{"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/63","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":63,"pages_in_order":146,"rows_per_page":100,"rows":[6201,6300],"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/62","next":"/task/classification/papers/64","papers":[{"url":null,"slug":"classification-algorithm-of-speech-data-of","title":"Classification Algorithm of Speech Data of Parkinsons Disease Based on Convolution Sparse Kernel Transfer Learning with Optimal Kernel and Parallel Sample Feature Selection","date":"2020-02-10","arxiv_id":"2002.03716","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-multi-task-multi-label-cnn-for-effective","title":"Deep Multi-task Multi-label CNN for Effective Facial Attribute Classification","date":"2020-02-10","arxiv_id":"2002.03683","repositories_listed":0,"syntology":null},{"url":null,"slug":"localized-flood-detectionwith-minimal-labeled","title":"Localized Flood DetectionWith Minimal Labeled Social Media Data Using Transfer Learning","date":"2020-02-10","arxiv_id":"2003.04973","repositories_listed":0,"syntology":null},{"url":null,"slug":"on-approximation-capabilities-of-relu","title":"On Approximation Capabilities of ReLU Activation and Softmax Output Layer in Neural Networks","date":"2020-02-10","arxiv_id":"2002.04060","repositories_listed":0,"syntology":null},{"url":null,"slug":"ugrwo-sampling-a-modified-random-walk-under","title":"UGRWO-Sampling for COVID-19 dataset: A modified random walk under-sampling approach based on graphs to imbalanced data classification","date":"2020-02-10","arxiv_id":"2002.03521","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-efficient-structured-dictionary-for","title":"Learning efficient structured dictionary for image classification","date":"2020-02-09","arxiv_id":"2002.03271","repositories_listed":0,"syntology":null},{"url":null,"slug":"privacy-preserving-image-classification-in","title":"Privacy-Preserving Image Classification in the Local Setting","date":"2020-02-09","arxiv_id":"2002.03261","repositories_listed":0,"syntology":null},{"url":null,"slug":"short-text-classification-via-knowledge","title":"Short Text Classification via Knowledge powered Attention with Similarity Matrix based CNN","date":"2020-02-09","arxiv_id":"2002.03350","repositories_listed":0,"syntology":null},{"url":null,"slug":"unlabeled-data-deployment-for-classification","title":"Unlabeled Data Deployment for Classification of Diabetic Retinopathy Images Using Knowledge Transfer","date":"2020-02-09","arxiv_id":"2002.03321","repositories_listed":0,"syntology":null},{"url":null,"slug":"weakly-supervised-attention-pyramid","title":"Weakly Supervised Attention Pyramid Convolutional Neural Network for Fine-Grained Visual Classification","date":"2020-02-09","arxiv_id":"2002.03353","repositories_listed":0,"syntology":null},{"url":null,"slug":"description-based-text-classification-with","title":"Description Based Text Classification with Reinforcement Learning","date":"2020-02-08","arxiv_id":"2002.03067","repositories_listed":0,"syntology":null},{"url":null,"slug":"intrinsic-dimension-estimation-via-nearest","title":"Intrinsic Dimension Estimation via Nearest Constrained Subspace Classifier","date":"2020-02-08","arxiv_id":"2002.03228","repositories_listed":0,"syntology":null},{"url":null,"slug":"on-a-scalable-entropic-breaching-of-the","title":"On a scalable entropic breaching of the overfitting barrier in machine learning","date":"2020-02-08","arxiv_id":"2002.03176","repositories_listed":0,"syntology":null},{"url":null,"slug":"pixelhop-a-small-successive-subspace-learning","title":"PixelHop++: A Small Successive-Subspace-Learning-Based (SSL-based) Model for Image Classification","date":"2020-02-08","arxiv_id":"2002.03141","repositories_listed":0,"syntology":null},{"url":"/paper/symbiotic-attention-with-privileged","slug":"symbiotic-attention-with-privileged","title":"Symbiotic Attention with Privileged Information for Egocentric Action Recognition","date":"2020-02-08","arxiv_id":"2002.03137","repositories_listed":0,"syntology":null},{"url":null,"slug":"assessing-the-adversarial-robustness-of-monte","title":"Assessing