{"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/43","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":43,"pages_in_order":55,"rows_per_page":100,"rows":[4201,4300],"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/42","next":"/method/lstm/papers/44","papers":[{"paper":"/paper/recursive-subtree-composition-in-lstm-based","slug":"recursive-subtree-composition-in-lstm-based","title":"Recursive Subtree Composition in LSTM-Based Dependency Parsing","date":"2019-02-26","arxiv_id":"1902.09781","n_code_links":1,"syntology":null},{"paper":"/paper/an-attention-enhanced-graph-convolutional","slug":"an-attention-enhanced-graph-convolutional","title":"An Attention Enhanced Graph Convolutional LSTM Network for Skeleton-Based Action Recognition","date":"2019-02-25","arxiv_id":"1902.09130","n_code_links":0,"syntology":null},{"paper":"/paper/gqa-a-new-dataset-for-compositional-question","slug":"gqa-a-new-dataset-for-compositional-question","title":"GQA: A New Dataset for Real-World Visual Reasoning and Compositional Question Answering","date":"2019-02-25","arxiv_id":"1902.09506","n_code_links":5,"syntology":null},{"paper":"/paper/improving-neural-response-diversity-with","slug":"improving-neural-response-diversity-with","title":"Improving Neural Response Diversity with Frequency-Aware Cross-Entropy Loss","date":"2019-02-25","arxiv_id":"1902.09191","n_code_links":2,"syntology":null},{"paper":"/paper/nas-bench-101-towards-reproducible-neural","slug":"nas-bench-101-towards-reproducible-neural","title":"NAS-Bench-101: Towards Reproducible Neural Architecture Search","date":"2019-02-25","arxiv_id":"1902.09635","n_code_links":4,"syntology":{"ran":9,"of":10,"n_ran_checked":9,"n_instrument":0,"unverified":1,"pointer_only":0,"phrase":"9 ran (of which 0 constructed an object rather than computing a result; 9 with no instrument failure: 0 honoured, 0 violated, 9 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","official":{"repos":["google-research/nasbench","automl/nas_benchmarks"],"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":"transfer-learning-for-sequences-via-learning","title":"Transfer Learning for Sequences via Learning to Collocate","date":"2019-02-25","arxiv_id":"1902.09092","n_code_links":0,"syntology":null},{"paper":"/paper/synchronous-bidirectional-inference-for","slug":"synchronous-bidirectional-inference-for","title":"Synchronous Bidirectional Inference for Neural Sequence Generation","date":"2019-02-24","arxiv_id":"1902.08955","n_code_links":1,"syntology":null},{"paper":null,"slug":"abi-neural-ensemble-model-for-gender","title":"ABI Neural Ensemble Model for Gender Prediction Adapt Bar-Ilan Submission for the CLIN29 Shared Task on Gender Prediction","date":"2019-02-23","arxiv_id":"1902.08856","n_code_links":0,"syntology":null},{"paper":"/paper/enhancing-clinical-concept-extraction-with","slug":"enhancing-clinical-concept-extraction-with","title":"Enhancing Clinical Concept Extraction with Contextual Embeddings","date":"2019-02-22","arxiv_id":"1902.08691","n_code_links":0,"syntology":null},{"paper":null,"slug":"learning-to-flip-successive-cancellation","title":"Learning to Flip Successive Cancellation Decoding of Polar Codes with LSTM Networks","date":"2019-02-22","arxiv_id":"1902.08394","n_code_links":0,"syntology":null},{"paper":null,"slug":"deep-speaker-embedding-learning-with-multi","title":"Deep Speaker Embedding Learning with Multi-Level Pooling for Text-Independent Speaker Verification","date":"2019-02-21","arxiv_id":"1902.07821","n_code_links":0,"syntology":null},{"paper":"/paper/ntuer-at-semeval-2019-task-3-emotion","slug":"ntuer-at-semeval-2019-task-3-emotion","title":"ntuer