{"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/81","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":81,"pages_in_order":129,"rows_per_page":100,"rows":[8001,8100],"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/80","next":"/task/classification-1/papers/82","papers":[{"url":null,"slug":"one-class-classification-a-survey","title":"One-Class Classification: A Survey","date":"2021-01-08","arxiv_id":"2101.03064","repositories_listed":0,"syntology":null},{"url":null,"slug":"comparing-classification-models-on-kepler","title":"Comparing Classification Models on Kepler Data","date":"2021-01-06","arxiv_id":"2101.01904","repositories_listed":0,"syntology":null},{"url":null,"slug":"explainable-systematic-analysis-for-synthetic","title":"Explainable Systematic Analysis for Synthetic Aperture Sonar Imagery","date":"2021-01-06","arxiv_id":"2101.03134","repositories_listed":0,"syntology":null},{"url":null,"slug":"on-device-document-classification-using","title":"On-Device Document Classification using multimodal features","date":"2021-01-06","arxiv_id":"2101.01880","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-unifying-approach-on-bias-and-variance","title":"A unifying approach on bias and variance analysis for classification","date":"2021-01-05","arxiv_id":"2101.01765","repositories_listed":0,"syntology":null},{"url":null,"slug":"to-do-or-not-to-do-cost-sensitive-causal","title":"To do or not to do: cost-sensitive causal decision-making","date":"2021-01-05","arxiv_id":"2101.01407","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-novel-bio-inspired-hybrid-multi-filter","title":"A Novel Bio-Inspired Hybrid Multi-Filter Wrapper Gene Selection Method with Ensemble Classifier for Microarray Data","date":"2021-01-04","arxiv_id":"2101.00819","repositories_listed":0,"syntology":null},{"url":null,"slug":"explainable-neural-network-based-modulation","title":"Explainable Neural Network-based Modulation Classification via Concept Bottleneck Models","date":"2021-01-04","arxiv_id":"2101.01239","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-network-traffic-monitoring-using-deep","title":"Towards Network Traffic Monitoring Using Deep Transfer Learning","date":"2021-01-04","arxiv_id":"2101.00731","repositories_listed":0,"syntology":null},{"url":null,"slug":"an-evolution-of-cnn-object-classifiers-on-low","title":"An Evolution of CNN Object Classifiers on Low-Resolution Images","date":"2021-01-03","arxiv_id":"2101.00686","repositories_listed":0,"syntology":null},{"url":null,"slug":"in-ear-spo2-for-classification-of-cognitive","title":"In-Ear SpO2 for Classification of Cognitive Workload","date":"2021-01-03","arxiv_id":"2101.00647","repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-label-ranking-mining-multi-label-and","title":"Multi-label Ranking: Mining Multi-label and Label Ranking Data","date":"2021-01-03","arxiv_id":"2101.00583","repositories_listed":0,"syntology":null},{"url":null,"slug":"multitask-learning-for-class-imbalanced","title":"Multitask Learning for Class-Imbalanced Discourse Classification","date":"2021-01-02","arxiv_id":"2101.00389","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-near-optimal-recipe-for-debiasing-trained","title":"A Near-Optimal Recipe for Debiasing Trained Machine Learning Models","date":"2021-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"ac-vae-learning-semantic-representation-with","title":"AC-VAE: Learning Semantic Representation with VAE for Adaptive Clustering","date":"2021-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"adaptive-stacked-graph-filter","title":"Adaptive Stacked Graph Filter","date":"2021-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"afinets-attentive-feature-integration","title":"AFINets: Attentive Feature Integration Networks for Image Classification","date":"2021-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"aspect-based-sentiment-classification-via","title":"Aspect-based Sentiment Classification via Reinforcement Learning","date":"2021-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"asynchronous-modeling-a-dual-phase","title":"Asynchronous Modeling: A Dual-phase Perspective for Long-Tailed Recognition","date":"2021-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"attention-based-joint-learning-for-supervised","title":"Attention Based Joint Learning for Supervised Premature Ventricular Contraction Differentiation