{"about":{"non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","site":"https://codewithpapers.app","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page","syntology":{"site":"https://syntology.ai","developers":"https://syntology.ai/developers","mcp":{"server":"https://syntology.ai/mcp","transport":"streamable-http","server_card":"https://syntology.ai/.well-known/mcp/server-card.json","auth":{"type":"trial token, no account","trial_token":"https://syntology.ai/api/oauth/trial/token","method":"POST","docs":"https://syntology.ai/developers"}},"have":"https://syntology.ai/api/graph/have?x=<method, arXiv id or title> (free, answers coverage only)","paper_base":"https://syntology.ai/paper/","atlas_base":"https://app.syntology.ai/?focus="},"machine_readable":[{"url":"https://codewithpapers.app/llms.txt","what":"the machine catalog: every machine-readable file, counted"},{"url":"https://codewithpapers.app/index/manifest.json","what":"paper-to-code index by arXiv id, with Syntology's counts"},{"url":"https://codewithpapers.app/search/manifest.json","what":"site search index (titles, authors) and its files"},{"url":"https://codewithpapers.app/download","what":"bulk files: Syntology's layer, described there"},{"url":"https://codewithpapers.app/build_manifest.json","what":"the build record: inputs, counts, exclusions, probes"}]},"url":"/task/classification/papers/104","list_of":"/task/classification","task":"General Classification","archive":{"snapshot":"2025-07-28"},"key_notes":{"n_ran_checked":"legacy name, kept unchanged so existing readers do not break: it counts the samples that ran with no instrument failure (honoured, violated, and ran with no contract checked); it does not mean a contract was checked, and the pages print it as 'K with no instrument failure', not 'K checked'","n_constructed":"a sub-count of the samples that ran, never subtracted from them and never a failure: an executed sample whose run returned an instance of its own class (fixture_out_type equals the entry name): the run built an object and did not compute a result (Syntology's RAN record, counts.constructed)"},"syntology_read_at":"2026-09-28T10:30:06+00:00","order":"archive","order_definition":"repositories listed in the archive (most first), then date (newest first), then slug","page":104,"pages_in_order":146,"rows_per_page":100,"rows":[10301,10400],"of":14581,"counts":{"archive_papers_tagged":14581,"with_a_code_link":3945,"where_syntology_ran_a_sample":713,"not_listed_spam_title":0,"listed":14581,"listed_where_code_ran":713,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":560,"every_run_a_failure_of_syntologys_instrument":153,"listed_with_a_run_with_no_instrument_failure":560,"listed_every_run_a_failure_of_syntologys_instrument":153,"filter":{"states":["a run with no instrument failure","any run, instrument failures included"],"default":"a run with no instrument failure","note":"on the 'only where code ran' pages the default hides, in the browser, the rows where every run was a failure of Syntology's instrument; the second state shows them again. Rows are hidden, never re-ordered; these twins list every row"}},"definition":"distinct papers the archive tags; 'where Syntology ran a sample' counts papers with at least one harvested sample that ran, which is not a correctness claim"},"first_page":"/task/classification","prev":"/task/classification/papers/103","next":"/task/classification/papers/105","papers":[{"url":null,"slug":"profit-driven-decision-trees-for-churn","title":"Profit Driven Decision Trees for Churn Prediction","date":"2017-12-21","arxiv_id":"1712.08101","repositories_listed":0,"syntology":null},{"url":null,"slug":"reabsnet-detecting-and-revising-adversarial","title":"ReabsNet: Detecting and Revising Adversarial Examples","date":"2017-12-21","arxiv_id":"1712.08250","repositories_listed":0,"syntology":null},{"url":null,"slug":"detection-and-classification-of-masses-in","title":"Detection and classification of masses in mammographic images in a multi-kernel approach","date":"2017-12-20","arxiv_id":"1712.07116","repositories_listed":0,"syntology":null},{"url":null,"slug":"fast-knn-mode-seeking-clustering-applied-to","title":"Fast kNN mode seeking clustering applied to active learning","date":"2017-12-20","arxiv_id":"1712.07454","repositories_listed":0,"syntology":null},{"url":null,"slug":"any-gram-kernels-for-sentence-classification","title":"Any-gram Kernels for Sentence Classification: A Sentiment Analysis Case Study","date":"2017-12-19","arxiv_id":"1712.07004","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":"y-net-3d-intracranial-artery-segmentation","title":"Y-net: 3D intracranial artery segmentation using a