{"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/75","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":75,"pages_in_order":146,"rows_per_page":100,"rows":[7401,7500],"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/74","next":"/task/classification/papers/76","papers":[{"url":null,"slug":"a-deep-learning-framework-for-classification","title":"A Deep Learning Framework for Classification of in vitro Multi-Electrode Array Recordings","date":"2019-06-05","arxiv_id":"1906.02241","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-feature-transfer-enabled-multi-task-deep","title":"A Feature Transfer Enabled Multi-Task Deep Learning Model on Medical Imaging","date":"2019-06-05","arxiv_id":"1906.01828","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-glcm-embedded-cnn-strategy-for-computer","title":"A GLCM Embedded CNN Strategy for Computer-aided Diagnosis in Intracerebral Hemorrhage","date":"2019-06-05","arxiv_id":"1906.02040","repositories_listed":0,"syntology":null},{"url":null,"slug":"ai-skin-skin-disease-recognition-based-on","title":"AI-Skin : Skin Disease Recognition based on Self-learning and Wide Data Collection through a Closed Loop Framework","date":"2019-06-05","arxiv_id":"1906.01895","repositories_listed":0,"syntology":null},{"url":null,"slug":"automated-classification-of-seizures-against","title":"Automated Classification of Seizures against Nonseizures: A Deep Learning Approach","date":"2019-06-05","arxiv_id":"1906.02745","repositories_listed":0,"syntology":null},{"url":null,"slug":"collage-inference-achieving-low-tail-latency","title":"Collage Inference: Achieving low tail latency during distributed image classification using coded redundancy models","date":"2019-06-05","arxiv_id":"1906.03999","repositories_listed":0,"syntology":null},{"url":null,"slug":"discriminative-few-shot-learning-based-on","title":"Discriminative Few-Shot Learning Based on Directional Statistics","date":"2019-06-05","arxiv_id":"1906.01819","repositories_listed":0,"syntology":null},{"url":null,"slug":"generalized-linear-rule-models","title":"Generalized Linear Rule Models","date":"2019-06-05","arxiv_id":"1906.01761","repositories_listed":0,"syntology":null},{"url":"/paper/neural-legal-judgment-prediction-in-english","slug":"neural-legal-judgment-prediction-in-english","title":"Neural Legal Judgment Prediction in English","date":"2019-06-05","arxiv_id":"1906.02059","repositories_listed":0,"syntology":null},{"url":null,"slug":"privacy-preserving-classification-of-personal","title":"Privacy-Preserving Classification of Personal Text Messages with Secure Multi-Party Computation: An Application to Hate-Speech Detection","date":"2019-06-05","arxiv_id":"1906.02325","repositories_listed":0,"syntology":null},{"url":"/paper/two-stream-region-convolutional-3d-network","slug":"two-stream-region-convolutional-3d-network","title":"Two-Stream Region Convolutional 3D Network for Temporal Activity Detection","date":"2019-06-05","arxiv_id":"1906.02182","repositories_listed":0,"syntology":null},{"url":null,"slug":"visual-confusion-label-tree-for-image","title":"Visual Confusion Label Tree For Image Classification","date":"2019-06-05","arxiv_id":"1906.02012","repositories_listed":0,"syntology":null},{"url":null,"slug":"190602679","title":"A Natural Language-Inspired Multi-label Video Streaming Traffic Classification Method Based on Deep Neural Networks","date":"2019-06-04","arxiv_id":"1906.02679","repositories_listed":0,"syntology":null},{"url":null,"slug":"assessing-the-robustness-of-bayesian-dark","title":"Assessing the Robustness of Bayesian Dark Knowledge to Posterior Uncertainty","date":"2019-06-04","arxiv_id":"1906.01724","repositories_listed":0,"syntology":null},{"url":null,"slug":"bayesian-active-learning-with-abstention","title":"Bayesian Active Learning With Abstention Feedbacks","date":"2019-06-04","arxiv_id":"1906.02179","repositories_listed":0,"syntology":null},{"url":null,"slug":"conditional-generative-models-are-not-robust","title":"Understanding the Limitations of Conditional Generative Models","date":"2019-06-04","arxiv_id":"1906.01171","repositories_listed":0,"syntology":null},{"url":null,"slug":"in-memory-hyperdimensional-computing","title":"In-memory hyperdimensional computing","date":"2019-06-04","arxiv_id":"1906.01548","repositories_listed":0,"syntology":null},{"url":null,"slug":"off-policy-evaluation-via-off-policy","title":"Off-Policy Evaluation