{"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/107","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":107,"pages_in_order":146,"rows_per_page":100,"rows":[10601,10700],"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/106","next":"/task/classification/papers/108","papers":[{"url":null,"slug":"beat-by-beat-classifying-cardiac-arrhythmias","title":"Beat by Beat: Classifying Cardiac Arrhythmias with Recurrent Neural Networks","date":"2017-10-17","arxiv_id":"1710.06319","repositories_listed":0,"syntology":null},{"url":null,"slug":"classification-and-geometry-of-general","title":"Classification and Geometry of General Perceptual Manifolds","date":"2017-10-17","arxiv_id":"1710.06487","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-to-learn-image-classifiers-with","title":"Learning to Learn Image Classifiers with Visual Analogy","date":"2017-10-17","arxiv_id":"1710.06177","repositories_listed":0,"syntology":null},{"url":null,"slug":"material-classification-using-neural-networks","title":"Material Classification using Neural Networks","date":"2017-10-17","arxiv_id":"1710.06854","repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-task-label-embedding-for-text","title":"Multi-Task Label Embedding for Text Classification","date":"2017-10-17","arxiv_id":"1710.07210","repositories_listed":0,"syntology":null},{"url":null,"slug":"classifying-web-exploits-with-topic-modeling","title":"Classifying Web Exploits with Topic Modeling","date":"2017-10-16","arxiv_id":"1710.05561","repositories_listed":0,"syntology":null},{"url":null,"slug":"convolutional-neural-networks-for-sentiment","title":"Convolutional Neural Networks for Sentiment Classification on Business Reviews","date":"2017-10-16","arxiv_id":"1710.05978","repositories_listed":0,"syntology":null},{"url":null,"slug":"entanglement-entropy-of-target-functions-for","title":"Entanglement Entropy of Target Functions for Image Classification and Convolutional Neural Network","date":"2017-10-16","arxiv_id":"1710.05520","repositories_listed":0,"syntology":null},{"url":null,"slug":"pushing-the-envelope-in-deep-visual","title":"Pushing the envelope in deep visual recognition for mobile platforms","date":"2017-10-16","arxiv_id":"1710.05982","repositories_listed":0,"syntology":null},{"url":null,"slug":"what-is-missing-or-wrong-in-the-scene-a","title":"What is (missing or wrong) in the scene? A Hybrid Deep Boltzmann Machine For Contextualized Scene Modeling","date":"2017-10-16","arxiv_id":"1710.05664","repositories_listed":0,"syntology":null},{"url":null,"slug":"information-theoretic-representation-learning","title":"Information-Theoretic Representation Learning for Positive-Unlabeled Classification","date":"2017-10-15","arxiv_id":"1710.05359","repositories_listed":0,"syntology":null},{"url":null,"slug":"brainsegnet-a-segmentation-network-for-human","title":"BrainSegNet : A Segmentation Network for Human Brain Fiber Tractography Data into Anatomically Meaningful Clusters","date":"2017-10-14","arxiv_id":"1710.05158","repositories_listed":0,"syntology":null},{"url":null,"slug":"community-aware-random-walk-for-network","title":"Community Aware Random Walk for Network Embedding","date":"2017-10-14","arxiv_id":"1710.05199","repositories_listed":0,"syntology":null},{"url":null,"slug":"simultaneous-matrix-diagonalization-for","title":"Simultaneous Matrix Diagonalization for Structural Brain Networks Classification","date":"2017-10-14","arxiv_id":"1710.05213","repositories_listed":0,"syntology":null},{"url":null,"slug":"video-classification-with-cnns-using-the","title":"Video Classification With CNNs: Using The Codec As A Spatio-Temporal Activity Sensor","date":"2017-10-14","arxiv_id":"1710.05112","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-deep-incremental-boltzmann-machine-for","title":"A Deep Incremental Boltzmann Machine for Modeling Context in