{"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":"/method/tanh-activation/papers/53","list_of":"/method/tanh-activation","method":"Tanh Activation","archive":{"snapshot":"2025-07-28"},"syntology_read_at":"2026-09-28T10:30:06+00:00","order":"archive","order_definition":"date (newest first), then slug","page":53,"pages_in_order":64,"rows_per_page":100,"rows":[5201,5300],"of":6333,"counts":{"archive_papers_tagged":6333,"with_a_code_link":2134,"where_syntology_ran_a_sample":386,"not_listed_spam_title":0,"listed":6333,"listed_where_code_ran":386,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":324,"every_run_a_failure_of_syntologys_instrument":62,"listed_with_a_run_with_no_instrument_failure":324,"listed_every_run_a_failure_of_syntologys_instrument":62,"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":"/method/tanh-activation","prev":"/method/tanh-activation/papers/52","next":"/method/tanh-activation/papers/54","papers":[{"paper":null,"slug":"lstm-hypertagging","title":"LSTM Hypertagging","date":"2018-11-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"macquarie-university-at-bioasq-6b-deep","title":"Macquarie University at BioASQ 6b: Deep learning and deep reinforcement learning for query-based summarisation","date":"2018-11-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"self-learning-architecture-for-natural","title":"Self-Learning Architecture for Natural Language Generation","date":"2018-11-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"towards-linear-time-neural-machine","title":"Towards Linear Time Neural Machine Translation with Capsule Networks","date":"2018-11-01","arxiv_id":"1811.00287","n_code_links":0,"syntology":null},{"paper":null,"slug":"cross-lingual-transfer-learning-for","title":"Cross-Lingual Transfer Learning for Multilingual Task Oriented Dialog","date":"2018-10-31","arxiv_id":"1810.13327","n_code_links":0,"syntology":null},{"paper":"/paper/advancing-pico-element-detection-in-medical","slug":"advancing-pico-element-detection-in-medical","title":"Advancing PICO Element Detection in Biomedical Text via Deep Neural Networks","date":"2018-10-30","arxiv_id":"1810.12780","n_code_links":1,"syntology":null},{"paper":"/paper/generative-adversarial-networks-for-unpaired","slug":"generative-adversarial-networks-for-unpaired","title":"Generative Adversarial Networks for Unpaired Voice Transformation on Impaired Speech","date":"2018-10-30","arxiv_id":"1810.12656","n_code_links":2,"syntology":null},{"paper":null,"slug":"long-short-term-attention","title":"Long Short-Term Attention","date":"2018-10-30","arxiv_id":"1810.12752","n_code_links":0,"syntology":null},{"paper":"/paper/subword-encoding-in-lattice-lstm-for-chinese","slug":"subword-encoding-in-lattice-lstm-for-chinese","title":"Subword Encoding in Lattice LSTM for Chinese Word Segmentation","date":"2018-10-30","arxiv_id":"1810.12594","n_code_links":1,"syntology":null},{"paper":null,"slug":"cascaded-cnn-resbilstm-ctc-an-end-to-end","title":"Cascaded CNN-resBiLSTM-CTC: An End-to-End Acoustic Model For Speech Recognition","date":"2018-10-29","arxiv_id":"1810.12001","n_code_links":0,"syntology":null},{"paper":null,"slug":"counting-in-language-with-rnns","title":"Counting in Language with RNNs","date":"2018-10-29","arxiv_id":"1810.12411","n_code_links":0,"syntology":null},{"paper":"/paper/investigation-of-enhanced-tacotron-text-to","slug":"investigation-of-enhanced-tacotron-text-to","title":"Investigation of enhanced Tacotron text-to-speech synthesis systems with self-attention for pitch accent language","date":"2018-10-29","arxiv_id":"1810.11960","n_code_links":1,"syntology":null},{"paper":null,"slug":"learning-comment-generation-by-leveraging","title":"Learning Comment Generation by Leveraging User-Generated Data","date":"2018-10-29","arxiv_id":"1810.12264","n_code_links":0,"syntology":null},{"paper":"/paper/lpcnet-improving-neural-speech-synthesis","slug":"lpcnet-improving-neural-speech-synthesis","title":"LPCNet: Improving Neural Speech Synthesis Through Linear Prediction","date":"2018-10-28","arxiv_id":"1810.11846","n_code_links":2,"syntology":null},{"paper":null,"slug":"can-entropy-explain-successor-surprisal","title":"Can Entropy Explain