{"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/125","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":125,"pages_in_order":146,"rows_per_page":100,"rows":[12401,12500],"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/124","next":"/task/classification/papers/126","papers":[{"url":null,"slug":"creating-annotated-dialogue-resources-cross","title":"Creating Annotated Dialogue Resources: Cross-domain Dialogue Act Classification","date":"2016-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"emotweet-28-a-fine-grained-emotion-corpus-for","title":"EmoTweet-28: A Fine-Grained Emotion Corpus for Sentiment Analysis","date":"2016-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"en-es-cs-an-english-spanish-code-switching","title":"EN-ES-CS: An English-Spanish Code-Switching Twitter Corpus for Multilingual Sentiment Analysis","date":"2016-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"ensemble-classification-of-grants-using-lda","title":"Ensemble Classification of Grants using LDA-based Features","date":"2016-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"explicit-fine-grained-syntactic-and-semantic","title":"Explicit Fine grained Syntactic and Semantic Annotation of the Idafa Construction in Arabic","date":"2016-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"flextag-a-highly-flexible-pos-tagging","title":"FlexTag: A Highly Flexible PoS Tagging Framework","date":"2016-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"integration-of-lexical-and-semantic-knowledge","title":"Integration of Lexical and Semantic Knowledge for Sentiment Analysis in SMS","date":"2016-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"introducing-the-weighted-trustability","title":"Introducing the Weighted Trustability Evaluator for Crowdsourcing Exemplified by Speaker Likability Classification","date":"2016-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"italian-verbnet-a-construction-based-approach","title":"Italian VerbNet: A Construction-based Approach to Italian Verb Classification","date":"2016-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"marsagram-an-excursion-in-the-forests-of","title":"MarsaGram: an excursion in the forests of parsing trees","date":"2016-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"on-the-use-of-a-serious-game-for-recording-a","title":"On the Use of a Serious Game for Recording a Speech Corpus of People with Intellectual Disabilities","date":"2016-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"pe2rr-corpus-manual-error-annotation-of","title":"PE2rr Corpus: Manual Error Annotation of Automatically Pre-annotated MT Post-edits","date":"2016-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"qtleap-wsdned-corpora-semantic-annotation-of","title":"QTLeap WSD/NED Corpora: Semantic Annotation of Parallel Corpora in Six Languages","date":"2016-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"rude-waiter-but-mouthwatering-pastries-an","title":"Rude waiter but mouthwatering pastries! An exploratory study into Dutch Aspect-Based Sentiment Analysis","date":"2016-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"spa-web-based-platform-for-easy-access-to","title":"SPA: Web-based Platform for easy Access to Speech Processing Modules","date":"2016-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"temporal-information-annotation-crowd-vs","title":"Temporal Information Annotation: Crowd vs. Experts","date":"2016-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"transfer-of-corpus-specific-dialogue-act","title":"Transfer of Corpus-Specific Dialogue Act Annotation to ISO Standard: Is it worth it?","date":"2016-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"using-data-mining-techniques-for-sentiment","title":"Using Data Mining Techniques for Sentiment Shifter Identification","date":"2016-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"visualisation-and-exploration-of-high","title":"Visualisation and Exploration of High-Dimensional Distributional Features in Lexical Semantic Classification","date":"2016-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"vocal-pathologies-detection-and-mispronounced","title":"Vocal Pathologies Detection and Mispronounced Phonemes Identification: Case of Arabic Continuous Speech","date":"2016-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"interactive-inter-layer-activeness","title":"InterActive: Inter-Layer Activeness Propagation","date":"2016-04-30","arxiv_id":"1605.00052","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-compact-structural-representations","title":"Learning Compact Structural Representations for Audio Events Using Regressor