{"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/multi-class-classification/papers/8","list_of":"/task/multi-class-classification","task":"Multi-class 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":8,"pages_in_order":10,"rows_per_page":100,"rows":[701,800],"of":903,"counts":{"archive_papers_tagged":903,"with_a_code_link":289,"where_syntology_ran_a_sample":34,"not_listed_spam_title":0,"listed":903,"listed_where_code_ran":34,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":28,"every_run_a_failure_of_syntologys_instrument":6,"listed_with_a_run_with_no_instrument_failure":28,"listed_every_run_a_failure_of_syntologys_instrument":6,"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/multi-class-classification","prev":"/task/multi-class-classification/papers/7","next":"/task/multi-class-classification/papers/9","papers":[{"url":null,"slug":"reconsidering-analytical-variational-bounds","title":"Reconsidering Analytical Variational Bounds for Output Layers of Deep Networks","date":"2019-10-02","arxiv_id":"1910.00877","repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-classifier-prediction-of-knee","title":"Multi-classifier prediction of knee osteoarthritis progression from incomplete imbalanced longitudinal data","date":"2019-09-30","arxiv_id":"1909.13408","repositories_listed":0,"syntology":null},{"url":null,"slug":"how-can-we-generalise-learning-distributed","title":"How can we generalise learning distributed representations of graphs?","date":"2019-09-25","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-with-protection-rejection-of","title":"Learning with Protection: Rejection of Suspicious Samples under Adversarial Environment","date":"2019-09-25","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"road-mapping-in-lidar-images-using-a-joint","title":"Road Mapping In LiDAR Images Using A Joint-Task Dense Dilated Convolutions Merging Network","date":"2019-09-07","arxiv_id":"1909.04588","repositories_listed":0,"syntology":null},{"url":null,"slug":"explicit-facial-expression-transfer-via-fine","title":"Explicit Facial Expression Transfer via Fine-Grained Representations","date":"2019-09-06","arxiv_id":"1909.02967","repositories_listed":0,"syntology":null},{"url":null,"slug":"student-performance-prediction-with-optimum","title":"Student Performance Prediction with Optimum Multilabel Ensemble Model","date":"2019-09-06","arxiv_id":"1909.07444","repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-layer-domain-adaptation-for-deep","title":"Multi-layer Domain Adaptation for Deep Convolutional Networks","date":"2019-09-05","arxiv_id":"1909.02620","repositories_listed":0,"syntology":null},{"url":null,"slug":"analysis-and-classification-of-heart-diseases","title":"Analysis and classification of heart diseases using heartbeat features and machine learning algorithms","date":"2019-08-31","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"the-efficacy-of-various-machine-learning","title":"The efficacy of various machine learning models for multi-class classification of RNA-seq expression data","date":"2019-08-19","arxiv_id":"1908.06817","repositories_listed":0,"syntology":null},{"url":null,"slug":"shallow-domain-adaptive-embeddings-for","title":"Shallow Domain Adaptive Embeddings for Sentiment Analysis","date":"2019-08-16","arxiv_id":"1908.06082","repositories_listed":0,"syntology":null},{"url":null,"slug":"optimal-multiclass-overfitting-by-sequence","title":"Optimal multiclass overfitting by sequence reconstruction from Hamming queries","date":"2019-08-08","arxiv_id":"1908.03156","repositories_listed":0,"syntology":null},{"url":null,"slug":"competing-ratio-loss-for-discriminative-multi","title":"Competing Ratio Loss for Discriminative Multi-class Image Classification","date":"2019-07-31","arxiv_id":"1907.13349","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-multi-task-self-normalizing-3d-cnn-to-infer","title":"A Multi-Task Self-Normalizing 3D-CNN to Infer Tuberculosis Radiological Manifestations","date":"2019-07-29","arxiv_id":"1907.12331","repositories_listed":0,"syntology":null},{"url":null,"slug":"collaborative-filtering-and-multi-label","title":"Collaborative Filtering and Multi-Label Classification with Matrix Factorization","date":"2019-07-23","arxiv_id":"1907.12365","repositories_listed":0,"syntology":null},{"url":"/paper/graphxnet-chest-x-ray-classification-under","slug":"graphxnet-chest-x-ray-classification-under","title":"GraphX$^{NET}-$ Chest X-Ray Classification Under Extreme Minimal