Successor Surprisal Effects in Reading?","date":"2018-10-26","arxiv_id":"1810.11481","n_code_links":0,"syntology":null},{"paper":null,"slug":"extractive-summarization-of-ehr-discharge","title":"Extractive Summarization of EHR Discharge Notes","date":"2018-10-26","arxiv_id":"1810.12085","n_code_links":0,"syntology":null},{"paper":"/paper/magnitude-a-fast-efficient-universal-vector","slug":"magnitude-a-fast-efficient-universal-vector","title":"Magnitude: A Fast, Efficient Universal Vector Embedding Utility Package","date":"2018-10-26","arxiv_id":"1810.11190","n_code_links":1,"syntology":null},{"paper":null,"slug":"tackling-sequence-to-sequence-mapping","title":"Tackling Sequence to Sequence Mapping Problems with Neural Networks","date":"2018-10-25","arxiv_id":"1810.10802","n_code_links":0,"syntology":null},{"paper":null,"slug":"deep-learning-with-long-short-term-memory-for","title":"Deep Learning with Long Short-Term Memory for Time Series Prediction","date":"2018-10-24","arxiv_id":"1810.10161","n_code_links":0,"syntology":null},{"paper":"/paper/forecasting-individualized-disease","slug":"forecasting-individualized-disease","title":"Forecasting Individualized Disease Trajectories using Interpretable Deep Learning","date":"2018-10-24","arxiv_id":"1810.10489","n_code_links":0,"syntology":null},{"paper":null,"slug":"learn-to-code-switch-data-augmentation-using","title":"Learn to Code-Switch: Data Augmentation using Copy Mechanism on Language Modeling","date":"2018-10-24","arxiv_id":"1810.10254","n_code_links":0,"syntology":null},{"paper":null,"slug":"multistep-speed-prediction-on-traffic","title":"Multistep Speed Prediction on Traffic Networks: A Graph Convolutional Sequence-to-Sequence Learning Approach with Attention Mechanism","date":"2018-10-24","arxiv_id":"1810.10237","n_code_links":0,"syntology":null},{"paper":null,"slug":"precipitation-nowcasting-leveraging","title":"Precipitation Nowcasting: Leveraging bidirectional LSTM and 1D CNN","date":"2018-10-24","arxiv_id":"1810.10485","n_code_links":0,"syntology":null},{"paper":null,"slug":"aint-nobody-got-time-for-coding-structure","title":"Ain't Nobody Got Time For Coding: Structure-Aware Program Synthesis From Natural Language","date":"2018-10-23","arxiv_id":"1810.09717","n_code_links":0,"syntology":null},{"paper":"/paper/interpretable-lstms-for-whole-brain","slug":"interpretable-lstms-for-whole-brain","title":"Analyzing Neuroimaging Data Through Recurrent Deep Learning Models","date":"2018-10-23","arxiv_id":"1810.09945","n_code_links":1,"syntology":null},{"paper":null,"slug":"using-deep-learning-for-price-prediction-by","title":"Using Deep Learning for price prediction by exploiting stationary limit order book features","date":"2018-10-23","arxiv_id":"1810.09965","n_code_links":0,"syntology":null},{"paper":"/paper/ordered-neurons-integrating-tree-structures","slug":"ordered-neurons-integrating-tree-structures","title":"Ordered Neurons: Integrating Tree Structures into Recurrent Neural Networks","date":"2018-10-22","arxiv_id":"1810.09536","n_code_links":7,"syntology":{"ran":11,"of":12,"n_ran_checked":9,"n_instrument":2,"unverified":1,"pointer_only":2,"phrase":"11 ran (of which 0 constructed an object rather than computing a result; 9 with no instrument failure: 1 honoured, 0 violated, 8 with no contract checked; 2 where Syntology's instrument failed) · 1 unverified","official":{"repos":["yikangshen/Ordered-Neurons"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed","official"]}}},{"paper":"/paper/viena2-a-driving-anticipation-dataset","slug":"viena2-a-driving-anticipation-dataset","title":"VIENA2: A Driving Anticipation Dataset","date":"2018-10-22","arxiv_id":"1810.09044","n_code_links":0,"syntology":null},{"paper":null,"slug":"sleep-arousal-detection-from-polysomnography","title":"Sleep Arousal Detection from Polysomnography using the Scattering Transform and Recurrent Neural Networks","date":"2018-10-21","arxiv_id":"1810.08875","n_code_links":0,"syntology":null},{"paper":"/paper/discourse-embellishment-using-a-deep-encoder","slug":"discourse-embellishment-using-a-deep-encoder","title":"Discourse Embellishment Using a Deep Encoder-Decoder