Robots","date":"2017-10-13","arxiv_id":"1710.04975","repositories_listed":0,"syntology":null},{"url":null,"slug":"complex-word-identification-challenges-in","title":"Complex Word Identification: Challenges in Data Annotation and System Performance","date":"2017-10-13","arxiv_id":"1710.04989","repositories_listed":0,"syntology":null},{"url":null,"slug":"object-classification-in-images-of","title":"Object Classification in Images of Neoclassical Artifacts Using Deep Learning","date":"2017-10-13","arxiv_id":"1710.04943","repositories_listed":0,"syntology":null},{"url":null,"slug":"retinal-fluid-segmentation-and-detection-in","title":"Retinal Fluid Segmentation and Detection in Optical Coherence Tomography Images using Fully Convolutional Neural Network","date":"2017-10-13","arxiv_id":"1710.04778","repositories_listed":0,"syntology":null},{"url":null,"slug":"two-stage-algorithm-for-fairness-aware","title":"Two-stage Algorithm for Fairness-aware Machine Learning","date":"2017-10-13","arxiv_id":"1710.04924","repositories_listed":0,"syntology":null},{"url":null,"slug":"an-improved-naive-bayes-classifier-based","title":"An Improved Naive Bayes Classifier-based Noise Detection Technique for Classifying User Phone Call Behavior","date":"2017-10-12","arxiv_id":"1710.04461","repositories_listed":0,"syntology":null},{"url":null,"slug":"can-the-early-human-visual-system-compete","title":"Can the early human visual system compete with Deep Neural Networks?","date":"2017-10-12","arxiv_id":"1710.04744","repositories_listed":0,"syntology":null},{"url":null,"slug":"effects-of-images-with-different-levels-of","title":"Effects of Images with Different Levels of Familiarity on EEG","date":"2017-10-12","arxiv_id":"1710.04462","repositories_listed":0,"syntology":null},{"url":null,"slug":"graph-convolutional-networks-for","title":"Graph Convolutional Networks for Classification with a Structured Label Space","date":"2017-10-12","arxiv_id":"1710.04908","repositories_listed":0,"syntology":null},{"url":null,"slug":"hyperspectral-band-selection-using-genetic","title":"Hyperspectral band selection using genetic algorithm and support vector machines for early identification of charcoal rot disease in soybean","date":"2017-10-12","arxiv_id":"1710.04681","repositories_listed":0,"syntology":null},{"url":null,"slug":"stdp-based-pruning-of-connections-and-weight","title":"STDP Based Pruning of Connections and Weight Quantization in Spiking Neural Networks for Energy Efficient Recognition","date":"2017-10-12","arxiv_id":"1710.04734","repositories_listed":0,"syntology":null},{"url":null,"slug":"convolutional-neural-networks-for-1","title":"Convolutional Neural Networks for Histopathology Image Classification: Training vs. Using Pre-Trained Networks","date":"2017-10-11","arxiv_id":"1710.05726","repositories_listed":0,"syntology":null},{"url":null,"slug":"decentralized-online-learning-with-kernels","title":"Decentralized Online Learning with Kernels","date":"2017-10-11","arxiv_id":"1710.04062","repositories_listed":0,"syntology":null},{"url":null,"slug":"lung-cancer-screening-using-adaptive-memory","title":"Lung Cancer Screening Using Adaptive Memory-Augmented Recurrent Networks","date":"2017-10-11","arxiv_id":"1710.05719","repositories_listed":0,"syntology":null},{"url":null,"slug":"application-of-deep-learning-in","title":"Application of Deep Learning in Neuroradiology: Automated Detection of Basal Ganglia Hemorrhage using 2D-Convolutional Neural Networks","date":"2017-10-10","arxiv_id":"1710.03823","repositories_listed":0,"syntology":null},{"url":null,"slug":"multilevel-modeling-with-structured-penalties","title":"Multilevel Modeling with Structured Penalties for Classification from Imaging Genetics data","date":"2017-10-10","arxiv_id":"1710.03627","repositories_listed":0,"syntology":null},{"url":null,"slug":"an-automatic-deep-learning-approach-for","title":"An automatic deep learning