at SemEval-2019 Task 3: Emotion Classification with Word and Sentence Representations in RCNN","date":"2019-02-21","arxiv_id":"1902.07867","n_code_links":1,"syntology":null},{"paper":null,"slug":"overcoming-multi-model-forgetting","title":"Overcoming Multi-Model Forgetting","date":"2019-02-21","arxiv_id":"1902.08232","n_code_links":0,"syntology":null},{"paper":"/paper/towards-real-time-eyeblink-detection-in-the","slug":"towards-real-time-eyeblink-detection-in-the","title":"Towards Real-time Eyeblink Detection in The Wild:Dataset,Theory and Practices","date":"2019-02-21","arxiv_id":"1902.07891","n_code_links":0,"syntology":null},{"paper":null,"slug":"grids-versus-graphs-partitioning-space-for","title":"Grids versus Graphs: Partitioning Space for Improved Taxi Demand-Supply Forecasts","date":"2019-02-18","arxiv_id":"1902.06515","n_code_links":0,"syntology":null},{"paper":"/paper/intra-and-inter-epoch-temporal-context","slug":"intra-and-inter-epoch-temporal-context","title":"Intra- and Inter-epoch Temporal Context Network (IITNet) Using Sub-epoch Features for Automatic Sleep Scoring on Raw Single-channel EEG","date":"2019-02-18","arxiv_id":"1902.06562","n_code_links":2,"syntology":null},{"paper":"/paper/multigrain-a-unified-image-embedding-for","slug":"multigrain-a-unified-image-embedding-for","title":"MultiGrain: a unified image embedding for classes and instances","date":"2019-02-14","arxiv_id":"1902.05509","n_code_links":3,"syntology":{"ran":4,"of":4,"n_ran_checked":3,"n_instrument":1,"unverified":0,"pointer_only":0,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","official":{"repos":["facebookresearch/multigrain"],"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":"modeling-default-rate-in-p2p-lending-via-lstm","title":"Risk Prediction of Peer-to-Peer Lending Market by a LSTM Model with Macroeconomic Factor","date":"2019-02-13","arxiv_id":"1902.04954","n_code_links":0,"syntology":null},{"paper":null,"slug":"probabilistic-neural-architecture-search","title":"Probabilistic Neural Architecture Search","date":"2019-02-13","arxiv_id":"1902.05116","n_code_links":0,"syntology":null},{"paper":null,"slug":"reinforcement-learning-to-optimize-long-term","title":"Reinforcement Learning to Optimize Long-term User Engagement in Recommender Systems","date":"2019-02-13","arxiv_id":"1902.05570","n_code_links":0,"syntology":null},{"paper":"/paper/sector-a-neural-model-for-coherent-topic","slug":"sector-a-neural-model-for-coherent-topic","title":"SECTOR: A Neural Model for Coherent Topic Segmentation and Classification","date":"2019-02-13","arxiv_id":"1902.04793","n_code_links":3,"syntology":null},{"paper":null,"slug":"rtbust-exploiting-temporal-patterns-for","title":"RTbust: Exploiting Temporal Patterns for Botnet Detection on Twitter","date":"2019-02-12","arxiv_id":"1902.04506","n_code_links":0,"syntology":null},{"paper":null,"slug":"fsnet-compression-of-deep-convolutional","title":"FSNet: Compression of Deep Convolutional Neural Networks by Filter Summary","date":"2019-02-08","arxiv_id":"1902.03264","n_code_links":0,"syntology":null},{"paper":null,"slug":"aspect-specific-opinion-expression-extraction","title":"Aspect Specific Opinion Expression Extraction using Attention based LSTM-CRF Network","date":"2019-02-07","arxiv_id":"1902.02709","n_code_links":0,"syntology":null},{"paper":null,"slug":"effectiveness-of-lstms-in-predicting","title":"Effectiveness of LSTMs in Predicting Congestive Heart Failure Onset","date":"2019-02-07","arxiv_id":"1902.02443","n_code_links":0,"syntology":null},{"paper":null,"slug":"high-performance-stock-index-trading-making","title":"High-performance