Network","date":"2018-10-18","arxiv_id":"1810.08076","n_code_links":1,"syntology":null},{"paper":"/paper/genetic-algorithm-optimized-long-short-term","slug":"genetic-algorithm-optimized-long-short-term","title":"Genetic Algorithm-Optimized Long Short-Term Memory Network for Stock Market Prediction","date":"2018-10-18","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"sequence-to-sequence-mixture-model-for","title":"Sequence to Sequence Mixture Model for Diverse Machine Translation","date":"2018-10-17","arxiv_id":"1810.07391","n_code_links":0,"syntology":null},{"paper":null,"slug":"xjtluindoorloc-a-new-fingerprinting-database","title":"XJTLUIndoorLoc: A New Fingerprinting Database for Indoor Localization and Trajectory Estimation Based on Wi-Fi RSS and Geomagnetic Field","date":"2018-10-17","arxiv_id":"1810.07377","n_code_links":0,"syntology":null},{"paper":null,"slug":"from-deep-to-physics-informed-learning-of","title":"From Deep to Physics-Informed Learning of Turbulence: Diagnostics","date":"2018-10-16","arxiv_id":"1810.07785","n_code_links":0,"syntology":null},{"paper":"/paper/reduced-gate-convolutional-lstm-using","slug":"reduced-gate-convolutional-lstm-using","title":"Reduced-Gate Convolutional LSTM Using Predictive Coding for Spatiotemporal Prediction","date":"2018-10-16","arxiv_id":"1810.07251","n_code_links":1,"syntology":null},{"paper":"/paper/refacing-reconstructing-anonymized-facial","slug":"refacing-reconstructing-anonymized-facial","title":"Refacing: reconstructing anonymized facial features using GANs","date":"2018-10-15","arxiv_id":"1810.06455","n_code_links":1,"syntology":null},{"paper":null,"slug":"virtualization-of-tissue-staining-in-digital","title":"Virtualization of tissue staining in digital pathology using an unsupervised deep learning approach","date":"2018-10-15","arxiv_id":"1810.06415","n_code_links":0,"syntology":null},{"paper":null,"slug":"robust-neural-abstractive-summarization","title":"Robust Neural Abstractive Summarization Systems and Evaluation against Adversarial Information","date":"2018-10-14","arxiv_id":"1810.06065","n_code_links":0,"syntology":null},{"paper":null,"slug":"improving-generalization-of-sequence-encoder","title":"Improving Generalization of Sequence Encoder-Decoder Networks for Inverse Imaging of Cardiac Transmembrane Potential","date":"2018-10-12","arxiv_id":"1810.05713","n_code_links":0,"syntology":null},{"paper":null,"slug":"sequential-learning-of-movement-prediction-in","title":"Sequential Learning of Movement Prediction in Dynamic Environments using LSTM Autoencoder","date":"2018-10-12","arxiv_id":"1810.05394","n_code_links":0,"syntology":null},{"paper":null,"slug":"persistence-pays-off-paying-attention-to-what","title":"Persistence pays off: Paying Attention to What the LSTM Gating Mechanism Persists","date":"2018-10-10","arxiv_id":"1810.04437","n_code_links":0,"syntology":null},{"paper":"/paper/nsga-net-a-multi-objective-genetic-algorithm","slug":"nsga-net-a-multi-objective-genetic-algorithm","title":"NSGA-Net: Neural Architecture Search using Multi-Objective Genetic Algorithm","date":"2018-10-08","arxiv_id":"1810.03522","n_code_links":2,"syntology":null},{"paper":"/paper/h-detach-modifying-the-lstm-gradient-towards","slug":"h-detach-modifying-the-lstm-gradient-towards","title":"h-detach: Modifying the LSTM Gradient Towards Better Optimization","date":"2018-10-06","arxiv_id":"1810.03023","n_code_links":1,"syntology":null},{"paper":"/paper/generating-diffusion-mri-scalar-maps-from-t1","slug":"generating-diffusion-mri-scalar-maps-from-t1","title":"Generating Diffusion MRI scalar maps from T1 weighted images using generative adversarial networks","date":"2018-10-05","arxiv_id":"1810.02683","n_code_links":1,"syntology":null},{"paper":null,"slug":"sentence-segmentation-for-classical-chinese","title":"Sentence Segmentation for Classical Chinese Based on LSTM with Radical Embedding","date":"2018-10-05","arxiv_id":"1810.03479","n_code_links":0,"syntology":null},{"paper":null,"slug":"multilingual-sequence-to-sequence-speech","title":"Multilingual sequence-to-sequence speech recognition: architecture, transfer learning, and language