{"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/24","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":24,"pages_in_order":64,"rows_per_page":100,"rows":[2301,2400],"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/23","next":"/method/tanh-activation/papers/25","papers":[{"paper":"/paper/emoji-prediction-from-twitter-data-using-deep","slug":"emoji-prediction-from-twitter-data-using-deep","title":"Emoji Prediction from Twitter Data using Deep Learning Approach","date":"2021-10-04","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":null,"slug":"graph-pointer-neural-networks","title":"Graph Pointer Neural Networks","date":"2021-10-03","arxiv_id":"2110.00973","n_code_links":0,"syntology":null},{"paper":"/paper/light-field-saliency-detection-with-dual","slug":"light-field-saliency-detection-with-dual","title":"Light Field Saliency Detection with Dual Local Graph Learning andReciprocative Guidance","date":"2021-10-02","arxiv_id":"2110.00698","n_code_links":1,"syntology":null},{"paper":null,"slug":"significance-of-data-augmentation-for","title":"Significance of Data Augmentation for Improving Cleft Lip and Palate Speech Recognition","date":"2021-10-02","arxiv_id":"2110.00797","n_code_links":0,"syntology":null},{"paper":null,"slug":"bitcoin-transaction-strategy-construction","title":"Bitcoin Transaction Strategy Construction Based on Deep Reinforcement Learning","date":"2021-09-30","arxiv_id":"2109.14789","n_code_links":0,"syntology":null},{"paper":"/paper/covid-19-fake-news-detection-using","slug":"covid-19-fake-news-detection-using","title":"COVID-19 Fake News Detection Using Bidirectional Encoder Representations from Transformers Based Models","date":"2021-09-30","arxiv_id":"2109.14816","n_code_links":1,"syntology":null},{"paper":"/paper/a-comprehensive-survey-and-performance","slug":"a-comprehensive-survey-and-performance","title":"Activation Functions in Deep Learning: A Comprehensive Survey and Benchmark","date":"2021-09-29","arxiv_id":"2109.14545","n_code_links":1,"syntology":null},{"paper":null,"slug":"a-study-of-aggregation-of-long-time-series","title":"A Study of Aggregation of Long Time-series Input for LSTM Neural Networks","date":"2021-09-29","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"cdnet-a-cascaded-decoupling-architecture-for","title":"CDNet: A cascaded decoupling architecture for video prediction","date":"2021-09-29","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"cross-architecture-distillation-using","title":"Cross-Architecture Distillation Using Bidirectional CMOW Embeddings","date":"2021-09-29","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"federated-contrastive-learning-for-privacy","title":"Federated Contrastive Learning for Privacy-Preserving Unpaired Image-to-Image Translation","date":"2021-09-29","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"hierarchical-character-tagger-for-short-text","title":"Hierarchical Character Tagger for Short Text Spelling Error Correction","date":"2021-09-29","arxiv_id":"2109.14259","n_code_links":0,"syntology":null},{"paper":"/paper/integrating-attention-feedback-into-the","slug":"integrating-attention-feedback-into-the","title":"Integrating Attention Feedback into the Recurrent Neural Network","date":"2021-09-29","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":"/paper/multilingual-fact-linking","slug":"multilingual-fact-linking","title":"Multilingual Fact Linking","date":"2021-09-29","arxiv_id":"2109.14364","n_code_links":1,"syntology":null},{"paper":null,"slug":"naspy-automated-extraction-of-automated","title":"NASPY: Automated Extraction of Automated Machine Learning Models","date":"2021-09-29","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"not-all-regions-are-worthy-to-be-distilled","title":"Not All Regions are Worthy to be Distilled: Region-aware Knowledge Distillation Towards Efficient Image-to-Image Translation","date":"2021-09-29","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"polyphonic-music-composition-an-adversarial","title":"Polyphonic Music Composition: An Adversarial Inverse Reinforcement Learning Approach","date":"2021-09-29","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"short-term-memory-in-neural-language-models","title":"Short-term memory in neural language