{"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/19","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":19,"pages_in_order":129,"rows_per_page":100,"rows":[1801,1900],"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/18","next":"/task/classification-1/papers/20","papers":[{"url":"/paper/strategic-classification-with-graph-neural","slug":"strategic-classification-with-graph-neural","title":"Strategic Classification with Graph Neural Networks","date":"2022-05-31","arxiv_id":"2205.15765","repositories_listed":1,"syntology":null},{"url":"/paper/robust-weight-perturbation-for-adversarial-1","slug":"robust-weight-perturbation-for-adversarial-1","title":"Robust Weight Perturbation for Adversarial Training","date":"2022-05-30","arxiv_id":"2205.14826","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":1,"n_ran_checked":1,"n_instrument":2,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":3,"phrase":"3 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; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/robust-weight-perturbation-for-adversarial-1#ran","syntology_url":"https://syntology.ai/paper/2205.14826","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2205.14826"}},"official":{"repos":["chaojianyu/robust-weight-perturbation"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":1,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/self-attention-based-deep-feature-fusion-for","slug":"self-attention-based-deep-feature-fusion-for","title":"Self-Attention-Based Deep Feature Fusion for Remote Sensing Scene Classification","date":"2022-05-30","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/3d-model-shapenet-core-classification-using","slug":"3d-model-shapenet-core-classification-using","title":"3D-model ShapeNet Core Classification using Meta-Semantic Learning","date":"2022-05-28","arxiv_id":"2205.15869","repositories_listed":1,"syntology":null},{"url":"/paper/prototype-based-classification-from-hierarchy-1","slug":"prototype-based-classification-from-hierarchy-1","title":"Prototype Based Classification from Hierarchy to Fairness","date":"2022-05-27","arxiv_id":"2205.13997","repositories_listed":1,"syntology":null},{"url":"/paper/traclets-harnessing-the-power-of-computer","slug":"traclets-harnessing-the-power-of-computer","title":"TraClets: Harnessing the power of computer vision for trajectory classification","date":"2022-05-27","arxiv_id":"2205.13880","repositories_listed":1,"syntology":null},{"url":"/paper/unequal-covariance-awareness-for-fisher","slug":"unequal-covariance-awareness-for-fisher","title":"Unequal Covariance Awareness for Fisher Discriminant Analysis and Its Variants in Classification","date":"2022-05-26","arxiv_id":"2205.13565","repositories_listed":1,"syntology":null},{"url":"/paper/unsupervised-reinforcement-adaptation-for-1","slug":"unsupervised-reinforcement-adaptation-for-1","title":"Unsupervised Reinforcement Adaptation for Class-Imbalanced Text Classification","date":"2022-05-26","arxiv_id":"2205.13139","repositories_listed":1,"syntology":null},{"url":"/paper/a-cnn-with-noise-inclined-module-and-denoise","slug":"a-cnn-with-noise-inclined-module-and-denoise","title":"A CNN with Noise Inclined Module and Denoise Framework for Hyperspectral Image Classification","date":"2022-05-25","arxiv_id":"2205.12459","repositories_listed":1,"syntology":null},{"url":"/paper/tsem-temporally-weighted-spatiotemporal","slug":"tsem-temporally-weighted-spatiotemporal","title":"TSEM: Temporally Weighted Spatiotemporal Explainable Neural Network for Multivariate Time Series","date":"2022-05-25","arxiv_id":"2205.13012","repositories_listed":1,"syntology":null},{"url":"/paper/highly-accurate-fmri-adhd-classification","slug":"highly-accurate-fmri-adhd-classification","title":"Highly Accurate FMRI ADHD Classification using time distributed multi modal 3D CNNs","date":"2022-05-24","arxiv_id":"2205.11993","repositories_listed":1,"syntology":null},{"url":"/paper/ranking-based-siamese-visual-tracking","slug":"ranking-based-siamese-visual-tracking","title":"Ranking-Based Siamese Visual Tracking","date":"2022-05-24","arxiv_id":"2205.11761","repositories_listed":1,"syntology":null},{"url":"/paper/conditional-supervised-contrastive-learning","slug":"conditional-supervised-contrastive-learning","title":"Conditional Supervised Contrastive Learning for Fair Text Classification","date":"2022-05-23","arxiv_id":"2205.11485","repositories_listed":1,"syntology":null},{"url":"/paper/discriminative-feature-learning-through","slug":"discriminative-feature-learning-through","title":"Discriminative