the Adversarial Robustness of Monte Carlo and Distillation Methods for Deep Bayesian Neural Network Classification","date":"2020-02-07","arxiv_id":"2002.02842","repositories_listed":0,"syntology":null},{"url":null,"slug":"certified-robustness-to-label-flipping","title":"Certified Robustness to Label-Flipping Attacks via Randomized Smoothing","date":"2020-02-07","arxiv_id":"2002.03018","repositories_listed":0,"syntology":null},{"url":null,"slug":"cognitive-anthropomorphism-of-ai-how-humans","title":"Cognitive Anthropomorphism of AI: How Humans and Computers Classify Images","date":"2020-02-07","arxiv_id":"2002.03024","repositories_listed":0,"syntology":null},{"url":null,"slug":"iqiyi-submission-to-activitynet-challenge","title":"iqiyi Submission to ActivityNet Challenge 2019 Kinetics-700 challenge: Hierarchical Group-wise Attention","date":"2020-02-07","arxiv_id":"2002.02918","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-hyperspectral-feature-extraction-and","title":"Learning Hyperspectral Feature Extraction and Classification with ResNeXt Network","date":"2020-02-07","arxiv_id":"2002.02585","repositories_listed":0,"syntology":null},{"url":null,"slug":"object-adaptive-lstm-network-for-real-time","title":"Object-Adaptive LSTM Network for Real-time Visual Tracking with Adversarial Data Augmentation","date":"2020-02-07","arxiv_id":"2002.02598","repositories_listed":0,"syntology":null},{"url":null,"slug":"the-effect-of-data-augmentation-on","title":"The Effect of Data Augmentation on Classification of Atrial Fibrillation in Short Single-Lead ECG Signals Using Deep Neural Networks","date":"2020-02-07","arxiv_id":"2002.02870","repositories_listed":0,"syntology":null},{"url":null,"slug":"an-information-rich-sampling-technique-over","title":"An Information-rich Sampling Technique over Spatio-Temporal CNN for Classification of Human Actions in Videos","date":"2020-02-06","arxiv_id":"2002.02100","repositories_listed":0,"syntology":null},{"url":null,"slug":"duality-of-width-and-depth-of-neural-networks","title":"Quasi-Equivalence of Width and Depth of Neural Networks","date":"2020-02-06","arxiv_id":"2002.02515","repositories_listed":0,"syntology":null},{"url":null,"slug":"end-to-end-models-for-the-analysis-of-pupil","title":"1-D Convlutional Neural Networks for the Analysis of Pupil Size Variations in Scotopic Conditions","date":"2020-02-06","arxiv_id":"2002.02383","repositories_listed":0,"syntology":null},{"url":null,"slug":"fixed-smooth-convolutional-layer-for-avoiding","title":"Fixed smooth convolutional layer for avoiding checkerboard artifacts in CNNs","date":"2020-02-06","arxiv_id":"2002.02117","repositories_listed":0,"syntology":null},{"url":null,"slug":"neural-network-representation-control","title":"GIM: Gaussian Isolation Machines","date":"2020-02-06","arxiv_id":"2002.02176","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-mind-reading","title":"Continuous Silent Speech Recognition using EEG","date":"2020-02-06","arxiv_id":"2002.03851","repositories_listed":0,"syntology":null},{"url":null,"slug":"automatic-image-based-identification-and","title":"Automatic image-based identification and biomass estimation of invertebrates","date":"2020-02-05","arxiv_id":"2002.03807","repositories_listed":0,"syntology":null},{"url":null,"slug":"automatic-location-type-classification-from","title":"Geosocial Location Classification: Associating Type to Places Based on Geotagged Social-Media Posts","date":"2020-02-05","arxiv_id":"2002.01846","repositories_listed":0,"syntology":null},{"url":null,"slug":"extracting-dispersion-curves-from-ambient","title":"Extracting dispersion curves from ambient noise correlations using deep learning","date":"2020-02-05","arxiv_id":"2002.02040","repositories_listed":0,"syntology":null},{"url":null,"slug":"illumination-adaptive-person-reid-based-on","title":"Illumination adaptive person reid based on teacher-student model and adversarial training","date":"2020-02-05","arxiv_id":"2002.01625","repositories_listed":0,"syntology":null},{"url":null,"slug":"improved-subsampled-randomized-hadamard","title":"Improved