stock index trading: making effective use of a deep LSTM neural network","date":"2019-02-07","arxiv_id":"1902.03125","n_code_links":0,"syntology":null},{"paper":"/paper/investigating-recurrent-neural-network-memory","slug":"investigating-recurrent-neural-network-memory","title":"Investigating Recurrent Neural Network Memory Structures using Neuro-Evolution","date":"2019-02-06","arxiv_id":"1902.02390","n_code_links":1,"syntology":null},{"paper":"/paper/abstractive-summarization-of-spoken-and","slug":"abstractive-summarization-of-spoken-and","title":"Restructuring Conversations using Discourse Relations for Zero-shot Abstractive Dialogue Summarization","date":"2019-02-05","arxiv_id":"1902.01615","n_code_links":1,"syntology":null},{"paper":null,"slug":"alphastar-an-evolutionary-computation","title":"AlphaStar: An Evolutionary Computation Perspective","date":"2019-02-05","arxiv_id":"1902.01724","n_code_links":0,"syntology":null},{"paper":"/paper/dvolver-efficient-pareto-optimal-neural","slug":"dvolver-efficient-pareto-optimal-neural","title":"DVOLVER: Efficient Pareto-Optimal Neural Network Architecture Search","date":"2019-02-05","arxiv_id":"1902.01654","n_code_links":1,"syntology":null},{"paper":null,"slug":"polyphonic-music-composition-with-lstm-neural","title":"Polyphonic Music Composition with LSTM Neural Networks and Reinforcement Learning","date":"2019-02-05","arxiv_id":"1902.01973","n_code_links":0,"syntology":null},{"paper":"/paper/a-comprehensive-exploration-on-wikisql-with","slug":"a-comprehensive-exploration-on-wikisql-with","title":"A Comprehensive Exploration on WikiSQL with Table-Aware Word Contextualization","date":"2019-02-04","arxiv_id":"1902.01069","n_code_links":5,"syntology":{"ran":21,"of":36,"n_ran_checked":5,"n_instrument":16,"unverified":15,"pointer_only":33,"phrase":"21 ran (of which 1 constructed an object rather than computing a result; 5 with no instrument failure: 2 honoured, 2 violated, 1 with no contract checked; 16 where Syntology's instrument failed) · 15 unverified","official":{"repos":["naver/sqlova"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["listed","official","unlocated"]}}},{"paper":null,"slug":"accuracy-vs-efficiency-achieving-both-through","title":"Accuracy vs. Efficiency: Achieving Both through FPGA-Implementation Aware Neural Architecture Search","date":"2019-01-31","arxiv_id":"1901.11211","n_code_links":0,"syntology":null},{"paper":null,"slug":"unsupervised-prediction-of-negative-health","title":"Unsupervised Prediction of Negative Health Events Ahead of Time","date":"2019-01-31","arxiv_id":"1901.11168","n_code_links":0,"syntology":null},{"paper":null,"slug":"hardware-guided-symbiotic-training-for","title":"Hardware-Guided Symbiotic Training for Compact, Accurate, yet Execution-Efficient LSTM","date":"2019-01-30","arxiv_id":"1901.10997","n_code_links":0,"syntology":null},{"paper":"/paper/the-evolved-transformer","slug":"the-evolved-transformer","title":"The Evolved Transformer","date":"2019-01-30","arxiv_id":"1901.11117","n_code_links":3,"syntology":null},{"paper":null,"slug":"deep-dust-predicting-concentrations-of-fine","title":"Deep-dust: Predicting concentrations of fine dust in Seoul using LSTM","date":"2019-01-29","arxiv_id":"1901.10106","n_code_links":0,"syntology":null},{"paper":null,"slug":"short-term-demand-forecasting-for-online-car","title":"Short-term Demand Forecasting for Online Car-hailing Services using Recurrent Neural