models","date":"2021-09-29","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"single-cell-capsule-attention-an","title":"Single-Cell Capsule Attention : an interpretable method of cell type classification for single-cell RNA-sequencing data","date":"2021-09-29","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"sketchode-learning-neural-sketch","title":"SketchODE: Learning neural sketch representation in continuous time","date":"2021-09-29","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"stable-cognitive-maps-for-path-integration","title":"Stable cognitive maps for Path Integration emerge from fusing visual and proprioceptive sensors","date":"2021-09-29","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"training-sequence-labeling-models-using-prior","title":"Training sequence labeling models using prior knowledge","date":"2021-09-29","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"variational-component-decoder-for-source","title":"Variational Component Decoder for Source Extraction from Nonlinear Mixture","date":"2021-09-29","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"an-adaptive-deep-learning-framework-for-day","title":"An Adaptive Deep Learning Framework for Day-ahead Forecasting of Photovoltaic Power Generation","date":"2021-09-28","arxiv_id":"2109.13442","n_code_links":0,"syntology":null},{"paper":"/paper/efficient-global-local-memory-for-real-time","slug":"efficient-global-local-memory-for-real-time","title":"Efficient Global-Local Memory for Real-time Instrument Segmentation of Robotic Surgical Video","date":"2021-09-28","arxiv_id":"2109.13593","n_code_links":1,"syntology":null},{"paper":null,"slug":"information-elevation-network-for-fast-online","title":"Information Elevation Network for Fast Online Action Detection","date":"2021-09-28","arxiv_id":"2109.13572","n_code_links":0,"syntology":null},{"paper":null,"slug":"lithium-ion-battery-state-of-health","title":"Lithium-ion Battery State of Health Estimation based on Cycle Synchronization using Dynamic Time Warping","date":"2021-09-28","arxiv_id":"2109.13448","n_code_links":0,"syntology":null},{"paper":null,"slug":"macroeconomic-forecasting-with-lstm-and-mixed","title":"Macroeconomic forecasting with LSTM and mixed frequency time series data","date":"2021-09-28","arxiv_id":"2109.13777","n_code_links":0,"syntology":null},{"paper":null,"slug":"msr-nv-neural-vocoder-using-multiple-sampling","title":"MSR-NV: Neural Vocoder Using Multiple Sampling Rates","date":"2021-09-28","arxiv_id":"2109.13714","n_code_links":0,"syntology":null},{"paper":null,"slug":"expressive-power-of-randomized-signature","title":"Expressive Power of Randomized Signature","date":"2021-09-27","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":"/paper/ganiry-bald-to-hairy-translation-using","slug":"ganiry-bald-to-hairy-translation-using","title":"GANiry: Bald-to-Hairy Translation Using CycleGAN","date":"2021-09-27","arxiv_id":"2109.13126","n_code_links":1,"syntology":null},{"paper":null,"slug":"long-time-prediction-of-nonlinear","title":"Long-time prediction of nonlinear parametrized dynamical systems by deep learning-based ROMs","date":"2021-09-27","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"biocopy-a-plug-and-play-span-copy-mechanism","title":"BioCopy: A Plug-And-Play Span Copy Mechanism in Seq2Seq Models","date":"2021-09-26","arxiv_id":"2109.12533","n_code_links":0,"syntology":null},{"paper":null,"slug":"efficient-non-linear-calculators","title":"Efficient Non-linear Calculators","date":"2021-09-26","arxiv_id":"2109.12686","n_code_links":0,"syntology":null},{"paper":null,"slug":"neural-augmentation-of-kalman-filter-with","title":"Neural Augmentation of Kalman Filter with Hypernetwork for Channel Tracking","date":"2021-09-26","arxiv_id":"2109.12561","n_code_links":0,"syntology":null},{"paper":"/paper/a-real-time-and-high-precision-method-for","slug":"a-real-time-and-high-precision-method-for","title":"A real-time and high-precision method for small traffic-signs recognition","date":"2021-09-25","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":null,"slug":"contrastive-unpaired-translation-using-focal","title":"Contrastive Unpaired Translation using Focal Loss for Patch Classification","date":"2021-09-25","arxiv_id":"2109.12431","n_code_links":0,"syntology":null},{"paper":null,"slug":"enhancing-latent-space-clustering-in-multi","title":"Self-Enhancing