{"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/110","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":110,"pages_in_order":129,"rows_per_page":100,"rows":[10901,11000],"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/109","next":"/task/classification-1/papers/111","papers":[{"url":null,"slug":"what-does-a-textcnn-learn","title":"What Does a TextCNN Learn?","date":"2018-01-19","arxiv_id":"1801.06287","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-graph-embedded-deep-feedforward-network-for","title":"A graph-embedded deep feedforward network for disease outcome classification and feature selection using gene expression data","date":"2018-01-18","arxiv_id":"1801.06202","repositories_listed":0,"syntology":null},{"url":null,"slug":"contextual-and-position-aware-factorization","title":"Contextual and Position-Aware Factorization Machines for Sentiment Classification","date":"2018-01-18","arxiv_id":"1801.06172","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-data-driven-approach-for-resting-state-eeg","title":"A Data Driven Approach for Resting-state EEG signal Classification of Schizophrenia with Control Participants using Random Matrix Theory","date":"2018-01-17","arxiv_id":"1712.05289","repositories_listed":0,"syntology":null},{"url":null,"slug":"face-recognition-via-centralized-coordinate","title":"Face Recognition via Centralized Coordinate Learning","date":"2018-01-17","arxiv_id":"1801.05678","repositories_listed":0,"syntology":null},{"url":null,"slug":"improvement-of-resting-state-eeg-analysis","title":"Improvement of Resting-state EEG Analysis Process with Spectrum Weight-Voting based on LES","date":"2018-01-17","arxiv_id":"1712.07369","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-network-for-simultaneous-decomposition","title":"Deep Network for Simultaneous Decomposition and Classification in UWB-SAR Imagery","date":"2018-01-16","arxiv_id":"1801.05458","repositories_listed":0,"syntology":null},{"url":null,"slug":"grounded-language-understanding-for","title":"Grounded Language Understanding for Manipulation Instructions Using GAN-Based Classification","date":"2018-01-16","arxiv_id":"1801.05096","repositories_listed":0,"syntology":null},{"url":null,"slug":"localization-aware-active-learning-for-object","title":"Localization-Aware Active Learning for Object Detection","date":"2018-01-16","arxiv_id":"1801.05124","repositories_listed":0,"syntology":null},{"url":null,"slug":"generalizing-decoding-and-optimizing-support","title":"Generalizing, Decoding, and Optimizing Support Vector Machine Classification","date":"2018-01-15","arxiv_id":"1801.04929","repositories_listed":0,"syntology":null},{"url":null,"slug":"brain-eeg-time-series-selection-a-novel-graph","title":"Brain EEG Time Series Selection: A Novel Graph-Based Approach for Classification","date":"2018-01-14","arxiv_id":"1801.04510","repositories_listed":0,"syntology":null},{"url":null,"slug":"semi-supervised-fisher-vector-network","title":"Semi-supervised Fisher vector network","date":"2018-01-13","arxiv_id":"1801.04438","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-classification-of-epileptic-signals","title":"Deep Classification of Epileptic Signals","date":"2018-01-11","arxiv_id":"1801.03610","repositories_listed":0,"syntology":null},{"url":null,"slug":"data-augmentation-by-pairing-samples-for","title":"Data Augmentation by Pairing Samples for Images Classification","date":"2018-01-09","arxiv_id":"1801.02929","repositories_listed":0,"syntology":null},{"url":null,"slug":"lifelong-learning-for-sentiment","title":"Lifelong Learning for Sentiment Classification","date":"2018-01-09","arxiv_id":"1801.02808","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-nearest-class-mean-model-for-incremental","title":"Deep Nearest Class Mean Model for Incremental Odor Classification","date":"2018-01-08","arxiv_id":"1801.02328","repositories_listed":0,"syntology":null},{"url":null,"slug":"synthetic-data-augmentation-using-gan-for","title":"Synthetic Data Augmentation using GAN for Improved Liver Lesion Classification","date":"2018-01-08","arxiv_id":"1801.02385","repositories_listed":0,"syntology":null},{"url":null,"slug":"anatomical-data-augmentation-for-cnn-based","title":"Anatomical Data Augmentation For CNN based Pixel-wise Classification","date":"2018-01-07","arxiv_id":"1801.02261","repositories_listed":0,"syntology":null},{"url":null,"slug":"applying-an-ensemble-learning-method-for","title":"Applying an Ensemble Learning Method for Improving Multi-label Classification