via Off-Policy Classification","date":"2019-06-04","arxiv_id":"1906.01624","repositories_listed":0,"syntology":null},{"url":null,"slug":"sparse-representation-classification-via","title":"Sparse Representation Classification via Screening for Graphs","date":"2019-06-04","arxiv_id":"1906.01601","repositories_listed":0,"syntology":null},{"url":null,"slug":"system-demo-for-transfer-learning-across","title":"System Demo for Transfer Learning across Vision and Text using Domain Specific CNN Accelerator for On-Device NLP Applications","date":"2019-06-04","arxiv_id":"1906.01145","repositories_listed":0,"syntology":null},{"url":null,"slug":"visual-tree-convolutional-neural-network-in","title":"Visual Tree Convolutional Neural Network in Image Classification","date":"2019-06-04","arxiv_id":"1906.01536","repositories_listed":0,"syntology":null},{"url":null,"slug":"190600512","title":"Stochastic Generalized Adversarial Label Learning","date":"2019-06-03","arxiv_id":"1906.00512","repositories_listed":0,"syntology":null},{"url":null,"slug":"190600634","title":"How Much Does Audio Matter to Recognize Egocentric Object Interactions?","date":"2019-06-03","arxiv_id":"1906.00634","repositories_listed":0,"syntology":null},{"url":null,"slug":"190600735","title":"Achieving Generalizable Robustness of Deep Neural Networks by Stability Training","date":"2019-06-03","arxiv_id":"1906.00735","repositories_listed":0,"syntology":null},{"url":null,"slug":"190600800","title":"Neural Network-based Object Classification by Known and Unknown Features (Based on Text Queries)","date":"2019-06-03","arxiv_id":"1906.00800","repositories_listed":0,"syntology":null},{"url":null,"slug":"190600852","title":"Hierarchical Auxiliary Learning","date":"2019-06-03","arxiv_id":"1906.00852","repositories_listed":0,"syntology":null},{"url":null,"slug":"190600917","title":"Learning Interpretable Shapelets for Time Series Classification through Adversarial Regularization","date":"2019-06-03","arxiv_id":"1906.00917","repositories_listed":0,"syntology":null},{"url":null,"slug":"190600939","title":"Cellular Traffic Prediction and Classification: a comparative evaluation of LSTM and ARIMA","date":"2019-06-03","arxiv_id":"1906.00939","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-curated-image-parameter-dataset-from-solar","title":"A Curated Image Parameter Dataset from Solar Dynamics Observatory Mission","date":"2019-06-03","arxiv_id":"1906.01062","repositories_listed":0,"syntology":null},{"url":null,"slug":"an-introduction-to-a-new-text-classification","title":"An Introduction to a New Text Classification and Visualization for Natural Language Processing Using Topological Data Analysis","date":"2019-06-03","arxiv_id":"1906.01726","repositories_listed":0,"syntology":null},{"url":null,"slug":"terminal-brain-damage-exposing-the-graceless","title":"Terminal Brain Damage: Exposing the Graceless Degradation in Deep Neural Networks Under Hardware Fault Attacks","date":"2019-06-03","arxiv_id":"1906.01017","repositories_listed":0,"syntology":null},{"url":null,"slug":"transfer-learning-in-the-field-of-renewable","title":"Transfer Learning in the Field of Renewable Energies -- A Transfer Learning Framework Providing Power Forecasts Throughout the Lifecycle of Wind Farms After Initial Connection to the Electrical Grid","date":"2019-06-03","arxiv_id":"1906.01168","repositories_listed":0,"syntology":null},{"url":null,"slug":"190600335","title":"Adversarial Examples for Edge Detection: They Exist, and They Transfer","date":"2019-06-02","arxiv_id":"1906.00335","repositories_listed":0,"syntology":null},{"url":"/paper/190600377","slug":"190600377","title":"Hierarchical Video Frame Sequence Representation with Deep Convolutional Graph Network","date":"2019-06-02","arxiv_id":"1906.00377","repositories_listed":0,"syntology":null},{"url":null,"slug":"190600460","title":"On The Radon--Nikodym Spectral Approach With Optimal Clustering","date":"2019-06-02","arxiv_id":"1906.00460","repositories_listed":0,"syntology":null},{"url":null,"slug":"statistically-significant-discriminative","title":"Statistically Significant Discriminative Patterns Searching","date":"2019-06-02","arxiv_id":"1906.01581","repositories_listed":0,"syntology":null},{"url":null,"slug":"190600128","title":"Achieving Fairness in Determining Medicaid Eligibility through Fairgroup Construction","date":"2019-06-01","arxiv_id":"1906.00128","repositories_listed":0,"syntology":null},{"url":null,"slug":"190600291","title":"Cooperative