Multi-filter Sequence-to-Sequence Model","date":"2021-09-25","arxiv_id":"2109.12399","n_code_links":0,"syntology":null},{"paper":null,"slug":"smart-home-energy-management-sequence-to","title":"Smart Home Energy Management: Sequence-to-Sequence Load Forecasting and Q-Learning","date":"2021-09-25","arxiv_id":"2109.12440","n_code_links":0,"syntology":null},{"paper":null,"slug":"indoor-localization-using-smartphone-magnetic","title":"Indoor Localization Using Smartphone Magnetic with Multi-Scale TCN and LSTM","date":"2021-09-24","arxiv_id":"2109.11750","n_code_links":0,"syntology":null},{"paper":null,"slug":"training-dataset-generation-for-bridge-game","title":"Training dataset generation for bridge game registration","date":"2021-09-24","arxiv_id":"2109.11861","n_code_links":0,"syntology":null},{"paper":null,"slug":"deep-learning-with-kernel-flow-regularization","title":"Deep Learning with Kernel Flow Regularization for Time Series Forecasting","date":"2021-09-23","arxiv_id":"2109.11649","n_code_links":0,"syntology":null},{"paper":null,"slug":"lstm-hyper-parameter-selection-for-malware","title":"LSTM Hyper-Parameter Selection for Malware Detection: Interaction Effects and Hierarchical Selection Approach","date":"2021-09-23","arxiv_id":"2109.11500","n_code_links":0,"syntology":null},{"paper":"/paper/unified-signal-compression-using-a-gan-with","slug":"unified-signal-compression-using-a-gan-with","title":"Unified Signal Compression Using a GAN with Iterative Latent Representation Optimization","date":"2021-09-23","arxiv_id":"2109.11168","n_code_links":1,"syntology":null},{"paper":"/paper/kohtd-kazakh-offline-handwritten-text-dataset","slug":"kohtd-kazakh-offline-handwritten-text-dataset","title":"KOHTD: Kazakh Offline Handwritten Text Dataset","date":"2021-09-22","arxiv_id":"2110.04075","n_code_links":1,"syntology":null},{"paper":"/paper/vehicle-behavior-prediction-and","slug":"vehicle-behavior-prediction-and","title":"Vehicle Behavior Prediction and Generalization Using Imbalanced Learning Techniques","date":"2021-09-22","arxiv_id":"2109.10656","n_code_links":1,"syntology":null},{"paper":null,"slug":"a-comprehensive-review-on-summarizing","title":"A Comprehensive Review on Summarizing Financial News Using Deep Learning","date":"2021-09-21","arxiv_id":"2109.10118","n_code_links":0,"syntology":null},{"paper":null,"slug":"short-term-traffic-prediction-using-physics","title":"Short-term traffic prediction using physics-aware neural networks","date":"2021-09-21","arxiv_id":"2109.10253","n_code_links":0,"syntology":null},{"paper":"/paper/rethnicity-predicting-ethnicity-from-names","slug":"rethnicity-predicting-ethnicity-from-names","title":"Rethnicity: Predicting Ethnicity from Names","date":"2021-09-19","arxiv_id":"2109.09228","n_code_links":1,"syntology":null},{"paper":"/paper/unsupervised-3d-pose-estimation-for","slug":"unsupervised-3d-pose-estimation-for","title":"Unsupervised 3D Pose Estimation for Hierarchical Dance Video Recognition","date":"2021-09-19","arxiv_id":"2109.09166","n_code_links":1,"syntology":null},{"paper":null,"slug":"hydroelectric-generation-forecasting-with","title":"Hydroelectric Generation Forecasting with Long Short Term Memory (LSTM) Based Deep Learning Model for Turkey","date":"2021-09-18","arxiv_id":"2109.09013","n_code_links":0,"syntology":null},{"paper":null,"slug":"a-deep-learning-based-optimization-approach","title":"A Deep-Learning Based Optimization Approach to Address Stop-Skipping Strategy in Urban Rail Transit Lines","date":"2021-09-17","arxiv_id":"2109.08786","n_code_links":0,"syntology":null},{"paper":null,"slug":"adaloss-a-computationally-efficient-and","title":"AdaLoss: A computationally-efficient and provably convergent adaptive gradient method","date":"2021-09-17","arxiv_id":"2109.08282","n_code_links":0,"syntology":null},{"paper":null,"slug":"comfetch-federated-learning-of-large-networks","title":"Comfetch: Federated Learning of Large Networks on Constrained Clients via Sketching","date":"2021-09-17","arxiv_id":"2109.08346","n_code_links":0,"syntology":null},{"paper":null,"slug":"continuous-streaming-multi-talker-asr-with","title":"Continuous Streaming Multi-Talker ASR with Dual-path