Performance","date":"2018-01-07","arxiv_id":"1801.02149","repositories_listed":0,"syntology":null},{"url":null,"slug":"architecture-based-classification-of-leaf","title":"Architecture Based Classification of Leaf Images","date":"2018-01-07","arxiv_id":"1801.02121","repositories_listed":0,"syntology":null},{"url":null,"slug":"accelerated-training-for-massive","title":"Accelerated Training for Massive Classification via Dynamic Class Selection","date":"2018-01-05","arxiv_id":"1801.01687","repositories_listed":0,"syntology":null},{"url":null,"slug":"efficient-image-evidence-analysis-of-cnn","title":"Efficient Image Evidence Analysis of CNN Classification Results","date":"2018-01-05","arxiv_id":"1801.01693","repositories_listed":0,"syntology":null},{"url":null,"slug":"enhanced-image-classification-with-data","title":"Enhanced Image Classification With Data Augmentation Using Position Coordinates","date":"2018-01-05","arxiv_id":"1802.02183","repositories_listed":0,"syntology":null},{"url":null,"slug":"implementation-of-deep-convolutional-neural","title":"Implementation of Deep Convolutional Neural Network in Multi-class Categorical Image Classification","date":"2018-01-03","arxiv_id":"1801.01397","repositories_listed":0,"syntology":null},{"url":null,"slug":"improved-eeg-event-classification-using","title":"Improved EEG Event Classification Using Differential Energy","date":"2018-01-03","arxiv_id":"1801.02477","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-classification-based-perspective-on-gan","title":"A Classification-Based Perspective on GAN Distributions","date":"2018-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"adversarial-examples-for-natural-language","title":"Adversarial Examples for Natural Language Classification Problems","date":"2018-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"cheap-dnn-pruning-with-performance-guarantees","title":"Cheap DNN Pruning with Performance Guarantees","date":"2018-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"classifier-to-generator-attack-estimation-of","title":"Classifier-to-Generator Attack: Estimation of Training Data Distribution from Classifier","date":"2018-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"continuous-convolutional-neural-networks-for","title":"Continuous Convolutional Neural Networks for Image Classification","date":"2018-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"dropmax-adaptive-stochastic-softmax","title":"DropMax: Adaptive Stochastic Softmax","date":"2018-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"enrichment-of-features-for-classification","title":"ENRICHMENT OF FEATURES FOR CLASSIFICATION USING AN OPTIMIZED LINEAR/NON-LINEAR COMBINATION OF INPUT FEATURES","date":"2018-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"facial-emotion-recognition-using-min-max","title":"Facial emotion recognition using min-max similarity classifier","date":"2018-01-01","arxiv_id":"1801.00451","repositories_listed":0,"syntology":null},{"url":null,"slug":"fast-and-accurate-text-classification","title":"Fast and Accurate Text Classification: Skimming, Rereading and Early Stopping","date":"2018-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"interpreting-deep-classification-models-with","title":"Interpreting Deep Classification Models With Bayesian Inference","date":"2018-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"key-protected-classification-for-gan-attack","title":"Key Protected Classification for GAN Attack Resilient Collaborative Learning","date":"2018-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"mach-embarrassingly-parallel-k-class","title":"MACH: Embarrassingly parallel $K$-class classification in $O(d\\log{K})$ memory and $O(K\\log{K} + d\\log{K})$ time, instead of $O(Kd)$","date":"2018-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-task-learning-on-mnist-image-datasets","title":"Multi-task Learning on MNIST Image Datasets","date":"2018-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"nearest-neighbour-radial-basis-function","title":"Nearest Neighbour Radial Basis Function Solvers for Deep Neural Networks","date":"2018-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-unsupervised-classification-with-deep","title":"Towards Unsupervised Classification with Deep Generative Models","date":"2018-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"vocabulary-informed-visual-feature","title":"VOCABULARY-INFORMED VISUAL FEATURE AUGMENTATION FOR ONE-SHOT