Feature Learning through Feature Distance Loss","date":"2022-05-23","arxiv_id":"2205.11606","repositories_listed":1,"syntology":null},{"url":"/paper/kold-korean-offensive-language-dataset","slug":"kold-korean-offensive-language-dataset","title":"KOLD: Korean Offensive Language Dataset","date":"2022-05-23","arxiv_id":"2205.11315","repositories_listed":1,"syntology":null},{"url":"/paper/facing-the-void-overcoming-missing-data-in","slug":"facing-the-void-overcoming-missing-data-in","title":"Facing the Void: Overcoming Missing Data in Multi-View Imagery","date":"2022-05-21","arxiv_id":"2205.10592","repositories_listed":1,"syntology":null},{"url":"/paper/fine-grained-visual-classification-using-self","slug":"fine-grained-visual-classification-using-self","title":"Fine-Grained Visual Classification using Self Assessment Classifier","date":"2022-05-21","arxiv_id":"2205.10529","repositories_listed":1,"syntology":null},{"url":"/paper/a-graph-transformer-for-whole-slide-image","slug":"a-graph-transformer-for-whole-slide-image","title":"A graph-transformer for whole slide image classification","date":"2022-05-19","arxiv_id":"2205.09671","repositories_listed":1,"syntology":{"n":10,"n_ran":10,"n_constructed":0,"n_ran_checked":9,"n_instrument":1,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":8,"n_pointer_only":1,"phrase":"10 ran (of which 0 constructed an object rather than computing a result; 9 with no instrument failure: 1 honoured, 0 violated, 8 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/a-graph-transformer-for-whole-slide-image#ran","syntology_url":"https://syntology.ai/paper/2205.09671","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2205.09671"}},"official":{"repos":["vkola-lab/tmi2022"],"state":"official (archive's flag): 10 ran","n_ran":10,"n_constructed":0,"n_ran_no_instrument_failure":9,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/clcnet-rethinking-of-ensemble-modeling-with","slug":"clcnet-rethinking-of-ensemble-modeling-with","title":"CLCNet: Rethinking of Ensemble Modeling with Classification Confidence Network","date":"2022-05-19","arxiv_id":"2205.09612","repositories_listed":1,"syntology":null},{"url":"/paper/pairwise-comparison-network-for-remote","slug":"pairwise-comparison-network-for-remote","title":"Pairwise Comparison Network for Remote Sensing Scene Classification","date":"2022-05-17","arxiv_id":"2205.08147","repositories_listed":1,"syntology":null},{"url":"/paper/graphhd-efficient-graph-classification-using","slug":"graphhd-efficient-graph-classification-using","title":"GraphHD: Efficient graph classification using hyperdimensional computing","date":"2022-05-16","arxiv_id":"2205.07826","repositories_listed":1,"syntology":null},{"url":"/paper/elodi-ensemble-logit-difference-inhibition","slug":"elodi-ensemble-logit-difference-inhibition","title":"ELODI: Ensemble Logit Difference Inhibition for Positive-Congruent Training","date":"2022-05-12","arxiv_id":"2205.06265","repositories_listed":1,"syntology":null},{"url":"/paper/mondrian-forest-for-data-stream","slug":"mondrian-forest-for-data-stream","title":"Mondrian Forest for Data Stream Classification Under Memory Constraints","date":"2022-05-12","arxiv_id":"2205.07871","repositories_listed":1,"syntology":null},{"url":"/paper/building-for-tomorrow-assessing-the-temporal","slug":"building-for-tomorrow-assessing-the-temporal","title":"Building for Tomorrow: Assessing the Temporal Persistence of Text Classifiers","date":"2022-05-11","arxiv_id":"2205.05435","repositories_listed":1,"syntology":null},{"url":"/paper/hyperspectral-image-classification-with-5","slug":"hyperspectral-image-classification-with-5","title":"Hyperspectral Image Classification With Contrastive Graph Convolutional Network","date":"2022-05-11","arxiv_id":"2205.11237","repositories_listed":1,"syntology":null},{"url":"/paper/making-pre-trained-language-models-good-long","slug":"making-pre-trained-language-models-good-long","title":"Making Pretrained Language Models Good Long-tailed Learners","date":"2022-05-11","arxiv_id":"2205.05461","repositories_listed":1,"syntology":null},{"url":"/paper/multi-label-logo-recognition-and-retrieval","slug":"multi-label-logo-recognition-and-retrieval","title":"Multi-Label Logo Recognition and Retrieval based on Weighted Fusion of Neural