Transducers","date":"2021-09-17","arxiv_id":"2109.08555","n_code_links":0,"syntology":null},{"paper":"/paper/new-students-on-sesame-street-what-order","slug":"new-students-on-sesame-street-what-order","title":"General Cross-Architecture Distillation of Pretrained Language Models into Matrix Embeddings","date":"2021-09-17","arxiv_id":"2109.08449","n_code_links":1,"syntology":null},{"paper":null,"slug":"on-device-neural-speech-synthesis","title":"On-device neural speech synthesis","date":"2021-09-17","arxiv_id":"2109.08710","n_code_links":0,"syntology":null},{"paper":null,"slug":"deep-spiking-neural-networks-with-resonate","title":"Deep Spiking Neural Networks with Resonate-and-Fire Neurons","date":"2021-09-16","arxiv_id":"2109.08234","n_code_links":0,"syntology":null},{"paper":"/paper/jointly-modeling-aspect-and-polarity-for","slug":"jointly-modeling-aspect-and-polarity-for","title":"Jointly Modeling Aspect and Polarity for Aspect-based Sentiment Analysis in Persian Reviews","date":"2021-09-16","arxiv_id":"2109.07680","n_code_links":1,"syntology":null},{"paper":"/paper/revisiting-tri-training-of-dependency-parsers","slug":"revisiting-tri-training-of-dependency-parsers","title":"Revisiting Tri-training of Dependency Parsers","date":"2021-09-16","arxiv_id":"2109.08122","n_code_links":2,"syntology":null},{"paper":"/paper/resolution-robust-large-mask-inpainting-with","slug":"resolution-robust-large-mask-inpainting-with","title":"Resolution-robust Large Mask Inpainting with Fourier Convolutions","date":"2021-09-15","arxiv_id":"2109.07161","n_code_links":8,"syntology":{"ran":3,"of":4,"n_ran_checked":3,"n_instrument":0,"unverified":1,"pointer_only":0,"phrase":"3 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; 0 where Syntology's instrument failed) · 1 unverified","official":{"repos":["saic-mdal/lama"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":1,"ran_from_kinds":["official"]}}},{"paper":"/paper/a-temporal-variational-model-for-story","slug":"a-temporal-variational-model-for-story","title":"A Temporal Variational Model for Story Generation","date":"2021-09-14","arxiv_id":"2109.06807","n_code_links":3,"syntology":null},{"paper":null,"slug":"a-three-step-training-approach-with-data","title":"A Three Step Training Approach with Data Augmentation for Morphological Inflection","date":"2021-09-14","arxiv_id":"2109.07006","n_code_links":0,"syntology":null},{"paper":null,"slug":"controllable-dialogue-generation-with","title":"Controllable Dialogue Generation with Disentangled Multi-grained Style Specification and Attribute Consistency Reward","date":"2021-09-14","arxiv_id":"2109.06717","n_code_links":0,"syntology":null},{"paper":null,"slug":"evaluating-biomedical-bert-models-for","title":"Evaluating Biomedical BERT Models for Vocabulary Alignment at Scale in the UMLS Metathesaurus","date":"2021-09-14","arxiv_id":"2109.13348","n_code_links":0,"syntology":null},{"paper":null,"slug":"oscillatory-fourier-neural-network-a-compact","title":"Oscillatory Fourier Neural Network: A Compact and Efficient Architecture for Sequential Processing","date":"2021-09-14","arxiv_id":"2109.13090","n_code_links":0,"syntology":null},{"paper":null,"slug":"stock-price-prediction-under-anomalous","title":"Stock Price Prediction Under Anomalous Circumstances","date":"2021-09-14","arxiv_id":"2109.15059","n_code_links":0,"syntology":null},{"paper":null,"slug":"beyond-isolated-utterances-conversational","title":"Beyond Isolated Utterances: Conversational Emotion Recognition","date":"2021-09-13","arxiv_id":"2109.06112","n_code_links":0,"syntology":null},{"paper":"/paper/flitext-a-faster-and-lighter-semi-supervised","slug":"flitext-a-faster-and-lighter-semi-supervised","title":"FLiText: A Faster and Lighter Semi-Supervised Text Classification with Convolution Networks","date":"2021-09-12","arxiv_id":"2110.11869","n_code_links":1,"syntology":null},{"paper":null,"slug":"clinical-trial-information-extraction-with","title":"Clinical Trial Information Extraction with BERT","date":"2021-09-11","arxiv_id":"2110.10027","n_code_links":0,"syntology":null},{"paper":null,"slug":"college-student-retention-risk-analysis-from","title":"College Student Retention Risk Analysis From Educational Database using Multi-Task Multi-Modal Neural