approach for coronary artery calcium segmentation","date":"2017-10-09","arxiv_id":"1710.03023","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-learning-paradigm-with-transformed","title":"Deep Learning Paradigm with Transformed Monolingual Word Embeddings for Multilingual Sentiment Analysis","date":"2017-10-09","arxiv_id":"1710.03203","repositories_listed":0,"syntology":null},{"url":null,"slug":"does-normalization-methods-play-a-role-for","title":"Does Normalization Methods Play a Role for Hyperspectral Image Classification?","date":"2017-10-09","arxiv_id":"1710.02939","repositories_listed":0,"syntology":null},{"url":null,"slug":"driving-behavior-analysis-through-can-bus","title":"Driving Behavior Analysis through CAN Bus Data in an Uncontrolled Environment","date":"2017-10-09","arxiv_id":"1710.04133","repositories_listed":0,"syntology":null},{"url":"/paper/ug2-a-video-benchmark-for-assessing-the","slug":"ug2-a-video-benchmark-for-assessing-the","title":"UG^2: a Video Benchmark for Assessing the Impact of Image Restoration and Enhancement on Automatic Visual Recognition","date":"2017-10-09","arxiv_id":"1710.02909","repositories_listed":0,"syntology":null},{"url":null,"slug":"vehicle-classification-based-on-convolutional","title":"Vehicle classification based on convolutional networks applied to FM-CW radar signals","date":"2017-10-09","arxiv_id":"1710.05718","repositories_listed":0,"syntology":null},{"url":null,"slug":"gender-and-ethnicity-classification-of-iris","title":"Gender and Ethnicity Classification of Iris Images using Deep Class-Encoder","date":"2017-10-08","arxiv_id":"1710.02856","repositories_listed":0,"syntology":null},{"url":null,"slug":"structural-feature-selection-for-event-logs","title":"Structural Feature Selection for Event Logs","date":"2017-10-08","arxiv_id":"1710.02823","repositories_listed":0,"syntology":null},{"url":null,"slug":"group-sparse-cnns-for-question-classification","title":"Group Sparse CNNs for Question Classification with Answer Sets","date":"2017-10-07","arxiv_id":"1710.02717","repositories_listed":0,"syntology":null},{"url":null,"slug":"bag-level-aggregation-for-multiple-instance","title":"Bag-Level Aggregation for Multiple Instance Active Learning in Instance Classification Problems","date":"2017-10-06","arxiv_id":"1710.02584","repositories_listed":0,"syntology":null},{"url":null,"slug":"czech-text-document-corpus-v-20","title":"Czech Text Document Corpus v 2.0","date":"2017-10-06","arxiv_id":"1710.02365","repositories_listed":0,"syntology":null},{"url":"/paper/deep-convolutional-neural-networks-as-generic","slug":"deep-convolutional-neural-networks-as-generic","title":"Deep Convolutional Neural Networks as Generic Feature Extractors","date":"2017-10-06","arxiv_id":"1710.02286","repositories_listed":0,"syntology":null},{"url":null,"slug":"discovering-playing-patterns-time-series","title":"Discovering Playing Patterns: Time Series Clustering of Free-To-Play Game Data","date":"2017-10-06","arxiv_id":"1710.02268","repositories_listed":0,"syntology":null},{"url":null,"slug":"linear-time-sequence-classification-using","title":"Linear-Time Sequence Classification using Restricted Boltzmann Machines","date":"2017-10-06","arxiv_id":"1710.02245","repositories_listed":0,"syntology":null},{"url":null,"slug":"anatomical-pattern-analysis-for-decoding","title":"Anatomical Pattern Analysis for decoding visual stimuli in human brains","date":"2017-10-05","arxiv_id":"1710.02113","repositories_listed":0,"syntology":null},{"url":null,"slug":"energy-based-spherical-sparse-coding","title":"Energy-Based Spherical Sparse Coding","date":"2017-10-04","arxiv_id":"1710.01820","repositories_listed":0,"syntology":null},{"url":null,"slug":"monitoring-tool-usage-in-surgery-videos-using","title":"Monitoring tool usage in surgery videos using boosted convolutional and recurrent neural