LEARNING","date":"2018-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"zero-shot-cross-language-text-classification","title":"Zero-shot Cross Language Text Classification","date":"2018-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"transfer-learning-for-diagnosis-of-congenital","title":"Transfer learning for diagnosis of congenital abnormalities of the kidney and urinary tract in children based on Ultrasound imaging data","date":"2017-12-31","arxiv_id":"1801.00224","repositories_listed":0,"syntology":null},{"url":null,"slug":"exploring-the-significance-of-using","title":"Exploring the significance of using perceptually relevant image decolorization method for scene classification","date":"2017-12-29","arxiv_id":"1712.10152","repositories_listed":0,"syntology":null},{"url":null,"slug":"objective-evaluation-metrics-for-automatic","title":"Objective evaluation metrics for automatic classification of EEG events","date":"2017-12-29","arxiv_id":"1712.10107","repositories_listed":0,"syntology":null},{"url":null,"slug":"accurate-bayesian-data-classification-without","title":"Accurate Bayesian Data Classification without Hyperparameter Cross-validation","date":"2017-12-28","arxiv_id":"1712.09813","repositories_listed":0,"syntology":null},{"url":null,"slug":"large-scale-3d-scene-classification-with","title":"Large-Scale 3D Scene Classification With Multi-View Volumetric CNN","date":"2017-12-26","arxiv_id":"1712.09216","repositories_listed":0,"syntology":null},{"url":null,"slug":"combining-weakly-and-webly-supervised","title":"Combining Weakly and Webly Supervised Learning for Classifying Food Images","date":"2017-12-23","arxiv_id":"1712.08730","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-the-kernel-for-classification-and","title":"Learning the Kernel for Classification and Regression","date":"2017-12-22","arxiv_id":"1712.08597","repositories_listed":0,"syntology":null},{"url":null,"slug":"linear-centralization-classifier","title":"Linear centralization classifier","date":"2017-12-22","arxiv_id":"1712.08259","repositories_listed":0,"syntology":null},{"url":null,"slug":"discovery-of-shifting-patterns-in-sequence","title":"Discovery of Shifting Patterns in Sequence Classification","date":"2017-12-19","arxiv_id":"1712.07203","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-fixation-point-strategy-for-object","title":"Learning Fixation Point Strategy for Object Detection and Classification","date":"2017-12-19","arxiv_id":"1712.06897","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-shapelet-transform-for-multivariate-time","title":"A Shapelet Transform for Multivariate Time Series Classification","date":"2017-12-18","arxiv_id":"1712.06428","repositories_listed":0,"syntology":null},{"url":null,"slug":"automatic-classification-of-functional-gait","title":"Automatic Classification of Functional Gait Disorders","date":"2017-12-18","arxiv_id":"1712.06405","repositories_listed":0,"syntology":null},{"url":null,"slug":"meboost-mixing-estimators-with-boosting-for","title":"MEBoost: Mixing Estimators with Boosting for Imbalanced Data Classification","date":"2017-12-18","arxiv_id":"1712.06658","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-novel-approach-for-effective-learning-in","title":"A Novel Approach for Effective Learning in Low Resourced Scenarios","date":"2017-12-15","arxiv_id":"1712.05608","repositories_listed":0,"syntology":null},{"url":null,"slug":"automated-image-analysis-framework-for-the","title":"Automated Image Analysis Framework for the High-Throughput Determination of Grapevine Berry Sizes Using Conditional Random Fields","date":"2017-12-15","arxiv_id":"1712.05647","repositories_listed":0,"syntology":null},{"url":null,"slug":"adaptive-knn-using-expected-accuracy-for","title":"Adaptive kNN using Expected Accuracy for Classification of Geo-Spatial Data","date":"2017-12-14","arxiv_id":"1801.01453","repositories_listed":0,"syntology":null},{"url":null,"slug":"exponential-convergence-of-testing-error-for","title":"Exponential convergence of testing error for stochastic gradient methods","date":"2017-12-13","arxiv_id":"1712.04755","repositories_listed":0,"syntology":null},{"url":null,"slug":"3d-object-classification-via-spherical","title":"3D Object Classification via Spherical Projections","date":"2017-12-12","arxiv_id":"1712.04426","repositories_listed":0,"syntology":null},{"url":null,"slug":"empirical-evaluation-of-kernel-pca","title":"Empirical Evaluation of Kernel PCA Approximation Methods in Classification Tasks","date":"2017-12-12","arxiv_id":"1712.04196","repositories_listed":0,"syntology":null},{"url":null,"slug":"identifying-the-mislabeled-training-samples","title":"Identifying