neural networks (CoNN): Exploiting prior independence structure for improved classification","date":"2019-06-01","arxiv_id":"1906.00291","repositories_listed":0,"syntology":null},{"url":null,"slug":"190600303","title":"Active Learning for Binary Classification with Abstention","date":"2019-06-01","arxiv_id":"1906.00303","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-semantic-cover-approach-for-topic-modeling","title":"A Semantic Cover Approach for Topic Modeling","date":"2019-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"a-soft-label-strategy-for-target-level","title":"A Soft Label Strategy for Target-Level Sentiment Classification","date":"2019-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"a-submodular-feature-aware-framework-for","title":"A Submodular Feature-Aware Framework for Label Subset Selection in Extreme Classification Problems","date":"2019-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"accurate-and-energy-efficient-classification","title":"Accurate and Energy-Efficient Classification with Spiking Random Neural Network: Corrected and Expanded Version","date":"2019-06-01","arxiv_id":"1906.08864","repositories_listed":0,"syntology":null},{"url":null,"slug":"adaptive-convolution-for-text-classification","title":"Adaptive Convolution for Text Classification","date":"2019-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"adversarial-category-alignment-network-for","title":"Adversarial Category Alignment Network for Cross-domain Sentiment Classification","date":"2019-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"amrita-school-of-engineering-cse-at-semeval","title":"Amrita School of Engineering - CSE at SemEval-2019 Task 6: Manipulating Attention with Temporal Convolutional Neural Network for Offense Identification and Classification","date":"2019-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"analyzing-the-use-of-existing-systems-for-the","title":"Analyzing the use of existing systems for the CLPsych 2019 Shared Task","date":"2019-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"are-fictional-voices-distinguishable","title":"Are Fictional Voices Distinguishable? Classifying Character Voices in Modern Drama","date":"2019-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"atalaya-at-semeval-2019-task-5-robust","title":"Atalaya at SemEval 2019 Task 5: Robust Embeddings for Tweet Classification","date":"2019-06-01","arxiv_id":null,"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":"bhanodaig-at-semeval-2019-task-6-categorizing","title":"bhanodaig at SemEval-2019 Task 6: Categorizing Offensive Language in social media","date":"2019-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"box-of-lies-multimodal-deception-detection-in","title":"Box of Lies: Multimodal Deception Detection in Dialogues","date":"2019-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"brainee-at-semeval-2019-task-3-ensembling","title":"BrainEE at SemEval-2019 Task 3: Ensembling Linear Classifiers for Emotion Prediction","date":"2019-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"building-detail-sensitive-semantic","title":"Building Detail-Sensitive Semantic Segmentation Networks With Polynomial Pooling","date":"2019-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"caire_hkust-at-semeval-2019-task-3","title":"CAiRE\\_HKUST at SemEval-2019 Task 3: Hierarchical Attention for Dialogue Emotion Classification","date":"2019-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"camsterdam-at-semeval-2019-task-6-neural-and","title":"CAMsterdam at SemEval-2019 Task 6: Neural and graph-based feature extraction for the identification of offensive tweets","date":"2019-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"classification-of-semantic-paraphasias","title":"Classification of Semantic Paraphasias: Optimization of a Word Embedding Model","date":"2019-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"clinical-data-classification-using","title":"Clinical Data Classification using Conditional Random Fields and Neural Parsing for Morphologically Rich Languages","date":"2019-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"coastal-at-semeval-2019-task-3-affect","title":"CoAStaL at SemEval-2019 Task 3: Affect Classification in Dialogue using Attentive BiLSTMs","date":"2019-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"collaborative-learning-of-semi-supervised","title":"Collaborative