networks","date":"2017-10-04","arxiv_id":"1710.01559","repositories_listed":0,"syntology":null},{"url":null,"slug":"secrets-in-computing-optical-flow-by","title":"Secrets in Computing Optical Flow by Convolutional Networks","date":"2017-10-04","arxiv_id":"1710.01462","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-concatenating-framework-of-shortcut","title":"A concatenating framework of shortcut convolutional neural networks","date":"2017-10-03","arxiv_id":"1710.00974","repositories_listed":0,"syntology":null},{"url":null,"slug":"decoding-visemes-improving-machine-lipreading","title":"Decoding visemes: improving machine lipreading","date":"2017-10-03","arxiv_id":"1710.01288","repositories_listed":0,"syntology":null},{"url":null,"slug":"decoding-visemes-improving-machine-lipreading-1","title":"Decoding visemes: improving machine lipreading","date":"2017-10-03","arxiv_id":"1710.01169","repositories_listed":0,"syntology":null},{"url":null,"slug":"supervised-q-walk-for-learning-vector","title":"Supervised Q-walk for Learning Vector Representation of Nodes in Networks","date":"2017-10-03","arxiv_id":"1710.00978","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-event-representation-as-sparse-as","title":"Learning event representation: As sparse as possible, but not sparser","date":"2017-10-02","arxiv_id":"1710.00448","repositories_listed":0,"syntology":null},{"url":null,"slug":"remote-sensing-image-classification-with","title":"Remote Sensing Image Classification with Large Scale Gaussian Processes","date":"2017-10-02","arxiv_id":"1710.00575","repositories_listed":0,"syntology":null},{"url":null,"slug":"scalable-nonlinear-auc-maximization-methods","title":"Scalable Nonlinear AUC Maximization Methods","date":"2017-10-02","arxiv_id":"1710.00760","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-determinantal-point-process-for-large","title":"Deep Determinantal Point Process for Large-Scale Multi-Label Classification","date":"2017-10-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-scene-image-classification-with-the","title":"Deep Scene Image Classification With the MFAFVNet","date":"2017-10-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"flip-invariant-motion-representation","title":"Flip-Invariant Motion Representation","date":"2017-10-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"increasing-cnn-robustness-to-occlusions-by","title":"Increasing CNN Robustness to Occlusions by Reducing Filter Support","date":"2017-10-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"introspective-neural-networks-for-generative","title":"Introspective Neural Networks for Generative Modeling","date":"2017-10-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-discriminative-latent-attributes-for","title":"Learning Discriminative Latent Attributes for Zero-Shot Classification","date":"2017-10-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"leveraging-weak-semantic-relevance-for","title":"Leveraging Weak Semantic Relevance for Complex Video Event Classification","date":"2017-10-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"locally-transferred-fisher-vectors-for","title":"Locally-Transferred Fisher Vectors for Texture Classification","date":"2017-10-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"punda-probabilistic-unsupervised-domain","title":"PUnDA: Probabilistic Unsupervised Domain Adaptation for Knowledge Transfer Across Visual Categories","date":"2017-10-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"pyramidal-ror-for-image-classification","title":"Pyramidal RoR for Image Classification","date":"2017-10-01","arxiv_id":"1710.00307","repositories_listed":0,"syntology":null},{"url":null,"slug":"reflectance-capture-using-univariate-sampling","title":"Reflectance Capture Using Univariate Sampling of BRDFs","date":"2017-10-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"semi-supervised-semantic-segmentation-using","title":"Semi