the Mislabeled Training Samples of ECG Signals using Machine Learning","date":"2017-12-11","arxiv_id":"1712.03792","repositories_listed":0,"syntology":null},{"url":null,"slug":"basic-thresholding-classification","title":"Basic Thresholding Classification","date":"2017-12-08","arxiv_id":"1712.03217","repositories_listed":0,"syntology":null},{"url":null,"slug":"blind-multiclass-ensemble-classification","title":"Blind Multiclass Ensemble Classification","date":"2017-12-08","arxiv_id":"1712.02903","repositories_listed":0,"syntology":null},{"url":null,"slug":"named-entity-sequence-classification","title":"Named Entity Sequence Classification","date":"2017-12-06","arxiv_id":"1712.02316","repositories_listed":0,"syntology":null},{"url":null,"slug":"single-trial-p300-classification-using-pca","title":"Single-trial P300 Classification using PCA with LDA, QDA and Neural Networks","date":"2017-12-06","arxiv_id":"1712.01977","repositories_listed":0,"syntology":null},{"url":null,"slug":"whats-in-my-closet-image-classification-using","title":"What's in my closet?: Image classification using fuzzy logic","date":"2017-12-05","arxiv_id":"1712.01970","repositories_listed":0,"syntology":null},{"url":null,"slug":"object-classification-using-ensemble-of-local","title":"Object Classification using Ensemble of Local and Deep Features","date":"2017-12-04","arxiv_id":"1712.04926","repositories_listed":0,"syntology":null},{"url":null,"slug":"raw-waveform-based-audio-classification-using","title":"Raw Waveform-based Audio Classification Using Sample-level CNN Architectures","date":"2017-12-04","arxiv_id":"1712.00866","repositories_listed":0,"syntology":null},{"url":null,"slug":"stochastic-maximum-likelihood-optimization","title":"Stochastic Maximum Likelihood Optimization via Hypernetworks","date":"2017-12-04","arxiv_id":"1712.01141","repositories_listed":0,"syntology":null},{"url":null,"slug":"avaliacao-da-doenca-de-alzheimer-pela-analise","title":"Avaliação da doença de Alzheimer pela análise multiespectral de imagens DW-MR por redes RBF como alternativa aos mapas ADC","date":"2017-12-03","arxiv_id":"1712.01700","repositories_listed":0,"syntology":null},{"url":null,"slug":"evaluation-of-alzheimers-disease-by-analysis","title":"Evaluation of Alzheimer's Disease by Analysis of MR Images using Multilayer Perceptrons and Kohonen SOM Classifiers as an Alternative to the ADC Maps","date":"2017-12-03","arxiv_id":"1712.00712","repositories_listed":0,"syntology":null},{"url":null,"slug":"fuzzy-based-dialectical-non-supervised-image","title":"Fuzzy-Based Dialectical Non-Supervised Image Classification and Clustering","date":"2017-12-03","arxiv_id":"1712.01694","repositories_listed":0,"syntology":null},{"url":null,"slug":"sentiment-classification-using-images-and","title":"Sentiment Classification using Images and Label Embeddings","date":"2017-12-03","arxiv_id":"1712.00725","repositories_listed":0,"syntology":null},{"url":null,"slug":"tensor-train-neighborhood-preserving","title":"Tensor Train Neighborhood Preserving Embedding","date":"2017-12-03","arxiv_id":"1712.00828","repositories_listed":0,"syntology":null},{"url":null,"slug":"triagem-virtual-de-imagens-de-imuno","title":"Triagem virtual de imagens de imuno-histoquímica usando redes neurais artificiais e espectro de padrões","date":"2017-12-03","arxiv_id":"1712.01695","repositories_listed":0,"syntology":null},{"url":null,"slug":"an-ensemble-of-deep-convolutional-neural","title":"An Ensemble of Deep Convolutional Neural Networks for Alzheimer's Disease Detection and Classification","date":"2017-12-02","arxiv_id":"1712.01675","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-sparse-adversarial-dictionaries-for","title":"Learning Sparse Adversarial Dictionaries For Multi-Class Audio Classification","date":"2017-12-02","arxiv_id":"1712.00640","repositories_listed":0,"syntology":null},{"url":null,"slug":"where-classification-fails-interpretation","title":"Where Classification Fails, Interpretation Rises","date":"2017-12-02","arxiv_id":"1712.00558","repositories_listed":0,"syntology":null},{"url":null,"slug":"adapt-at-ijcnlp-2017-task-4-a-multinomial","title":"ADAPT at IJCNLP-2017 Task 4: A Multinomial Naive Bayes Classification Approach for Customer Feedback Analysis task","date":"2017-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"group-sparse-additive-machine","title":"Group Sparse Additive