Features","date":"2022-05-11","arxiv_id":"2205.05419","repositories_listed":1,"syntology":null},{"url":"/paper/quantum-self-attention-neural-networks-for","slug":"quantum-self-attention-neural-networks-for","title":"Quantum Self-Attention Neural Networks for Text Classification","date":"2022-05-11","arxiv_id":"2205.05625","repositories_listed":1,"syntology":null},{"url":"/paper/towards-unified-prompt-tuning-for-few-shot-1","slug":"towards-unified-prompt-tuning-for-few-shot-1","title":"Towards Unified Prompt Tuning for Few-shot Text Classification","date":"2022-05-11","arxiv_id":"2205.05313","repositories_listed":1,"syntology":null},{"url":"/paper/object-detection-with-spiking-neural-networks","slug":"object-detection-with-spiking-neural-networks","title":"Object Detection with Spiking Neural Networks on Automotive Event Data","date":"2022-05-09","arxiv_id":"2205.04339","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":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/object-detection-with-spiking-neural-networks#ran","syntology_url":"https://syntology.ai/paper/2205.04339","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2205.04339"}},"official":{"repos":["loiccordone/object-detection-with-spiking-neural-networks"],"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/comparison-knowledge-translation-for","slug":"comparison-knowledge-translation-for","title":"Comparison Knowledge Translation for Generalizable Image Classification","date":"2022-05-07","arxiv_id":"2205.03633","repositories_listed":1,"syntology":null},{"url":"/paper/investigating-and-explaining-the-frequency","slug":"investigating-and-explaining-the-frequency","title":"Investigating and Explaining the Frequency Bias in Image Classification","date":"2022-05-06","arxiv_id":"2205.03154","repositories_listed":1,"syntology":null},{"url":"/paper/lpgnet-link-private-graph-networks-for-node","slug":"lpgnet-link-private-graph-networks-for-node","title":"LPGNet: Link Private Graph Networks for Node Classification","date":"2022-05-06","arxiv_id":"2205.03105","repositories_listed":1,"syntology":{"n":12,"n_ran":10,"n_constructed":0,"n_ran_checked":10,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":10,"n_pointer_only":0,"phrase":"10 ran (of which 0 constructed an object rather than computing a result; 10 with no instrument failure: 0 honoured, 0 violated, 10 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/lpgnet-link-private-graph-networks-for-node#ran","syntology_url":"https://syntology.ai/paper/2205.03105","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2205.03105"}},"official":{"repos":["ashgeek/lpgnet-prototype"],"state":"official (archive's flag): 10 ran","n_ran":10,"n_constructed":0,"n_ran_no_instrument_failure":10,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/exploiting-global-and-local-hierarchies-for","slug":"exploiting-global-and-local-hierarchies-for","title":"Exploiting Global and Local Hierarchies for Hierarchical Text Classification","date":"2022-05-05","arxiv_id":"2205.02613","repositories_listed":1,"syntology":null},{"url":"/paper/faith-few-shot-graph-classification-with","slug":"faith-few-shot-graph-classification-with","title":"FAITH: Few-Shot Graph Classification with Hierarchical Task Graphs","date":"2022-05-05","arxiv_id":"2205.02435","repositories_listed":1,"syntology":null},{"url":"/paper/image-classification-with-small-datasets","slug":"image-classification-with-small-datasets","title":"Image Classification With Small Datasets: Overview and Benchmark","date":"2022-05-05","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/unsupervised-meta-learning-with-multiview","slug":"unsupervised-meta-learning-with-multiview","title":"Unsupervised Meta Learning With Multiview Constraints for Hyperspectral Image Small Sample set Classification","date":"2022-05-05","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/immiscible-color-flows-in-optimal-transport","slug":"immiscible-color-flows-in-optimal-transport","title":"Immiscible Color Flows in Optimal Transport Networks for Image Classification","date":"2022-05-04","arxiv_id":"2205.02938","repositories_listed":1,"syntology":null},{"url":"/paper/learning-label-initialization-for-time","slug":"learning-label-initialization-for-time","title":"Learning Label Initialization for Time-Dependent Harmonic Extension","date":"2022-05-03","arxiv_id":"2205.01358","repositories_listed":1,"syntology":null},{"url":"/paper/orcas-i-queries-annotated-with-intent-using","slug":"orcas-i-queries-annotated-with-intent-using","title":"ORCAS-I: