{"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/lstm/papers/50","list_of":"/method/lstm","method":"LSTM","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":50,"pages_in_order":55,"rows_per_page":100,"rows":[4901,5000],"of":5448,"counts":{"archive_papers_tagged":5448,"with_a_code_link":1823,"where_syntology_ran_a_sample":335,"not_listed_spam_title":0,"listed":5448,"listed_where_code_ran":335,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":283,"every_run_a_failure_of_syntologys_instrument":52,"listed_with_a_run_with_no_instrument_failure":283,"listed_every_run_a_failure_of_syntologys_instrument":52,"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/lstm","prev":"/method/lstm/papers/49","next":"/method/lstm/papers/51","papers":[{"paper":"/paper/learning-semantic-concepts-and-order-for","slug":"learning-semantic-concepts-and-order-for","title":"Learning Semantic Concepts and Order for Image and Sentence Matching","date":"2017-12-06","arxiv_id":"1712.02036","n_code_links":0,"syntology":null},{"paper":"/paper/obamanet-photo-realistic-lip-sync-from-text","slug":"obamanet-photo-realistic-lip-sync-from-text","title":"ObamaNet: Photo-realistic lip-sync from text","date":"2017-12-06","arxiv_id":"1801.01442","n_code_links":1,"syntology":null},{"paper":"/paper/state-of-the-art-speech-recognition-with","slug":"state-of-the-art-speech-recognition-with","title":"State-of-the-art Speech Recognition With Sequence-to-Sequence Models","date":"2017-12-05","arxiv_id":"1712.01769","n_code_links":4,"syntology":null},{"paper":null,"slug":"iiit-h-at-ijcnlp-2017-task-3-a-bidirectional","title":"IIIT-H at IJCNLP-2017 Task 3: A Bidirectional-LSTM Approach for Review Opinion Diversification","date":"2017-12-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"iiit-h-at-ijcnlp-2017-task-4-customer","title":"IIIT-H at IJCNLP-2017 Task 4: Customer Feedback Analysis using Machine Learning and Neural Network Approaches","date":"2017-12-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":"/paper/predrnn-recurrent-neural-networks-for","slug":"predrnn-recurrent-neural-networks-for","title":"PredRNN: Recurrent Neural Networks for Predictive Learning using Spatiotemporal LSTMs","date":"2017-12-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":"/paper/predrnn-recurrent-neural-networks-for-1","slug":"predrnn-recurrent-neural-networks-for-1","title":"PredRNN: Recurrent Neural Networks for Predictive Learning using Spatiotemporal LSTMs","date":"2017-12-01","arxiv_id":null,"n_code_links":2,"syntology":null},{"paper":null,"slug":"thu_ngn-at-ijcnlp-2017-task-2-dimensional","title":"THU\\_NGN at IJCNLP-2017 Task 2: Dimensional Sentiment Analysis for Chinese Phrases with Deep LSTM","date":"2017-12-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"ynu-hpcc-at-ijcnlp-2017-task-5-multi-choice","title":"YNU-HPCC at IJCNLP-2017 Task 5: Multi-choice Question Answering in Exams Using an Attention-based LSTM Model","date":"2017-12-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"state-space-lstm-models-with-particle-mcmc","title":"State Space LSTM Models with Particle MCMC Inference","date":"2017-11-30","arxiv_id":"1711.11179","n_code_links":0,"syntology":null},{"paper":null,"slug":"role-of-deep-lstm-neural-networks-and-wifi","title":"Role of Deep LSTM Neural Networks And WiFi Networks in Support of Occupancy Prediction in Smart Buildings","date":"2017-11-28","arxiv_id":"1711.10355","n_code_links":0,"syntology":null},{"paper":null,"slug":"ostsc-over-sampling-for-time-series","title":"OSTSC: Over Sampling for Time Series Classification in R","date":"2017-11-27","arxiv_id":"1711.09545","n_code_links":0,"syntology":null},{"paper":"/paper/convolutional-image-captioning","slug":"convolutional-image-captioning","title":"Convolutional