{"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/feature-engineering/papers/15","list_of":"/task/feature-engineering","task":"Feature Engineering","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":15,"pages_in_order":18,"rows_per_page":100,"rows":[1401,1500],"of":1706,"counts":{"archive_papers_tagged":1706,"with_a_code_link":472,"where_syntology_ran_a_sample":60,"not_listed_spam_title":0,"listed":1706,"listed_where_code_ran":60,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":51,"every_run_a_failure_of_syntologys_instrument":9,"listed_with_a_run_with_no_instrument_failure":51,"listed_every_run_a_failure_of_syntologys_instrument":9,"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/feature-engineering","prev":"/task/feature-engineering/papers/14","next":"/task/feature-engineering/papers/16","papers":[{"url":null,"slug":"chinese-zero-pronoun-resolution-with-deep","title":"Chinese Zero Pronoun Resolution with Deep Memory Network","date":"2017-09-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"content-selection-for-real-time-sports-news","title":"Content Selection for Real-time Sports News Construction from Commentary Texts","date":"2017-09-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":"/paper/deep-neural-solver-for-math-word-problems","slug":"deep-neural-solver-for-math-word-problems","title":"Deep Neural Solver for Math Word Problems","date":"2017-09-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"do-lstms-really-work-so-well-for-pos-tagging","title":"Do LSTMs really work so well for PoS tagging? -- A replication study","date":"2017-09-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"dont-throw-those-morphological-analyzers-away","title":"Don't Throw Those Morphological Analyzers Away Just Yet: Neural Morphological Disambiguation for Arabic","date":"2017-09-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"fast-and-accurate-decision-trees-for-natural","title":"Fast and Accurate Decision Trees for Natural Language Processing Tasks","date":"2017-09-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"feature-enriched-character-level-convolutions","title":"Feature-Enriched Character-Level Convolutions for Text Regression","date":"2017-09-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":"/paper/getting-the-most-out-of-amr-parsing","slug":"getting-the-most-out-of-amr-parsing","title":"Getting the Most out of AMR Parsing","date":"2017-09-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-contextually-informed","title":"Learning Contextually Informed Representations for Linear-Time Discourse Parsing","date":"2017-09-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"syntax-aware-lstm-model-for-semantic-role","title":"Syntax Aware LSTM model for Semantic Role Labeling","date":"2017-09-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"transparent-text-quality-assessment-with","title":"Transparent text quality assessment with convolutional neural networks","date":"2017-09-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"unity-in-diversity-a-unified-parsing-strategy","title":"Unity in Diversity: A Unified Parsing Strategy for Major Indian Languages","date":"2017-09-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"an-empirical-study-of-discriminative-sequence","title":"An Empirical Study of Discriminative Sequence Labeling Models for Vietnamese Text Processing","date":"2017-08-30","arxiv_id":"1708.09163","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-style-match-for-complementary","title":"Deep Style Match for Complementary Recommendation","date":"2017-08-26","arxiv_id":"1708.07938","repositories_listed":0,"syntology":null},{"url":null,"slug":"sales-forecast-in-e-commerce-using","title":"Sales Forecast in E-commerce using Convolutional Neural Network","date":"2017-08-26","arxiv_id":"1708.07946","repositories_listed":0,"syntology":null},{"url":null,"slug":"energy-based-models-for-video-anomaly","title":"Energy-based Models for Video Anomaly Detection","date":"2017-08-17","arxiv_id":"1708.05211","repositories_listed":0,"syntology":null},{"url":null,"slug":"determining-whether-the-non-protein-coding","title":"Determining whether the non-protein-coding DNA sequences are in a complex interactive relationship by using an artificial intelligence method","date":"2017-08-14","arxiv_id":"1708.04019","repositories_listed":0,"syntology":null},{"url":null,"slug":"argument-labeling-of-explicit-discourse","title":"Argument Labeling of Explicit Discourse Relations using LSTM Neural