Fusion","date":"2021-09-11","arxiv_id":"2109.05178","n_code_links":0,"syntology":null},{"paper":"/paper/implicit-premise-generation-with-discourse","slug":"implicit-premise-generation-with-discourse","title":"Implicit Premise Generation with Discourse-aware Commonsense Knowledge Models","date":"2021-09-11","arxiv_id":"2109.05358","n_code_links":1,"syntology":null},{"paper":"/paper/multilingual-translation-via-grafting-pre","slug":"multilingual-translation-via-grafting-pre","title":"Multilingual Translation via Grafting Pre-trained Language Models","date":"2021-09-11","arxiv_id":"2109.05256","n_code_links":1,"syntology":null},{"paper":null,"slug":"remaining-useful-life-estimation-of-hard-disk","title":"Remaining Useful Life Estimation of Hard Disk Drives using Bidirectional LSTM Networks","date":"2021-09-11","arxiv_id":"2109.05351","n_code_links":0,"syntology":null},{"paper":"/paper/how-may-i-help-you-using-neural-text","slug":"how-may-i-help-you-using-neural-text","title":"How May I Help You? Using Neural Text Simplification to Improve Downstream NLP Tasks","date":"2021-09-10","arxiv_id":"2109.04604","n_code_links":1,"syntology":null},{"paper":"/paper/modeling-human-sentence-processing-with-left","slug":"modeling-human-sentence-processing-with-left","title":"Modeling Human Sentence Processing with Left-Corner Recurrent Neural Network Grammars","date":"2021-09-10","arxiv_id":"2109.04939","n_code_links":2,"syntology":null},{"paper":null,"slug":"erfact-non-monotonic-smooth-trainable","title":"ErfAct and Pserf: Non-monotonic Smooth Trainable Activation Functions","date":"2021-09-09","arxiv_id":"2109.04386","n_code_links":0,"syntology":null},{"paper":"/paper/multi-granularity-textual-adversarial-attack","slug":"multi-granularity-textual-adversarial-attack","title":"Multi-granularity Textual Adversarial Attack with Behavior Cloning","date":"2021-09-09","arxiv_id":"2109.04367","n_code_links":1,"syntology":null},{"paper":"/paper/thinking-clearly-talking-fast-concept-guided","slug":"thinking-clearly-talking-fast-concept-guided","title":"Thinking Clearly, Talking Fast: Concept-Guided Non-Autoregressive Generation for Open-Domain Dialogue Systems","date":"2021-09-09","arxiv_id":"2109.04084","n_code_links":1,"syntology":{"ran":5,"of":10,"n_ran_checked":5,"n_instrument":0,"unverified":5,"pointer_only":2,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 1 violated, 4 with no contract checked; 0 where Syntology's instrument failed) · 5 unverified","official":{"repos":["rowitzou/cg-nar"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":5,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"learning-to-combine-the-modalities-of","title":"Learning to Combine the Modalities of Language and Video for Temporal Moment Localization","date":"2021-09-07","arxiv_id":"2109.02925","n_code_links":0,"syntology":null},{"paper":"/paper/optimal-reservoir-operations-using-long-short","slug":"optimal-reservoir-operations-using-long-short","title":"Optimal Reservoir Operations using Long Short-Term Memory Network","date":"2021-09-07","arxiv_id":"2109.04255","n_code_links":1,"syntology":null},{"paper":null,"slug":"paraphrase-generation-as-unsupervised-machine","title":"Paraphrase Generation as Unsupervised Machine Translation","date":"2021-09-07","arxiv_id":"2109.02950","n_code_links":0,"syntology":null},{"paper":null,"slug":"knowledge-graph-question-answering-via-sparql","title":"Knowledge Graph Question Answering via SPARQL Silhouette Generation","date":"2021-09-06","arxiv_id":"2109.09475","n_code_links":0,"syntology":null},{"paper":"/paper/text-to-table-a-new-way-of-information","slug":"text-to-table-a-new-way-of-information","title":"Text-to-Table: A New Way of Information Extraction","date":"2021-09-06","arxiv_id":"2109.02707","n_code_links":1,"syntology":{"ran":3,"of":3,"n_ran_checked":0,"n_instrument":3,"unverified":0,"pointer_only":0,"phrase":"3 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; 3 where Syntology's instrument failed) · 0 unverified","official":{"repos":["shirley-wu/text_to_table"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":"/paper/image-compression-with-recurrent-neural-1","slug":"image-compression-with-recurrent-neural-1","title":"Image Compression with Recurrent Neural Network and Generalized Divisive Normalization","date":"2021-09-05","arxiv_id":"2109.01999","n_code_links":2,"syntology":null},{"paper":null,"slug":"error-detection-in-large-scale-natural","title":"Error