convolutional autoencoder","date":"2017-12-19","arxiv_id":"1712.07194","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":"when-not-to-classify-anomaly-detection-of","title":"When Not to Classify: Anomaly Detection of Attacks (ADA) on DNN Classifiers at Test Time","date":"2017-12-18","arxiv_id":"1712.06646","repositories_listed":0,"syntology":null},{"url":null,"slug":"deepnorm-a-deep-learning-approach-to-text","title":"DeepNorm-A Deep Learning Approach to Text Normalization","date":"2017-12-17","arxiv_id":"1712.06994","repositories_listed":0,"syntology":null},{"url":null,"slug":"super-sparse-learning-in-similarity-spaces","title":"Super-sparse Learning in Similarity Spaces","date":"2017-12-17","arxiv_id":"1712.06131","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-a-science-of-human-stories-using","title":"Towards a science of human stories: using sentiment analysis and emotional arcs to understand the building blocks of complex social systems","date":"2017-12-17","arxiv_id":"1712.06163","repositories_listed":0,"syntology":null},{"url":null,"slug":"mitigating-asymmetric-nonlinear-weight-update","title":"Mitigating Asymmetric Nonlinear Weight Update Effects in Hardware Neural Network based on Analog Resistive Synapse","date":"2017-12-16","arxiv_id":"1712.05895","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":"bt-nets-simplifying-deep-neural-networks-via","title":"BT-Nets: Simplifying Deep Neural Networks via Block Term Decomposition","date":"2017-12-15","arxiv_id":"1712.05689","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-when-to-skim-and-when-to-read","title":"Learning when to skim and when to read","date":"2017-12-15","arxiv_id":"1712.05483","repositories_listed":0,"syntology":null},{"url":null,"slug":"lightweight-neural-networks","title":"Lightweight Neural Networks","date":"2017-12-15","arxiv_id":"1712.05695","repositories_listed":0,"syntology":null},{"url":null,"slug":"pre-training-attention-mechanisms","title":"Pre-training Attention Mechanisms","date":"2017-12-15","arxiv_id":"1712.05652","repositories_listed":0,"syntology":null},{"url":null,"slug":"reducing-deep-network-complexity-with-fourier","title":"Reducing Deep Network Complexity via Sparse Hierarchical Fourier Interaction Networks","date":"2017-12-15","arxiv_id":"1801.01451","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":"rate-of-change-analysis-for-interestingness","title":"Rate of Change Analysis for Interestingness Measures","date":"2017-12-14","arxiv_id":"1712.05193","repositories_listed":0,"syntology":null},{"url":null,"slug":"evolving-unsupervised-deep-neural-networks","title":"Evolving Unsupervised Deep Neural Networks for Learning Meaningful Representations","date":"2017-12-13","arxiv_id":"1712.05043","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":"gmm-based-synthetic-samples-for","title":"GMM-Based Synthetic Samples for Classification of Hyperspectral Images With Limited Training Data","date":"2017-12-13","arxiv_id":"1712.04778","repositories_listed":0,"syntology":null},{"url":null,"slug":"mathematics-of-deep-learning","title":"Mathematics of Deep Learning","date":"2017-12-13","arxiv_id":"1712.04741","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":"multi-speaker-localization-using","title":"Multi-Speaker Localization Using Convolutional Neural Network Trained with Noise","date":"2017-12-12","arxiv_id":"1712.04276","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-novel-way-of-identifying-cyber-predators","title":"A Novel Way of Identifying Cyber Predators","date":"2017-12-11","arxiv_id":"1712.03903","repositories_listed":0,"syntology":null},{"url":null,"slug":"fast-nearest-neighbor-classification-using","title":"Fast Nearest-Neighbor Classification using RNN in Domains with Large Number of Classes","date":"2017-12-11","arxiv_id":"1712.03941","repositories_listed":0,"syntology":null},{"url":null,"slug":"generalized-zero-shot-learning-via","title":"Generalized Zero-Shot Learning via Synthesized Examples","date":"2017-12-11","arxiv_id":"1712.03878","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":"the-effectiveness-of-data-augmentation-for","title":"The Effectiveness of Data Augmentation for Detection of Gastrointestinal Diseases from Endoscopical Images","date":"2017-12-11","arxiv_id":"1712.03689","repositories_listed":0,"syntology":null},{"url":null,"slug":"gradient-normalization-depth-based-decay-for","title":"Gradient Normalization & Depth Based Decay For Deep Learning","date":"2017-12-10","arxiv_id":"1712.03607","repositories_listed":0,"syntology":null},{"url":null,"slug":"aspect-extraction-and-sentiment","title":"Aspect Extraction and Sentiment Classification of Mobile Apps using