Networks","date":"2017-08-11","arxiv_id":"1708.03425","repositories_listed":0,"syntology":null},{"url":null,"slug":"unified-neural-architecture-for-drug-disease","title":"Unified Neural Architecture for Drug, Disease and Clinical Entity Recognition","date":"2017-08-11","arxiv_id":"1708.03447","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-non-dnn-feature-engineering-approach-to","title":"A non-DNN Feature Engineering Approach to Dependency Parsing -- FBAML at CoNLL 2017 Shared Task","date":"2017-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"attention-based-recurrent-convolutional","title":"Attention-based Recurrent Convolutional Neural Network for Automatic Essay Scoring","date":"2017-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"automatic-diagnosis-coding-of-radiology","title":"Automatic Diagnosis Coding of Radiology Reports: A Comparison of Deep Learning and Conventional Classification Methods","date":"2017-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"bucc-2017-shared-task-a-first-attempt-toward","title":"BUCC 2017 Shared Task: a First Attempt Toward a Deep Learning Framework for Identifying Parallel Sentences in Comparable Corpora","date":"2017-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"classifying-semantic-clause-types-modeling","title":"Classifying Semantic Clause Types: Modeling Context and Genre Characteristics with Recurrent Neural Networks and Attention","date":"2017-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"clcl-geneva-dinn-parser-a-neural-network","title":"CLCL (Geneva) DINN Parser: a Neural Network Dependency Parser Ten Years Later","date":"2017-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"clinical-event-detection-with-hybrid-neural","title":"Clinical Event Detection with Hybrid Neural Architecture","date":"2017-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"datastories-at-semeval-2017-task-6-siamese","title":"DataStories at SemEval-2017 Task 6: Siamese LSTM with Attention for Humorous Text Comparison","date":"2017-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"duth-at-semeval-2017-task-5-sentiment","title":"DUTH at SemEval-2017 Task 5: Sentiment Predictability in Financial Microblogging and News Articles","date":"2017-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"ecnu-at-semeval-2017-task-4-evaluating","title":"ECNU at SemEval-2017 Task 4: Evaluating Effective Features on Machine Learning Methods for Twitter Message Polarity Classification","date":"2017-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"ecnu-at-semeval-2017-task-5-an-ensemble-of","title":"ECNU at SemEval-2017 Task 5: An Ensemble of Regression Algorithms with Effective Features for Fine-Grained Sentiment Analysis in Financial Domain","date":"2017-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"eica-at-semeval-2017-task-4-a-simple","title":"EICA at SemEval-2017 Task 4: A Simple Convolutional Neural Network for Topic-based Sentiment Classification","date":"2017-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"eica-team-at-semeval-2017-task-3-semantic-and","title":"EICA Team at SemEval-2017 Task 3: Semantic and Metadata-based Features for Community Question Answering","date":"2017-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"extracting-drug-drug-interactions-with","title":"Extracting Drug-Drug Interactions with Attention CNNs","date":"2017-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"fermi-at-semeval-2017-task-7-detection-and","title":"Fermi at SemEval-2017 Task 7: Detection and Interpretation of Homographic puns in English Language","date":"2017-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"itnlp-aikf-at-semeval-2017-task-1-rich","title":"ITNLP-AiKF at SemEval-2017 Task 1: Rich Features Based SVR for Semantic Textual Similarity Computing","date":"2017-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-contextual-embeddings-for-structural","title":"Learning Contextual Embeddings for Structural Semantic Similarity using Categorical Information","date":"2017-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-local-and-global-contexts-using-a","title":"Learning local and global contexts using a convolutional recurrent network model for relation classification in biomedical text","date":"2017-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"nnembs-at-semeval-2017-task-4-neural-twitter","title":"NNEMBs at SemEval-2017 Task 4: Neural Twitter Sentiment Classification: a Simple Ensemble Method with Different Embeddings","date":"2017-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"pku_icl-at-semeval-2017-task-10-keyphrase","title":"PKU\\_ICL at SemEval-2017 