{"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/34","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":34,"pages_in_order":64,"rows_per_page":100,"rows":[3301,3400],"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/33","next":"/method/tanh-activation/papers/35","papers":[{"paper":null,"slug":"lstms-compose-and-learn-bottom-up","title":"LSTMs Compose (and Learn) Bottom-Up","date":"2020-10-06","arxiv_id":"2010.04650","n_code_links":0,"syntology":null},{"paper":null,"slug":"tackling-the-low-resource-challenge-for","title":"Tackling the Low-resource Challenge for Canonical Segmentation","date":"2020-10-06","arxiv_id":"2010.02804","n_code_links":0,"syntology":null},{"paper":"/paper/the-sequence-to-sequence-baseline-for-the","slug":"the-sequence-to-sequence-baseline-for-the","title":"The Sequence-to-Sequence Baseline for the Voice Conversion Challenge 2020: Cascading ASR and TTS","date":"2020-10-06","arxiv_id":"2010.02434","n_code_links":3,"syntology":null},{"paper":null,"slug":"deep-reinforcement-learning-for-electric-1","title":"Deep Reinforcement Learning for Electric Vehicle Routing Problem with Time Windows","date":"2020-10-05","arxiv_id":"2010.02068","n_code_links":0,"syntology":null},{"paper":null,"slug":"gauravarora-hasoc-dravidian-codemix-fire2020","title":"Gauravarora@HASOC-Dravidian-CodeMix-FIRE2020: Pre-training ULMFiT on Synthetically Generated Code-Mixed Data for Hate Speech Detection","date":"2020-10-05","arxiv_id":"2010.02094","n_code_links":0,"syntology":null},{"paper":"/paper/improving-amr-parsing-with-sequence-to","slug":"improving-amr-parsing-with-sequence-to","title":"Improving AMR Parsing with Sequence-to-Sequence Pre-training","date":"2020-10-05","arxiv_id":"2010.01771","n_code_links":1,"syntology":null},{"paper":"/paper/neurally-augmented-alista-1","slug":"neurally-augmented-alista-1","title":"Neurally Augmented ALISTA","date":"2020-10-05","arxiv_id":"2010.01930","n_code_links":1,"syntology":null},{"paper":"/paper/on-the-universality-of-the-double-descent-1","slug":"on-the-universality-of-the-double-descent-1","title":"On the Universality of the Double Descent Peak in Ridgeless Regression","date":"2020-10-05","arxiv_id":"2010.01851","n_code_links":1,"syntology":{"ran":5,"of":5,"n_ran_checked":3,"n_instrument":2,"unverified":0,"pointer_only":5,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 3 honoured, 0 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","official":{"repos":["dholzmueller/universal_double_descent"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":"/paper/legan-disentangled-manipulation-of","slug":"legan-disentangled-manipulation-of","title":"LEGAN: Disentangled Manipulation of Directional Lighting and Facial Expressions by Leveraging Human Perceptual Judgements","date":"2020-10-04","arxiv_id":"2010.01464","n_code_links":1,"syntology":null},{"paper":null,"slug":"analysis-of-three-dimensional-potential","title":"Analysis of three dimensional potential problems in non-homogeneous media with physics-informed deep collocation method using material transfer learning and sensitivity analysis","date":"2020-10-03","arxiv_id":"2010.12060","n_code_links":0,"syntology":null},{"paper":"/paper/doubleensemble-a-new-ensemble-method-based-on","slug":"doubleensemble-a-new-ensemble-method-based-on","title":"DoubleEnsemble: A New Ensemble Method Based on Sample Reweighting and Feature Selection for Financial Data Analysis","date":"2020-10-03","arxiv_id":"2010.01265","n_code_links":1,"syntology":null},{"paper":null,"slug":"cycle-consistent-adversarial-autoencoders-for","title":"Cycle-Consistent Adversarial Autoencoders for Unsupervised Text Style Transfer","date":"2020-10-02","arxiv_id":"2010.00735","n_code_links":0,"syntology":null},{"paper":"/paper/time-matters-time-aware-lstms-for-predictive","slug":"time-matters-time-aware-lstms-for-predictive","title":"Time Matters: Time-Aware LSTMs for Predictive Business Process Monitoring","date":"2020-10-02","arxiv_id":"2010.00889","n_code_links":1,"syntology":null},{"paper":null,"slug":"how-lstm-encodes-syntax-exploring-context","title":"How