App-Store Reviews","date":"2017-12-09","arxiv_id":"1712.03430","repositories_listed":0,"syntology":null},{"url":null,"slug":"cost-sensitive-approach-to-batch-size","title":"Cost-Sensitive Approach to Batch Size Adaptation for Gradient Descent","date":"2017-12-09","arxiv_id":"1712.03428","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":"combining-deep-universal-features-semantic","title":"Combining Deep Universal Features, Semantic Attributes, and Hierarchical Classification for Zero-Shot Learning","date":"2017-12-08","arxiv_id":"1712.03151","repositories_listed":0,"syntology":null},{"url":null,"slug":"representations-of-sound-in-deep-learning-of","title":"Representations of Sound in Deep Learning of Audio Features from Music","date":"2017-12-08","arxiv_id":"1712.02898","repositories_listed":0,"syntology":null},{"url":null,"slug":"cost-sensitive-detection-with-variational","title":"Cost-sensitive detection with variational autoencoders for environmental acoustic sensing","date":"2017-12-07","arxiv_id":"1712.02488","repositories_listed":0,"syntology":null},{"url":null,"slug":"incremental-learning-in-deep-convolutional","title":"Incremental Learning in Deep Convolutional Neural Networks Using Partial Network Sharing","date":"2017-12-07","arxiv_id":"1712.02719","repositories_listed":0,"syntology":null},{"url":null,"slug":"semi-supervised-learning-with-ipm-based-gans","title":"Semi-Supervised Learning with IPM-based GANs: an Empirical Study","date":"2017-12-07","arxiv_id":"1712.02505","repositories_listed":0,"syntology":null},{"url":null,"slug":"automatic-segmentation-and-overall-survival","title":"Automatic Segmentation and Overall Survival Prediction in Gliomas using Fully Convolutional Neural Network and Texture Analysis","date":"2017-12-06","arxiv_id":"1712.02066","repositories_listed":0,"syntology":null},{"url":null,"slug":"cnn-training-with-graph-based-sample","title":"CNN training with graph-based sample preselection: application to handwritten character recognition","date":"2017-12-06","arxiv_id":"1712.02122","repositories_listed":0,"syntology":null},{"url":null,"slug":"discourse-aware-rumour-stance-classification","title":"Discourse-Aware Rumour Stance Classification in Social Media Using Sequential Classifiers","date":"2017-12-06","arxiv_id":"1712.02223","repositories_listed":0,"syntology":null},{"url":null,"slug":"distribution-based-categorization-of","title":"Distribution-Based Categorization of Classifier Transfer Learning","date":"2017-12-06","arxiv_id":"1712.02159","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":"s-shaped-vs-v-shaped-transfer-functions-for","title":"S-Shaped vs. V-Shaped Transfer Functions for Antlion Optimization Algorithm in Feature Selection Problems","date":"2017-12-06","arxiv_id":"1712.03223","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":"sparsity-regularization-and-feature-selection","title":"Regularization and feature selection for large dimensional data","date":"2017-12-06","arxiv_id":"1712.01975","repositories_listed":0,"syntology":null},{"url":null,"slug":"co-domain-embedding-using-deep-quadruplet","title":"Co-domain Embedding using Deep Quadruplet Networks for Unseen Traffic Sign Recognition","date":"2017-12-05","arxiv_id":"1712.01907","repositories_listed":0,"syntology":null},{"url":null,"slug":"human-activity-recognition-from-mobile","title":"Human activity recognition from mobile inertial sensors using recurrence plots","date":"2017-12-05","arxiv_id":"1712.01429","repositories_listed":0,"syntology":null},{"url":null,"slug":"recognizing-gender-from-human-facial-regions","title":"Recognizing Gender from Human Facial Regions using Genetic Algorithm","date":"2017-12-05","arxiv_id":"1712.01661","repositories_listed":0,"syntology":null},{"url":null,"slug":"sequence-mining-and-pattern-analysis-in","title":"Sequence Mining and Pattern Analysis in Drilling Reports with Deep Natural Language Processing","date":"2017-12-05","arxiv_id":"1712.01476","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":"leaf-identification-using-a-deep","title":"Leaf Identification Using a Deep Convolutional Neural Network","date":"2017-12-04","arxiv_id":"1712.00967","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-fast-and-slow-propedeutica-for-real","title":"Learning Fast and Slow: PROPEDEUTICA for Real-time Malware Detection","date":"2017-12-04","arxiv_id":"1712.01145","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-to-detect-chest-radiographs","title":"Learning to detect chest radiographs containing lung nodules using visual attention networks","date":"2017-12-04","arxiv_id":"1712.00996","repositories_listed":0,"syntology":null},{"url":null,"slug":"mining-supervisor-evaluation-and-peer","title":"Mining