Subsampled Randomized Hadamard Transform for Linear SVM","date":"2020-02-05","arxiv_id":"2002.01628","repositories_listed":0,"syntology":null},{"url":null,"slug":"online-passive-aggressive-total-error-rate","title":"Online Passive-Aggressive Total-Error-Rate Minimization","date":"2020-02-05","arxiv_id":"2002.01771","repositories_listed":0,"syntology":null},{"url":null,"slug":"renyi-entropy-bounds-on-the-active-learning","title":"Rényi Entropy Bounds on the Active Learning Cost-Performance Tradeoff","date":"2020-02-05","arxiv_id":"2002.02025","repositories_listed":0,"syntology":null},{"url":null,"slug":"classification-of-hyperspectral-and-lidar","title":"Classification of Hyperspectral and LiDAR Data Using Coupled CNNs","date":"2020-02-04","arxiv_id":"2002.01144","repositories_listed":0,"syntology":null},{"url":null,"slug":"determination-of-the-relative-inclination-and","title":"Determination of the relative inclination and the viewing angle of an interacting pair of galaxies using convolutional neural networks","date":"2020-02-04","arxiv_id":"2002.01238","repositories_listed":0,"syntology":null},{"url":null,"slug":"introduction-to-quasi-open-set-semi","title":"Open-set learning with augmented categories by exploiting unlabelled data","date":"2020-02-04","arxiv_id":"2002.01368","repositories_listed":0,"syntology":null},{"url":null,"slug":"iterative-data-programming-for-expanding-text","title":"Iterative Data Programming for Expanding Text Classification Corpora","date":"2020-02-04","arxiv_id":"2002.01412","repositories_listed":0,"syntology":null},{"url":null,"slug":"motor-imagery-classification-of-single-arm","title":"Motor Imagery Classification of Single-Arm Tasks Using Convolutional Neural Network based on Feature Refining","date":"2020-02-04","arxiv_id":"2002.01122","repositories_listed":0,"syntology":null},{"url":null,"slug":"texture-classification-using-block-intensity","title":"Texture Classification using Block Intensity and Gradient Difference (BIGD) Descriptor","date":"2020-02-04","arxiv_id":"2002.01154","repositories_listed":0,"syntology":null},{"url":null,"slug":"to-sequencemulti-label-relation-modeling-in","title":"Multi-label Relation Modeling in Facial Action Units Detection","date":"2020-02-04","arxiv_id":"2002.01105","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-neural-network-model-that-learns","title":"A neural network model that learns differences in diagnosis strategies among radiologists has an improved area under the curve for aneurysm status classification in magnetic resonance angiography image series","date":"2020-02-03","arxiv_id":"2002.01891","repositories_listed":0,"syntology":null},{"url":null,"slug":"classification-of-chest-diseases-using","title":"Classification of Chest Diseases using Wavelet Transforms and Transfer Learning","date":"2020-02-03","arxiv_id":"2002.00625","repositories_listed":0,"syntology":null},{"url":null,"slug":"defending-adversarial-attacks-via-semantic","title":"Defending Adversarial Attacks via Semantic Feature Manipulation","date":"2020-02-03","arxiv_id":"2002.02007","repositories_listed":0,"syntology":null},{"url":null,"slug":"interference-classification-using-deep-neural","title":"Interference Classification Using Deep Neural Networks","date":"2020-02-03","arxiv_id":"2002.00533","repositories_listed":0,"syntology":null},{"url":null,"slug":"medicine-strip-identification-using-2-d","title":"Medicine Strip Identification using 2-D Cepstral Feature Extraction and Multiclass Classification Methods","date":"2020-02-03","arxiv_id":"2003.00810","repositories_listed":0,"syntology":null},{"url":null,"slug":"on-device-information-extraction-from-sms","title":"On-Device Information Extraction from SMS using Hybrid Hierarchical Classification","date":"2020-02-03","arxiv_id":"2002.02755","repositories_listed":0,"syntology":null},{"url":null,"slug":"speech-emotion-recognition-using-support","title":"Speech Emotion Recognition using Support Vector Machine","date":"2020-02-03","arxiv_id":"2002.07590","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-brain-computer-interfaces-for-drone","title":"Towards