Supervised Semantic Segmentation Using Generative Adversarial Network","date":"2017-10-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"summarization-and-classification-of-wearable","title":"Summarization and Classification of Wearable Camera Streams by Learning the Distributions Over Deep Features of Out-Of-Sample Image Sequences","date":"2017-10-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"weakly-supervised-manifold-learning-for-dense","title":"Weakly Supervised Manifold Learning for Dense Semantic Object Correspondence","date":"2017-10-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"decontamination-of-mutual-contamination","title":"Decontamination of Mutual Contamination Models","date":"2017-09-30","arxiv_id":"1710.01167","repositories_listed":0,"syntology":null},{"url":null,"slug":"fast-fine-grained-image-classification-via","title":"Fast Fine-grained Image Classification via Weakly Supervised Discriminative Localization","date":"2017-09-30","arxiv_id":"1710.01168","repositories_listed":0,"syntology":null},{"url":null,"slug":"fine-grained-event-learning-of-human-object","title":"Fine-grained Event Learning of Human-Object Interaction with LSTM-CRF","date":"2017-09-30","arxiv_id":"1710.00262","repositories_listed":0,"syntology":null},{"url":null,"slug":"improved-training-for-self-training-by","title":"Improved Training for Self-Training by Confidence Assessments","date":"2017-09-30","arxiv_id":"1710.00209","repositories_listed":0,"syntology":null},{"url":null,"slug":"unsupervised-classification-of-intrusive","title":"Unsupervised Classification of Intrusive Igneous Rock Thin Section Images using Edge Detection and Colour Analysis","date":"2017-09-30","arxiv_id":"1710.00189","repositories_listed":0,"syntology":null},{"url":null,"slug":"light-cascaded-convolutional-neural-networks","title":"Light Cascaded Convolutional Neural Networks for Accurate Player Detection","date":"2017-09-29","arxiv_id":"1709.10230","repositories_listed":0,"syntology":null},{"url":null,"slug":"toward-scalable-machine-learning-and-data","title":"Toward Scalable Machine Learning and Data Mining: the Bioinformatics Case","date":"2017-09-29","arxiv_id":"1710.00112","repositories_listed":0,"syntology":null},{"url":null,"slug":"efficient-convolutional-neural-network-for","title":"Efficient Convolutional Neural Network For Audio Event Detection","date":"2017-09-28","arxiv_id":"1709.09888","repositories_listed":0,"syntology":null},{"url":null,"slug":"jointly-trained-sequential-labeling-and","title":"Jointly Trained Sequential Labeling and Classification by Sparse Attention Neural Networks","date":"2017-09-28","arxiv_id":"1709.10191","repositories_listed":0,"syntology":null},{"url":null,"slug":"sentiment-classification-with-word-attention","title":"Sentiment Classification with Word Attention based on Weakly Supervised Learning with a Convolutional Neural Network","date":"2017-09-28","arxiv_id":"1709.09885","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-comparative-study-of-cnn-bovw-and-lbp-for","title":"A Comparative Study of CNN, BoVW and LBP for Classification of Histopathological Images","date":"2017-09-27","arxiv_id":"1710.01249","repositories_listed":0,"syntology":null},{"url":null,"slug":"combining-real-valued-and-binary-gabor-radon","title":"Combining Real-Valued and Binary Gabor-Radon Features for Classification and Search in Medical Imaging Archives","date":"2017-09-27","arxiv_id":"1709.09754","repositories_listed":0,"syntology":null},{"url":null,"slug":"foodnet-recognizing-foods-using-ensemble-of","title":"FoodNet: Recognizing Foods Using Ensemble of Deep Networks","date":"2017-09-27","arxiv_id":"1709.09429","repositories_listed":0,"syntology":null},{"url":null,"slug":"leveraging-weakly-annotated-data-for-fashion","title":"Leveraging Weakly Annotated Data for Fashion Image Retrieval and Label