Supervision","date":"2019-07-23","arxiv_id":"1907.10085","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-multi-label-classification-in-affine","title":"Deep Multi Label Classification in Affine Subspaces","date":"2019-07-10","arxiv_id":"1907.04563","repositories_listed":0,"syntology":null},{"url":null,"slug":"predicting-customer-call-intent-by-analyzing","title":"Predicting Customer Call Intent by Analyzing Phone Call Transcripts based on CNN for Multi-Class Classification","date":"2019-07-08","arxiv_id":"1907.03715","repositories_listed":0,"syntology":null},{"url":null,"slug":"resource-efficient-wearable-computing-for","title":"Resource-Efficient Wearable Computing for Real-Time Reconfigurable Machine Learning: A Cascading Binary Classification","date":"2019-07-07","arxiv_id":"1907.03250","repositories_listed":0,"syntology":null},{"url":null,"slug":"neuralclassifier-an-open-source-neural","title":"NeuralClassifier: An Open-source Neural Hierarchical Multi-label Text Classification Toolkit","date":"2019-07-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"progressive-fashion-attribute-extraction","title":"Progressive Fashion Attribute Extraction","date":"2019-06-29","arxiv_id":"1907.00157","repositories_listed":0,"syntology":null},{"url":null,"slug":"binary-stochastic-representations-for-large","title":"Binary Stochastic Representations for Large Multi-class Classification","date":"2019-06-24","arxiv_id":"1906.09838","repositories_listed":0,"syntology":null},{"url":null,"slug":"beyond-supervised-classification-extreme","title":"Energy Models for Better Pseudo-Labels: Improving Semi-Supervised Classification with the 1-Laplacian Graph Energy","date":"2019-06-20","arxiv_id":"1906.08635","repositories_listed":0,"syntology":null},{"url":null,"slug":"consistency-of-semi-supervised-learning","title":"Consistency of semi-supervised learning algorithms on graphs: Probit and one-hot methods","date":"2019-06-18","arxiv_id":"1906.07658","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-integration-of-statistical-hypothesis","title":"Towards Integration of Statistical Hypothesis Tests into Deep Neural Networks","date":"2019-06-15","arxiv_id":"1906.06550","repositories_listed":0,"syntology":null},{"url":null,"slug":"190600294","title":"On the computational complexity of the probabilistic label tree algorithms","date":"2019-06-01","arxiv_id":"1906.00294","repositories_listed":0,"syntology":null},{"url":null,"slug":"beyond-context-a-new-perspective-for-word","title":"Beyond Context: A New Perspective for Word Embeddings","date":"2019-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"modeling-personal-biases-in-language-use-by","title":"Modeling Personal Biases in Language Use by Inducing Personalized Word Embeddings","date":"2019-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"sc-upb-at-the-vardial-2019-evaluation","title":"SC-UPB at the VarDial 2019 Evaluation Campaign: Moldavian vs. Romanian Cross-Dialect Topic Identification","date":"2019-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"a-generalization-error-bound-for-multi-class","title":"A Generalization Error Bound for Multi-class Domain Generalization","date":"2019-05-24","arxiv_id":"1905.10392","repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-scale-dual-branch-fully-convolutional","title":"Multi-Scale Dual-Branch Fully Convolutional Network for Hand Parsing","date":"2019-05-24","arxiv_id":"1905.10100","repositories_listed":0,"syntology":null},{"url":null,"slug":"label-mapping-neural-networks-with-response","title":"Label Mapping Neural Networks with Response Consolidation for Class Incremental Learning","date":"2019-05-20","arxiv_id":"1905.07835","repositories_listed":0,"syntology":null},{"url":null,"slug":"trk-cnn-transferable-ranking-cnn-for-image","title":"TRk-CNN: Transferable Ranking-CNN for image classification of glaucoma, glaucoma suspect, and normal eyes","date":"2019-05-16","arxiv_id":"1905.06509","repositories_listed":0,"syntology":null},{"url":null,"slug":"multiclass-language-identification-using-deep","title":"Multiclass Language Identification using Deep Learning on Spectral Images of Audio Signals","date":"2019-05-10","arxiv_id":"1905.04348","repositories_listed":0,"syntology":null},{"url":null,"slug":"prediction-and-outlier-detection-a","title":"Prediction and outlier detection in classification problems","date":"2019-05-10","arxiv_id":"1905.04396","repositories_listed":0,"syntology":null},{"url":null,"slug":"ocular-diseases-diagnosis-in-fundus-images","title":"Ocular Diseases Diagnosis in Fundus Images using a Deep Learning: Approaches, tools and Performance