LSTM Encodes Syntax: Exploring Context Vectors and Semi-Quantization on Natural Text","date":"2020-10-01","arxiv_id":"2010.00363","n_code_links":0,"syntology":null},{"paper":"/paper/learning-variational-word-masks-to-improve","slug":"learning-variational-word-masks-to-improve","title":"Learning Variational Word Masks to Improve the Interpretability of Neural Text Classifiers","date":"2020-10-01","arxiv_id":"2010.00667","n_code_links":3,"syntology":{"ran":1,"of":1,"n_ran_checked":1,"n_instrument":0,"unverified":0,"pointer_only":1,"phrase":"1 ran (of which 1 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; the one sample that ran constructed an object rather than computing a result","official":{"repos":["UVa-NLP/VMASK"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":1,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"predictive-spectral-analysis-using-an-end-to","title":"Predictive spectral analysis using an end-to-end deep model from hyperspectral images for high-throughput plant phenotyping","date":"2020-10-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":"/paper/tabular-gans-for-uneven-distribution","slug":"tabular-gans-for-uneven-distribution","title":"Tabular GANs for uneven distribution","date":"2020-10-01","arxiv_id":"2010.00638","n_code_links":1,"syntology":null},{"paper":null,"slug":"a-deep-learning-framework-for-covid-outbreak","title":"A Deep Learning Framework for COVID Outbreak Prediction","date":"2020-09-30","arxiv_id":"2010.00382","n_code_links":0,"syntology":null},{"paper":null,"slug":"a-wavelet-cnn-lstm-model-for-tailings-pond","title":"A Wavelet-CNN-LSTM Model for Tailings Pond Risk Prediction","date":"2020-09-30","arxiv_id":"2010.00518","n_code_links":0,"syntology":null},{"paper":"/paper/cardiogan-attentive-generative-adversarial","slug":"cardiogan-attentive-generative-adversarial","title":"CardioGAN: Attentive Generative Adversarial Network with Dual Discriminators for Synthesis of ECG from PPG","date":"2020-09-30","arxiv_id":"2010.00104","n_code_links":2,"syntology":null},{"paper":null,"slug":"rain-code-forecasting-spatiotemporal","title":"Rain-Code Fusion : Code-to-code ConvLSTM Forecasting Spatiotemporal Precipitation","date":"2020-09-30","arxiv_id":"2009.14573","n_code_links":0,"syntology":null},{"paper":null,"slug":"real-time-implementation-of-rmnv2-classifier","title":"Real-time Implementation of RMNv2 Classifier in NXP Bluebox 2.0 and NXP i.MX RT1060","date":"2020-09-30","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":"/paper/transfer-learning-from-monolingual-asr-to","slug":"transfer-learning-from-monolingual-asr-to","title":"Transfer Learning from Monolingual ASR to Transcription-free Cross-lingual Voice Conversion","date":"2020-09-30","arxiv_id":"2009.14668","n_code_links":1,"syntology":null},{"paper":"/paper/align-rudder-learning-from-few-demonstrations","slug":"align-rudder-learning-from-few-demonstrations","title":"Align-RUDDER: Learning From Few Demonstrations by Reward Redistribution","date":"2020-09-29","arxiv_id":"2009.14108","n_code_links":1,"syntology":{"ran":0,"of":3,"n_ran_checked":0,"n_instrument":0,"unverified":3,"pointer_only":0,"phrase":"0 ran · 3 unverified","official":{"repos":["ml-jku/align-rudder"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":3,"ran_from_kinds":[]}}},{"paper":"/paper/learning-to-compress-videos-without-computing","slug":"learning-to-compress-videos-without-computing","title":"Learning to Compress Videos without Computing Motion","date":"2020-09-29","arxiv_id":"2009.14110","n_code_links":1,"syntology":null},{"paper":"/paper/sequence-to-sequence-learning-for-indonesian","slug":"sequence-to-sequence-learning-for-indonesian","title":"Sequence-to-Sequence Learning for Indonesian Automatic Question Generator","date":"2020-09-29","arxiv_id":"2009.13889","n_code_links":1,"syntology":null},{"paper":null,"slug":"a-simple-and-efficient-ensemble-classifier","title":"A Simple and Efficient Ensemble Classifier Combining Multiple Neural Network Models on Social Media Datasets in