Prediction","date":"2017-09-27","arxiv_id":"1709.09426","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-deep-learning-model-for-traffic-flow-state","title":"A Deep Learning Model for Traffic Flow State Classification Based on Smart Phone Sensor Data","date":"2017-09-26","arxiv_id":"1709.08802","repositories_listed":0,"syntology":null},{"url":null,"slug":"automatic-error-analysis-of-human-motor","title":"Automatic Error Analysis of Human Motor Performance for Interactive Coaching in Virtual Reality","date":"2017-09-26","arxiv_id":"1709.09131","repositories_listed":0,"syntology":null},{"url":null,"slug":"fsl-bm-fuzzy-supervised-learning-with-binary","title":"FSL-BM: Fuzzy Supervised Learning with Binary Meta-Feature for Classification","date":"2017-09-26","arxiv_id":"1709.09268","repositories_listed":0,"syntology":null},{"url":null,"slug":"output-range-analysis-for-deep-neural","title":"Output Range Analysis for Deep Neural Networks","date":"2017-09-26","arxiv_id":"1709.09130","repositories_listed":0,"syntology":null},{"url":null,"slug":"ubsegnet-unified-biometric-region-of-interest","title":"UBSegNet: Unified Biometric Region of Interest Segmentation Network","date":"2017-09-26","arxiv_id":"1709.08924","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-learning-based-cryptographic-primitive","title":"Deep Learning Based Cryptographic Primitive Classification","date":"2017-09-25","arxiv_id":"1709.08385","repositories_listed":0,"syntology":null},{"url":null,"slug":"doc-deep-open-classification-of-text","title":"DOC: Deep Open Classification of Text Documents","date":"2017-09-25","arxiv_id":"1709.08716","repositories_listed":0,"syntology":null},{"url":null,"slug":"ensemble-classifier-for-eye-state","title":"Ensemble Classifier for Eye State Classification using EEG Signals","date":"2017-09-25","arxiv_id":"1709.08590","repositories_listed":0,"syntology":null},{"url":null,"slug":"fine-grained-discriminative-localization-via","title":"Fine-grained Discriminative Localization via Saliency-guided Faster R-CNN","date":"2017-09-25","arxiv_id":"1709.08295","repositories_listed":0,"syntology":null},{"url":null,"slug":"using-objective-words-in-the-reviews-to","title":"Using objective words in the reviews to improve the colloquial arabic sentiment analysis","date":"2017-09-25","arxiv_id":"1709.08521","repositories_listed":0,"syntology":null},{"url":null,"slug":"comparison-of-batch-normalization-and-weight","title":"Comparison of Batch Normalization and Weight Normalization Algorithms for the Large-scale Image Classification","date":"2017-09-24","arxiv_id":"1709.08145","repositories_listed":0,"syntology":null},{"url":null,"slug":"tensor-based-classifiers-for-hyperspectral","title":"Tensor-Based Classifiers for Hyperspectral Data Analysis","date":"2017-09-24","arxiv_id":"1709.08164","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-generic-regression-framework-for-pose","title":"A Generic Regression Framework for Pose Recognition on Color and Depth Images","date":"2017-09-23","arxiv_id":"1709.08068","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-classification-of-web-ontologies","title":"Towards Classification of Web ontologies using the Horizontal and Vertical Segmentation","date":"2017-09-23","arxiv_id":"1709.08028","repositories_listed":0,"syntology":null},{"url":null,"slug":"drought-stress-classification-using-3d-plant","title":"Drought Stress Classification using 3D Plant Models","date":"2017-09-21","arxiv_id":"1709.09496","repositories_listed":0,"syntology":null},{"url":null,"slug":"inducing-distant-supervision-in-suggestion","title":"Inducing Distant Supervision in Suggestion Mining through Part-of-Speech Embeddings","date":"2017-09-21","arxiv_id":"1709.07403","repositories_listed":0,"syntology":null}],"record_sha256":"7cf7dcae11bd9098273504db54fd549eabacc8af93c46253dbe1bcf4342cb107","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}