"url":"/paper/deep-neural-network-for-traffic-sign","slug":"deep-neural-network-for-traffic-sign","title":"Deep neural network for traffic sign recognition systems: An analysis of spatial transformers and stochastic optimisation methods","date":"2018-03-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/learning-longer-term-dependencies-in-rnns","slug":"learning-longer-term-dependencies-in-rnns","title":"Learning Longer-term Dependencies in RNNs with Auxiliary Losses","date":"2018-03-01","arxiv_id":"1803.00144","repositories_listed":1,"syntology":null},{"url":"/paper/an-emg-gesture-recognition-system-with","slug":"an-emg-gesture-recognition-system-with","title":"An EMG Gesture Recognition System with Flexible High-Density Sensors and Brain-Inspired High-Dimensional Classifier","date":"2018-02-28","arxiv_id":"1802.10237","repositories_listed":1,"syntology":null},{"url":"/paper/convolutional-neural-networks-for-toxic","slug":"convolutional-neural-networks-for-toxic","title":"Convolutional Neural Networks for Toxic Comment Classification","date":"2018-02-27","arxiv_id":"1802.09957","repositories_listed":1,"syntology":null},{"url":"/paper/directional-statistics-based-deep-metric","slug":"directional-statistics-based-deep-metric","title":"Directional Statistics-based Deep Metric Learning for Image Classification and Retrieval","date":"2018-02-27","arxiv_id":"1802.09662","repositories_listed":1,"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":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) · 0 unverified","sample_list":"/paper/directional-statistics-based-deep-metric#ran","syntology_url":"https://syntology.ai/paper/1802.09662","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1802.09662"}},"official":null}},{"url":"/paper/multi-task-learning-of-pairwise-sequence","slug":"multi-task-learning-of-pairwise-sequence","title":"Multi-task Learning of Pairwise Sequence Classification Tasks Over Disparate Label Spaces","date":"2018-02-27","arxiv_id":"1802.09913","repositories_listed":1,"syntology":null},{"url":"/paper/n-gcn-multi-scale-graph-convolution-for-semi","slug":"n-gcn-multi-scale-graph-convolution-for-semi","title":"N-GCN: Multi-scale Graph Convolution for Semi-supervised Node Classification","date":"2018-02-24","arxiv_id":"1802.08888","repositories_listed":1,"syntology":null},{"url":"/paper/classification-of-breast-cancer-histology","slug":"classification-of-breast-cancer-histology","title":"Classification of Breast Cancer Histology using Deep Learning","date":"2018-02-22","arxiv_id":"1802.08080","repositories_listed":1,"syntology":null},{"url":"/paper/adversarial-classification-an-adversarial","slug":"adversarial-classification-an-adversarial","title":"Adversarial classification: An adversarial risk analysis approach","date":"2018-02-21","arxiv_id":"1802.07513","repositories_listed":1,"syntology":null},{"url":"/paper/collaboratively-weighting-deep-and-classic","slug":"collaboratively-weighting-deep-and-classic","title":"Collaboratively Weighting Deep and Classic Representation via L2 Regularization for Image Classification","date":"2018-02-21","arxiv_id":"1802.07589","repositories_listed":1,"syntology":null},{"url":"/paper/determining-the-best-classifier-for","slug":"determining-the-best-classifier-for","title":"Determining the best classifier for predicting the value of a boolean field on a blood donor database using genetic algorithms","date":"2018-02-21","arxiv_id":"1802.07756","repositories_listed":1,"syntology":null},{"url":"/paper/smooth-loss-functions-for-deep-top-k","slug":"smooth-loss-functions-for-deep-top-k","title":"Smooth Loss Functions for Deep Top-k Classification","date":"2018-02-21","arxiv_id":"1802.07595","repositories_listed":1,"syntology":{"n":8,"n_ran":7,"n_constructed":0,"n_ran_checked":7,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":7,"n_pointer_only":0,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 