with Unsupervised Abnormal Beat Segmentation","date":"2021-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"brain-tumor-detection-and-classification","title":"Brain Tumor Detection and Classification based on Hybrid Ensemble Classifier","date":"2021-01-01","arxiv_id":"2101.00216","repositories_listed":0,"syntology":null},{"url":null,"slug":"buffer-zone-based-defense-against-adversarial","title":"Buffer Zone based Defense against Adversarial Examples in Image Classification","date":"2021-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"certified-robustness-against-physically","title":"Certified robustness against physically-realizable patch attack via randomized cropping","date":"2021-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"cluster-tune-enhance-bert-performance-in-low","title":"Cluster & Tune: Enhance BERT Performance in Low Resource Text Classification","date":"2021-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"consistent-instance-classification-for","title":"Consistent Instance Classification for Unsupervised Representation Learning","date":"2021-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"context-agnostic-learning-using-synthetic","title":"Context-Agnostic Learning Using Synthetic Data","date":"2021-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"contrastive-self-supervised-learning-of","title":"Contrastive Self-Supervised Learning of Global-Local Audio-Visual Representations","date":"2021-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"demystifying-loss-functions-for","title":"Demystifying Loss Functions for Classification","date":"2021-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"distribution-embedding-network-for-meta","title":"Distribution Embedding Network for Meta-Learning with Variable-Length Input","date":"2021-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"dual-tree-wavelet-packet-cnns-for-image","title":"Dual-Tree Wavelet Packet CNNs for Image Classification","date":"2021-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"end-to-end-trainable-trident-person-search","title":"End-to-End Trainable Trident Person Search Network Using Adaptive Gradient Propagation","date":"2021-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"energy-based-out-of-distribution-detection","title":"Energy-based Out-of-distribution Detection for Multi-label Classification","date":"2021-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"exploring-target-driven-image-classification","title":"Exploring Target Driven Image Classification","date":"2021-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"fasg-feature-aggregation-self-training-gcn","title":"FASG: Feature Aggregation Self-training GCN for Semi-supervised Node Classification","date":"2021-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"foc-osod-focus-on-classification-one-shot","title":"FOC OSOD: Focus on Classification One-Shot Object Detection","date":"2021-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"foreground-activation-maps-for-weakly","title":"Foreground Activation Maps for Weakly Supervised Object Localization","date":"2021-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"graph-graph-similarity-network","title":"Graph-Graph Similarity Network","date":"2021-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"graph-structural-aggregation-for-explainable","title":"Graph Structural Aggregation for Explainable Learning","date":"2021-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"identifying-the-sources-of-uncertainty-in","title":"Identifying the Sources of Uncertainty in Object Classification","date":"2021-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-flexible-classifiers-with-shot","title":"Learning Flexible Classifiers with Shot-CONditional Episodic (SCONE) Training","date":"2021-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"modeling-human-development-effects-of-blurred","title":"Modeling Human Development: Effects of Blurred Vision on Category Learning in CNNs","date":"2021-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"monolingual-word-sense-alignment-as-a","title":"Monolingual Word Sense Alignment as a Classification Problem","date":"2021-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"neural-pooling-for-graph-neural-networks","title":"Neural Pooling for Graph Neural Networks","date":"2021-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"on-single-environment-extrapolations-in-graph","title":"On