Queries Annotated with Intent using Weak Supervision","date":"2022-05-02","arxiv_id":"2205.00926","repositories_listed":1,"syntology":null},{"url":"/paper/augmented-balanced-image-dataset-generator","slug":"augmented-balanced-image-dataset-generator","title":"Augmented Balanced Image Dataset Generator Using AugStatic Library","date":"2022-05-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/augstatic-a-light-weight-image-augmentation","slug":"augstatic-a-light-weight-image-augmentation","title":"AugStatic - A Light-Weight Image Augmentation Library","date":"2022-05-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/composing-structure-aware-batches-for-1","slug":"composing-structure-aware-batches-for-1","title":"Composing Structure-Aware Batches for Pairwise Sentence Classification","date":"2022-05-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/dddm-a-brain-inspired-framework-for-robust","slug":"dddm-a-brain-inspired-framework-for-robust","title":"DDDM: a Brain-Inspired Framework for Robust Classification","date":"2022-05-01","arxiv_id":"2205.10117","repositories_listed":1,"syntology":null},{"url":"/paper/early-stopping-based-on-unlabeled-samples-in","slug":"early-stopping-based-on-unlabeled-samples-in","title":"Early Stopping Based on Unlabeled Samples in Text Classification","date":"2022-05-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/filipn-lt-edi-acl2022-detecting-signs-of","slug":"filipn-lt-edi-acl2022-detecting-signs-of","title":"FilipN@LT-EDI-ACL2022-Detecting signs of Depression from Social Media: Examining the use of summarization methods as data augmentation for text classification","date":"2022-05-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/generalized-reference-kernel-for-one-class","slug":"generalized-reference-kernel-for-one-class","title":"Generalized Reference Kernel for One-class Classification","date":"2022-05-01","arxiv_id":"2205.00534","repositories_listed":1,"syntology":null},{"url":"/paper/learn-to-adapt-for-generalized-zero-shot-text-1","slug":"learn-to-adapt-for-generalized-zero-shot-text-1","title":"Learn to Adapt for Generalized Zero-Shot Text Classification","date":"2022-05-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/pixie-preference-in-implicit-and-explicit","slug":"pixie-preference-in-implicit-and-explicit","title":"Pixie: Preference in Implicit and Explicit Comparisons","date":"2022-05-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/uncertainty-estimation-of-transformer","slug":"uncertainty-estimation-of-transformer","title":"Uncertainty Estimation of Transformer Predictions for Misclassification Detection","date":"2022-05-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/end-to-end-signal-classification-in-signed","slug":"end-to-end-signal-classification-in-signed","title":"End-to-End Signal Classification in Signed Cumulative Distribution Transform Space","date":"2022-04-30","arxiv_id":"2205.00348","repositories_listed":1,"syntology":null},{"url":"/paper/novel-optimized-crow-search-algorithm-for","slug":"novel-optimized-crow-search-algorithm-for","title":"Novel optimized crow search algorithm for feature selection","date":"2022-04-30","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/deep-learning-for-musical-form-recognition","slug":"deep-learning-for-musical-form-recognition","title":"Deep Learning for Musical Form: Recognition and Analysis","date":"2022-04-29","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/costi-a-new-classifier-for-sequences-of","slug":"costi-a-new-classifier-for-sequences-of","title":"COSTI: a New Classifier for Sequences of Temporal Intervals","date":"2022-04-28","arxiv_id":"2204.13467","repositories_listed":1,"syntology":null},{"url":"/paper/hpt-hierarchy-aware-prompt-tuning-for","slug":"hpt-hierarchy-aware-prompt-tuning-for","title":"HPT: Hierarchy-aware Prompt Tuning for Hierarchical Text Classification","date":"2022-04-28","arxiv_id":"2204.13413","repositories_listed":1,"syntology":{"n":7,"n_ran":2,"n_constructed":2,"n_ran_checked":2,"n_instrument":0,"n_unverified":5,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":0,"phrase":"2 ran (of which 2 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) · 5 unverified; every one of the 2 samples that ran constructed an object rather than computing a