Image Captioning","date":"2017-11-24","arxiv_id":"1711.09151","n_code_links":4,"syntology":null},{"paper":"/paper/3d-anisotropic-hybrid-network-transferring","slug":"3d-anisotropic-hybrid-network-transferring","title":"3D Anisotropic Hybrid Network: Transferring Convolutional Features from 2D Images to 3D Anisotropic Volumes","date":"2017-11-23","arxiv_id":"1711.08580","n_code_links":1,"syntology":{"ran":2,"of":2,"n_ran_checked":0,"n_instrument":2,"unverified":0,"pointer_only":2,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","official":null}},{"paper":"/paper/exploiting-temporal-information-for-3d-pose","slug":"exploiting-temporal-information-for-3d-pose","title":"Exploiting temporal information for 3D pose estimation","date":"2017-11-23","arxiv_id":"1711.08585","n_code_links":1,"syntology":{"ran":2,"of":2,"n_ran_checked":0,"n_instrument":2,"unverified":0,"pointer_only":2,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","official":null}},{"paper":null,"slug":"customized-nonlinear-bandits-for-online","title":"Customized Nonlinear Bandits for Online Response Selection in Neural Conversation Models","date":"2017-11-22","arxiv_id":"1711.08493","n_code_links":0,"syntology":null},{"paper":"/paper/does-higher-order-lstm-have-better-accuracy","slug":"does-higher-order-lstm-have-better-accuracy","title":"Does Higher Order LSTM Have Better Accuracy for Segmenting and Labeling Sequence Data?","date":"2017-11-22","arxiv_id":"1711.08231","n_code_links":1,"syntology":null},{"paper":"/paper/on-the-automatic-generation-of-medical","slug":"on-the-automatic-generation-of-medical","title":"On the Automatic Generation of Medical Imaging Reports","date":"2017-11-22","arxiv_id":"1711.08195","n_code_links":4,"syntology":null},{"paper":null,"slug":"deep-long-short-term-memory-adaptive","title":"Deep Long Short-Term Memory Adaptive Beamforming Networks For Multichannel Robust Speech Recognition","date":"2017-11-21","arxiv_id":"1711.08016","n_code_links":0,"syntology":null},{"paper":"/paper/jambot-music-theory-aware-chord-based","slug":"jambot-music-theory-aware-chord-based","title":"JamBot: Music Theory Aware Chord Based Generation of Polyphonic Music with LSTMs","date":"2017-11-21","arxiv_id":"1711.07682","n_code_links":1,"syntology":null},{"paper":null,"slug":"e-pur-an-energy-efficient-processing-unit-for","title":"E-PUR: An Energy-Efficient Processing Unit for Recurrent Neural Networks","date":"2017-11-20","arxiv_id":"1711.07480","n_code_links":0,"syntology":null},{"paper":null,"slug":"pixel-wise-object-tracking","title":"Pixel-wise object tracking","date":"2017-11-20","arxiv_id":"1711.07377","n_code_links":0,"syntology":null},{"paper":null,"slug":"a-classifying-variational-autoencoder-with","title":"A Classifying Variational Autoencoder with Application to Polyphonic Music Generation","date":"2017-11-19","arxiv_id":"1711.07050","n_code_links":0,"syntology":null},{"paper":null,"slug":"diverse-and-accurate-image-description-using","title":"Diverse and Accurate Image Description Using a Variational Auto-Encoder with an Additive Gaussian Encoding Space","date":"2017-11-19","arxiv_id":"1711.07068","n_code_links":0,"syntology":null},{"paper":"/paper/convamr-abstract-meaning-representation","slug":"convamr-abstract-meaning-representation","title":"ConvAMR: Abstract meaning representation parsing for legal document","date":"2017-11-16","arxiv_id":"1711.06141","n_code_links":1,"syntology":null},{"paper":null,"slug":"a-sequential-neural-encoder-with-latent","title":"A Sequential Neural Encoder with Latent Structured Description for Modeling Sentences","date":"2017-11-15","arxiv_id":"1711.05433","n_code_links":0,"syntology":null},{"paper":"/paper/skipflow-incorporating-neural-coherence","slug":"skipflow-incorporating-neural-coherence","title":"SkipFlow: Incorporating Neural Coherence Features for End-to-End Automatic Text Scoring","date":"2017-11-14","arxiv_id":"1711.04981","n_code_links":1,"syntology":null},{"paper":null,"slug":"attend-and-diagnose-clinical-time-series","title":"Attend and Diagnose: Clinical Time Series Analysis using Attention Models","date":"2017-11-10","arxiv_id":"1711.03905","n_code_links":0,"syntology":null},{"paper":"/paper/breaking-the-softmax-bottleneck-a-high-rank","slug":"breaking-the-softmax-bottleneck-a-high-rank","title":"Breaking the Softmax Bottleneck: A High-Rank RNN Language Model","date":"2017-11-10","arxiv_id":"1711.03953","n_code_links":9,"syntology":{"ran":12,"of":23,"n_ran_checked":11,"n_instrument":1,"unverified":11,"pointer_only":1,"phrase":"12 ran (of which 0 constructed an object rather than computing a result; 11 with no instrument failure: 0 honoured, 0 violated, 11 with no contract checked; 1 where Syntology's instrument failed) · 11 unverified","official":{"repos":["zihangdai/mos"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":1,"ran_from_kinds":["listed","official"]}}},{"paper":null,"slug":"integrating-user-and-agent-models-a-deep-task","title":"Integrating User and Agent Models: A Deep Task-Oriented Dialogue System","date":"2017-11-10","arxiv_id":"1711.03697","n_code_links":0,"syntology":null},{"paper":null,"slug":"intelligent-fault-analysis-in-electrical","title":"Intelligent Fault Analysis in Electrical Power Grids","date":"2017-11-08","arxiv_id":"1711.03026","n_code_links":0,"syntology":null},{"paper":null,"slug":"multi-label-image-recognition-by-recurrently","title":"Multi-label Image Recognition by Recurrently Discovering Attentional Regions","date":"2017-11-08","arxiv_id":"1711.02816","n_code_links":0,"syntology":null},{"paper":null,"slug":"traffic-prediction-based-on-random","title":"Traffic Prediction Based on Random Connectivity in Deep Learning with Long Short-Term Memory","date":"2017-11-08","arxiv_id":"1711.02833","n_code_links":0,"syntology":null},{"paper":null,"slug":"cortical-microcircuits-as-gated-recurrent","title":"Cortical microcircuits as gated-recurrent neural networks","date":"2017-11-07","arxiv_id":"1711.02448","n_code_links":0,"syntology":null},{"paper":null,"slug":"deeprain-convlstm-network-for-precipitation","title":"DeepRain: ConvLSTM Network for Precipitation Prediction using Multichannel Radar Data","date":"2017-11-07","arxiv_id":"1711.02316","n_code_links":0,"syntology":null},{"paper":null,"slug":"wider-and-deeper-cheaper-and-faster","title":"Wider and Deeper, Cheaper and Faster: Tensorized LSTMs for Sequence Learning","date":"2017-11-05","arxiv_id":"1711.01577","n_code_links":0,"syntology":null},{"paper":null,"slug":"multi-glimpse-lstm-with-color-depth-feature","title":"Multi-Glimpse LSTM with Color-Depth Feature Fusion for Human Detection","date":"2017-11-03","arxiv_id":"1711.01062","n_code_links":0,"syntology":null},{"paper":null,"slug":"diversifying-neural-conversation-model-with","title":"Diversifying Neural Conversation Model with Maximal Marginal Relevance","date":"2017-11-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"fake-news-detection-through-multi-perspective","title":"Fake News Detection Through Multi-Perspective Speaker Profiles","date":"2017-11-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":"/paper/hierarchical-representations-for-efficient","slug":"hierarchical-representations-for-efficient","title":"Hierarchical Representations for Efficient Architecture Search","date":"2017-11-01","arxiv_id":"1711.00436","n_code_links":1,"syntology":null},{"paper":null,"slug":"integrating-subject-type-and-property","title":"Integrating