Brain-Computer Interfaces for Drone Swarm Control","date":"2020-02-03","arxiv_id":"2002.00519","repositories_listed":0,"syntology":null},{"url":null,"slug":"effect-of-analysis-window-and-feature","title":"Effect of Analysis Window and Feature Selection on Classification of Hand Movements Using EMG Signal","date":"2020-02-02","arxiv_id":"2002.00461","repositories_listed":0,"syntology":null},{"url":null,"slug":"explain-graph-neural-networks-to-understand","title":"Explain Graph Neural Networks to Understand Weighted Graph Features in Node Classification","date":"2020-02-02","arxiv_id":"2002.00514","repositories_listed":0,"syntology":null},{"url":null,"slug":"evaluating-semantic-feature-representations","title":"Evaluating Semantic Feature Representations to Efficiently Detect Hate Intent on Social Media","date":"2020-02-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-modal-music-information-retrieval","title":"Multi-Modal Music Information Retrieval: Augmenting Audio-Analysis with Visual Computing for Improved Music Video Analysis","date":"2020-02-01","arxiv_id":"2002.00251","repositories_listed":0,"syntology":null},{"url":null,"slug":"crowdsourced-classification-with-xor-queries","title":"Binary Classification with XOR Queries: Fundamental Limits and An Efficient Algorithm","date":"2020-01-31","arxiv_id":"2001.11775","repositories_listed":0,"syntology":null},{"url":null,"slug":"fast-monte-carlo-dropout-and-error-correction","title":"Fast Monte Carlo Dropout and Error Correction for Radio Transmitter Classification","date":"2020-01-31","arxiv_id":"2001.11963","repositories_listed":0,"syntology":null},{"url":null,"slug":"fastwordbug-a-fast-method-to-generate","title":"FastWordBug: A Fast Method To Generate Adversarial Text Against NLP Applications","date":"2020-01-31","arxiv_id":"2002.00760","repositories_listed":0,"syntology":null},{"url":null,"slug":"hybrid-tiled-convolutional-neural-networks","title":"Hybrid Tiled Convolutional Neural Networks for Text Sentiment Classification","date":"2020-01-31","arxiv_id":"2001.11857","repositories_listed":0,"syntology":null},{"url":null,"slug":"localizing-multi-scale-semantic-patches-for","title":"Localizing Interpretable Multi-scale informative Patches Derived from Media Classification Task","date":"2020-01-31","arxiv_id":"2002.03737","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-cnn-with-multi-scale-convolution-for","title":"A CNN With Multi-scale Convolution for Hyperspectral Image Classification using Target-Pixel-Orientation scheme","date":"2020-01-30","arxiv_id":"2001.11198","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-rule-based-eeg-classification-system-for","title":"A Rule-Based EEG Classification System for Discrimination of Hand Motor Attempts in Stroke Patients","date":"2020-01-30","arxiv_id":"2001.11278","repositories_listed":0,"syntology":null},{"url":null,"slug":"analytic-study-of-double-descent-in-binary","title":"Analytic Study of Double Descent in Binary Classification: The Impact of Loss","date":"2020-01-30","arxiv_id":"2001.11572","repositories_listed":0,"syntology":null},{"url":null,"slug":"better-multi-class-probability-estimates-for","title":"Better Multi-class Probability Estimates for Small Data Sets","date":"2020-01-30","arxiv_id":"2001.11242","repositories_listed":0,"syntology":null},{"url":null,"slug":"fase-al-adaptation-of-fast-adaptive-stacking","title":"Fase-AL -- Adaptation of Fast Adaptive Stacking of Ensembles for Supporting Active Learning","date":"2020-01-30","arxiv_id":"2001.11466","repositories_listed":0,"syntology":null},{"url":null,"slug":"introducing-the-diagrammatic-mode","title":"Introducing the diagrammatic semiotic mode","date":"2020-01-30","arxiv_id":"2001.11224","repositories_listed":0,"syntology":null},{"url":null,"slug":"weakly-supervised-segmentation-of-cracks-on","title":"Weakly Supervised Segmentation of Cracks on Solar Cells using Normalized Lp Norm","date":"2020-01-30","arxiv_id":"2001.11248","repositories_listed":0,"syntology":null},{"url":null,"slug":"binary-classification-from-positive-data-with","title":"Binary