Banks","date":"2016-04-29","arxiv_id":"1604.08716","repositories_listed":0,"syntology":null},{"url":null,"slug":"detecting-smart-spammers-on-social-network-a","title":"Detecting \"Smart\" Spammers On Social Network: A Topic Model Approach","date":"2016-04-28","arxiv_id":"1604.08504","repositories_listed":0,"syntology":null},{"url":null,"slug":"detection-of-epileptic-seizure-in-eeg-signals","title":"Detection of epileptic seizure in EEG signals using linear least squares preprocessing","date":"2016-04-27","arxiv_id":"1604.08500","repositories_listed":0,"syntology":null},{"url":null,"slug":"image-colorization-using-a-deep-convolutional","title":"Image Colorization Using a Deep Convolutional Neural Network","date":"2016-04-27","arxiv_id":"1604.07904","repositories_listed":0,"syntology":null},{"url":null,"slug":"interpretable-deep-neural-networks-for-single","title":"Interpretable Deep Neural Networks for Single-Trial EEG Classification","date":"2016-04-27","arxiv_id":"1604.08201","repositories_listed":0,"syntology":null},{"url":null,"slug":"ubl-an-r-package-for-utility-based-learning","title":"UBL: an R package for Utility-based Learning","date":"2016-04-27","arxiv_id":"1604.08079","repositories_listed":0,"syntology":null},{"url":null,"slug":"unsupervised-classification-in-hyperspectral","title":"Unsupervised Classification in Hyperspectral Imagery with Nonlocal Total Variation and Primal-Dual Hybrid Gradient Algorithm","date":"2016-04-27","arxiv_id":"1604.08182","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-new-approach-in-persian-handwritten-letters","title":"A New Approach in Persian Handwritten Letters Recognition Using Error Correcting Output Coding","date":"2016-04-26","arxiv_id":"1604.07554","repositories_listed":0,"syntology":null},{"url":null,"slug":"modern-physiognomy-an-investigation-on","title":"Modern Physiognomy: An Investigation on Predicting Personality Traits and Intelligence from the Human Face","date":"2016-04-26","arxiv_id":"1604.07499","repositories_listed":0,"syntology":null},{"url":"/paper/attributes-for-improved-attributes-a-multi","slug":"attributes-for-improved-attributes-a-multi","title":"Attributes for Improved Attributes: A Multi-Task Network for Attribute Classification","date":"2016-04-25","arxiv_id":"1604.07360","repositories_listed":0,"syntology":null},{"url":null,"slug":"scalable-gaussian-processes-for-supervised","title":"Scalable Gaussian Processes for Supervised Hashing","date":"2016-04-25","arxiv_id":"1604.07335","repositories_listed":0,"syntology":null},{"url":null,"slug":"supervised-incremental-hashing","title":"Supervised Incremental Hashing","date":"2016-04-25","arxiv_id":"1604.07342","repositories_listed":0,"syntology":null},{"url":null,"slug":"an-information-theoretic-formulation-of-the","title":"An information theoretic formulation of the Dictionary Learning and Sparse Coding Problems on Statistical Manifolds","date":"2016-04-23","arxiv_id":"1604.06939","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-classifier-guided-approach-for-top-down","title":"A Classifier-guided Approach for Top-down Salient Object Detection","date":"2016-04-22","arxiv_id":"1604.06570","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-adaptive-network-an-efficient-deep","title":"Deep Adaptive Network: An Efficient Deep Neural Network with Sparse Binary Connections","date":"2016-04-21","arxiv_id":"1604.06154","repositories_listed":0,"syntology":null},{"url":null,"slug":"improving-human-action-recognition-by-non","title":"Improving Human Action Recognition by Non-action Classification","date":"2016-04-21","arxiv_id":"1604.06397","repositories_listed":0,"syntology":null},{"url":null,"slug":"ocr-error-correction-using-character","title":"OCR Error Correction Using Character Correction and Feature-Based Word Classification","date":"2016-04-21","arxiv_id":"1604.06225","repositories_listed":0,"syntology":null},{"url":null,"slug":"the-thumos-challenge-on-action-recognition","title":"The THUMOS Challenge on Action Recognition for Videos \"in the Wild\"","date":"2016-04-21","arxiv_id":"1604.06182","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-cnns-for-hep-2-cells-classification-a","title":"Deep CNNs for HEp-2 Cells Classification : A Cross-specimen Analysis","date":"2016-04-20","arxiv_id":"1604.05816","repositories_listed":0,"syntology":null},{"url":null,"slug":"an-attentive-neural-architecture-for-fine","title":"An Attentive Neural Architecture for Fine-grained Entity Type Classification","date":"2016-04-19","arxiv_id":"1604.05525","repositories_listed":0,"syntology":null},{"url":null,"slug":"cognitive-state-classification-using","title":"Cognitive state classification using transformed