modeling","date":"2018-10-04","arxiv_id":"1810.03459","n_code_links":0,"syntology":null},{"paper":"/paper/recurrent-transition-networks-for-character","slug":"recurrent-transition-networks-for-character","title":"Recurrent Transition Networks for Character Locomotion","date":"2018-10-04","arxiv_id":"1810.02363","n_code_links":2,"syntology":null},{"paper":null,"slug":"a-unified-neural-network-model-for","title":"A Unified Neural Network Model for Geolocating Twitter Users","date":"2018-10-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"antnlp-at-conll-2018-shared-task-a-graph","title":"AntNLP at CoNLL 2018 Shared Task: A Graph-Based Parser for Universal Dependency Parsing","date":"2018-10-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"automatic-identification-of-drugs-and-adverse","title":"Automatic Identification of Drugs and Adverse Drug Reaction Related Tweets","date":"2018-10-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":"/paper/classification-of-medication-related-tweets","slug":"classification-of-medication-related-tweets","title":"Classification of Medication-Related Tweets Using Stacked Bidirectional LSTMs with Context-Aware Attention","date":"2018-10-01","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":null,"slug":"de-identifying-free-text-of-japanese-dummy","title":"De-identifying Free Text of Japanese Dummy Electronic Health Records","date":"2018-10-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"deep-attentive-sentence-ordering-network","title":"Deep Attentive Sentence Ordering Network","date":"2018-10-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"deep-learning-for-social-media-health-text","title":"Deep Learning for Social Media Health Text Classification","date":"2018-10-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":"/paper/did-you-offend-me-classification-of-offensive","slug":"did-you-offend-me-classification-of-offensive","title":"Did you offend me? Classification of Offensive Tweets in Hinglish Language","date":"2018-10-01","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":null,"slug":"disney-at-iest-2018-predicting-emotions-using","title":"Disney at IEST 2018: Predicting Emotions using an Ensemble","date":"2018-10-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"elmolex-connecting-elmo-and-lexicon-features","title":"ELMoLex: Connecting ELMo and Lexicon Features for Dependency Parsing","date":"2018-10-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"humir-at-iest-2018-lexicon-sensitive-and-left","title":"HUMIR at IEST-2018: Lexicon-Sensitive and Left-Right Context-Sensitive BiLSTM for Implicit Emotion Recognition","date":"2018-10-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"in-domain-context-aware-token-embeddings","title":"In-domain Context-aware Token Embeddings Improve Biomedical Named Entity Recognition","date":"2018-10-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"incorporating-background-knowledge-into-video","title":"Incorporating Background Knowledge into Video Description Generation","date":"2018-10-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"interpretable-spatio-temporal-attention-for","title":"Interpretable Spatio-temporal Attention for Video Action Recognition","date":"2018-10-01","arxiv_id":"1810.04511","n_code_links":0,"syntology":null},{"paper":"/paper/large-scale-exploration-of-neural-relation","slug":"large-scale-exploration-of-neural-relation","title":"Large-scale Exploration of Neural Relation Classification Architectures","date":"2018-10-01","arxiv_id":null,"n_code_links":2,"syntology":null},{"paper":"/paper/learning-text-representations-for-500k","slug":"learning-text-representations-for-500k","title":"Learning Text Representations for 500K Classification Tasks on Named Entity Disambiguation","date":"2018-10-01","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":null,"slug":"leveraging-writing-systems-change-for-deep","title":"Leveraging Writing Systems Change for Deep Learning Based Chinese Emotion Analysis","date":"2018-10-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"n-ary-relation-extraction-using-graph-state-1","title":"N-ary Relation Extraction using Graph-State LSTM","date":"2018-10-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":"/paper/neural-maximum-subgraph-parsing-for-cross","slug":"neural-maximum-subgraph-parsing-for-cross","title":"Neural