Machine","date":"2017-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"hierarchical-bayesian-image-analysis-from-low","title":"Hierarchical Bayesian image analysis: from low-level modeling to robust supervised learning","date":"2017-12-01","arxiv_id":"1712.00368","repositories_listed":0,"syntology":null},{"url":null,"slug":"ju-nitm-at-ijcnlp-2017-task-5-a","title":"JU NITM at IJCNLP-2017 Task 5: A Classification Approach for Answer Selection in Multi-choice Question Answering System","date":"2017-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"leveraging-linguistic-resources-for-improving","title":"Leveraging Linguistic Resources for Improving Neural Text Classification","date":"2017-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"open-set-text-classification-using-cnns","title":"Open Set Text Classification Using CNNs","date":"2017-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"ynu-hpcc-at-ijcnlp-2017-task-4-attention","title":"YNU-HPCC at IJCNLP-2017 Task 4: Attention-based Bi-directional GRU Model for Customer Feedback Analysis Task of English","date":"2017-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":"/paper/paris-lille-3d-a-large-and-high-quality","slug":"paris-lille-3d-a-large-and-high-quality","title":"Paris-Lille-3D: a large and high-quality ground truth urban point cloud dataset for automatic segmentation and classification","date":"2017-11-30","arxiv_id":"1712.00032","repositories_listed":0,"syntology":null},{"url":null,"slug":"single-epoch-supernova-classification-with","title":"Single-epoch supernova classification with deep convolutional neural networks","date":"2017-11-30","arxiv_id":"1711.11526","repositories_listed":0,"syntology":null},{"url":null,"slug":"modeling-information-flow-through-deep-neural","title":"Modeling Information Flow Through Deep Neural Networks","date":"2017-11-29","arxiv_id":"1712.00003","repositories_listed":0,"syntology":null},{"url":null,"slug":"technical-report-a-new-decision-theory-based","title":"Technical Report: A New Decision-Theory-Based Framework for Echo Canceler Control","date":"2017-11-29","arxiv_id":"1711.11454","repositories_listed":0,"syntology":null},{"url":null,"slug":"classification-of-entities-via-their","title":"Classification of entities via their descriptive sentences","date":"2017-11-28","arxiv_id":"1711.10317","repositories_listed":0,"syntology":null},{"url":"/paper/dfunet-convolutional-neural-networks-for","slug":"dfunet-convolutional-neural-networks-for","title":"DFUNet: Convolutional Neural Networks for Diabetic Foot Ulcer Classification","date":"2017-11-28","arxiv_id":"1711.10448","repositories_listed":0,"syntology":null},{"url":null,"slug":"butterfly-effect-bidirectional-control-of","title":"Butterfly Effect: Bidirectional Control of Classification Performance by Small Additive Perturbation","date":"2017-11-27","arxiv_id":"1711.09681","repositories_listed":0,"syntology":null},{"url":null,"slug":"lexical-semantic-resources-yet-powerful","title":"Lexical-semantic resources: yet powerful resources for automatic personality classification","date":"2017-11-27","arxiv_id":"1711.09824","repositories_listed":0,"syntology":null},{"url":null,"slug":"ostsc-over-sampling-for-time-series","title":"OSTSC: Over Sampling for Time Series Classification in R","date":"2017-11-27","arxiv_id":"1711.09545","repositories_listed":0,"syntology":null},{"url":null,"slug":"local-jet-pattern-a-robust-descriptor-for","title":"Local Jet Pattern: A Robust Descriptor for Texture Classification","date":"2017-11-26","arxiv_id":"1711.10921","repositories_listed":0,"syntology":null},{"url":null,"slug":"micro-doppler-based-human-robot","title":"Micro-Doppler Based Human-Robot Classification Using Ensemble and Deep Learning Approaches","date":"2017-11-25","arxiv_id":"1711.09177","repositories_listed":0,"syntology":null},{"url":null,"slug":"persistent-homology-machine-learning-for","title":"Persistent homology machine learning for fingerprint classification","date":"2017-11-24","arxiv_id":"1711.09158","repositories_listed":0,"syntology":null},{"url":null,"slug":"warped-linear-models-for-time-series","title":"Warped-Linear Models for Time Series Classification","date":"2017-11-24","arxiv_id":"1711.09156","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-pitfall-of-unsupervised-pre-training","title":"A Pitfall of Unsupervised Pre-Training","date":"2017-11-23","arxiv_id":"1712.01655","repositories_listed":0,"syntology":null}],"record_sha256":"47ec0237643a49dca5a55980259774b0193c3fccc4bdda28f8065b0f2ea71e36","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}