Vietnamese","date":"2020-09-28","arxiv_id":"2009.13060","n_code_links":0,"syntology":null},{"paper":null,"slug":"conversational-semantic-parsing","title":"Conversational Semantic Parsing","date":"2020-09-28","arxiv_id":"2009.13655","n_code_links":0,"syntology":null},{"paper":"/paper/detecting-soccer-balls-with-reduced-neural","slug":"detecting-soccer-balls-with-reduced-neural","title":"Detecting soccer balls with reduced neural networks: a comparison of multiple architectures under constrained hardware scenarios","date":"2020-09-28","arxiv_id":"2009.13684","n_code_links":1,"syntology":null},{"paper":null,"slug":"early-detection-of-parkinson-disease-using","title":"Early Detection of Parkinson Disease using Deep Neural Networks on Gait Dynamics","date":"2020-09-28","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"fancy-man-lauches-zippo-at-wnut-2020-shared","title":"Fancy Man Lauches Zippo at WNUT 2020 Shared Task-1: A Bert Case Model for Wet Lab Entity Extraction","date":"2020-09-28","arxiv_id":"2009.12997","n_code_links":0,"syntology":null},{"paper":null,"slug":"multi-timescale-representation-learning-in","title":"Multi-timescale Representation Learning in LSTM Language Models","date":"2020-09-27","arxiv_id":"2009.12727","n_code_links":0,"syntology":null},{"paper":null,"slug":"metaphor-detection-using-deep-contextualized","title":"Metaphor Detection using Deep Contextualized Word Embeddings","date":"2020-09-26","arxiv_id":"2009.12565","n_code_links":0,"syntology":null},{"paper":"/paper/developing-fb-chatbot-based-on-deep-learning-1","slug":"developing-fb-chatbot-based-on-deep-learning-1","title":"Developing FB Chatbot Based on Deep Learning Using RASA Framework for University Enquiries","date":"2020-09-25","arxiv_id":"2009.12341","n_code_links":1,"syntology":null},{"paper":"/paper/focus-constrained-attention-mechanism-for","slug":"focus-constrained-attention-mechanism-for","title":"Focus-Constrained Attention Mechanism for CVAE-based Response Generation","date":"2020-09-25","arxiv_id":"2009.12102","n_code_links":1,"syntology":null},{"paper":"/paper/mintl-minimalist-transfer-learning-for-task","slug":"mintl-minimalist-transfer-learning-for-task","title":"MinTL: Minimalist Transfer Learning for Task-Oriented Dialogue Systems","date":"2020-09-25","arxiv_id":"2009.12005","n_code_links":1,"syntology":{"ran":7,"of":9,"n_ran_checked":5,"n_instrument":2,"unverified":2,"pointer_only":3,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 1 honoured, 0 violated, 4 with no contract checked; 2 where Syntology's instrument failed) · 2 unverified","official":{"repos":["zlinao/MinTL"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":2,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"neural-network-based-ranging-with-lte-channel","title":"Neural Network-Based Ranging with LTE Channel Impulse Response for Localization in Indoor Environments","date":"2020-09-24","arxiv_id":"2009.11907","n_code_links":0,"syntology":null},{"paper":"/paper/deep-multi-stations-weather-forecasting","slug":"deep-multi-stations-weather-forecasting","title":"Deep multi-stations weather forecasting: explainable recurrent convolutional neural networks","date":"2020-09-23","arxiv_id":"2009.11239","n_code_links":2,"syntology":null},{"paper":"/paper/seq2edits-sequence-transduction-using-span","slug":"seq2edits-sequence-transduction-using-span","title":"Seq2Edits: Sequence Transduction Using Span-level Edit Operations","date":"2020-09-23","arxiv_id":"2009.11136","n_code_links":1,"syntology":null},{"paper":null,"slug":"triple-gan-with-variable-fractional-order","title":"Triple-GAN with Variable Fractional Order Gradient Descent Method and Mish Activation Function","date":"2020-09-23","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"spatial-temporal-block-and-lstm-network-for","title":"Spatial-Temporal Block and LSTM Network for Pedestrian Trajectories Prediction","date":"2020-09-22","arxiv_id":"2009.10468","n_code_links":0,"syntology":null},{"paper":null,"slug":"a-experimental-study-of-weight-initialization","title":"An Experimental Study of Weight