Maximum Subgraph Parsing for Cross-Domain Semantic Dependency Analysis","date":"2018-10-01","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":null,"slug":"neural-transition-based-parsing-of-web","title":"Neural Transition Based Parsing of Web Queries: An Entity Based Approach","date":"2018-10-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":"/paper/nl-fiit-at-iest-2018-emotion-recognition","slug":"nl-fiit-at-iest-2018-emotion-recognition","title":"NL-FIIT at IEST-2018: Emotion Recognition utilizing Neural Networks and Multi-level Preprocessing","date":"2018-10-01","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":null,"slug":"nlp-at-iest-2018-bilstm-attention-and-lstm","title":"NLP at IEST 2018: BiLSTM-Attention and LSTM-Attention via Soft Voting in Emotion Classification","date":"2018-10-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":"/paper/paragraph-level-neural-question-generation","slug":"paragraph-level-neural-question-generation","title":"Paragraph-level Neural Question Generation with Maxout Pointer and Gated Self-attention Networks","date":"2018-10-01","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":null,"slug":"possessors-change-over-time-a-case-study-with","title":"Possessors Change Over Time: A Case Study with Artworks","date":"2018-10-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":"/paper/sex-bist-a-multi-source-trainable-parser-with","slug":"sex-bist-a-multi-source-trainable-parser-with","title":"SEx BiST: A Multi-Source Trainable Parser with Deep Contextualized Lexical Representations","date":"2018-10-01","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":null,"slug":"shot-or-not-comparison-of-nlp-approaches-for","title":"Shot Or Not: Comparison of NLP Approaches for Vaccination Behaviour Detection","date":"2018-10-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"somm-into-the-model","title":"Somm: Into the Model","date":"2018-10-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"spot-the-odd-man-out-exploring-the","title":"Spot the Odd Man Out: Exploring the Associative Power of Lexical Resources","date":"2018-10-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"translating-a-math-word-problem-to-a","title":"Translating a Math Word Problem to a Expression Tree","date":"2018-10-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":"/paper/tree-stack-lstm-in-transition-based","slug":"tree-stack-lstm-in-transition-based","title":"Tree-Stack LSTM in Transition Based Dependency Parsing","date":"2018-10-01","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":null,"slug":"using-multi-task-and-transfer-learning-to","title":"Using Multi-task and Transfer Learning to Solve Working Memory Tasks","date":"2018-09-28","arxiv_id":"1809.10847","n_code_links":0,"syntology":null},{"paper":"/paper/an-empirical-comparison-of-syllabuses-for","slug":"an-empirical-comparison-of-syllabuses-for","title":"An Empirical Comparison of Syllabuses for Curriculum Learning","date":"2018-09-27","arxiv_id":"1809.10789","n_code_links":1,"syntology":null},{"paper":"/paper/batch-normalized-recurrent-highway-networks","slug":"batch-normalized-recurrent-highway-networks","title":"Batch-normalized Recurrent Highway Networks","date":"2018-09-26","arxiv_id":"1809.10271","n_code_links":1,"syntology":null},{"paper":"/paper/deep-contextualized-word-representations-for","slug":"deep-contextualized-word-representations-for","title":"Deep contextualized word representations for detecting sarcasm and irony","date":"2018-09-26","arxiv_id":"1809.09795","n_code_links":1,"syntology":null},{"paper":null,"slug":"language-modeling-teaches-you-more-syntax","title":"Language Modeling Teaches You More Syntax than Translation Does: Lessons Learned Through Auxiliary Task Analysis","date":"2018-09-26","arxiv_id":"1809.10040","n_code_links":0,"syntology":null},{"paper":null,"slug":"supervised-neural-models-revitalize-the-open","title":"Chinese User Service Intention Classification Based on Hybrid Neural Network","date":"2018-09-25","arxiv_id":"1809.09408","n_code_links":0,"syntology":null},{"paper":null,"slug":"information-weighted-neural-cache-language","title":"Information-Weighted