Subject, Type, and Property Identification for Simple Question Answering over Knowledge Base","date":"2017-11-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"procedural-text-generation-from-an-execution","title":"Procedural Text Generation from an Execution Video","date":"2017-11-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"text-sentiment-analysis-based-on-fusion-of","title":"Text Sentiment Analysis based on Fusion of Structural Information and Serialization Information","date":"2017-11-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":"/paper/long-term-forecasting-using-tensor-train-rnns","slug":"long-term-forecasting-using-tensor-train-rnns","title":"Long-term Forecasting using Higher Order Tensor RNNs","date":"2017-10-31","arxiv_id":"1711.00073","n_code_links":1,"syntology":null},{"paper":null,"slug":"learning-neural-trans-dimensional-random","title":"Learning neural trans-dimensional random field language models with noise-contrastive estimation","date":"2017-10-30","arxiv_id":"1710.10739","n_code_links":0,"syntology":null},{"paper":null,"slug":"prototype-matching-networks-for-large-scale","title":"Prototype Matching Networks for Large-Scale Multi-label Genomic Sequence Classification","date":"2017-10-30","arxiv_id":"1710.11238","n_code_links":0,"syntology":null},{"paper":null,"slug":"transfer-learning-to-learn-with-multitask","title":"Transfer Learning to Learn with Multitask Neural Model Search","date":"2017-10-30","arxiv_id":"1710.10776","n_code_links":0,"syntology":null},{"paper":"/paper/attention-based-models-for-text-dependent","slug":"attention-based-models-for-text-dependent","title":"Attention-Based Models for Text-Dependent Speaker Verification","date":"2017-10-28","arxiv_id":"1710.10470","n_code_links":2,"syntology":null},{"paper":null,"slug":"advanced-lstm-a-study-about-better-time","title":"Advanced LSTM: A Study about Better Time Dependency Modeling in Emotion Recognition","date":"2017-10-27","arxiv_id":"1710.10197","n_code_links":0,"syntology":null},{"paper":null,"slug":"improving-deep-learning-by-inverse-square","title":"Improving Deep Learning by Inverse Square Root Linear Units (ISRLUs)","date":"2017-10-27","arxiv_id":"1710.09967","n_code_links":0,"syntology":null},{"paper":null,"slug":"deep-spatial-regression-model-for-image-crowd","title":"Deep Spatial Regression Model for Image Crowd Counting","date":"2017-10-26","arxiv_id":"1710.09757","n_code_links":0,"syntology":null},{"paper":"/paper/lip2audspec-speech-reconstruction-from-silent","slug":"lip2audspec-speech-reconstruction-from-silent","title":"Lip2AudSpec: Speech reconstruction from silent lip movements video","date":"2017-10-26","arxiv_id":"1710.09798","n_code_links":1,"syntology":{"ran":1,"of":1,"n_ran_checked":0,"n_instrument":1,"unverified":0,"pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","official":{"repos":["hassanhub/LipReading"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"geoseq2seq-information-geometric-sequence-to","title":"GeoSeq2Seq: Information Geometric Sequence-to-Sequence Networks","date":"2017-10-25","arxiv_id":"1710.09363","n_code_links":0,"syntology":null},{"paper":"/paper/on-the-long-term-memory-of-deep-recurrent","slug":"on-the-long-term-memory-of-deep-recurrent","title":"On the Long-Term Memory of Deep Recurrent Networks","date":"2017-10-25","arxiv_id":"1710.09431","n_code_links":1,"syntology":null},{"paper":null,"slug":"unsupervised-and-semi-supervised-anomaly","title":"Unsupervised and Semi-supervised Anomaly Detection with LSTM Neural Networks","date":"2017-10-25","arxiv_id":"1710.09207","n_code_links":0,"syntology":null},{"paper":null,"slug":"deep-health-care-text-classification","title":"Deep Health Care Text