evaluation","date":"2019-05-07","arxiv_id":"1905.02544","repositories_listed":0,"syntology":null},{"url":null,"slug":"question-relatedness-on-stack-overflow-the","title":"Question Relatedness on Stack Overflow: The Task, Dataset, and Corpus-inspired Models","date":"2019-05-03","arxiv_id":"1905.01966","repositories_listed":0,"syntology":null},{"url":null,"slug":"outlier-detection-from-image-data","title":"Outlier Detection from Image Data","date":"2019-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"a-dual-branch-deep-neural-network-for","title":"Weakly and Semi Supervised Detection in Medical Imaging via Deep Dual Branch Net","date":"2019-04-29","arxiv_id":"1904.12589","repositories_listed":0,"syntology":null},{"url":null,"slug":"190412319","title":"Classification and Detection in Mammograms with Weak Supervision via Dual Branch Deep Neural Net","date":"2019-04-28","arxiv_id":"1904.12319","repositories_listed":0,"syntology":null},{"url":null,"slug":"kfhe-homer-kalman-filter-based-heuristic","title":"ML-KFHE: Multi-label ensemble classification algorithm exploiting sensor fusion properties of the Kalman filter","date":"2019-04-23","arxiv_id":"1904.10552","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-bayesian-approach-for-accurate","title":"A Bayesian Approach for Accurate Classification-Based Aggregates","date":"2019-02-06","arxiv_id":"1902.02412","repositories_listed":0,"syntology":null},{"url":null,"slug":"domain-discrepancy-measure-using-complex","title":"Domain Discrepancy Measure for Complex Models in Unsupervised Domain Adaptation","date":"2019-01-30","arxiv_id":"1901.10654","repositories_listed":0,"syntology":null},{"url":null,"slug":"it-s-only-words-and-words-are-all-i-have","title":"It's Only Words And Words Are All I Have","date":"2019-01-16","arxiv_id":"1901.05227","repositories_listed":0,"syntology":null},{"url":null,"slug":"elimination-of-all-bad-local-minima-in-deep","title":"Elimination of All Bad Local Minima in Deep Learning","date":"2019-01-02","arxiv_id":"1901.00279","repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-label-adversarial-perturbations","title":"Multi-Label Adversarial Perturbations","date":"2019-01-02","arxiv_id":"1901.00546","repositories_listed":0,"syntology":null},{"url":null,"slug":"sentence-wise-smooth-regularization-for","title":"Sentence-wise Smooth Regularization for Sequence to Sequence Learning","date":"2018-12-12","arxiv_id":"1812.04784","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-multi-class-structured-dictionary-learning","title":"A multi-class structured dictionary learning method using discriminant atom selection","date":"2018-12-04","arxiv_id":"1812.01389","repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-class-learning-from-theory-to-algorithm","title":"Multi-Class Learning: From Theory to Algorithm","date":"2018-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"memoir-multi-class-extreme-classification","title":"MEMOIR: Multi-class Extreme Classification with Inexact Margin","date":"2018-11-24","arxiv_id":"1811.09863","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-auto-set-a-deep-auto-encoder-set-network","title":"Deep Auto-Set: A Deep Auto-Encoder-Set Network for Activity Recognition Using Wearables","date":"2018-11-20","arxiv_id":"1811.08127","repositories_listed":0,"syntology":null},{"url":null,"slug":"single-label-multi-class-image-classification","title":"Single-Label Multi-Class Image Classification by Deep Logistic Regression","date":"2018-11-20","arxiv_id":"1811.08400","repositories_listed":0,"syntology":null},{"url":null,"slug":"streaming-network-embedding-through-local","title":"Streaming Network Embedding through Local Actions","date":"2018-11-14","arxiv_id":"1811.05932","repositories_listed":0,"syntology":null},{"url":null,"slug":"theoretical-analysis-of-adversarial-learning","title":"Theoretical Analysis of Adversarial Learning: A Minimax Approach","date":"2018-11-13","arxiv_id":"1811.05232","repositories_listed":0,"syntology":null},{"url":null,"slug":"adversarial-learning-of-label-dependency-a","title":"Adversarial Learning of Label Dependency: A Novel Framework for Multi-class Classification","date":"2018-11-12","arxiv_id":"1811.04689","repositories_listed":0,"syntology":null},{"url":null,"slug":"automated-multi-label-classification-based-on","title":"Automated Multi-Label Classification based on ML-Plan","date":"2018-11-09","arxiv_id":"1811.04060","repositories_listed":0,"syntology":null},{"url":null,"slug":"automated-fact-checking-of-claims-in","title":"Automated Fact-Checking of Claims in Argumentative