Detection in Large-Scale Natural Language Understanding Systems Using Transformer Models","date":"2021-09-04","arxiv_id":"2109.01754","n_code_links":0,"syntology":null},{"paper":"/paper/pushing-paraphrase-away-from-original","slug":"pushing-paraphrase-away-from-original","title":"Pushing Paraphrase Away from Original Sentence: A Multi-Round Paraphrase Generation Approach","date":"2021-09-04","arxiv_id":"2109.01862","n_code_links":1,"syntology":{"ran":4,"of":4,"n_ran_checked":4,"n_instrument":0,"unverified":0,"pointer_only":0,"phrase":"4 ran (of which 3 constructed an object rather than computing a result; 4 with no instrument failure: 1 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","official":{"repos":["l-zhe/btmpg"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":3,"n_ran_no_instrument_failure":4,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"estimating-demand-flexibility-using-siamese","title":"Estimating Demand Flexibility Using Siamese LSTM Neural Networks","date":"2021-09-03","arxiv_id":"2109.01258","n_code_links":0,"syntology":null},{"paper":null,"slug":"no-need-to-know-everything-efficiently","title":"No Need to Know Everything! Efficiently Augmenting Language Models With External Knowledge","date":"2021-09-03","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"evaluating-the-single-shot-multibox-detector","title":"Evaluating the Single-Shot MultiBox Detector and YOLO Deep Learning Models for the Detection of Tomatoes in a Greenhouse","date":"2021-09-02","arxiv_id":"2109.00810","n_code_links":0,"syntology":null},{"paper":"/paper/macrpo-multi-agent-cooperative-recurrent","slug":"macrpo-multi-agent-cooperative-recurrent","title":"MACRPO: Multi-Agent Cooperative Recurrent Policy Optimization","date":"2021-09-02","arxiv_id":"2109.00882","n_code_links":1,"syntology":null},{"paper":"/paper/assessing-domain-adaptation-techniques-for","slug":"assessing-domain-adaptation-techniques-for","title":"Assessing domain adaptation techniques for mitosis detection in multi-scanner breast cancer histopathology images","date":"2021-09-01","arxiv_id":"2109.00869","n_code_links":1,"syntology":null},{"paper":"/paper/text-autoaugment-learning-compositional","slug":"text-autoaugment-learning-compositional","title":"Text AutoAugment: Learning Compositional Augmentation Policy for Text Classification","date":"2021-09-01","arxiv_id":"2109.00523","n_code_links":1,"syntology":null},{"paper":null,"slug":"automatic-non-invasive-cough-detection-based","title":"Automatic non-invasive Cough Detection based on Accelerometer and Audio Signals","date":"2021-08-31","arxiv_id":"2109.00103","n_code_links":0,"syntology":null},{"paper":null,"slug":"medical-sansformers-training-self-supervised","title":"SANSformers: Self-Supervised Forecasting in Electronic Health Records with Attention-Free Models","date":"2021-08-31","arxiv_id":"2108.13672","n_code_links":0,"syntology":null},{"paper":null,"slug":"neural-sequence-to-sequence-speech-synthesis","title":"Neural Sequence-to-Sequence Speech Synthesis Using a Hidden Semi-Markov Model Based Structured Attention Mechanism","date":"2021-08-31","arxiv_id":"2108.13985","n_code_links":0,"syntology":null},{"paper":null,"slug":"quantization-of-generative-adversarial","title":"Quantization of Generative Adversarial Networks for Efficient Inference: a Methodological Study","date":"2021-08-31","arxiv_id":"2108.13996","n_code_links":0,"syntology":null},{"paper":null,"slug":"sense-representations-for-portuguese","title":"Sense representations for Portuguese: experiments with sense embeddings and deep neural language models","date":"2021-08-31","arxiv_id":"2109.00025","n_code_links":0,"syntology":null},{"paper":null,"slug":"temporal-deep-learning-architecture-for","title":"Temporal Deep Learning Architecture for Prediction of COVID-19 Cases in India","date":"2021-08-31","arxiv_id":"2108.13823","n_code_links":0,"syntology":null},{"paper":null,"slug":"working-memory-connections-for-lstm","title":"Working Memory Connections for LSTM","date":"2021-08-31","arxiv_id":"2109.00020","n_code_links":0,"syntology":null}],"record_sha256":"6ed84dd4e278358af32c5e4218fd9850a51a5086a1c210a417256d41944ae6bb","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}