Classification","date":"2017-10-23","arxiv_id":"1710.08396","n_code_links":0,"syntology":null},{"paper":null,"slug":"multimodal-fusion-with-recurrent-neural","title":"Multimodal Fusion with Recurrent Neural Networks for Rumor Detection on Microblogs","date":"2017-10-23","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"testing-the-limits-of-unsupervised-learning","title":"Testing the limits of unsupervised learning for semantic similarity","date":"2017-10-23","arxiv_id":"1710.08246","n_code_links":0,"syntology":null},{"paper":null,"slug":"combining-multiple-views-for-visual-speech","title":"Combining Multiple Views for Visual Speech Recognition","date":"2017-10-19","arxiv_id":"1710.07168","n_code_links":0,"syntology":null},{"paper":null,"slug":"meta-learning-via-feature-label-memory","title":"Meta-Learning via Feature-Label Memory Network","date":"2017-10-19","arxiv_id":"1710.07110","n_code_links":0,"syntology":null},{"paper":"/paper/sling-a-framework-for-frame-semantic-parsing","slug":"sling-a-framework-for-frame-semantic-parsing","title":"SLING: A framework for frame semantic parsing","date":"2017-10-19","arxiv_id":"1710.07032","n_code_links":2,"syntology":{"ran":5,"of":5,"n_ran_checked":4,"n_instrument":1,"unverified":0,"pointer_only":0,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","official":{"repos":["google/sling"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":0,"ran_from_kinds":["listed","official"]}}},{"paper":"/paper/learning-differentially-private-recurrent","slug":"learning-differentially-private-recurrent","title":"Learning Differentially Private Recurrent Language Models","date":"2017-10-18","arxiv_id":"1710.06963","n_code_links":1,"syntology":null},{"paper":null,"slug":"ohiostate-at-ijcnlp-2017-task-4-exploring","title":"OhioState at IJCNLP-2017 Task 4: Exploring Neural Architectures for Multilingual Customer Feedback Analysis","date":"2017-10-18","arxiv_id":"1710.06931","n_code_links":0,"syntology":null},{"paper":null,"slug":"convolutional-attention-based-seq2seq-neural","title":"Convolutional Attention-based Seq2Seq Neural Network for End-to-End ASR","date":"2017-10-12","arxiv_id":"1710.04515","n_code_links":0,"syntology":null},{"paper":null,"slug":"deep-learning-in-multiple-multistep-time","title":"Deep Learning in Multiple Multistep Time Series Prediction","date":"2017-10-12","arxiv_id":"1710.04373","n_code_links":0,"syntology":null},{"paper":"/paper/dissent-sentence-representation-learning-from","slug":"dissent-sentence-representation-learning-from","title":"DisSent: Sentence Representation Learning from Explicit Discourse Relations","date":"2017-10-12","arxiv_id":"1710.04334","n_code_links":3,"syntology":null},{"paper":"/paper/discrete-event-continuous-time-rnns","slug":"discrete-event-continuous-time-rnns","title":"Discrete Event, Continuous Time RNNs","date":"2017-10-11","arxiv_id":"1710.04110","n_code_links":1,"syntology":null},{"paper":null,"slug":"stackseq2seq-dual-encoder-seq2seq-recurrent","title":"StackSeq2Seq: Dual Encoder Seq2Seq Recurrent Networks","date":"2017-10-11","arxiv_id":"1710.04211","n_code_links":0,"syntology":null},{"paper":null,"slug":"network-of-recurrent-neural-networks","title":"Network of Recurrent Neural Networks","date":"2017-10-10","arxiv_id":"1710.03414","n_code_links":0,"syntology":null},{"paper":null,"slug":"optimizing-long-short-term-memory-recurrent","title":"Optimizing Long Short-Term Memory Recurrent Neural Networks Using Ant Colony Optimization to Predict Turbine Engine Vibration","date":"2017-10-10","arxiv_id":"1710.03753","n_code_links":0,"syntology":null},{"paper":"/paper/forecasting-across-time-series-databases","slug":"forecasting-across-time-series-databases","title":"Forecasting Across Time Series Databases using