result","sample_list":"/paper/hpt-hierarchy-aware-prompt-tuning-for#ran","syntology_url":"https://syntology.ai/paper/2204.13413","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2204.13413"}},"official":{"repos":["wzh9969/hpt"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":2,"n_ran_no_instrument_failure":2,"n_unverified":5,"ran_from_kinds":["official"]}}},{"url":"/paper/what-do-you-mean-by-relation-extraction-a","slug":"what-do-you-mean-by-relation-extraction-a","title":"What do You Mean by Relation Extraction? A Survey on Datasets and Study on Scientific Relation Classification","date":"2022-04-28","arxiv_id":"2204.13516","repositories_listed":1,"syntology":null},{"url":"/paper/the-multimarginal-optimal-transport","slug":"the-multimarginal-optimal-transport","title":"The Multimarginal Optimal Transport Formulation of Adversarial Multiclass Classification","date":"2022-04-27","arxiv_id":"2204.12676","repositories_listed":1,"syntology":{"n":4,"n_ran":4,"n_constructed":0,"n_ran_checked":3,"n_instrument":1,"n_unverified":0,"n_honours":2,"n_violates":1,"n_no_contract":0,"n_pointer_only":4,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 2 honoured, 1 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/the-multimarginal-optimal-transport#ran","syntology_url":"https://syntology.ai/paper/2204.12676","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2204.12676"}},"official":null}},{"url":"/paper/end-to-end-audio-strikes-back-boosting","slug":"end-to-end-audio-strikes-back-boosting","title":"End-to-End Audio Strikes Back: Boosting Augmentations Towards An Efficient Audio Classification Network","date":"2022-04-25","arxiv_id":"2204.11479","repositories_listed":1,"syntology":null},{"url":"/paper/diffusion-kernel-attention-network-for-brain","slug":"diffusion-kernel-attention-network-for-brain","title":"Diffusion Kernel Attention Network for Brain Disorder Classification","date":"2022-04-23","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/au-nn-anfis-unit-neural-network","slug":"au-nn-anfis-unit-neural-network","title":"AU-NN: ANFIS Unit Neural Network","date":"2022-04-21","arxiv_id":"2204.11839","repositories_listed":1,"syntology":null},{"url":"/paper/graph-neural-networks-and-attention-based-cnn","slug":"graph-neural-networks-and-attention-based-cnn","title":"Graph neural networks and attention-based CNN-LSTM for protein classification","date":"2022-04-20","arxiv_id":"2204.09486","repositories_listed":1,"syntology":null},{"url":"/paper/neurochaos-feature-transformation-and","slug":"neurochaos-feature-transformation-and","title":"Neurochaos Feature Transformation and Classification for Imbalanced Learning","date":"2022-04-20","arxiv_id":"2205.06742","repositories_listed":1,"syntology":null},{"url":"/paper/self-supervised-learning-for-sonar-image","slug":"self-supervised-learning-for-sonar-image","title":"Self-supervised Learning for Sonar Image Classification","date":"2022-04-20","arxiv_id":"2204.09323","repositories_listed":1,"syntology":null},{"url":"/paper/radio-galaxy-zoo-using-semi-supervised","slug":"radio-galaxy-zoo-using-semi-supervised","title":"Radio Galaxy Zoo: Using semi-supervised learning to leverage large unlabelled data-sets for radio galaxy classification under data-set shift","date":"2022-04-19","arxiv_id":"2204.08816","repositories_listed":1,"syntology":null},{"url":"/paper/neural-structured-prediction-for-inductive-1","slug":"neural-structured-prediction-for-inductive-1","title":"Neural Structured Prediction for Inductive Node Classification","date":"2022-04-15","arxiv_id":"2204.07524","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/neural-structured-prediction-for-inductive-1#ran","syntology_url":"https://syntology.ai/paper/2204.07524","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2204.07524"}},"official":{"repos":["deepgraphlearning/spn"],"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/label-semantic-aware-pre-training-for-few","slug":"label-semantic-aware-pre-training-for-few","title":"Label Semantic Aware Pre-training for Few-shot Text Classification","date":"2022-04-14","arxiv_id":"2204.07128","repositories_listed":1,"syntology":null},{"url":"/paper/revisiting-transformer-based-models-for-long-1","slug":"revisiting-transformer-based-models-for-long-1","title":"Revisiting Transformer-based Models for Long Document Classification","date":"2022-04-14","arxiv_id":"2204.06683","repositories_listed":1,"syntology":null},{"url":"/paper/lifelonger-a-benchmark-for-continual-disease","slug":"lifelonger-a-benchmark-for-continual-disease","title":"LifeLonger: A Benchmark for Continual Disease