Task 10: Keyphrase Extraction with Model Ensemble and External Knowledge","date":"2017-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"semantic-frame-labeling-with-target-based","title":"Semantic Frame Labeling with Target-based Neural Model","date":"2017-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"takelab-at-semeval-2017-task-5-linear","title":"TakeLab at SemEval-2017 Task 5: Linear aggregation of word embeddings for fine-grained sentiment analysis of financial news","date":"2017-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-a-general-continuous-model-of-turn","title":"Towards a General, Continuous Model of Turn-taking in Spoken Dialogue using LSTM Recurrent Neural Networks","date":"2017-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"umdeep-at-semeval-2017-task-1-end-to-end","title":"UMDeep at SemEval-2017 Task 1: End-to-End Shared Weight LSTM Model for Semantic Textual Similarity","date":"2017-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"cross-lingual-induction-and-transfer-of-verb","title":"Cross-Lingual Induction and Transfer of Verb Classes Based on Word Vector Space Specialisation","date":"2017-07-21","arxiv_id":"1707.06945","repositories_listed":0,"syntology":null},{"url":null,"slug":"generalized-convolutional-neural-networks-for","title":"Generalized Convolutional Neural Networks for Point Cloud Data","date":"2017-07-20","arxiv_id":"1707.06719","repositories_listed":0,"syntology":null},{"url":null,"slug":"automation-of-feature-engineering-for-iot","title":"Automation of Feature Engineering for IoT Analytics","date":"2017-07-13","arxiv_id":"1707.04067","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-generalised-seizure-prediction-with","title":"A Generalised Seizure Prediction with Convolutional Neural Networks for Intracranial and Scalp Electroencephalogram Data Analysis","date":"2017-07-06","arxiv_id":"1707.01976","repositories_listed":0,"syntology":null},{"url":null,"slug":"dag-based-long-short-term-memory-for-neural","title":"DAG-based Long Short-Term Memory for Neural Word Segmentation","date":"2017-07-02","arxiv_id":"1707.00248","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-local-detection-approach-for-named-entity","title":"A Local Detection Approach for Named Entity Recognition and Mention Detection","date":"2017-07-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-learning-in-semantic-kernel-spaces","title":"Deep Learning in Semantic Kernel Spaces","date":"2017-07-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":"/paper/dynamic-facial-analysis-from-bayesian","slug":"dynamic-facial-analysis-from-bayesian","title":"Dynamic Facial Analysis: From Bayesian Filtering to Recurrent Neural Network","date":"2017-07-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"evinets-neural-networks-for-combining","title":"EviNets: Neural Networks for Combining Evidence Signals for Factoid Question Answering","date":"2017-07-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"predicting-depression-for-japanese-blog-text","title":"Predicting Depression for Japanese Blog Text","date":"2017-07-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"varying-linguistic-purposes-of-emoji-in","title":"Varying Linguistic Purposes of Emoji in (Twitter) Context","date":"2017-07-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"autosvd-an-efficient-hybrid-collaborative","title":"AutoSVD++: An Efficient Hybrid Collaborative Filtering Model via Contractive Auto-encoders","date":"2017-06-13","arxiv_id":"1704.00551","repositories_listed":0,"syntology":null},{"url":null,"slug":"recognizing-irregular-entities-in-biomedical","title":"Recognizing irregular entities in biomedical text via deep neural networks","date":"2017-06-13","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"random-forests-decision-trees-and-categorical","title":"Random Forests, Decision Trees, and Categorical Predictors: The \"Absent Levels\" Problem","date":"2017-06-12","arxiv_id":"1706.03492","repositories_listed":0,"syntology":null},{"url":null,"slug":"one-button-machine-for-automating-feature","title":"One button machine for automating feature engineering in relational databases","date":"2017-06-01","arxiv_id":"1706.00327","repositories_listed":0,"syntology":null},{"url":null,"slug":"sequential-dynamic-decision-making-with-deep","title":"Sequential Dynamic Decision Making with Deep Neural Nets on a Test-Time Budget","date":"2017-05-31","arxiv_id":"1705.10924","repositories_listed":0,"syntology":null},{"url":null,"slug":"fine-grained-acceleration-control-for","title":"Fine-grained