Networks","date":"2019-01-29","arxiv_id":"1901.10821","n_code_links":0,"syntology":null},{"paper":null,"slug":"visual-rhythm-prediction-with-feature","title":"Visual Rhythm Prediction with Feature-Aligning Network","date":"2019-01-29","arxiv_id":"1901.10163","n_code_links":0,"syntology":null},{"paper":"/paper/neural-related-work-summarization-with-a","slug":"neural-related-work-summarization-with-a","title":"Neural Related Work Summarization with a Joint Context-driven Attention Mechanism","date":"2019-01-28","arxiv_id":"1901.09492","n_code_links":1,"syntology":null},{"paper":"/paper/gcn-gan-a-non-linear-temporal-link-prediction","slug":"gcn-gan-a-non-linear-temporal-link-prediction","title":"GCN-GAN: A Non-linear Temporal Link Prediction Model for Weighted Dynamic Networks","date":"2019-01-26","arxiv_id":"1901.09165","n_code_links":1,"syntology":{"ran":1,"of":1,"n_ran_checked":1,"n_instrument":0,"unverified":0,"pointer_only":1,"phrase":"1 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; 0 where Syntology's instrument failed) · 0 unverified","official":null}},{"paper":null,"slug":"intrinsically-sparse-long-short-term-memory","title":"Intrinsically Sparse Long Short-Term Memory Networks","date":"2019-01-26","arxiv_id":"1901.09208","n_code_links":0,"syntology":null},{"paper":null,"slug":"a-neurally-inspired-hierarchical-prediction","title":"A Neurally-Inspired Hierarchical Prediction Network for Spatiotemporal Sequence Learning and Prediction","date":"2019-01-25","arxiv_id":"1901.09002","n_code_links":0,"syntology":null},{"paper":null,"slug":"dynamical-isometry-and-a-mean-field-theory-of-1","title":"Dynamical Isometry and a Mean Field Theory of LSTMs and GRUs","date":"2019-01-25","arxiv_id":"1901.08987","n_code_links":0,"syntology":null},{"paper":null,"slug":"hybrid-ner-system-for-multi-source-offer","title":"Hybrid NER System for Multi-Source Offer Feeds","date":"2019-01-24","arxiv_id":"1901.08406","n_code_links":0,"syntology":null},{"paper":"/paper/large-batch-training-for-lstm-and-beyond","slug":"large-batch-training-for-lstm-and-beyond","title":"Large-Batch Training for LSTM and Beyond","date":"2019-01-24","arxiv_id":"1901.08256","n_code_links":1,"syntology":null},{"paper":null,"slug":"evaluating-the-state-of-the-art-of-end-to-end","title":"Evaluating the State-of-the-Art of End-to-End Natural Language Generation: The E2E NLG Challenge","date":"2019-01-23","arxiv_id":"1901.07931","n_code_links":0,"syntology":null},{"paper":"/paper/transfertransfo-a-transfer-learning-approach","slug":"transfertransfo-a-transfer-learning-approach","title":"TransferTransfo: A Transfer Learning Approach for Neural Network Based Conversational Agents","date":"2019-01-23","arxiv_id":"1901.08149","n_code_links":23,"syntology":{"ran":18,"of":26,"n_ran_checked":11,"n_instrument":7,"unverified":8,"pointer_only":7,"phrase":"18 ran (of which 0 constructed an object rather than computing a result; 11 with no instrument failure: 1 honoured, 0 violated, 10 with no contract checked; 7 where Syntology's instrument failed) · 8 unverified","official":null}},{"paper":null,"slug":"an-adversarial-approach-to-high-quality","title":"An Adversarial Approach to High-Quality, Sentiment-Controlled Neural Dialogue Generation","date":"2019-01-22","arxiv_id":"1901.07129","n_code_links":0,"syntology":null},{"paper":null,"slug":"reducing-state-updates-via-gaussian-gated","title":"Reducing state updates via Gaussian-gated LSTMs","date":"2019-01-22","arxiv_id":"1901.07334","n_code_links":0,"syntology":null},{"paper":"/paper/towards-non-saturating-recurrent-units-for","slug":"towards-non-saturating-recurrent-units-for","title":"Towards Non-saturating Recurrent Units for