Initialization and Weight Inheritance Effects on Neuroevolution","date":"2020-09-21","arxiv_id":"2009.09644","n_code_links":0,"syntology":null},{"paper":"/paper/multitask-pointer-network-for-multi","slug":"multitask-pointer-network-for-multi","title":"Multitask Pointer Network for Multi-Representational Parsing","date":"2020-09-21","arxiv_id":"2009.09730","n_code_links":1,"syntology":null},{"paper":"/paper/stock-price-prediction-using-machine-learning","slug":"stock-price-prediction-using-machine-learning","title":"Stock Price Prediction Using Machine Learning and LSTM-Based Deep Learning Models","date":"2020-09-20","arxiv_id":"2009.10819","n_code_links":4,"syntology":null},{"paper":null,"slug":"unsupervised-anomaly-detection-on-temporal","title":"Unsupervised Anomaly Detection on Temporal Multiway Data","date":"2020-09-20","arxiv_id":"2009.09443","n_code_links":0,"syntology":null},{"paper":null,"slug":"vicomtech-at-ehealth-kd-challenge-2020-deep","title":"Vicomtech at eHealth-KD Challenge 2020: Deep End-to-End Model for Entity and Relation Extraction in Medical Text","date":"2020-09-20","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"a-deep-learning-based-approach-for-the","title":"A deep learning-based approach for the automated surface inspection of copper clad laminate images","date":"2020-09-19","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":"/paper/towards-computational-linguistics-in","slug":"towards-computational-linguistics-in","title":"Towards Computational Linguistics in Minangkabau Language: Studies on Sentiment Analysis and Machine Translation","date":"2020-09-19","arxiv_id":"2009.09309","n_code_links":1,"syntology":null},{"paper":null,"slug":"hyperspectral-image-classification-method","title":"Hyperspectral Image Classification Method Based on 2D–3D CNN and Multibranch Feature Fusion","date":"2020-09-18","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":"/paper/a-probabilistic-end-to-end-task-oriented","slug":"a-probabilistic-end-to-end-task-oriented","title":"A Probabilistic End-To-End Task-Oriented Dialog Model with Latent Belief States towards Semi-Supervised Learning","date":"2020-09-17","arxiv_id":"2009.08115","n_code_links":1,"syntology":{"ran":4,"of":10,"n_ran_checked":4,"n_instrument":0,"unverified":6,"pointer_only":0,"phrase":"4 ran (of which 2 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 1 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 6 unverified","official":{"repos":["thu-spmi/LABES"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":2,"n_ran_no_instrument_failure":4,"n_unverified":6,"ran_from_kinds":["official"]}}},{"paper":"/paper/distilled-one-shot-federated-learning","slug":"distilled-one-shot-federated-learning","title":"Distilled One-Shot Federated Learning","date":"2020-09-17","arxiv_id":"2009.07999","n_code_links":1,"syntology":null},{"paper":null,"slug":"urban-traffic-flow-forecast-based-on","title":"Urban Traffic Flow Forecast Based on FastGCRNN","date":"2020-09-17","arxiv_id":"2009.08087","n_code_links":0,"syntology":null},{"paper":"/paper/a-convolutional-lstm-based-residual-network","slug":"a-convolutional-lstm-based-residual-network","title":"A Convolutional LSTM based Residual Network for Deepfake Video Detection","date":"2020-09-16","arxiv_id":"2009.07480","n_code_links":1,"syntology":null},{"paper":null,"slug":"an-analysis-of-deep-neural-networks-for","title":"An analysis of deep neural networks for predicting trends in time series data","date":"2020-09-16","arxiv_id":"2009.07943","n_code_links":0,"syntology":null},{"paper":null,"slug":"graph-to-sequence-neural-machine-translation","title":"Graph-to-Sequence Neural Machine Translation","date":"2020-09-16","arxiv_id":"2009.07489","n_code_links":0,"syntology":null},{"paper":null,"slug":"mimic-and-conquer-heterogeneous-tree","title":"Mimic and Conquer: Heterogeneous Tree Structure Distillation for Syntactic NLP","date":"2020-09-16","arxiv_id":"2009.07411","n_code_links":0,"syntology":null},{"paper":"/paper/minimize-exposure-bias-of-seq2seq-models-in","slug":"minimize-exposure-bias-of-seq2seq-models-in","title":"Minimize