Neural Cache Language Models for ASR","date":"2018-09-24","arxiv_id":"1809.08826","n_code_links":0,"syntology":null},{"paper":null,"slug":"neural-transductive-learning-and-beyond","title":"Neural Transductive Learning and Beyond: Morphological Generation in the Minimal-Resource Setting","date":"2018-09-24","arxiv_id":"1809.08733","n_code_links":0,"syntology":null},{"paper":null,"slug":"mind-your-language-abuse-and-offense","title":"Mind Your Language: Abuse and Offense Detection for Code-Switched Languages","date":"2018-09-23","arxiv_id":"1809.08652","n_code_links":0,"syntology":null},{"paper":"/paper/semi-supervised-sequence-modeling-with-cross","slug":"semi-supervised-sequence-modeling-with-cross","title":"Semi-Supervised Sequence Modeling with Cross-View Training","date":"2018-09-22","arxiv_id":"1809.08370","n_code_links":2,"syntology":null},{"paper":"/paper/recurrent-flow-guided-semantic-forecasting","slug":"recurrent-flow-guided-semantic-forecasting","title":"Recurrent Flow-Guided Semantic Forecasting","date":"2018-09-21","arxiv_id":"1809.08318","n_code_links":1,"syntology":null},{"paper":null,"slug":"lstm-based-whisper-detection","title":"LSTM-based Whisper Detection","date":"2018-09-20","arxiv_id":"1809.07832","n_code_links":0,"syntology":null},{"paper":"/paper/investigating-linguistic-pattern-ordering-in","slug":"investigating-linguistic-pattern-ordering-in","title":"Investigating Linguistic Pattern Ordering in Hierarchical Natural Language Generation","date":"2018-09-19","arxiv_id":"1809.07629","n_code_links":1,"syntology":null},{"paper":null,"slug":"latent-topic-conversational-models","title":"Latent Topic Conversational Models","date":"2018-09-19","arxiv_id":"1809.07070","n_code_links":0,"syntology":null},{"paper":null,"slug":"bidirectional-attentional-encoder-decoder","title":"Bidirectional Attentional Encoder-Decoder Model and Bidirectional Beam Search for Abstractive Summarization","date":"2018-09-18","arxiv_id":"1809.06662","n_code_links":0,"syntology":null},{"paper":null,"slug":"language-identification-with-deep-bottleneck","title":"Language Identification with Deep Bottleneck Features","date":"2018-09-18","arxiv_id":"1809.08909","n_code_links":0,"syntology":null},{"paper":null,"slug":"power-market-price-forecasting-via-deep","title":"Power Market Price Forecasting via Deep Learning","date":"2018-09-18","arxiv_id":"1809.08092","n_code_links":0,"syntology":null},{"paper":null,"slug":"learning-of-multi-context-models-for","title":"Learning of Multi-Context Models for Autonomous Underwater Vehicles","date":"2018-09-17","arxiv_id":"1809.06179","n_code_links":0,"syntology":null},{"paper":"/paper/cadp-a-novel-dataset-for-cctv-traffic-camera","slug":"cadp-a-novel-dataset-for-cctv-traffic-camera","title":"CADP: A Novel Dataset for CCTV Traffic Camera based Accident Analysis","date":"2018-09-16","arxiv_id":"1809.05782","n_code_links":1,"syntology":null},{"paper":"/paper/geometry-consistent-generative-adversarial","slug":"geometry-consistent-generative-adversarial","title":"Geometry-Consistent Generative Adversarial Networks for One-Sided Unsupervised Domain Mapping","date":"2018-09-16","arxiv_id":"1809.05852","n_code_links":1,"syntology":null},{"paper":null,"slug":"brain-decoding-from-functional-mri-using-long","title":"Brain decoding from functional MRI using long short-term memory recurrent neural networks","date":"2018-09-14","arxiv_id":"1809.05561","n_code_links":0,"syntology":null},{"paper":null,"slug":"macquarie-university-at-bioasq-6b-deep-1","title":"Macquarie University at BioASQ 6b: Deep learning and deep reinforcement learning for query-based multi-document summarisation","date":"2018-09-14","arxiv_id":"1809.05283","n_code_links":0,"syntology":null},{"paper":"/paper/online-cyber-attack-detection-in-smart-grid-a","slug":"online-cyber-attack-detection-in-smart-grid-a","title":"Online Cyber-Attack Detection in Smart Grid: A Reinforcement Learning Approach","date":"2018-09-14","arxiv_id":"1809.05258","n_code_links":1,"syntology":null}],"record_sha256":"1367ae8385ec8239e837284daa83813bab35cff2d0cb127dd96a09678ba97954","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}