Modelling Long-term Dependencies","date":"2019-01-22","arxiv_id":"1902.06704","n_code_links":2,"syntology":{"ran":8,"of":9,"n_ran_checked":8,"n_instrument":0,"unverified":1,"pointer_only":0,"phrase":"8 ran (of which 0 constructed an object rather than computing a result; 8 with no instrument failure: 0 honoured, 0 violated, 8 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","official":{"repos":["apsarath/NRU"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":1,"ran_from_kinds":["listed","official"]}}},{"paper":"/paper/a-deep-learning-approach-to-real-time-parking","slug":"a-deep-learning-approach-to-real-time-parking","title":"A deep learning approach to real-time parking occupancy prediction in spatio-temporal networks incorporating multiple spatio-temporal data sources","date":"2019-01-21","arxiv_id":"1901.06758","n_code_links":1,"syntology":null},{"paper":null,"slug":"semantic-image-networks-for-human-action","title":"Semantic Image Networks for Human Action Recognition","date":"2019-01-21","arxiv_id":"1901.06792","n_code_links":0,"syntology":null},{"paper":"/paper/st-lstm-a-deep-learning-approach-combined","slug":"st-lstm-a-deep-learning-approach-combined","title":"ST-LSTM: A Deep Learning Approach Combined Spatio-Temporal Features for Short-Term","date":"2019-01-21","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":null,"slug":"deep-recurrent-factor-model-interpretable-non","title":"Deep Recurrent Factor Model: Interpretable Non-Linear and Time-Varying Multi-Factor Model","date":"2019-01-20","arxiv_id":"1901.11493","n_code_links":0,"syntology":null},{"paper":null,"slug":"da-lstm-a-long-short-term-memory-with-depth","title":"DA-LSTM: A Long Short-Term Memory with Depth Adaptive to Non-uniform Information Flow in Sequential Data","date":"2019-01-18","arxiv_id":"1903.02082","n_code_links":0,"syntology":null},{"paper":null,"slug":"slim-lstm-networks-lstm_6-and-lstm_c6","title":"Slim LSTM networks: LSTM_6 and LSTM_C6","date":"2019-01-18","arxiv_id":"1901.06401","n_code_links":0,"syntology":null},{"paper":"/paper/eat-nas-elastic-architecture-transfer-for","slug":"eat-nas-elastic-architecture-transfer-for","title":"EAT-NAS: Elastic Architecture Transfer for Accelerating Large-scale Neural Architecture Search","date":"2019-01-17","arxiv_id":"1901.05884","n_code_links":1,"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, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","official":{"repos":["JaminFong/EAT-NAS"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"enhance-the-motion-cues-for-face-anti","title":"Enhance the Motion Cues for Face Anti-Spoofing using CNN-LSTM Architecture","date":"2019-01-17","arxiv_id":"1901.05635","n_code_links":0,"syntology":null},{"paper":null,"slug":"robust-chinese-word-segmentation-with","title":"Robust Chinese Word Segmentation with Contextualized Word Representations","date":"2019-01-17","arxiv_id":"1901.05816","n_code_links":0,"syntology":null},{"paper":null,"slug":"exploiting-synchronized-lyrics-and-vocal","title":"Exploiting Synchronized Lyrics And Vocal Features For Music Emotion Detection","date":"2019-01-15","arxiv_id":"1901.04831","n_code_links":0,"syntology":null},{"paper":null,"slug":"predicting-the-mumble-of-wireless-channel","title":"Predicting the Mumble of Wireless Channel with Sequence-to-Sequence Models","date":"2019-01-14","arxiv_id":"1901.04119","n_code_links":0,"syntology":null},{"paper":"/paper/passage-re-ranking-with-bert","slug":"passage-re-ranking-with-bert","title":"Passage Re-ranking with