fMRI data","date":"2016-04-19","arxiv_id":"1604.05413","repositories_listed":0,"syntology":null},{"url":null,"slug":"comparative-study-of-instance-based-learning","title":"Comparative Study of Instance Based Learning and Back Propagation for Classification Problems","date":"2016-04-19","arxiv_id":"1604.05429","repositories_listed":0,"syntology":null},{"url":null,"slug":"right-whale-recognition-using-convolutional","title":"Right whale recognition using convolutional neural networks","date":"2016-04-19","arxiv_id":"1604.05605","repositories_listed":0,"syntology":null},{"url":null,"slug":"syntactic-and-semantic-classification-of-verb","title":"Syntactic and semantic classification of verb arguments using dependency-based and rich semantic features","date":"2016-04-19","arxiv_id":"1604.05747","repositories_listed":0,"syntology":null},{"url":null,"slug":"churn-analysis-using-deep-convolutional","title":"Churn analysis using deep convolutional neural networks and autoencoders","date":"2016-04-18","arxiv_id":"1604.05377","repositories_listed":0,"syntology":null},{"url":null,"slug":"end-to-end-tracking-and-semantic-segmentation","title":"End-to-End Tracking and Semantic Segmentation Using Recurrent Neural Networks","date":"2016-04-18","arxiv_id":"1604.05091","repositories_listed":0,"syntology":null},{"url":null,"slug":"finding-common-characteristics-among-nba","title":"Finding Common Characteristics Among NBA Playoff and Championship Teams: A Machine Learning Approach","date":"2016-04-18","arxiv_id":"1604.05266","repositories_listed":0,"syntology":null},{"url":null,"slug":"mahalanobis-distance-metric-learning","title":"Mahalanobis Distance Metric Learning Algorithm for Instance-based Data Stream Classification","date":"2016-04-17","arxiv_id":"1604.04879","repositories_listed":0,"syntology":null},{"url":null,"slug":"acd-action-concept-discovery-from-image","title":"ACD: Action Concept Discovery from Image-Sentence Corpora","date":"2016-04-16","arxiv_id":"1604.04784","repositories_listed":0,"syntology":null},{"url":null,"slug":"generating-binary-tags-for-fast-medical-image","title":"Generating Binary Tags for Fast Medical Image Retrieval Based on Convolutional Nets and Radon Transform","date":"2016-04-16","arxiv_id":"1604.04676","repositories_listed":0,"syntology":null},{"url":"/paper/learning-models-for-actions-and-person-object","slug":"learning-models-for-actions-and-person-object","title":"Learning Models for Actions and Person-Object Interactions with Transfer to Question Answering","date":"2016-04-16","arxiv_id":"1604.04808","repositories_listed":0,"syntology":null},{"url":null,"slug":"improving-the-robustness-of-deep-neural","title":"Improving the Robustness of Deep Neural Networks via Stability Training","date":"2016-04-15","arxiv_id":"1604.04326","repositories_listed":0,"syntology":null},{"url":null,"slug":"latent-model-ensemble-with-auto-localization","title":"Latent Model Ensemble with Auto-localization","date":"2016-04-15","arxiv_id":"1604.04333","repositories_listed":0,"syntology":null},{"url":null,"slug":"probing-the-intra-component-correlations","title":"Probing the Intra-Component Correlations within Fisher Vector for Material Classification","date":"2016-04-15","arxiv_id":"1604.04473","repositories_listed":0,"syntology":null},{"url":null,"slug":"unsupervised-single-particle-deep-clustering","title":"Unsupervised single-particle deep clustering via statistical manifold learning","date":"2016-04-15","arxiv_id":"1604.04539","repositories_listed":0,"syntology":null},{"url":null,"slug":"animation-and-chirplet-based-development-of-a","title":"Animation and Chirplet-Based Development of a PIR Sensor Array for Intruder Classification in an Outdoor Environment","date":"2016-04-13","arxiv_id":"1604.03829","repositories_listed":0,"syntology":null},{"url":null,"slug":"an-incremental-linear-time-learning-algorithm","title":"An incremental linear-time learning algorithm for the Optimum-Path Forest classifier","date":"2016-04-12","arxiv_id":"1604.03346","repositories_listed":0,"syntology":null},{"url":null,"slug":"efficient-classification-of-multi-labelled","title":"Efficient Classification of Multi-Labelled Text Streams by Clashing","date":"2016-04-12","arxiv_id":"1604.03200","repositories_listed":0,"syntology":null},{"url":"/paper/orientation-boosted-voxel-nets-for-3d-object","slug":"orientation-boosted-voxel-nets-for-3d-object","title":"Orientation-boosted Voxel Nets for 3D Object Recognition","date":"2016-04-12","arxiv_id":"1604.03351","repositories_listed":0,"syntology":null},{"url":null,"slug":"the-univariate-flagging-algorithm-ufa-a-fully","title":"The Univariate Flagging Algorithm (UFA): a Fully-Automated