Single-environment Extrapolations in Graph Classification and Regression Tasks","date":"2021-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"one-class-classification-robust-to-geometric","title":"One-class Classification Robust to Geometric Transformation","date":"2021-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"out-of-distribution-classification-and","title":"Out-of-Distribution Classification and Clustering","date":"2021-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"predet-large-scale-weakly-supervised-pre","title":"PreDet: Large-Scale Weakly Supervised Pre-Training for Detection","date":"2021-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"quantifying-task-complexity-through","title":"Quantifying Task Complexity Through Generalized Information Measures","date":"2021-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"task-calibration-for-distributional","title":"Task Calibration for Distributional Uncertainty in Few-Shot Classification","date":"2021-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"uncertainty-aware-active-learning-for-optimal","title":"Uncertainty-aware Active Learning for Optimal Bayesian Classifier","date":"2021-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"uncertainty-calibration-error-a-new-metric","title":"Uncertainty Calibration Error: A New Metric for Multi-Class Classification","date":"2021-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"vem-gcn-topology-optimization-with","title":"VEM-GCN: Topology Optimization with Variational EM for Graph Convolutional Networks","date":"2021-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"watch-only-once-an-end-to-end-video-action","title":"Watch Only Once: An End-to-End Video Action Detection Framework","date":"2021-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"wordsworth-scores-for-attacking-cnns-and","title":"WordsWorth Scores for Attacking CNNs and LSTMs for Text Classification","date":"2021-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"3d-human-motion-anticipation-and","title":"3D Human motion anticipation and classification","date":"2020-12-31","arxiv_id":"2012.15378","repositories_listed":0,"syntology":null},{"url":null,"slug":"an-integrated-attribute-guided-dense","title":"Integrated Generalized Zero-Shot Learning for Fine-Grained Classification","date":"2020-12-31","arxiv_id":"2101.02141","repositories_listed":0,"syntology":null},{"url":null,"slug":"unsupervised-label-aware-event-trigger-and","title":"Unsupervised Label-aware Event Trigger and Argument Classification","date":"2020-12-30","arxiv_id":"2012.15243","repositories_listed":0,"syntology":null},{"url":null,"slug":"cascaded-framework-for-automatic-evaluation","title":"Cascaded Framework for Automatic Evaluation of Myocardial Infarction from Delayed-Enhancement Cardiac MRI","date":"2020-12-29","arxiv_id":"2012.14556","repositories_listed":0,"syntology":null},{"url":null,"slug":"mgml-multi-granularity-multi-level-feature","title":"MGML: Multi-Granularity Multi-Level Feature Ensemble Network for Remote Sensing Scene Classification","date":"2020-12-29","arxiv_id":"2012.14569","repositories_listed":0,"syntology":null},{"url":null,"slug":"3d-axial-attention-for-lung-nodule","title":"3D Axial-Attention for Lung Nodule Classification","date":"2020-12-28","arxiv_id":"2012.14117","repositories_listed":0,"syntology":null},{"url":null,"slug":"classification-of-pathological-and-normal","title":"Classification of Pathological and Normal Gait: A Survey","date":"2020-12-28","arxiv_id":"2012.14465","repositories_listed":0,"syntology":null},{"url":null,"slug":"convolutional-neural-networks-in-multi-class","title":"Convolutional Neural Networks in Multi-Class Classification of Medical Data","date":"2020-12-28","arxiv_id":"2012.14059","repositories_listed":0,"syntology":null},{"url":null,"slug":"lookhops-light-multi-order-convolution-and","title":"LookHops: light multi-order convolution and pooling for graph classification","date":"2020-12-28","arxiv_id":"2012.15741","repositories_listed":0,"syntology":null},{"url":"/paper/whu-hi-uav-borne-hyperspectral-with-high","slug":"whu-hi-uav-borne-hyperspectral-with-high","title":"WHU-Hi: UAV-borne hyperspectral with high spatial resolution (H2) benchmark datasets for hyperspectral image