BERT","date":"2019-01-13","arxiv_id":"1901.04085","n_code_links":6,"syntology":{"ran":11,"of":13,"n_ran_checked":9,"n_instrument":2,"unverified":2,"pointer_only":2,"phrase":"11 ran (of which 0 constructed an object rather than computing a result; 9 with no instrument failure: 0 honoured, 0 violated, 9 with no contract checked; 2 where Syntology's instrument failed) · 2 unverified","official":{"repos":["nyu-dl/dl4marco-bert"],"state":"official (archive's flag): 11 ran","n_ran":11,"n_constructed":0,"n_ran_no_instrument_failure":9,"n_unverified":2,"ran_from_kinds":["official"]}}},{"paper":"/paper/sales-demand-forecast-in-e-commerce-using-a","slug":"sales-demand-forecast-in-e-commerce-using-a","title":"Sales Demand Forecast in E-commerce using a Long Short-Term Memory Neural Network Methodology","date":"2019-01-13","arxiv_id":"1901.04028","n_code_links":1,"syntology":null},{"paper":null,"slug":"grammatical-analysis-of-pretrained-sentence","title":"Linguistic Analysis of Pretrained Sentence Encoders with Acceptability Judgments","date":"2019-01-11","arxiv_id":"1901.03438","n_code_links":0,"syntology":null},{"paper":"/paper/auto-deeplab-hierarchical-neural-architecture","slug":"auto-deeplab-hierarchical-neural-architecture","title":"Auto-DeepLab: Hierarchical Neural Architecture Search for Semantic Image Segmentation","date":"2019-01-10","arxiv_id":"1901.02985","n_code_links":12,"syntology":{"ran":4,"of":4,"n_ran_checked":3,"n_instrument":1,"unverified":0,"pointer_only":1,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","official":{"repos":["tensorflow/models"],"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/is-it-time-to-swish-comparing-deep-learning","slug":"is-it-time-to-swish-comparing-deep-learning","title":"Is it Time to Swish? Comparing Deep Learning Activation Functions Across NLP tasks","date":"2019-01-09","arxiv_id":"1901.02671","n_code_links":1,"syntology":null},{"paper":"/paper/choosing-the-right-word-using-bidirectional","slug":"choosing-the-right-word-using-bidirectional","title":"Choosing the Right Word: Using Bidirectional LSTM Tagger for Writing Support Systems","date":"2019-01-08","arxiv_id":"1901.02490","n_code_links":1,"syntology":null},{"paper":null,"slug":"multi-stream-cnn-based-video-semantic","title":"Multi-stream CNN based Video Semantic Segmentation for Automated Driving","date":"2019-01-08","arxiv_id":"1901.02511","n_code_links":0,"syntology":null},{"paper":null,"slug":"forecasting-people-trajectories-and-head","title":"Forecasting People Trajectories and Head Poses by Jointly Reasoning on Tracklets and Vislets","date":"2019-01-07","arxiv_id":"1901.02000","n_code_links":0,"syntology":null},{"paper":null,"slug":"team-ep-at-tac-2018-automating-data","title":"Team EP at TAC 2018: Automating data extraction in systematic reviews of environmental agents","date":"2019-01-07","arxiv_id":"1901.02081","n_code_links":0,"syntology":null},{"paper":"/paper/multi-objective-reinforced-evolution-in","slug":"multi-objective-reinforced-evolution-in","title":"Multi-Objective Reinforced Evolution in Mobile Neural Architecture Search","date":"2019-01-04","arxiv_id":"1901.01074","n_code_links":4,"syntology":null},{"paper":null,"slug":"learning-a-generator-model-from-terminal-bus","title":"Learning a Generator Model from Terminal Bus Data","date":"2019-01-03","arxiv_id":"1901.00781","n_code_links":0,"syntology":null},{"paper":null,"slug":"performance-of-three-slim-variants-of-the","title":"Performance of Three Slim Variants of The Long Short-Term Memory (LSTM) Layer","date":"2019-01-02","arxiv_id":"1901.00525","n_code_links":0,"syntology":null},{"paper":null,"slug":"augmentation-scheme-for-dealing-with","title":"Augmentation