Approach for Identifying Optimal Thresholds in Data","date":"2016-04-12","arxiv_id":"1604.03248","repositories_listed":0,"syntology":null},{"url":null,"slug":"gaussian-process-domain-experts-for-model","title":"Gaussian Process Domain Experts for Model Adaptation in Facial Behavior Analysis","date":"2016-04-11","arxiv_id":"1604.02917","repositories_listed":0,"syntology":null},{"url":null,"slug":"reservoir-computing-for-spatiotemporal-signal","title":"Reservoir computing for spatiotemporal signal classification without trained output weights","date":"2016-04-11","arxiv_id":"1604.03073","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-general-retraining-framework-for-scalable","title":"A General Retraining Framework for Scalable Adversarial Classification","date":"2016-04-09","arxiv_id":"1604.02606","repositories_listed":0,"syntology":null},{"url":null,"slug":"machine-learning-for-visual-navigation-of","title":"Machine Learning for Visual Navigation of Unmanned Ground Vehicles","date":"2016-04-08","arxiv_id":"1604.02485","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-classification-leveraged-object-detector","title":"A Classification Leveraged Object Detector","date":"2016-04-07","arxiv_id":"1604.01841","repositories_listed":0,"syntology":null},{"url":null,"slug":"building-ensembles-of-adaptive-nested","title":"Building Ensembles of Adaptive Nested Dichotomies with Random-Pair Selection","date":"2016-04-07","arxiv_id":"1604.01854","repositories_listed":0,"syntology":null},{"url":null,"slug":"multilevel-weighted-support-vector-machine","title":"Multilevel Weighted Support Vector Machine for Classification on Healthcare Data with Missing Values","date":"2016-04-07","arxiv_id":"1604.02123","repositories_listed":0,"syntology":null},{"url":null,"slug":"generalising-the-discriminative-restricted","title":"Generalising the Discriminative Restricted Boltzmann Machine","date":"2016-04-06","arxiv_id":"1604.01806","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-label-imbalance-in-multi-label","title":"Towards Label Imbalance in Multi-label Classification with Many Labels","date":"2016-04-05","arxiv_id":"1604.01304","repositories_listed":0,"syntology":null},{"url":null,"slug":"topic-model-based-multi-label-classification","title":"Topic Model Based Multi-Label Classification from the Crowd","date":"2016-04-04","arxiv_id":"1604.00783","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-new-learning-method-for-inference-accuracy","title":"A New Learning Method for Inference Accuracy, Core Occupation, and Performance Co-optimization on TrueNorth Chip","date":"2016-04-03","arxiv_id":"1604.00697","repositories_listed":0,"syntology":null},{"url":null,"slug":"good-practice-in-cnn-feature-transfer","title":"Good Practice in CNN Feature Transfer","date":"2016-04-01","arxiv_id":"1604.00133","repositories_listed":0,"syntology":null},{"url":null,"slug":"neural-attention-models-for-sequence","title":"Neural Attention Models for Sequence Classification: Analysis and Application to Key Term Extraction and Dialogue Act Detection","date":"2016-03-31","arxiv_id":"1604.00077","repositories_listed":0,"syntology":null},{"url":null,"slug":"degrees-of-freedom-in-deep-neural-networks","title":"Degrees of Freedom in Deep Neural Networks","date":"2016-03-30","arxiv_id":"1603.09260","repositories_listed":0,"syntology":null},{"url":null,"slug":"dense-image-representation-with-spatial","title":"Dense Image Representation with Spatial Pyramid VLAD Coding of CNN for Locally Robust Captioning","date":"2016-03-30","arxiv_id":"1603.09046","repositories_listed":0,"syntology":null},{"url":null,"slug":"classification-based-financial-markets","title":"Classification-based Financial Markets Prediction using Deep Neural Networks","date":"2016-03-29","arxiv_id":"1603.08604","repositories_listed":0,"syntology":null},{"url":null,"slug":"classification-of-alzheimers-disease-using","title":"Classification of Alzheimer's Disease using fMRI Data and Deep Learning Convolutional Neural Networks","date":"2016-03-29","arxiv_id":"1603.08631","repositories_listed":0,"syntology":null},{"url":null,"slug":"face-image-analysis-using-aam-gabor-lbp-and","title":"Face Image Analysis using AAM, Gabor, LBP and WD features for Gender, Age, Expression and Ethnicity Classification","date":"2016-03-29","arxiv_id":"1604.01684","repositories_listed":0,"syntology":null},{"url":null,"slug":"latent-embeddings-for-zero-shot","title":"Latent Embeddings for Zero-shot Classification","date":"2016-03-29","arxiv_id":"1603.08895","repositories_listed":0,"syntology":null},{"url":null,"slug":"root13-spotting-hypernyms-co-hyponyms-and","title":"ROOT13: Spotting