classification","date":"2020-12-27","arxiv_id":"2012.13920","repositories_listed":0,"syntology":null},{"url":null,"slug":"explainable-multi-class-classification-of","title":"Explainable Multi-class Classification of Medical Data","date":"2020-12-26","arxiv_id":"2012.13796","repositories_listed":0,"syntology":null},{"url":null,"slug":"grapth-theory-in-the-classification-of","title":"Graph Theory in the Classification of Information Systems","date":"2020-12-24","arxiv_id":"2012.13182","repositories_listed":0,"syntology":null},{"url":null,"slug":"quackie-a-nlp-classification-task-with-ground","title":"QUACKIE: A NLP Classification Task With Ground Truth Explanations","date":"2020-12-24","arxiv_id":"2012.13190","repositories_listed":0,"syntology":null},{"url":null,"slug":"semi-supervised-node-classification-on-graphs","title":"Semi-Supervised Node Classification on Graphs: Markov Random Fields vs. Graph Neural Networks","date":"2020-12-24","arxiv_id":"2012.13085","repositories_listed":0,"syntology":null},{"url":null,"slug":"comparison-of-privacy-preserving-distributed","title":"Comparison of Privacy-Preserving Distributed Deep Learning Methods in Healthcare","date":"2020-12-23","arxiv_id":"2012.12591","repositories_listed":0,"syntology":null},{"url":null,"slug":"unbiased-subdata-selection-for-fair","title":"Unbiased Subdata Selection for Fair Classification: A Unified Framework and Scalable Algorithms","date":"2020-12-22","arxiv_id":"2012.12356","repositories_listed":0,"syntology":null},{"url":null,"slug":"binary-classification-tests-imperfect","title":"Binary Classification Tests, Imperfect Standards, and Ambiguous Information","date":"2020-12-21","arxiv_id":"2012.11215","repositories_listed":0,"syntology":null},{"url":null,"slug":"cost-sensitive-semi-supervised-classification","title":"Cost-sensitive Semi-supervised Classification for Fraud Applications","date":"2020-12-21","arxiv_id":"2012.11743","repositories_listed":0,"syntology":null},{"url":null,"slug":"resting-state-eeg-sex-classification-using","title":"Resting-state EEG sex classification using selected brain connectivity representation","date":"2020-12-21","arxiv_id":"2012.11105","repositories_listed":0,"syntology":null},{"url":null,"slug":"techtexc-classification-of-technical-texts","title":"TechTexC: Classification of Technical Texts using Convolution and Bidirectional Long Short Term Memory Network","date":"2020-12-21","arxiv_id":"2012.11420","repositories_listed":0,"syntology":null},{"url":null,"slug":"semi-supervised-hyperspectral-image","title":"Semi-supervised Hyperspectral Image Classification with Graph Clustering Convolutional Networks","date":"2020-12-20","arxiv_id":"2012.10932","repositories_listed":0,"syntology":null},{"url":null,"slug":"communication-aware-collaborative-learning","title":"Communication-Aware Collaborative Learning","date":"2020-12-19","arxiv_id":"2012.10569","repositories_listed":0,"syntology":null},{"url":null,"slug":"sample-complexity-of-adversarially-robust","title":"Sample Complexity of Adversarially Robust Linear Classification on Separated Data","date":"2020-12-19","arxiv_id":"2012.10794","repositories_listed":0,"syntology":null},{"url":null,"slug":"3d-object-classification-on-partial-point","title":"Classification of Single-View Object Point Clouds","date":"2020-12-18","arxiv_id":"2012.10042","repositories_listed":0,"syntology":null},{"url":null,"slug":"automatic-detection-of-abnormal-eeg-signals","title":"Automatic detection of abnormal EEG signals using wavelet feature extraction and gradient boosting decision tree","date":"2020-12-18","arxiv_id":"2012.10034","repositories_listed":0,"syntology":null},{"url":null,"slug":"fair-for-all-best-effort-fairness-guarantees","title":"Fair for All: Best-effort Fairness Guarantees for Classification","date":"2020-12-18","arxiv_id":"2012.10216","repositories_listed":0,"syntology":null},{"url":null,"slug":"leveraging-meta-path-contexts-for","title":"Leveraging Meta-path Contexts for Classification in Heterogeneous Information Networks","date":"2020-12-18","arxiv_id":"2012.10024","repositories_listed":0,"syntology":null},{"url":null,"slug":"reinforcement-learning-based-multi-robot","title":"Distributed Map Classification using Local Observations","date":"2020-12-18","arxiv_id":"2012.10480","repositories_listed":0,"syntology":null},{"url":null,"slug":"technical-progress-analysis-using-a-dynamic","title":"Technical Progress Analysis Using a Dynamic Topic Model for Technical Terms to