Classification","date":"2022-04-12","arxiv_id":"2204.05737","repositories_listed":1,"syntology":null},{"url":"/paper/multi-view-breast-cancer-classification-via","slug":"multi-view-breast-cancer-classification-via","title":"Multi-View Hypercomplex Learning for Breast Cancer Screening","date":"2022-04-12","arxiv_id":"2204.05798","repositories_listed":1,"syntology":{"n":7,"n_ran":7,"n_constructed":0,"n_ran_checked":7,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":7,"n_pointer_only":3,"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) · 0 unverified","sample_list":"/paper/multi-view-breast-cancer-classification-via#ran","syntology_url":"https://syntology.ai/paper/2204.05798","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2204.05798"}},"official":{"repos":["ispamm/phbreast"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/redwood-using-collision-detection-to-grow-a","slug":"redwood-using-collision-detection-to-grow-a","title":"Redwood: Using Collision Detection to Grow a Large-Scale Intent Classification Dataset","date":"2022-04-12","arxiv_id":"2204.05483","repositories_listed":1,"syntology":null},{"url":"/paper/doctor-xavier-explainable-diagnosis-using","slug":"doctor-xavier-explainable-diagnosis-using","title":"Doctor XAvIer: Explainable Diagnosis on Physician-Patient Dialogues and XAI Evaluation","date":"2022-04-11","arxiv_id":"2204.10178","repositories_listed":1,"syntology":null},{"url":"/paper/from-cnns-to-vision-transformers-a","slug":"from-cnns-to-vision-transformers-a","title":"From Modern CNNs to Vision Transformers: Assessing the Performance, Robustness, and Classification Strategies of Deep Learning Models in Histopathology","date":"2022-04-11","arxiv_id":"2204.05044","repositories_listed":1,"syntology":{"n":5,"n_ran":4,"n_constructed":0,"n_ran_checked":0,"n_instrument":4,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":5,"phrase":"4 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; 4 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/from-cnns-to-vision-transformers-a#ran","syntology_url":"https://syntology.ai/paper/2204.05044","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2204.05044"}},"official":{"repos":["hhi-aml/histobenchmark"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/physically-disentangled-representations","slug":"physically-disentangled-representations","title":"Physically Disentangled Representations","date":"2022-04-11","arxiv_id":"2204.05281","repositories_listed":1,"syntology":null},{"url":"/paper/joint-distribution-matters-deep-brownian","slug":"joint-distribution-matters-deep-brownian","title":"Joint Distribution Matters: Deep Brownian Distance Covariance for Few-Shot Classification","date":"2022-04-09","arxiv_id":"2204.04567","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":1,"n_ran_checked":1,"n_instrument":2,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":3,"phrase":"3 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; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/joint-distribution-matters-deep-brownian#ran","syntology_url":"https://syntology.ai/paper/2204.04567","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2204.04567"}},"official":{"repos":["Fei-Long121/DeepBDC"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":1,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/bag-of-words-vs-sequence-vs-graph-vs","slug":"bag-of-words-vs-sequence-vs-graph-vs","title":"Are We Really Making Much Progress in Text Classification? A Comparative Review","date":"2022-04-08","arxiv_id":"2204.03954","repositories_listed":1,"syntology":null},{"url":"/paper/few-shot-forecasting-of-time-series-with","slug":"few-shot-forecasting-of-time-series-with","title":"Few-Shot Forecasting of Time-Series with Heterogeneous Channels","date":"2022-04-07","arxiv_id":"2204.03456","repositories_listed":1,"syntology":null},{"url":"/paper/surface-vision-transformers-flexible","slug":"surface-vision-transformers-flexible","title":"Surface Vision Transformers: Flexible Attention-Based Modelling of Biomedical Surfaces","date":"2022-04-07","arxiv_id":"2204.03408","repositories_listed":1,"syntology":null},{"url":"/paper/rf-signal-transformation-and-classification","slug":"rf-signal-transformation-and-classification","title":"RF Signal Transformation and Classification using Deep Neural Networks","date":"2022-04-06","arxiv_id":"2204.03564","repositories_listed":1,"syntology":null},{"url":"/paper/metaaudio-a-few-shot-audio-classification","slug":"metaaudio-a-few-shot-audio-classification","title":"MetaAudio: A Few-Shot Audio Classification Benchmark","date":"2022-04-05","arxiv_id":"2204.02121","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/metaaudio-a-few-shot-audio-classification#ran","syntology_url":"https://syntology.ai/paper/2204.02121","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2204.02121"}},"official":{"repos":["cheggan/metaaudio-a-few-shot-audio-classification-benchmark"],"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/a-pipeline-and-comparative-study-of-12","slug":"a-pipeline-and-comparative-study-of-12","title":"A pipeline and comparative study of 12 machine learning models for text classification","date":"2022-04-04","arxiv_id":"2204.06518","repositories_listed":1,"syntology":null},{"url":"/paper/long-movie-clip-classification-with-state","slug":"long-movie-clip-classification-with-state","title":"Long Movie Clip Classification with State-Space Video Models","date":"2022-04-04","arxiv_id":"2204.01692","repositories_listed":1,"syntology":{"n":18,"n_ran":11,"n_constructed":2,"n_ran_checked":3,"n_instrument":8,"n_unverified":7,"n_honours":1,"n_violates":0,"n_no_contract":2,"n_pointer_only":2,"phrase":"11 ran (of which 2 constructed an object rather than computing a result; 3 with no instrument failure: 1 honoured, 0 violated, 2 with no contract checked; 8 where Syntology's instrument failed) · 7 unverified","sample_list":"/paper/long-movie-clip-classification-with-state#ran","syntology_url":"https://syntology.ai/paper/2204.01692","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2204.01692"}},"official":{"repos":["md-mohaiminul/ViS4mer"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":2,"n_ran_no_instrument_failure":3,"n_unverified":7,"ran_from_kinds":["official","unlocated"]}}},{"url":"/paper/discrete-wavelet-transform-for-generative","slug":"discrete-wavelet-transform-for-generative","title":"Discrete Wavelet Transform for Generative Adversarial Network to Identify Drivers Using Gyroscope and Accelerometer Sensors","date":"2022-04-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/proper-reuse-of-image-classification-features","slug":"proper-reuse-of-image-classification-features","title":"Proper Reuse of Image Classification Features Improves Object Detection","date":"2022-04-01","arxiv_id":"2204.00484","repositories_listed":1,"syntology":null},{"url":"/paper/simplicial-embeddings-in-self-supervised","slug":"simplicial-embeddings-in-self-supervised","title":"Simplicial Embeddings in Self-Supervised Learning and Downstream Classification","date":"2022-04-01","arxiv_id":"2204.00616","repositories_listed":1,"syntology":{"n":6,"n_ran":5,"n_constructed":0,"n_ran_checked":4,"n_instrument":1,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":3,"phrase":"5 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; 1 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/simplicial-embeddings-in-self-supervised#ran","syntology_url":"https://syntology.ai/paper/2204.00616","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2204.00616"}},"official":{"repos":["lavoiems/simplicial-embeddings"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/fine-grained-temporal-contrastive-learning","slug":"fine-grained-temporal-contrastive-learning","title":"Fine-grained Temporal Contrastive Learning for Weakly-supervised Temporal Action Localization","date":"2022-03-31","arxiv_id":"2203.16800","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/fine-grained-temporal-contrastive-learning#ran","syntology_url":"https://syntology.ai/paper/2203.16800","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2203.16800"}},"official":{"repos":["mengyuanchen21/cvpr2022-ftcl"],"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/challenges-in-leveraging-gans-for-few-shot","slug":"challenges-in-leveraging-gans-for-few-shot","title":"Overcoming challenges in leveraging GANs for few-shot data augmentation","date":"2022-03-30","arxiv_id":"2203.16662","repositories_listed":1,"syntology":null},{"url":"/paper/fair-contrastive-learning-for-facial","slug":"fair-contrastive-learning-for-facial","title":"Fair Contrastive Learning for Facial Attribute Classification","date":"2022-03-30","arxiv_id":"2203.16209","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/fair-contrastive-learning-for-facial#ran","syntology_url":"https://syntology.ai/paper/2203.16209","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2203.16209"}},"official":{"repos":["sungho-coolg/fscl"],"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/on-uncertainty-tempering-and-data","slug":"on-uncertainty-tempering-and-data","title":"On