Scheme for Dealing with Imbalanced Network Traffic Classification Using Deep Learning","date":"2019-01-01","arxiv_id":"1901.00204","n_code_links":0,"syntology":null},{"paper":null,"slug":"recurrent-neural-networks-for-time-series","title":"Recurrent Neural Networks for Time Series Forecasting","date":"2019-01-01","arxiv_id":"1901.00069","n_code_links":0,"syntology":null},{"paper":"/paper/multilingual-constituency-parsing-with-self","slug":"multilingual-constituency-parsing-with-self","title":"Multilingual Constituency Parsing with Self-Attention and Pre-Training","date":"2018-12-31","arxiv_id":"1812.11760","n_code_links":4,"syntology":{"ran":22,"of":28,"n_ran_checked":19,"n_instrument":3,"unverified":6,"pointer_only":3,"phrase":"22 ran (of which 0 constructed an object rather than computing a result; 19 with no instrument failure: 0 honoured, 0 violated, 19 with no contract checked; 3 where Syntology's instrument failed) · 6 unverified","official":{"repos":["nikitakit/self-attentive-parser"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":8,"n_unverified":2,"ran_from_kinds":["listed","official"]}}},{"paper":null,"slug":"slim-lstms","title":"SLIM LSTMs","date":"2018-12-29","arxiv_id":"1812.11391","n_code_links":0,"syntology":null},{"paper":null,"slug":"a-framework-for-automated-pop-song-melody","title":"A Framework for Automated Pop-song Melody Generation with Piano Accompaniment Arrangement","date":"2018-12-28","arxiv_id":"1812.10906","n_code_links":0,"syntology":null},{"paper":null,"slug":"looking-for-elmos-friends-sentence-level","title":"Can You Tell Me How to Get Past Sesame Street? Sentence-Level Pretraining Beyond Language Modeling","date":"2018-12-28","arxiv_id":"1812.10860","n_code_links":0,"syntology":null},{"paper":null,"slug":"hierarchical-lstms-with-adaptive-attention","title":"Hierarchical LSTMs with Adaptive Attention for Visual Captioning","date":"2018-12-26","arxiv_id":"1812.11004","n_code_links":0,"syntology":null},{"paper":"/paper/massively-multilingual-sentence-embeddings","slug":"massively-multilingual-sentence-embeddings","title":"Massively Multilingual Sentence Embeddings for Zero-Shot Cross-Lingual Transfer and Beyond","date":"2018-12-26","arxiv_id":"1812.10464","n_code_links":13,"syntology":{"ran":10,"of":10,"n_ran_checked":8,"n_instrument":2,"unverified":0,"pointer_only":4,"phrase":"10 ran (of which 0 constructed an object rather than computing a result; 8 with no instrument failure: 2 honoured, 0 violated, 6 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","official":{"repos":["facebookresearch/LASER"],"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":"tensor-train-long-short-term-memory-for","title":"Tensor-Train Long Short-Term Memory for Monaural Speech Enhancement","date":"2018-12-25","arxiv_id":"1812.10095","n_code_links":0,"syntology":null},{"paper":null,"slug":"precision-highway-for-ultra-low-precision","title":"Precision Highway for Ultra Low-Precision Quantization","date":"2018-12-24","arxiv_id":"1812.09818","n_code_links":0,"syntology":null},{"paper":"/paper/snas-stochastic-neural-architecture-search","slug":"snas-stochastic-neural-architecture-search","title":"SNAS: Stochastic Neural Architecture Search","date":"2018-12-24","arxiv_id":"1812.09926","n_code_links":2,"syntology":null},{"paper":null,"slug":"artificial-neural-networks-condensation-a","title":"Artificial neural networks condensation: A strategy to facilitate adaption of machine learning in medical settings by reducing computational burden","date":"2018-12-23","arxiv_id":"1812.09659","n_code_links":0,"syntology":null},{"paper":null,"slug":"sources-of-complexity-in-semantic-frame","title":"Sources