acceleration control for autonomous intersection management using deep reinforcement learning","date":"2017-05-30","arxiv_id":"1705.10432","repositories_listed":0,"syntology":null},{"url":null,"slug":"robust-tracking-using-region-proposal","title":"Robust Tracking Using Region Proposal Networks","date":"2017-05-30","arxiv_id":"1705.10447","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-deep-multi-view-learning-framework-for-city","title":"A Deep Multi-View Learning Framework for City Event Extraction from Twitter Data Streams","date":"2017-05-28","arxiv_id":"1705.09975","repositories_listed":0,"syntology":null},{"url":null,"slug":"shallow-updates-for-deep-reinforcement","title":"Shallow Updates for Deep Reinforcement Learning","date":"2017-05-21","arxiv_id":"1705.07461","repositories_listed":0,"syntology":null},{"url":null,"slug":"spelling-correction-as-a-foreign-language","title":"Spelling Correction as a Foreign Language","date":"2017-05-21","arxiv_id":"1705.07371","repositories_listed":0,"syntology":null},{"url":null,"slug":"effective-representations-of-clinical-notes","title":"Effective Representations of Clinical Notes","date":"2017-05-19","arxiv_id":"1705.07025","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-a-bidirectional-mapping-between","title":"Learning a bidirectional mapping between human whole-body motion and natural language using deep recurrent neural networks","date":"2017-05-18","arxiv_id":"1705.06400","repositories_listed":0,"syntology":null},{"url":null,"slug":"practical-processing-of-mobile-sensor-data","title":"Practical Processing of Mobile Sensor Data for Continual Deep Learning Predictions","date":"2017-05-17","arxiv_id":"1705.06224","repositories_listed":0,"syntology":null},{"url":null,"slug":"r2-d2-color-inspired-convolutional-neural","title":"R2-D2: ColoR-inspired Convolutional NeuRal Network (CNN)-based AndroiD Malware Detections","date":"2017-05-12","arxiv_id":"1705.04448","repositories_listed":0,"syntology":null},{"url":null,"slug":"drug-drug-interaction-extraction-via","title":"Drug-drug Interaction Extraction via Recurrent Neural Network with Multiple Attention Layers","date":"2017-05-09","arxiv_id":"1705.03261","repositories_listed":0,"syntology":null},{"url":null,"slug":"on-the-effectiveness-of-feature-set","title":"On the effectiveness of feature set augmentation using clusters of word embeddings","date":"2017-05-03","arxiv_id":"1705.01265","repositories_listed":0,"syntology":null},{"url":null,"slug":"optimizing-a-pos-tagset-for-norwegian","title":"Optimizing a PoS Tagset for Norwegian Dependency Parsing","date":"2017-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"leveraging-patient-similarity-and-time-series","title":"Leveraging Patient Similarity and Time Series Data in Healthcare Predictive Models","date":"2017-04-25","arxiv_id":"1704.07498","repositories_listed":0,"syntology":null},{"url":null,"slug":"an-entity-driven-recursive-neural-network","title":"An entity-driven recursive neural network model for chinese discourse coherence modeling","date":"2017-04-14","arxiv_id":"1704.04336","repositories_listed":0,"syntology":null},{"url":null,"slug":"fast-learning-and-prediction-for-object","title":"Fast Learning and Prediction for Object Detection using Whitened CNN Features","date":"2017-04-10","arxiv_id":"1704.02930","repositories_listed":0,"syntology":null},{"url":null,"slug":"syntax-aware-lstm-model-for-chinese-semantic","title":"Syntax Aware LSTM Model for Chinese Semantic Role Labeling","date":"2017-04-03","arxiv_id":"1704.00405","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-characterization-study-of-arabic-twitter","title":"A Characterization Study of Arabic Twitter Data with a Benchmarking for State-of-the-Art Opinion Mining Models","date":"2017-04-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"a-multi-task-approach-to-predict-likability","title":"A Multi-task Approach to Predict Likability of Books","date":"2017-04-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"arabic-pos-tagging-dont-abandon-feature","title":"Arabic POS Tagging: Don't Abandon Feature Engineering Just Yet","date":"2017-04-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"if-you-cant-beat-them-join-them-handcrafted","title":"If You Can't Beat Them Join Them: Handcrafted Features Complement Neural Nets for Non-Factoid Answer Reranking","date":"2017-04-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"large-scale-categorization-of-japanese","title":"Large-Scale Categorization of Japanese Product Titles Using Neural Attention