Recurrent Neural Networks on Groups of Similar Series: A Clustering Approach","date":"2017-10-09","arxiv_id":"1710.03222","n_code_links":3,"syntology":null},{"paper":"/paper/to-prune-or-not-to-prune-exploring-the","slug":"to-prune-or-not-to-prune-exploring-the","title":"To prune, or not to prune: exploring the efficacy of pruning for model compression","date":"2017-10-05","arxiv_id":"1710.01878","n_code_links":4,"syntology":{"ran":2,"of":2,"n_ran_checked":0,"n_instrument":2,"unverified":0,"pointer_only":0,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","official":null}},{"paper":null,"slug":"identifying-clickbait-a-multi-strategy","title":"Identifying Clickbait: A Multi-Strategy Approach Using Neural Networks","date":"2017-10-04","arxiv_id":"1710.01507","n_code_links":0,"syntology":null},{"paper":null,"slug":"person-re-identification-with-vision-and","title":"Person Re-Identification with Vision and Language","date":"2017-10-03","arxiv_id":"1710.01202","n_code_links":0,"syntology":null},{"paper":null,"slug":"3dof-pedestrian-trajectory-prediction-learned","title":"3DOF Pedestrian Trajectory Prediction Learned from Long-Term Autonomous Mobile Robot Deployment Data","date":"2017-09-30","arxiv_id":"1710.00126","n_code_links":0,"syntology":null},{"paper":null,"slug":"model-free-prediction-of-noisy-chaotic-time","title":"Model-free prediction of noisy chaotic time series by deep learning","date":"2017-09-29","arxiv_id":"1710.01693","n_code_links":0,"syntology":null},{"paper":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","n_code_links":0,"syntology":null},{"paper":"/paper/tensor-product-generation-networks-for-deep","slug":"tensor-product-generation-networks-for-deep","title":"Tensor Product Generation Networks for Deep NLP Modeling","date":"2017-09-26","arxiv_id":"1709.09118","n_code_links":2,"syntology":null},{"paper":null,"slug":"house-price-prediction-using-lstm","title":"House Price Prediction Using LSTM","date":"2017-09-25","arxiv_id":"1709.08432","n_code_links":0,"syntology":null},{"paper":null,"slug":"cross-modal-recurrent-models-for-weight","title":"Cross-modal Recurrent Models for Weight Objective Prediction from Multimodal Time-series Data","date":"2017-09-23","arxiv_id":"1709.08073","n_code_links":0,"syntology":null},{"paper":"/paper/deep-recurrent-nmf-for-speech-separation-by","slug":"deep-recurrent-nmf-for-speech-separation-by","title":"Deep Recurrent NMF for Speech Separation by Unfolding Iterative Thresholding","date":"2017-09-21","arxiv_id":"1709.07124","n_code_links":1,"syntology":null},{"paper":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","n_code_links":0,"syntology":null},{"paper":null,"slug":"de-identification-of-medical-records-using","title":"De-identification of medical records using conditional random fields and long short-term memory networks","date":"2017-09-20","arxiv_id":"1709.06901","n_code_links":0,"syntology":null},{"paper":null,"slug":"language-modeling-with-highway-lstm","title":"Language Modeling with Highway LSTM","date":"2017-09-19","arxiv_id":"1709.06436","n_code_links":0,"syntology":null},{"paper":"/paper/reducing-complexity-of-hevc-a-deep-learning","slug":"reducing-complexity-of-hevc-a-deep-learning","title":"Reducing Complexity of HEVC: A Deep Learning Approach","date":"2017-09-19","arxiv_id":"1710.01218","n_code_links":1,"syntology":null},{"paper":null,"slug":"social-style-characterization-from-egocentric","title":"Social Style Characterization from Egocentric Photo-streams","date":"2017-09-18","arxiv_id":"1709.05775","n_code_links":0,"syntology":null},{"paper":null,"slug":"are-you-serious-rhetorical-questions-and","title":"Are you serious?: Rhetorical Questions and Sarcasm in Social Media