of Complexity in Semantic Frame Parsing for Information Extraction","date":"2018-12-21","arxiv_id":"1812.09193","n_code_links":0,"syntology":null},{"paper":null,"slug":"squantizer-simultaneous-learning-for-both","title":"SQuantizer: Simultaneous Learning for Both Sparse and Low-precision Neural Networks","date":"2018-12-20","arxiv_id":"1812.08301","n_code_links":0,"syntology":null},{"paper":null,"slug":"learning-on-road-visual-control-for-self","title":"Learning On-Road Visual Control for Self-Driving Vehicles with Auxiliary Tasks","date":"2018-12-19","arxiv_id":"1812.07760","n_code_links":0,"syntology":null},{"paper":null,"slug":"a-comparison-of-lstms-and-attention","title":"A Comparison of LSTMs and Attention Mechanisms for Forecasting Financial Time Series","date":"2018-12-18","arxiv_id":"1812.07699","n_code_links":0,"syntology":null},{"paper":"/paper/deep-gated-recurrent-and-convolutional","slug":"deep-gated-recurrent-and-convolutional","title":"Deep Gated Recurrent and Convolutional Network Hybrid Model for Univariate Time Series Classification","date":"2018-12-18","arxiv_id":"1812.07683","n_code_links":1,"syntology":null},{"paper":null,"slug":"multi-level-sequence-gan-for-group-activity","title":"Multi-Level Sequence GAN for Group Activity Recognition","date":"2018-12-18","arxiv_id":"1812.07124","n_code_links":0,"syntology":null},{"paper":null,"slug":"a-robust-deep-learning-approach-for-automatic","title":"A Robust Deep Learning Approach for Automatic Classification of Seizures Against Non-seizures","date":"2018-12-17","arxiv_id":"1812.06562","n_code_links":0,"syntology":null},{"paper":null,"slug":"code-failure-prediction-and-pattern","title":"Code Failure Prediction and Pattern Extraction using LSTM Networks","date":"2018-12-13","arxiv_id":"1812.05237","n_code_links":0,"syntology":null},{"paper":"/paper/a-multimodal-lstm-for-predicting-listener","slug":"a-multimodal-lstm-for-predicting-listener","title":"A Multimodal LSTM for Predicting Listener Empathic Responses Over Time","date":"2018-12-12","arxiv_id":"1812.04891","n_code_links":1,"syntology":null},{"paper":"/paper/bayesian-sparsification-of-gated-recurrent","slug":"bayesian-sparsification-of-gated-recurrent","title":"Bayesian Sparsification of Gated Recurrent Neural Networks","date":"2018-12-12","arxiv_id":"1812.05692","n_code_links":1,"syntology":null},{"paper":null,"slug":"lstm-based-ecg-classification-for-continuous","title":"LSTM-Based ECG Classification for Continuous Monitoring on Personal Wearable Devices","date":"2018-12-12","arxiv_id":"1812.04818","n_code_links":0,"syntology":null},{"paper":"/paper/taxi-demand-supply-forecasting-impact-of","slug":"taxi-demand-supply-forecasting-impact-of","title":"Taxi Demand-Supply Forecasting: Impact of Spatial Partitioning on the Performance of Neural Networks","date":"2018-12-10","arxiv_id":"1812.03699","n_code_links":1,"syntology":null},{"paper":null,"slug":"a-hybrid-long-term-load-forecasting-model-for","title":"A Hybrid Distribution Feeder Long-Term Load Forecasting Method Based on Sequence Prediction","date":"2018-12-09","arxiv_id":"1812.04480","n_code_links":0,"syntology":null},{"paper":null,"slug":"zero-initialization-of-modified-gated","title":"Zero Initialization of modified Gated Recurrent Encoder-Decoder Network for Short Term Load Forecasting","date":"2018-12-09","arxiv_id":"1812.03425","n_code_links":0,"syntology":null}],"record_sha256":"973a6a28d2aaecef0fc935dc4346a24d88eb631ef8e40bd567de3268978ee5eb","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}