0 honoured, 0 violated, 7 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/smooth-loss-functions-for-deep-top-k#ran","syntology_url":"https://syntology.ai/paper/1802.07595","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1802.07595"}},"official":{"repos":["oval-group/smooth-topk"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/selective-classification-via-curve","slug":"selective-classification-via-curve","title":"A General Framework for Abstention Under Label Shift","date":"2018-02-20","arxiv_id":"1802.07024","repositories_listed":1,"syntology":null},{"url":"/paper/music-genre-classification-using-masked","slug":"music-genre-classification-using-masked","title":"Music Genre Classification using Masked Conditional Neural Networks","date":"2018-02-18","arxiv_id":"1802.06432","repositories_listed":1,"syntology":null},{"url":"/paper/node-centralities-and-classification","slug":"node-centralities-and-classification","title":"Node Centralities and Classification Performance for Characterizing Node Embedding Algorithms","date":"2018-02-18","arxiv_id":"1802.06368","repositories_listed":1,"syntology":null},{"url":"/paper/structured-label-inference-for-visual","slug":"structured-label-inference-for-visual","title":"Structured Label Inference for Visual Understanding","date":"2018-02-18","arxiv_id":"1802.06459","repositories_listed":1,"syntology":null},{"url":"/paper/capsulegan-generative-adversarial-capsule","slug":"capsulegan-generative-adversarial-capsule","title":"CapsuleGAN: Generative Adversarial Capsule Network","date":"2018-02-17","arxiv_id":"1802.06167","repositories_listed":1,"syntology":null},{"url":"/paper/multinomial-adversarial-networks-for-multi","slug":"multinomial-adversarial-networks-for-multi","title":"Multinomial Adversarial Networks for Multi-Domain Text Classification","date":"2018-02-15","arxiv_id":"1802.05694","repositories_listed":1,"syntology":null},{"url":"/paper/augment-and-reduce-stochastic-inference-for-1","slug":"augment-and-reduce-stochastic-inference-for-1","title":"Augment and Reduce: Stochastic Inference for Large Categorical Distributions","date":"2018-02-12","arxiv_id":"1802.04220","repositories_listed":1,"syntology":null},{"url":"/paper/dcfnet-deep-neural-network-with-decomposed","slug":"dcfnet-deep-neural-network-with-decomposed","title":"DCFNet: Deep Neural Network with Decomposed Convolutional Filters","date":"2018-02-12","arxiv_id":"1802.04145","repositories_listed":1,"syntology":null},{"url":"/paper/revisiting-the-vector-space-model-sparse","slug":"revisiting-the-vector-space-model-sparse","title":"Revisiting the Vector Space Model: Sparse Weighted Nearest-Neighbor Method for Extreme Multi-Label Classification","date":"2018-02-12","arxiv_id":"1802.03938","repositories_listed":1,"syntology":null},{"url":"/paper/subspace-support-vector-data-description","slug":"subspace-support-vector-data-description","title":"Subspace Support Vector Data Description","date":"2018-02-12","arxiv_id":"1802.03989","repositories_listed":1,"syntology":null},{"url":"/paper/hydra-an-ensemble-of-convolutional-neural","slug":"hydra-an-ensemble-of-convolutional-neural","title":"Hydra: an Ensemble of Convolutional Neural Networks for Geospatial Land Classification","date":"2018-02-10","arxiv_id":"1802.03518","repositories_listed":1,"syntology":null},{"url":"/paper/atpboost-learning-premise-selection-in-binary","slug":"atpboost-learning-premise-selection-in-binary","title":"ATPboost: Learning Premise Selection in Binary Setting with ATP Feedback","date":"2018-02-09","arxiv_id":"1802.03375","repositories_listed":1,"syntology":null},{"url":"/paper/intentional-control-of-type-i-error-over","slug":"intentional-control-of-type-i-error-over","title":"Intentional Control of Type I Error over Unconscious