Models","date":"2017-04-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"neural-networks-for-negation-cue-detection-in","title":"Neural Networks for Negation Cue Detection in Chinese","date":"2017-04-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"on-the-relevance-of-syntactic-and-discourse","title":"On the Relevance of Syntactic and Discourse Features for Author Profiling and Identification","date":"2017-04-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"the-atilf-llf-system-for-parseme-shared-task","title":"The ATILF-LLF System for Parseme Shared Task: a Transition-based Verbal Multiword Expression Tagger","date":"2017-04-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"supervised-typing-of-big-graphs-using","title":"Supervised Typing of Big Graphs using Semantic Embeddings","date":"2017-03-22","arxiv_id":"1703.07805","repositories_listed":0,"syntology":null},{"url":null,"slug":"one-shot-imitation-learning","title":"One-Shot Imitation Learning","date":"2017-03-21","arxiv_id":"1703.07326","repositories_listed":0,"syntology":null},{"url":null,"slug":"machine-learning-approach-for-early-detection","title":"Machine learning approach for early detection of autism by combining questionnaire and home video screening","date":"2017-03-15","arxiv_id":"1703.06076","repositories_listed":0,"syntology":null},{"url":null,"slug":"building-automated-vandalism-detection-tools","title":"Building automated vandalism detection tools for Wikidata","date":"2017-03-10","arxiv_id":"1703.03861","repositories_listed":0,"syntology":null},{"url":null,"slug":"application-of-multi-channel-3d-cube","title":"Application of Multi-channel 3D-cube Successive Convolution Network for Convective Storm Nowcasting","date":"2017-02-15","arxiv_id":"1702.04517","repositories_listed":0,"syntology":null},{"url":null,"slug":"small-boxes-big-data-a-deep-learning-approach","title":"Small Boxes Big Data: A Deep Learning Approach to Optimize Variable Sized Bin Packing","date":"2017-02-14","arxiv_id":"1702.04415","repositories_listed":0,"syntology":null},{"url":null,"slug":"multitask-learning-with-deep-neural-networks","title":"Multitask Learning with Deep Neural Networks for Community Question Answering","date":"2017-02-13","arxiv_id":"1702.03706","repositories_listed":0,"syntology":null},{"url":null,"slug":"graph-neural-networks-and-boolean","title":"Graph Neural Networks and Boolean Satisfiability","date":"2017-02-12","arxiv_id":"1702.03592","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-deep-convolutional-neural-network-for","title":"A Deep Convolutional Neural Network for Background Subtraction","date":"2017-02-06","arxiv_id":"1702.01731","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-recurrent-neural-network-for-protein","title":"Deep Recurrent Neural Network for Protein Function Prediction from Sequence","date":"2017-01-28","arxiv_id":"1701.08318","repositories_listed":0,"syntology":null},{"url":null,"slug":"exploration-of-proximity-heuristics-in-length","title":"Exploration of Proximity Heuristics in Length Normalization","date":"2017-01-05","arxiv_id":"1701.01417","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-feature-engineering-for","title":"Learning Feature Engineering for Classification","date":"2017-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"sparse-coding-of-neural-word-embeddings-for-1","title":"Sparse Coding of Neural Word Embeddings for Multilingual Sequence Labeling","date":"2017-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":"/paper/training-end-to-end-dialogue-systems-with-the","slug":"training-end-to-end-dialogue-systems-with-the","title":"Training End-to-End Dialogue Systems with the Ubuntu Dialogue Corpus","date":"2017-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"predicting-the-industry-of-users-on-social","title":"Predicting the Industry of Users on Social Media","date":"2016-12-24","arxiv_id":"1612.08205","repositories_listed":0,"syntology":null},{"url":null,"slug":"neural-symbolic-machines-learning-semantic","title":"Neural Symbolic Machines: Learning Semantic Parsers on Freebase with Weak Supervision (Short Version)","date":"2016-12-04","arxiv_id":"1612.01197","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-recurrent-and-compositional-model-for","title":"A Recurrent and Compositional Model for Personality Trait Recognition from Short Texts","date":"2016-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null}],"record_sha256":"2dbbb7668012ed7000c5ae98e5bea25f33c5503875a124c2e3e95bde8b8eebf2","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}