Dialog","date":"2017-09-15","arxiv_id":"1709.05305","n_code_links":0,"syntology":null},{"paper":null,"slug":"learning-intrinsic-sparse-structures-within","title":"Learning Intrinsic Sparse Structures within Long Short-Term Memory","date":"2017-09-15","arxiv_id":"1709.05027","n_code_links":0,"syntology":null},{"paper":null,"slug":"self-guiding-multimodal-lstm-when-we-do-not","title":"Self-Guiding Multimodal LSTM - when we do not have a perfect training dataset for image captioning","date":"2017-09-15","arxiv_id":"1709.05038","n_code_links":0,"syntology":null},{"paper":null,"slug":"deep-learning-for-automatic-stereotypical","title":"Deep Learning for Automatic Stereotypical Motor Movement Detection using Wearable Sensors in Autism Spectrum Disorders","date":"2017-09-14","arxiv_id":"1709.05956","n_code_links":0,"syntology":null},{"paper":"/paper/dialogue-act-sequence-labeling-using","slug":"dialogue-act-sequence-labeling-using","title":"Dialogue Act Sequence Labeling using Hierarchical encoder with CRF","date":"2017-09-13","arxiv_id":"1709.04250","n_code_links":3,"syntology":null},{"paper":"/paper/rra-recurrent-residual-attention-for-sequence","slug":"rra-recurrent-residual-attention-for-sequence","title":"RRA: Recurrent Residual Attention for Sequence Learning","date":"2017-09-12","arxiv_id":"1709.03714","n_code_links":1,"syntology":null},{"paper":null,"slug":"systran-purely-neural-mt-engines-for-wmt2017","title":"SYSTRAN Purely Neural MT Engines for WMT2017","date":"2017-09-12","arxiv_id":"1709.03814","n_code_links":0,"syntology":null},{"paper":"/paper/lstm-fully-convolutional-networks-for-time","slug":"lstm-fully-convolutional-networks-for-time","title":"LSTM Fully Convolutional Networks for Time Series Classification","date":"2017-09-08","arxiv_id":"1709.05206","n_code_links":9,"syntology":{"ran":2,"of":2,"n_ran_checked":1,"n_instrument":1,"unverified":0,"pointer_only":2,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","official":{"repos":["titu1994/LSTM-FCN"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]}}},{"paper":"/paper/simple-recurrent-units-for-highly","slug":"simple-recurrent-units-for-highly","title":"Simple Recurrent Units for Highly Parallelizable Recurrence","date":"2017-09-08","arxiv_id":"1709.02755","n_code_links":11,"syntology":{"ran":1,"of":1,"n_ran_checked":1,"n_instrument":0,"unverified":0,"pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 1 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","official":{"repos":["asappresearch/sru"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]}}},{"paper":null,"slug":"an-unsupervised-long-short-term-memory-neural","title":"An unsupervised long short-term memory neural network for event detection in cell videos","date":"2017-09-07","arxiv_id":"1709.02081","n_code_links":0,"syntology":null},{"paper":null,"slug":"towards-social-pattern-characterization-in","title":"Towards social pattern characterization in egocentric photo-streams","date":"2017-09-05","arxiv_id":"1709.01424","n_code_links":0,"syntology":null},{"paper":null,"slug":"a-bilstm-based-system-for-cross-lingual","title":"A BiLSTM-based System for Cross-lingual Pronoun Prediction","date":"2017-09-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"a-joint-sequential-and-relational-model-for","title":"A Joint Sequential and Relational Model for Frame-Semantic Parsing","date":"2017-09-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"deep-multi-task-learning-for-aspect-term","title":"Deep Multi-Task Learning for Aspect Term Extraction with Memory Interaction","date":"2017-09-01","arxiv_id":null,"n_code_links":0,"syntology":null}],"record_sha256":"7a52a427a11539bad07506a3f296968c3020c63b16ce2af3cccbd0efd6ea8660","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}