Data Distortion: a Neyman-Pearson Approach to Text Classification","date":"2018-02-07","arxiv_id":"1802.02558","repositories_listed":1,"syntology":null},{"url":"/paper/classsim-similarity-between-classes-defined","slug":"classsim-similarity-between-classes-defined","title":"ClassSim: Similarity between Classes Defined by Misclassification Ratios of Trained Classifiers","date":"2018-02-05","arxiv_id":"1802.01267","repositories_listed":1,"syntology":null},{"url":"/paper/deep-learning-with-a-rethinking-structure-for","slug":"deep-learning-with-a-rethinking-structure-for","title":"Deep Learning with a Rethinking Structure for Multi-label Classification","date":"2018-02-05","arxiv_id":"1802.01697","repositories_listed":1,"syntology":null},{"url":"/paper/onto2vec-joint-vector-based-representation-of","slug":"onto2vec-joint-vector-based-representation-of","title":"Onto2Vec: joint vector-based representation of biological entities and their ontology-based annotations","date":"2018-01-31","arxiv_id":"1802.00864","repositories_listed":1,"syntology":null},{"url":"/paper/diagnose-like-a-radiologist-attention-guided","slug":"diagnose-like-a-radiologist-attention-guided","title":"Diagnose like a Radiologist: Attention Guided Convolutional Neural Network for Thorax Disease Classification","date":"2018-01-30","arxiv_id":"1801.09927","repositories_listed":1,"syntology":null},{"url":"/paper/helping-crisis-responders-find-the","slug":"helping-crisis-responders-find-the","title":"Helping Crisis Responders Find the Informative Needle in the Tweet Haystack","date":"2018-01-29","arxiv_id":"1801.09633","repositories_listed":1,"syntology":null},{"url":"/paper/improving-review-representations-with-user","slug":"improving-review-representations-with-user","title":"Improving Review Representations with User Attention and Product Attention for Sentiment Classification","date":"2018-01-24","arxiv_id":"1801.07861","repositories_listed":1,"syntology":null},{"url":"/paper/a-practitioners-guide-to-transfer-learning","slug":"a-practitioners-guide-to-transfer-learning","title":"A Practitioners' Guide to Transfer Learning for Text Classification using Convolutional Neural Networks","date":"2018-01-19","arxiv_id":"1801.06480","repositories_listed":1,"syntology":null},{"url":"/paper/investigating-the-working-of-text-classifiers","slug":"investigating-the-working-of-text-classifiers","title":"Investigating the Working of Text Classifiers","date":"2018-01-19","arxiv_id":"1801.06261","repositories_listed":1,"syntology":null},{"url":"/paper/transfer-learning-for-improving-speech","slug":"transfer-learning-for-improving-speech","title":"Transfer Learning for Improving Speech Emotion Classification Accuracy","date":"2018-01-19","arxiv_id":"1801.06353","repositories_listed":1,"syntology":null},{"url":"/paper/unseen-class-discovery-in-open-world","slug":"unseen-class-discovery-in-open-world","title":"Unseen Class Discovery in Open-world Classification","date":"2018-01-17","arxiv_id":"1801.05609","repositories_listed":1,"syntology":null},{"url":"/paper/automatic-classification-of-music-genre-using","slug":"automatic-classification-of-music-genre-using","title":"Automatic Classification of Music Genre using Masked Conditional Neural Networks","date":"2018-01-16","arxiv_id":"1801.05504","repositories_listed":1,"syntology":null},{"url":"/paper/low-shot-learning-from-imaginary-data","slug":"low-shot-learning-from-imaginary-data","title":"Low-Shot Learning from Imaginary Data","date":"2018-01-16","arxiv_id":"1801.05401","repositories_listed":1,"syntology":null},{"url":"/paper/predicting-movie-genres-based-on-plot","slug":"predicting-movie-genres-based-on-plot","title":"Predicting Movie Genres Based on Plot Summaries","date":"2018-01-15","arxiv_id":"1801.04813","repositories_listed":1,"syntology":null},{"url":"/paper/detecting-offensive-language-in-tweets-using","slug":"detecting-offensive-language-in-tweets-using","title":"Detecting