Classification from Positive Data with Skewed Confidence","date":"2020-01-29","arxiv_id":"2001.10642","repositories_listed":0,"syntology":null},{"url":null,"slug":"developing-a-gender-classification-approach","title":"Developing a gender classification approach in human face images using modified local binary patterns and tani-moto based nearest neighbor algorithm","date":"2020-01-29","arxiv_id":"2001.10966","repositories_listed":0,"syntology":null},{"url":null,"slug":"early-detection-and-classification-of-live","title":"Early-detection and classification of live bacteria using time-lapse coherent imaging and deep learning","date":"2020-01-29","arxiv_id":"2001.10695","repositories_listed":0,"syntology":null},{"url":null,"slug":"evaluating-the-progress-of-deep-learning-for","title":"Evaluating the Progress of Deep Learning for Visual Relational Concepts","date":"2020-01-29","arxiv_id":"2001.10857","repositories_listed":0,"syntology":null},{"url":null,"slug":"iot-behavioral-monitoring-via-network-traffic","title":"IoT Behavioral Monitoring via Network Traffic Analysis","date":"2020-01-28","arxiv_id":"2001.10632","repositories_listed":0,"syntology":null},{"url":null,"slug":"wisdom-a-framework-for-the-analysis-of","title":"WISDoM: characterizing neurological timeseries with the Wishart distribution","date":"2020-01-28","arxiv_id":"2001.10342","repositories_listed":0,"syntology":null},{"url":null,"slug":"an-ontology-aware-framework-for-audio-event","title":"An Ontology-Aware Framework for Audio Event Classification","date":"2020-01-27","arxiv_id":"2001.10048","repositories_listed":0,"syntology":null},{"url":null,"slug":"black-box-explanation-by-learning-image","title":"Black Box Explanation by Learning Image Exemplars in the Latent Feature Space","date":"2020-01-27","arxiv_id":"2002.03746","repositories_listed":0,"syntology":null},{"url":null,"slug":"convolution-neural-network-architecture","title":"Convolution Neural Network Architecture Learning for Remote Sensing Scene Classification","date":"2020-01-27","arxiv_id":"2001.09614","repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-label-prediction-in-time-series-data","title":"Multi-label Prediction in Time Series Data using Deep Neural Networks","date":"2020-01-27","arxiv_id":"2001.10098","repositories_listed":0,"syntology":null},{"url":null,"slug":"performance-analysis-and-comparison-of","title":"Performance Analysis and Comparison of Machine and Deep Learning Algorithms for IoT Data Classification","date":"2020-01-27","arxiv_id":"2001.09636","repositories_listed":0,"syntology":null},{"url":null,"slug":"point-of-care-diabetic-retinopathy-diagnosis","title":"Point-of-Care Diabetic Retinopathy Diagnosis: A Standalone Mobile Application Approach","date":"2020-01-26","arxiv_id":"2002.04066","repositories_listed":0,"syntology":null},{"url":null,"slug":"generation-distillation-for-efficient-natural-1","title":"Generation-Distillation for Efficient Natural Language Understanding in Low-Data Settings","date":"2020-01-25","arxiv_id":"2002.00733","repositories_listed":0,"syntology":null},{"url":null,"slug":"modular-network-for-high-accuracy-object","title":"Modular network for high accuracy object detection","date":"2020-01-24","arxiv_id":"2001.09203","repositories_listed":0,"syntology":null},{"url":null,"slug":"mt-bioner-multi-task-learning-for-biomedical","title":"MT-BioNER: Multi-task Learning for Biomedical Named Entity Recognition using Deep Bidirectional Transformers","date":"2020-01-24","arxiv_id":"2001.08904","repositories_listed":0,"syntology":null},{"url":null,"slug":"polarimetric-guided-nonlocal-means-covariance","title":"Polarimetric Guided Nonlocal Means Covariance Matrix Estimation for Defoliation Mapping","date":"2020-01-24","arxiv_id":"2001.08976","repositories_listed":0,"syntology":null},{"url":null,"slug":"simple-and-effective-prevention-of-mode","title":"Simple and Effective Prevention of Mode Collapse in Deep One-Class Classification","date":"2020-01-24","arxiv_id":"2001.08873","repositories_listed":0,"syntology":null},{"url":null,"slug":"continual-local-replacement-for-few-shot","title":"Continual