Exposure Bias of Seq2Seq Models in Joint Entity and Relation Extraction","date":"2020-09-16","arxiv_id":"2009.07503","n_code_links":1,"syntology":{"ran":1,"of":4,"n_ran_checked":1,"n_instrument":0,"unverified":3,"pointer_only":4,"phrase":"1 ran (of which 1 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) · 3 unverified; the one sample that ran constructed an object rather than computing a result","official":{"repos":["WindChimeRan/OpenJERE"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":1,"n_ran_no_instrument_failure":1,"n_unverified":3,"ran_from_kinds":["official"]}}},{"paper":"/paper/rcnn-for-region-of-interest-detection-in","slug":"rcnn-for-region-of-interest-detection-in","title":"RCNN for Region of Interest Detection in Whole Slide Images","date":"2020-09-16","arxiv_id":"2009.07532","n_code_links":1,"syntology":null},{"paper":"/paper/tadgan-time-series-anomaly-detection-using","slug":"tadgan-time-series-anomaly-detection-using","title":"TadGAN: Time Series Anomaly Detection Using Generative Adversarial Networks","date":"2020-09-16","arxiv_id":"2009.07769","n_code_links":5,"syntology":null},{"paper":null,"slug":"learning-functors-using-gradient-descent","title":"Learning Functors using Gradient Descent","date":"2020-09-15","arxiv_id":"2009.06837","n_code_links":0,"syntology":null},{"paper":null,"slug":"lessons-learned-from-applying-off-the-shelf","title":"Lessons Learned from Applying off-the-shelf BERT: There is no Silver Bullet","date":"2020-09-15","arxiv_id":"2009.07238","n_code_links":0,"syntology":null},{"paper":null,"slug":"composing-answer-from-multi-spans-for-reading","title":"Composing Answer from Multi-spans for Reading Comprehension","date":"2020-09-14","arxiv_id":"2009.06141","n_code_links":0,"syntology":null},{"paper":null,"slug":"controllable-neural-text-to-speech-synthesis","title":"Controllable neural text-to-speech synthesis using intuitive prosodic features","date":"2020-09-14","arxiv_id":"2009.06775","n_code_links":0,"syntology":null},{"paper":null,"slug":"short-term-forecasting-covid-19-cases-in","title":"Short-Term Forecasting COVID-19 Cases In Turkey Using Long Short-Term Memory Network","date":"2020-09-14","arxiv_id":"2009.06343","n_code_links":0,"syntology":null},{"paper":"/paper/pairwise-gan-pose-based-view-synthesis","slug":"pairwise-gan-pose-based-view-synthesis","title":"Pairwise-GAN: Pose-based View Synthesis through Pair-Wise Training","date":"2020-09-13","arxiv_id":"2009.06053","n_code_links":1,"syntology":null},{"paper":"/paper/span-based-semantic-parsing-for-compositional","slug":"span-based-semantic-parsing-for-compositional","title":"Span-based Semantic Parsing for Compositional Generalization","date":"2020-09-13","arxiv_id":"2009.06040","n_code_links":1,"syntology":null},{"paper":null,"slug":"abstractive-information-extraction-from","title":"Abstractive Information Extraction from Scanned Invoices (AIESI) using End-to-end Sequential Approach","date":"2020-09-12","arxiv_id":"2009.05728","n_code_links":0,"syntology":null},{"paper":null,"slug":"corrective-feedback-emphatic-speech-synthesis","title":"Visual-speech Synthesis of Exaggerated Corrective Feedback","date":"2020-09-12","arxiv_id":"2009.05748","n_code_links":0,"syntology":null},{"paper":null,"slug":"fine-tuning-pre-trained-contextual-embeddings","title":"Fine-tuning Pre-trained Contextual Embeddings for Citation Content Analysis in Scholarly Publication","date":"2020-09-12","arxiv_id":"2009.05836","n_code_links":0,"syntology":null},{"paper":null,"slug":"relation-detection-for-indonesian-language","title":"Relation Detection for Indonesian Language using Deep Neural Network -- Support Vector Machine","date":"2020-09-12","arxiv_id":"2009.05698","n_code_links":0,"syntology":null},{"paper":"/paper/yolobile-real-time-object-detection-on-mobile","slug":"yolobile-real-time-object-detection-on-mobile","title":"YOLObile: Real-Time Object Detection on Mobile Devices via Compression-Compilation Co-Design","date":"2020-09-12","arxiv_id":"2009.05697","n_code_links":3,"syntology":null},{"paper":null,"slug":"a-comparison-of-lstm-and-bert-for-small","title":"A