Uncertainty, Tempering, and Data Augmentation in Bayesian Classification","date":"2022-03-30","arxiv_id":"2203.16481","repositories_listed":1,"syntology":{"n":16,"n_ran":14,"n_constructed":1,"n_ran_checked":14,"n_instrument":0,"n_unverified":2,"n_honours":2,"n_violates":1,"n_no_contract":11,"n_pointer_only":0,"phrase":"14 ran (of which 1 constructed an object rather than computing a result; 14 with no instrument failure: 2 honoured, 1 violated, 11 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/on-uncertainty-tempering-and-data#ran","syntology_url":"https://syntology.ai/paper/2203.16481","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2203.16481"}},"official":{"repos":["activatedgeek/bayesian-classification"],"state":"official (archive's flag): 14 ran","n_ran":14,"n_constructed":1,"n_ran_no_instrument_failure":14,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/span-classification-with-structured","slug":"span-classification-with-structured","title":"Span Classification with Structured Information for Disfluency Detection in Spoken Utterances","date":"2022-03-30","arxiv_id":"2203.16028","repositories_listed":1,"syntology":null},{"url":"/paper/stylefool-fooling-video-classification","slug":"stylefool-fooling-video-classification","title":"StyleFool: Fooling Video Classification Systems via Style Transfer","date":"2022-03-30","arxiv_id":"2203.16000","repositories_listed":1,"syntology":null},{"url":"/paper/understanding-graph-convolutional-networks","slug":"understanding-graph-convolutional-networks","title":"Understanding Graph Convolutional Networks for Text Classification","date":"2022-03-30","arxiv_id":"2203.16060","repositories_listed":1,"syntology":null},{"url":"/paper/integrative-few-shot-learning-for","slug":"integrative-few-shot-learning-for","title":"Integrative Few-Shot Learning for Classification and Segmentation","date":"2022-03-29","arxiv_id":"2203.15712","repositories_listed":1,"syntology":null},{"url":"/paper/robust-structured-declarative-classifiers-for","slug":"robust-structured-declarative-classifiers-for","title":"Robust Structured Declarative Classifiers for 3D Point Clouds: Defending Adversarial Attacks with Implicit Gradients","date":"2022-03-29","arxiv_id":"2203.15245","repositories_listed":1,"syntology":null},{"url":"/paper/filler-word-detection-and-classification-a","slug":"filler-word-detection-and-classification-a","title":"Filler Word Detection and Classification: A Dataset and Benchmark","date":"2022-03-28","arxiv_id":"2203.15135","repositories_listed":1,"syntology":null},{"url":"/paper/knowledge-mining-with-scene-text-for-fine","slug":"knowledge-mining-with-scene-text-for-fine","title":"Knowledge Mining with Scene Text for Fine-Grained Recognition","date":"2022-03-27","arxiv_id":"2203.14215","repositories_listed":1,"syntology":null},{"url":"/paper/contrastive-graph-learning-for-population","slug":"contrastive-graph-learning-for-population","title":"Contrastive Graph Learning for Population-based fMRI Classification","date":"2022-03-26","arxiv_id":"2203.14044","repositories_listed":1,"syntology":null},{"url":"/paper/improving-robustness-of-jet-tagging","slug":"improving-robustness-of-jet-tagging","title":"Improving Robustness of Jet Tagging Algorithms with Adversarial Training","date":"2022-03-25","arxiv_id":"2203.13890","repositories_listed":1,"syntology":null},{"url":"/paper/effectively-leveraging-multi-modal-features","slug":"effectively-leveraging-multi-modal-features","title":"Movie Genre Classification by Language Augmentation and Shot Sampling","date":"2022-03-24","arxiv_id":"2203.13281","repositories_listed":1,"syntology":null},{"url":"/paper/on-robust-classification-using-contractive","slug":"on-robust-classification-using-contractive","title":"Robust Classification using Contractive Hamiltonian Neural ODEs","date":"2022-03-22","arxiv_id":"2203.11805","repositories_listed":1,"syntology":null},{"url":"/paper/suum-cuique-studying-bias-in-taboo-detection","slug":"suum-cuique-studying-bias-in-taboo-detection","title":"Suum Cuique: Studying Bias in Taboo Detection with a Community Perspective","date":"2022-03-22","arxiv_id":"2203.11401","repositories_listed":1,"syntology":null}],"record_sha256":"c1ec9ea1281a28669675dff2be7beaaff10e26adf9784f3c61110d0d358b0f61","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}