Offensive Language in Tweets Using Deep Learning","date":"2018-01-13","arxiv_id":"1801.04433","repositories_listed":1,"syntology":null},{"url":"/paper/fwlbp-a-scale-invariant-descriptor-for","slug":"fwlbp-a-scale-invariant-descriptor-for","title":"FWLBP: A Scale Invariant Descriptor for Texture Classification","date":"2018-01-10","arxiv_id":"1801.03228","repositories_listed":1,"syntology":null},{"url":"/paper/a-new-method-of-region-embedding-for-text","slug":"a-new-method-of-region-embedding-for-text","title":"A New Method of Region Embedding for Text Classification","date":"2018-01-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/make-svm-great-again-with-siamese-kernel-for","slug":"make-svm-great-again-with-siamese-kernel-for","title":"Make SVM great again with Siamese kernel for few-shot learning","date":"2018-01-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/wavelet-pooling-for-convolutional-neural","slug":"wavelet-pooling-for-convolutional-neural","title":"Wavelet Pooling for Convolutional Neural Networks","date":"2018-01-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/towards-building-an-intelligent-anti-malware","slug":"towards-building-an-intelligent-anti-malware","title":"Towards Building an Intelligent Anti-Malware System: A Deep Learning Approach using Support Vector Machine (SVM) for Malware Classification","date":"2017-12-31","arxiv_id":"1801.00318","repositories_listed":1,"syntology":null},{"url":"/paper/extrapolating-expected-accuracies-for-large","slug":"extrapolating-expected-accuracies-for-large","title":"Extrapolating Expected Accuracies for Large Multi-Class Problems","date":"2017-12-27","arxiv_id":"1712.09713","repositories_listed":1,"syntology":null},{"url":"/paper/robust-loss-functions-under-label-noise-for","slug":"robust-loss-functions-under-label-noise-for","title":"Robust Loss Functions under Label Noise for Deep Neural Networks","date":"2017-12-27","arxiv_id":"1712.09482","repositories_listed":1,"syntology":null},{"url":"/paper/brain-tumor-segmentation-based-on-refined","slug":"brain-tumor-segmentation-based-on-refined","title":"Brain Tumor Segmentation Based on Refined Fully Convolutional Neural Networks with A Hierarchical Dice Loss","date":"2017-12-25","arxiv_id":"1712.09093","repositories_listed":1,"syntology":null},{"url":"/paper/building-a-sentiment-corpus-of-tweets-in-1","slug":"building-a-sentiment-corpus-of-tweets-in-1","title":"Building a Sentiment Corpus of Tweets in Brazilian Portuguese","date":"2017-12-24","arxiv_id":"1712.08917","repositories_listed":1,"syntology":null},{"url":"/paper/a-mixture-of-matrix-variate-bilinear-factor","slug":"a-mixture-of-matrix-variate-bilinear-factor","title":"A Mixture of Matrix Variate Bilinear Factor Analyzers","date":"2017-12-22","arxiv_id":"1712.08664","repositories_listed":1,"syntology":null},{"url":"/paper/deep-hashing-with-category-mask-for-fast","slug":"deep-hashing-with-category-mask-for-fast","title":"Deep Hashing with Category Mask for Fast Video Retrieval","date":"2017-12-22","arxiv_id":"1712.08315","repositories_listed":1,"syntology":null},{"url":"/paper/combining-static-and-dynamic-features-for","slug":"combining-static-and-dynamic-features-for","title":"Combining Static and Dynamic Features for Multivariate Sequence Classification","date":"2017-12-20","arxiv_id":"1712.08160","repositories_listed":1,"syntology":null},{"url":"/paper/detecting-hate-speech-in-social-media","slug":"detecting-hate-speech-in-social-media","title":"Detecting Hate Speech in Social Media","date":"2017-12-18","arxiv_id":"1712.06427","repositories_listed":1,"syntology":null}],"record_sha256":"bfefea7e43fbf666a60fb1306ac468a85f7030ebeb54b96893921696bc9420e4","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}