Comparison of LSTM and BERT for Small Corpus","date":"2020-09-11","arxiv_id":"2009.05451","n_code_links":0,"syntology":null},{"paper":null,"slug":"deep-learning-interference-cancellation-in","title":"Deep Learning Interference Cancellation in Wireless Networks","date":"2020-09-11","arxiv_id":"2009.05533","n_code_links":0,"syntology":null},{"paper":null,"slug":"enabling-image-recognition-on-constrained","title":"Enabling Image Recognition on Constrained Devices Using Neural Network Pruning and a CycleGAN","date":"2020-09-11","arxiv_id":"2009.05300","n_code_links":0,"syntology":null},{"paper":"/paper/gtea-representation-learning-for-temporal","slug":"gtea-representation-learning-for-temporal","title":"GTEA: Inductive Representation Learning on Temporal Interaction Graphs via Temporal Edge Aggregation","date":"2020-09-11","arxiv_id":"2009.05266","n_code_links":2,"syntology":null},{"paper":null,"slug":"investigating-bi-lstm-and-crf-with-pos-tag","title":"Investigating Bi-LSTM and CRF with POS Tag Embedding for Indonesian Named Entity Tagger","date":"2020-09-11","arxiv_id":"2009.05687","n_code_links":0,"syntology":null},{"paper":null,"slug":"multi-modal-embeddings-using-multi-task","title":"Multi-modal embeddings using multi-task learning for emotion recognition","date":"2020-09-10","arxiv_id":"2009.05019","n_code_links":0,"syntology":null},{"paper":null,"slug":"predicting-covid-19-cases-using-bidirectional","title":"Predicting COVID-19 cases using Bidirectional LSTM on multivariate time series","date":"2020-09-10","arxiv_id":"2009.12325","n_code_links":0,"syntology":null},{"paper":null,"slug":"unsupervised-domain-adaptation-via-cyclegan","title":"Unsupervised Domain Adaptation via CycleGAN for White Matter Hyperintensity Segmentation in Multicenter MR Images","date":"2020-09-10","arxiv_id":"2009.04985","n_code_links":0,"syntology":null},{"paper":null,"slug":"central-yup-ik-and-machine-translation-of-low","title":"Central Yup'ik and Machine Translation of Low-Resource Polysynthetic Languages","date":"2020-09-09","arxiv_id":"2009.04087","n_code_links":0,"syntology":null},{"paper":"/paper/mu-gan-facial-attribute-editing-based-on","slug":"mu-gan-facial-attribute-editing-based-on","title":"MU-GAN: Facial Attribute Editing based on Multi-attention Mechanism","date":"2020-09-09","arxiv_id":"2009.04177","n_code_links":1,"syntology":null},{"paper":null,"slug":"enhancing-the-interpretability-of-deep-models","title":"Enhancing the Interpretability of Deep Models in Heathcare Through Attention: Application to Glucose Forecasting for Diabetic People","date":"2020-09-08","arxiv_id":"2009.03732","n_code_links":0,"syntology":null},{"paper":"/paper/neural-time-dependent-partial-differential","slug":"neural-time-dependent-partial-differential","title":"Neural-PDE: A RNN based neural network for solving time dependent PDEs","date":"2020-09-08","arxiv_id":"2009.03892","n_code_links":1,"syntology":null},{"paper":null,"slug":"prediction-coherent-lstm-based-recurrent","title":"Prediction-Coherent LSTM-based Recurrent Neural Network for Safer Glucose Predictions in Diabetic People","date":"2020-09-08","arxiv_id":"2009.03722","n_code_links":0,"syntology":null},{"paper":"/paper/revisiting-lstm-networks-for-semi-supervised-1","slug":"revisiting-lstm-networks-for-semi-supervised-1","title":"Revisiting LSTM Networks for Semi-Supervised Text Classification via Mixed Objective Function","date":"2020-09-08","arxiv_id":"2009.04007","n_code_links":1,"syntology":{"ran":2,"of":3,"n_ran_checked":0,"n_instrument":2,"unverified":1,"pointer_only":3,"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) · 1 unverified","official":null}},{"paper":null,"slug":"study-of-short-term-personalized-glucose","title":"Study of Short-Term Personalized Glucose Predictive Models on Type-1 Diabetic Children","date":"2020-09-08","arxiv_id":"2009.04409","n_code_links":0,"syntology":null},{"paper":null,"slug":"tanhsoft-a-family-of-activation-functions","title":"TanhSoft -- a family of