Local Replacement for Few-shot Learning","date":"2020-01-23","arxiv_id":"2001.08366","repositories_listed":0,"syntology":null},{"url":null,"slug":"tensor-based-grading-a-novel-patch-based","title":"Tensor-Based Grading: A Novel Patch-Based Grading Approach for the Analysis of Deformation Fields in Huntington's Disease","date":"2020-01-23","arxiv_id":"2001.08651","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-multi-scale-tensor-network-architecture-for","title":"A Multi-Scale Tensor Network Architecture for Classification and Regression","date":"2020-01-22","arxiv_id":"2001.08286","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-neural-architecture-for-person-ontology","title":"A Neural Architecture for Person Ontology population","date":"2020-01-22","arxiv_id":"2001.08013","repositories_listed":0,"syntology":null},{"url":null,"slug":"automatic-phantom-test-pattern-classification","title":"Automatic phantom test pattern classification through transfer learning with deep neural networks","date":"2020-01-22","arxiv_id":"2001.08189","repositories_listed":0,"syntology":null},{"url":null,"slug":"coarse-grain-cluster-analysis-of-tensors-with","title":"Coarse-Grain Cluster Analysis of Tensors with Application to Climate Biome Identification","date":"2020-01-22","arxiv_id":"2001.07827","repositories_listed":0,"syntology":null},{"url":null,"slug":"investigating-classification-techniques-with","title":"Investigating Classification Techniques with Feature Selection For Intention Mining From Twitter Feed","date":"2020-01-22","arxiv_id":"2001.10380","repositories_listed":0,"syntology":null},{"url":null,"slug":"optimized-generic-feature-learning-for-few","title":"Optimized Generic Feature Learning for Few-shot Classification across Domains","date":"2020-01-22","arxiv_id":"2001.07926","repositories_listed":0,"syntology":null},{"url":null,"slug":"classifying-wikipedia-in-a-fine-grained","title":"Classifying Wikipedia in a fine-grained hierarchy: what graphs can contribute","date":"2020-01-21","arxiv_id":"2001.07558","repositories_listed":0,"syntology":null},{"url":null,"slug":"motif-difference-field-a-simple-and-effective","title":"Motif Difference Field: A Simple and Effective Image Representation of Time Series for Classification","date":"2020-01-21","arxiv_id":"2001.07582","repositories_listed":0,"syntology":null},{"url":null,"slug":"unsupervisedly-learned-representations-should","title":"Unsupervisedly Learned Representations: Should the Quest be Over?","date":"2020-01-21","arxiv_id":"2001.07495","repositories_listed":0,"syntology":null},{"url":null,"slug":"early-forecasting-of-text-classification","title":"Early Forecasting of Text Classification Accuracy and F-Measure with Active Learning","date":"2020-01-20","arxiv_id":"2001.10337","repositories_listed":0,"syntology":null},{"url":null,"slug":"plane-pair-matching-for-efficient-3d-view","title":"Plane Pair Matching for Efficient 3D View Registration","date":"2020-01-20","arxiv_id":"2001.07058","repositories_listed":0,"syntology":null},{"url":null,"slug":"short-text-classification-via-term-graph","title":"Short Text Classification via Term Graph","date":"2020-01-20","arxiv_id":"2001.10338","repositories_listed":0,"syntology":null},{"url":null,"slug":"spectral-pyramid-graph-attention-network-for","title":"Spectral Pyramid Graph Attention Network for Hyperspectral Image Classification","date":"2020-01-20","arxiv_id":"2001.07108","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-meta-algorithm-for-classification-using","title":"A meta-algorithm for classification using random recursive tree ensembles: A high energy physics application","date":"2020-01-19","arxiv_id":"2001.06880","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-learning-for-hindi-text-classification-a","title":"Deep Learning for Hindi Text Classification: A Comparison","date":"2020-01-19","arxiv_id":"2001.10340","repositories_listed":0,"syntology":null}],"record_sha256":"88993e4559f460b25d5848afc9fd02b57bfc1c53cdbca7a45cce2f2116af0bd2","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}