Parliamentary Debates","date":"2018-11-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"quantifying-learning-guarantees-for-convex","title":"Quantifying Learning Guarantees for Convex but Inconsistent Surrogates","date":"2018-10-26","arxiv_id":"1810.11544","repositories_listed":0,"syntology":null},{"url":null,"slug":"dcsvm-fast-multi-class-classification-using","title":"DCSVM: Fast Multi-class Classification using Support Vector Machines","date":"2018-10-23","arxiv_id":"1810.09828","repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-class-classification-model-inspired-by","title":"Multi-class Classification Model Inspired by Quantum Detection Theory","date":"2018-10-10","arxiv_id":"1810.04491","repositories_listed":0,"syntology":null},{"url":null,"slug":"improved-generalization-bounds-for-robust","title":"Improved Generalization Bounds for Adversarially Robust Learning","date":"2018-10-04","arxiv_id":"1810.02180","repositories_listed":0,"syntology":null},{"url":null,"slug":"model-transfer-with-explicit-knowledge-of-the","title":"Model Transfer with Explicit Knowledge of the Relation between Class Definitions","date":"2018-10-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"phrase-level-self-attention-networks-for","title":"Phrase-level Self-Attention Networks for Universal Sentence Encoding","date":"2018-10-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"unsupervised-adversarial-invariance","title":"Unsupervised Adversarial Invariance","date":"2018-09-26","arxiv_id":"1809.10083","repositories_listed":0,"syntology":null},{"url":null,"slug":"no-multiplication-no-floating-point-no","title":"No Multiplication? No Floating Point? No Problem! Training Networks for Efficient Inference","date":"2018-09-24","arxiv_id":"1809.09244","repositories_listed":0,"syntology":null},{"url":null,"slug":"solving-for-multi-class-a-survey-and","title":"Solving for multi-class: a survey and synthesis","date":"2018-09-16","arxiv_id":"1809.05929","repositories_listed":0,"syntology":null},{"url":null,"slug":"cofga-classification-of-fine-grained-features","title":"COFGA: Classification Of Fine-Grained Features In Aerial Images","date":"2018-08-27","arxiv_id":"1808.09001","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-overparameterized-neural-networks","title":"Learning Overparameterized Neural Networks via Stochastic Gradient Descent on Structured Data","date":"2018-08-03","arxiv_id":"1808.01204","repositories_listed":0,"syntology":null},{"url":null,"slug":"filtering-aggression-from-the-multilingual","title":"Filtering Aggression from the Multilingual Social Media Feed","date":"2018-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"punctuation-as-native-language-interference","title":"Punctuation as Native Language Interference","date":"2018-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"dermoscopic-image-analysis-for-isic-challenge","title":"Dermoscopic Image Analysis for ISIC Challenge 2018","date":"2018-07-24","arxiv_id":"1807.08948","repositories_listed":0,"syntology":null},{"url":null,"slug":"identifying-domain-independent-update-intents","title":"Identifying Domain Independent Update Intents in Task Based Dialogs","date":"2018-07-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"dynamic-spectrum-matching-with-one-shot","title":"Dynamic Spectrum Matching with One-shot Learning","date":"2018-06-23","arxiv_id":"1806.09981","repositories_listed":0,"syntology":null},{"url":null,"slug":"analysis-of-the-effect-of-unexpected-outliers","title":"Analysis of the Effect of Unexpected Outliers in the Classification of Spectroscopy Data","date":"2018-06-14","arxiv_id":"1806.05455","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-one-sided-classification-toolkit-with","title":"A One-Sided Classification Toolkit with Applications in the Analysis of Spectroscopy Data","date":"2018-06-12","arxiv_id":"1806.06915","repositories_listed":0,"syntology":null},{"url":null,"slug":"large-scale-classification-in-deep-neural","title":"Large scale classification in deep neural network with Label Mapping","date":"2018-06-07","arxiv_id":"1806.02507","repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-function-convolutional-neural-networks","title":"Multi-function Convolutional Neural Networks for Improving Image Classification Performance","date":"2018-05-30","arxiv_id":"1805.11788","repositories_listed":0,"syntology":null},{"url":null,"slug":"confidence-prediction-for-lexicon-free-ocr","title":"Confidence Prediction for Lexicon-Free