activation functions combining Tanh and Softplus","date":"2020-09-08","arxiv_id":"2009.03863","n_code_links":0,"syntology":null},{"paper":"/paper/automatic-detection-of-microsleep-episodes","slug":"automatic-detection-of-microsleep-episodes","title":"Automatic detection of microsleep episodes with deep learning","date":"2020-09-07","arxiv_id":"2009.03027","n_code_links":1,"syntology":null},{"paper":"/paper/deep-cyclic-generative-adversarial-residual","slug":"deep-cyclic-generative-adversarial-residual","title":"Deep Cyclic Generative Adversarial Residual Convolutional Networks for Real Image Super-Resolution","date":"2020-09-07","arxiv_id":"2009.03693","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":["RaoUmer/SRResCycGAN"],"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":"detecting-and-adapting-to-crisis-pattern-with","title":"Detecting and adapting to crisis pattern with context based Deep Reinforcement Learning","date":"2020-09-07","arxiv_id":"2009.07200","n_code_links":0,"syntology":null},{"paper":"/paper/forecasting-the-leading-indicator-of-a","slug":"forecasting-the-leading-indicator-of-a","title":"Forecasting the Leading Indicator of a Recession: The 10-Year minus 3-Month Treasury Yield Spread","date":"2020-09-07","arxiv_id":"2009.05507","n_code_links":1,"syntology":null},{"paper":"/paper/any-to-many-voice-conversion-with-location","slug":"any-to-many-voice-conversion-with-location","title":"Any-to-Many Voice Conversion with Location-Relative Sequence-to-Sequence Modeling","date":"2020-09-06","arxiv_id":"2009.02725","n_code_links":1,"syntology":null},{"paper":"/paper/calciumgan-a-generative-adversarial-network","slug":"calciumgan-a-generative-adversarial-network","title":"Synthesising Realistic Calcium Traces of Neuronal Populations Using GAN","date":"2020-09-06","arxiv_id":"2009.02707","n_code_links":1,"syntology":null},{"paper":"/paper/a-hybrid-deep-learning-model-for-arabic-text","slug":"a-hybrid-deep-learning-model-for-arabic-text","title":"A Hybrid Deep Learning Model for Arabic Text Recognition","date":"2020-09-04","arxiv_id":"2009.01987","n_code_links":4,"syntology":null},{"paper":"/paper/kilt-a-benchmark-for-knowledge-intensive","slug":"kilt-a-benchmark-for-knowledge-intensive","title":"KILT: a Benchmark for Knowledge Intensive Language Tasks","date":"2020-09-04","arxiv_id":"2009.02252","n_code_links":3,"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":{"repos":["facebookresearch/KILT"],"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":"linguistically-inspired-morphological","title":"Linguistically inspired morphological inflection with a sequence to sequence model","date":"2020-09-04","arxiv_id":"2009.02073","n_code_links":0,"syntology":null},{"paper":"/paper/hifisinger-towards-high-fidelity-neural","slug":"hifisinger-towards-high-fidelity-neural","title":"HiFiSinger: Towards High-Fidelity Neural Singing Voice Synthesis","date":"2020-09-03","arxiv_id":"2009.01776","n_code_links":1,"syntology":{"ran":7,"of":10,"n_ran_checked":7,"n_instrument":0,"unverified":3,"pointer_only":0,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 0 honoured, 0 violated, 7 with no contract checked; 0 where Syntology's instrument failed) · 3 unverified","official":null}},{"paper":null,"slug":"quantum-long-short-term-memory","title":"Quantum Long Short-Term Memory","date":"2020-09-03","arxiv_id":"2009.01783","n_code_links":0,"syntology":null},{"paper":"/paper/a-practical-chinese-dependency-parser-based","slug":"a-practical-chinese-dependency-parser-based","title":"A Practical Chinese Dependency Parser Based on A Large-scale Dataset","date":"2020-09-02","arxiv_id":"2009.00901","n_code_links":2,"syntology":null},{"paper":null,"slug":"application-of-lstm-architectures-for-next","title":"Application of LSTM architectures for next frame forecasting in Sentinel-1 images time series","date":"2020-09-02","arxiv_id":"2009.00841","n_code_links":0,"syntology":null}],"record_sha256":"c0b3470f969d4cbf1de99ece851e644b5e7138589a84f5a460df2260f97f684b","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}