Supervisor Evaluation and Peer Feedback in Performance Appraisals","date":"2017-12-04","arxiv_id":"1712.00991","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":"topics-and-label-propagation-best-of-both","title":"Topics and Label Propagation: Best of Both Worlds for Weakly Supervised Text Classification","date":"2017-12-04","arxiv_id":"1712.02767","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":"dialectical-multispectral-classification-of","title":"Dialectical Multispectral Classification of Diffusion-Weighted Magnetic Resonance Images as an Alternative to Apparent Diffusion Coefficients Maps to Perform Anatomical Analysis","date":"2017-12-03","arxiv_id":"1712.01697","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":"a-global-feature-extraction-model-for-the","title":"A global feature extraction model for the effective computer aided diagnosis of mild cognitive impairment using structural MRI images","date":"2017-12-02","arxiv_id":"1712.00556","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":"3d-facial-action-units-recognition-for","title":"3D Facial Action Units Recognition for Emotional Expression","date":"2017-12-01","arxiv_id":"1712.00195","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-deep-learning-based-feature-selection","title":"A Deep Learning based Feature Selection Method with Multi Level Feature Identification and Extraction using Convolutional Neural Network","date":"2017-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"a-learning-error-analysis-for-structured","title":"A Learning Error Analysis for Structured Prediction with Approximate Inference","date":"2017-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"a-modified-cosine-similarity-based-log-kernel","title":"A Modified Cosine-Similarity based Log Kernel for Support Vector Machines in the Domain of Text Classification","date":"2017-12-01","arxiv_id":null,"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":"adversarial-surrogate-losses-for-ordinal","title":"Adversarial Surrogate Losses for Ordinal Regression","date":"2017-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"all-in-1-at-ijcnlp-2017-task-4-short-text","title":"All-In-1 at IJCNLP-2017 Task 4: Short Text Classification with One Model for All Languages","date":"2017-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"bingo-at-ijcnlp-2017-task-4-augmenting-data","title":"Bingo at IJCNLP-2017 Task 4: Augmenting Data using Machine Translation for Cross-linguistic Customer Feedback Classification","date":"2017-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-learning-with-permutation-invariant","title":"Deep Learning with Permutation-invariant Operator for Multi-instance Histopathology Classification","date":"2017-12-01","arxiv_id":"1712.00310","repositories_listed":0,"syntology":null},{"url":null,"slug":"delineation-of-skin-strata-in-reflectance","title":"Delineation of Skin Strata in Reflectance Confocal Microscopy Images using Recurrent Convolutional Networks with Toeplitz Attention","date":"2017-12-01","arxiv_id":"1712.00192","repositories_listed":0,"syntology":null},{"url":null,"slug":"experiments-with-domain-dependent-dialogue","title":"Experiments with Domain Dependent Dialogue Act Classification using Open-Domain Dialogue Corpora","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":"hierarchical-clustering-beyond-the-worst-case","title":"Hierarchical Clustering Beyond the Worst-Case","date":"2017-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"houdini-fooling-deep-structured-visual-and","title":"Houdini: Fooling Deep Structured Visual and Speech Recognition Models with Adversarial Examples","date":"2017-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"joint-sentence-document-model-for-manifesto","title":"Joint Sentence-Document Model for Manifesto Text Analysis","date":"2017-12-01","arxiv_id":null,"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":"linguistic-approach-based-transfer-learning","title":"Linguistic approach based Transfer Learning for Sentiment Classification in Hindi","date":"2017-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"lower-bounds-on-the-robustness-to-adversarial","title":"Lower bounds on the robustness to adversarial perturbations","date":"2017-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"maximizing-subset-accuracy-with-recurrent","title":"Maximizing Subset Accuracy with Recurrent Neural Networks in Multi-label Classification","date":"2017-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null}],"record_sha256":"a4c053477576cad8c34e3e8c2e1d0b895e47f16d1fdff46631d704dc17461662","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}