OCR","date":"2018-05-28","arxiv_id":"1805.11161","repositories_listed":0,"syntology":null},{"url":null,"slug":"curriculum-learning-for-speech-emotion","title":"Curriculum Learning for Speech Emotion Recognition from Crowdsourced Labels","date":"2018-05-25","arxiv_id":"1805.10339","repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-task-determinantal-point-processes-for","title":"Multi-Task Determinantal Point Processes for Recommendation","date":"2018-05-24","arxiv_id":"1805.09916","repositories_listed":0,"syntology":null},{"url":null,"slug":"stacked-semantic-guided-attention-model-for","title":"Stacked Semantic-Guided Attention Model for Fine-Grained Zero-Shot Learning","date":"2018-05-21","arxiv_id":"1805.08113","repositories_listed":0,"syntology":null},{"url":null,"slug":"several-tunable-gmm-kernels","title":"Several Tunable GMM Kernels","date":"2018-05-08","arxiv_id":"1805.02830","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-recurrent-cnn-for-automatic-detection-and","title":"A Recurrent CNN for Automatic Detection and Classification of Coronary Artery Plaque and Stenosis in Coronary CT Angiography","date":"2018-04-12","arxiv_id":"1804.04360","repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-level-activation-for-segmentation-of","title":"Multi-level Activation for Segmentation of Hierarchically-nested Classes","date":"2018-04-05","arxiv_id":"1804.01910","repositories_listed":0,"syntology":null},{"url":null,"slug":"on-the-resistance-of-neural-nets-to-label","title":"The Resistance to Label Noise in K-NN and DNN Depends on its Concentration","date":"2018-03-30","arxiv_id":"1803.11410","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-attention-model-for-triage-of-emergency","title":"Deep Attention Model for Triage of Emergency Department Patients","date":"2018-03-28","arxiv_id":"1804.03240","repositories_listed":0,"syntology":null},{"url":null,"slug":"model-agnostic-private-learning-via-stability","title":"Model-Agnostic Private Learning via Stability","date":"2018-03-14","arxiv_id":"1803.05101","repositories_listed":0,"syntology":null},{"url":null,"slug":"robust-mcmc-sampling-with-non-gaussian-and","title":"Dimension-Robust MCMC in Bayesian Inverse Problems","date":"2018-03-09","arxiv_id":"1803.03344","repositories_listed":0,"syntology":null},{"url":null,"slug":"cross-domain-recommendation-via-deep-domain","title":"Cross-domain Recommendation via Deep Domain Adaptation","date":"2018-03-08","arxiv_id":"1803.03018","repositories_listed":0,"syntology":null},{"url":null,"slug":"cognitive-radar-antenna-selection-via-deep","title":"Cognitive Radar Antenna Selection via Deep Learning","date":"2018-02-27","arxiv_id":"1802.09736","repositories_listed":0,"syntology":null},{"url":null,"slug":"bayes-optimal-hierarchical-classification","title":"Bayes-optimal Hierarchical Classification over Asymmetric Tree-Distance Loss","date":"2018-02-17","arxiv_id":"1802.06771","repositories_listed":0,"syntology":null},{"url":null,"slug":"enhancing-multi-class-classification-of","title":"Enhancing Multi-Class Classification of Random Forest using Random Vector Functional Neural Network and Oblique Decision Surfaces","date":"2018-02-05","arxiv_id":"1802.01240","repositories_listed":0,"syntology":null},{"url":null,"slug":"solving-for-multi-class-using-orthogonal","title":"Solving for multi-class using orthogonal coding matrices","date":"2018-01-27","arxiv_id":"1801.09055","repositories_listed":0,"syntology":null},{"url":null,"slug":"binary-output-layer-of-feedforward-neural","title":"Binary output layer of feedforward neural networks for solving multi-class classification problems","date":"2018-01-22","arxiv_id":"1801.07599","repositories_listed":0,"syntology":null},{"url":null,"slug":"semi-supervised-outlier-detection-using","title":"Semi-supervised Outlier Detection using Generative And Adversary Framework","date":"2018-01-01","arxiv_id":null,"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":"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":"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":"3d-shape-classification-using-collaborative","title":"3D Shape Classification Using Collaborative Representation based Projections","date":"2017-11-13","arxiv_id":"1711.04875","repositories_listed":0,"syntology":null},{"url":null,"slug":"tensor-decompositions-for-modeling-inverse","title":"Tensor Decompositions for Modeling Inverse Dynamics","date":"2017-11-13","arxiv_id":"1711.04683","repositories_listed":0,"syntology":null}],"record_sha256":"0ed582e37836eb66df5d8f141a04fddcf996e4733b070c2c147f709d5be4ccfb","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}