Learning of Semi-Supervised Segmentation and Classification for Medical Images","date":"2019-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"columbia-at-semeval-2019-task-7-multi-task","title":"Columbia at SemEval-2019 Task 7: Multi-task Learning for Stance Classification and Rumour Verification","date":"2019-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"columbianlp-at-semeval-2019-task-8-the-answer","title":"ColumbiaNLP at SemEval-2019 Task 8: The Answer is Language Model Fine-tuning","date":"2019-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"combining-discourse-markers-and-cross-lingual","title":"Combining Discourse Markers and Cross-lingual Embeddings for Synonym--Antonym Classification","date":"2019-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"compact-feature-learning-for-multi-domain","title":"Compact Feature Learning for Multi-Domain Image Classification","date":"2019-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"compressing-unknown-images-with-product","title":"Compressing Unknown Images With Product Quantizer for Efficient Zero-Shot Classification","date":"2019-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"cross-atlas-convolution-for-parameterization","title":"Cross-Atlas Convolution for Parameterization Invariant Learning on Textured Mesh Surface","date":"2019-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"da-ld-hildesheim-at-semeval-2019-task-6","title":"DA-LD-Hildesheim at SemEval-2019 Task 6: Tracking Offensive Content with Deep Learning using Shallow Representation","date":"2019-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"dataset-mention-extraction-and-classification","title":"Dataset Mention Extraction and Classification","date":"2019-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"development-and-deployment-of-a-large-scale","title":"Development and Deployment of a Large-Scale Dialog-based Intelligent Tutoring System","date":"2019-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"dick-preston-and-morbo-at-semeval-2019-task-4","title":"Dick-Preston and Morbo at SemEval-2019 Task 4: Transfer Learning for Hyperpartisan News Detection","date":"2019-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"doris-martin-at-semeval-2019-task-4","title":"Doris Martin at SemEval-2019 Task 4: Hyperpartisan News Detection with Generic Semi-supervised Features","date":"2019-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"ds-at-semeval-2019-task-9-from-suggestion","title":"DS at SemEval-2019 Task 9: From Suggestion Mining with neural networks to adversarial cross-domain classification","date":"2019-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"dteam-vardial-2019-ensemble-based-on-skip","title":"DTeam @ VarDial 2019: Ensemble based on skip-gram and triplet loss neural networks for Moldavian vs. Romanian cross-dialect topic identification","date":"2019-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"duth-at-semeval-2019-task-8-part-of-speech","title":"DUTH at SemEval-2019 Task 8: Part-Of-Speech Features for Question Classification","date":"2019-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"embedding-complementary-deep-networks-for","title":"Embedding Complementary Deep Networks for Image Classification","date":"2019-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"enhancing-triplegan-for-semi-supervised","title":"Enhancing TripleGAN for Semi-Supervised Conditional Instance Synthesis and Classification","date":"2019-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"eventai-at-semeval-2019-task-7-rumor","title":"eventAI at SemEval-2019 Task 7: Rumor Detection on Social Media by Exploiting Content, User Credibility and Propagation Information","date":"2019-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"exploiting-edge-features-for-graph-neural","title":"Exploiting Edge Features for Graph Neural Networks","date":"2019-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"exploiting-noisy-data-in-distant-supervision","title":"Exploiting Noisy Data in Distant Supervision Relation Classification","date":"2019-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"exploration-of-noise-strategies-in-semi","title":"Exploration of Noise Strategies in Semi-supervised Named Entity Classification","date":"2019-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"fermi-at-semeval-2019-task-4-the-sarah-jane","title":"Fermi