Hypernyms, Co-Hyponyms and Randoms","date":"2016-03-29","arxiv_id":"1603.08705","repositories_listed":0,"syntology":null},{"url":null,"slug":"audio-visual-emotion-recognition-with","title":"Audio Visual Emotion Recognition with Temporal Alignment and Perception Attention","date":"2016-03-28","arxiv_id":"1603.08321","repositories_listed":0,"syntology":null},{"url":null,"slug":"generating-visual-explanations","title":"Generating Visual Explanations","date":"2016-03-28","arxiv_id":"1603.08507","repositories_listed":0,"syntology":null},{"url":null,"slug":"longitudinal-analysis-of-discussion-topics-in","title":"Longitudinal Analysis of Discussion Topics in an Online Breast Cancer Community using Convolutional Neural Networks","date":"2016-03-28","arxiv_id":"1603.08458","repositories_listed":0,"syntology":null},{"url":"/paper/sparse-activity-and-sparse-connectivity-in","slug":"sparse-activity-and-sparse-connectivity-in","title":"Sparse Activity and Sparse Connectivity in Supervised Learning","date":"2016-03-28","arxiv_id":"1603.08367","repositories_listed":0,"syntology":null},{"url":null,"slug":"evolution-of-active-categorical-image","title":"Evolution of active categorical image classification via saccadic eye movement","date":"2016-03-27","arxiv_id":"1603.08233","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-generalized-flow-for-multi-class-and-binary","title":"A generalized flow for multi-class and binary classification tasks: An Azure ML approach","date":"2016-03-26","arxiv_id":"1603.08070","repositories_listed":0,"syntology":null},{"url":null,"slug":"classification-of-large-scale-fundus-image","title":"Classification of Large-Scale Fundus Image Data Sets: A Cloud-Computing Framework","date":"2016-03-26","arxiv_id":"1603.08071","repositories_listed":0,"syntology":null},{"url":null,"slug":"unsupervised-domain-adaptation-in-the-wild","title":"Unsupervised Domain Adaptation in the Wild: Dealing with Asymmetric Label Sets","date":"2016-03-26","arxiv_id":"1603.08105","repositories_listed":0,"syntology":null},{"url":null,"slug":"classifying-syntactic-regularities-for","title":"Classifying Syntactic Regularities for Hundreds of Languages","date":"2016-03-25","arxiv_id":"1603.08016","repositories_listed":0,"syntology":null},{"url":null,"slug":"joint-projection-and-dictionary-learning","title":"Joint Projection and Dictionary Learning using Low-rank Regularization and Graph Constraints","date":"2016-03-24","arxiv_id":"1603.07697","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-tutorial-on-deep-neural-networks-for","title":"A Tutorial on Deep Neural Networks for Intelligent Systems","date":"2016-03-23","arxiv_id":"1603.07249","repositories_listed":0,"syntology":null},{"url":"/paper/deep-multimodal-feature-analysis-for-action","slug":"deep-multimodal-feature-analysis-for-action","title":"Deep Multimodal Feature Analysis for Action Recognition in RGB+D Videos","date":"2016-03-23","arxiv_id":"1603.07120","repositories_listed":0,"syntology":null},{"url":null,"slug":"energy-efficient-convnets-through-approximate","title":"Energy-Efficient ConvNets Through Approximate Computing","date":"2016-03-22","arxiv_id":"1603.06777","repositories_listed":0,"syntology":null},{"url":null,"slug":"enhanced-perceptrons-using-contrastive","title":"Enhanced perceptrons using contrastive biclusters","date":"2016-03-22","arxiv_id":"1603.06859","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-discriminative-features-using","title":"Learning Discriminative Features using Encoder-Decoder type Deep Neural Nets","date":"2016-03-22","arxiv_id":"1607.01354","repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-velocity-neural-networks-for-gesture","title":"Multi-velocity neural networks for gesture recognition in videos","date":"2016-03-22","arxiv_id":"1603.06829","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-system-for-probabilistic-linking-of","title":"A System for Probabilistic Linking of Thesauri and Classification Systems","date":"2016-03-21","arxiv_id":"1603.06485","repositories_listed":0,"syntology":null},{"url":null,"slug":"action-affect-classification-and-morphing","title":"Action-Affect Classification and Morphing using Multi-Task Representation Learning","date":"2016-03-21","arxiv_id":"1603.06554","repositories_listed":0,"syntology":null},{"url":null,"slug":"the-svm-classifier-based-on-the-modified","title":"The SVM Classifier Based on the Modified Particle Swarm Optimization","date":"2016-03-21","arxiv_id":"1603.08296","repositories_listed":0,"syntology":null}],"record_sha256":"252f33f146771369e3776e8b235ff2ef410b0d231b4859dc1eefd3614c029bb1","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}