Revise Patent Classification Codes","date":"2020-12-18","arxiv_id":"2012.10120","repositories_listed":0,"syntology":null},{"url":null,"slug":"temporal-bilinear-encoding-network-of-audio","title":"Temporal Bilinear Encoding Network of Audio-Visual Features at Low Sampling Rates","date":"2020-12-18","arxiv_id":"2012.10283","repositories_listed":0,"syntology":null},{"url":null,"slug":"smoothed-gaussian-mixture-models-for-video","title":"Smoothed Gaussian Mixture Models for Video Classification and Recommendation","date":"2020-12-17","arxiv_id":"2012.11673","repositories_listed":0,"syntology":null},{"url":null,"slug":"weakly-supervised-action-localization-and","title":"Weakly-Supervised Action Localization and Action Recognition using Global-Local Attention of 3D CNN","date":"2020-12-17","arxiv_id":"2012.09542","repositories_listed":0,"syntology":null},{"url":null,"slug":"cross-cohort-generalizability-of-deep-and","title":"Cross-Cohort Generalizability of Deep and Conventional Machine Learning for MRI-based Diagnosis and Prediction of Alzheimer's Disease","date":"2020-12-16","arxiv_id":"2012.08769","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-learning-of-cell-classification-using","title":"Deep Learning of Cell Classification using Microscope Images of Intracellular Microtubule Networks","date":"2020-12-16","arxiv_id":"2012.12125","repositories_listed":0,"syntology":null},{"url":"/paper/melinda-a-multimodal-dataset-for-biomedical","slug":"melinda-a-multimodal-dataset-for-biomedical","title":"MELINDA: A Multimodal Dataset for Biomedical Experiment Method Classification","date":"2020-12-16","arxiv_id":"2012.09216","repositories_listed":0,"syntology":null},{"url":null,"slug":"convolutional-neural-networks-from-image","title":"Convolutional Neural Networks from Image Markers","date":"2020-12-15","arxiv_id":"2012.12108","repositories_listed":0,"syntology":null},{"url":null,"slug":"frozen-to-paraffin-categorization-of","title":"Frozen-to-Paraffin: Categorization of Histological Frozen Sections by the Aid of Paraffin Sections and Generative Adversarial Networks","date":"2020-12-15","arxiv_id":"2012.08158","repositories_listed":0,"syntology":null},{"url":null,"slug":"robust-optimal-classification-trees-under","title":"Robust Optimal Classification Trees under Noisy Labels","date":"2020-12-15","arxiv_id":"2012.08560","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-t-sne-based-classification-approach-to","title":"A t-SNE Based Classification Approach to Compositional Microbiome Data","date":"2020-12-14","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"application-of-the-neural-network","title":"Application of the Neural Network Dependability Kit in Real-World Environments","date":"2020-12-14","arxiv_id":"2012.09602","repositories_listed":0,"syntology":null},{"url":null,"slug":"one-shot-learning-with-triplet-loss-for","title":"One-Shot Learning with Triplet Loss for Vegetation Classification Tasks","date":"2020-12-14","arxiv_id":"2012.07403","repositories_listed":0,"syntology":null},{"url":null,"slug":"perceptron-theory-for-predicting-the-accuracy","title":"Perceptron Theory Can Predict the Accuracy of Neural Networks","date":"2020-12-14","arxiv_id":"2012.07881","repositories_listed":0,"syntology":null},{"url":null,"slug":"improving-the-classification-of-rare-chords","title":"Improving the Classification of Rare Chords with Unlabeled Data","date":"2020-12-13","arxiv_id":"2012.07055","repositories_listed":0,"syntology":null},{"url":"/paper/fine-grained-classification-via-categorical","slug":"fine-grained-classification-via-categorical","title":"Fine-grained Classification via Categorical Memory Networks","date":"2020-12-12","arxiv_id":"2012.06793","repositories_listed":0,"syntology":null},{"url":null,"slug":"light-weight-1-d-convolutional-neural-network","title":"Light-Weight 1-D Convolutional Neural Network Architecture for Mental Task Identification and Classification Based on Single-Channel EEG","date":"2020-12-12","arxiv_id":"2012.06782","repositories_listed":0,"syntology":null},{"url":null,"slug":"one-point-is-all-you-need-directional","title":"On Learning the Right Attention Point for Feature Enhancement","date":"2020-12-11","arxiv_id":"2012.06257","repositories_listed":0,"syntology":null}],"record_sha256":"52e18a79796929e13f0d596707958fca89a571fa715a70cb13b42962a6f64348","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}