at SemEval-2019 Task 4: The sarah-jane-smith Hyperpartisan News Detector","date":"2019-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"graph-convolutional-networks-for-exploring","title":"Graph convolutional networks for exploring authorship hypotheses","date":"2019-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"gwu-nlp-at-semeval-2019-task-7-hybrid","title":"GWU NLP at SemEval-2019 Task 7: Hybrid Pipeline for Rumour Veracity and Stance Classification on Social Media","date":"2019-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"hateminer-at-semeval-2019-task-5-hate-speech","title":"HATEMINER at SemEval-2019 Task 5: Hate speech detection against Immigrants and Women in Twitter using a Multinomial Naive Bayes Classifier","date":"2019-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":"/paper/hltsuda-at-semeval-2019-task-1-ucca-graph-1","slug":"hltsuda-at-semeval-2019-task-1-ucca-graph-1","title":"HLT@SUDA at SemEval-2019 Task 1: UCCA Graph Parsing as Constituent Tree Parsing","date":"2019-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"identifying-therapist-conversational-actions","title":"Identifying therapist conversational actions across diverse psychotherapeutic approaches","date":"2019-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"imagettr-grounding-type-theory-with-records","title":"ImageTTR: Grounding Type Theory with Records in Image Classification for Visual Question Answering","date":"2019-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"improving-human-needs-categorization-of","title":"Improving Human Needs Categorization of Events with Semantic Classification","date":"2019-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"incorporating-emoji-descriptions-improves","title":"Incorporating Emoji Descriptions Improves Tweet Classification","date":"2019-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"ingeotec-at-semeval-2019-task-5-and-task-6-a","title":"INGEOTEC at SemEval-2019 Task 5 and Task 6: A Genetic Programming Approach for Text Classification","date":"2019-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"integration-of-knowledge-graph-embedding-into","title":"Integration of Knowledge Graph Embedding Into Topic Modeling with Hierarchical Dirichlet Process","date":"2019-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"iterative-alignment-network-for-continuous","title":"Iterative Alignment Network for Continuous Sign Language Recognition","date":"2019-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"jctdhs-at-semeval-2019-task-5-detection-of","title":"JCTDHS at SemEval-2019 Task 5: Detection of Hate Speech in Tweets using Deep Learning Methods, Character N-gram Features, and Preprocessing Methods","date":"2019-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"joint-multiple-intent-detection-and-slot","title":"Joint Multiple Intent Detection and Slot Labeling for Goal-Oriented Dialog","date":"2019-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"lirmm-advanse-at-semeval-2019-task-3","title":"LIRMM-Advanse at SemEval-2019 Task 3: Attentive Conversation Modeling for Emotion Detection and Classification","date":"2019-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"local-to-global-learning-gradually-adding","title":"Local to Global Learning: Gradually Adding Classes for Training Deep Neural Networks","date":"2019-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"lt3-at-semeval-2019-task-5-multilingual","title":"LT3 at SemEval-2019 Task 5: Multilingual Detection of Hate Speech Against Immigrants and Women in Twitter (hatEval)","date":"2019-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"ltl-ude-at-semeval-2019-task-6-bert-and-two","title":"LTL-UDE at SemEval-2019 Task 6: BERT and Two-Vote Classification for Categorizing Offensiveness","date":"2019-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"maxpoolnms-getting-rid-of-nms-bottlenecks-in","title":"MaxpoolNMS: Getting Rid of NMS Bottlenecks in Two-Stage Object Detectors","date":"2019-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"midas-at-semeval-2019-task-9-suggestion","title":"MIDAS at SemEval-2019 Task 9: Suggestion Mining from Online Reviews using ULMFit","date":"2019-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null}],"record_sha256":"25b9e19666971bfb21de03cd4bee1f5e5c111990ab21acbc4167dae9af735496","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}