{"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/31","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":31,"pages_in_order":64,"rows_per_page":100,"rows":[3001,3100],"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/30","next":"/method/tanh-activation/papers/32","papers":[{"paper":null,"slug":"icmsc-intra-and-cross-modality-semantic","title":"ICMSC: Intra- and Cross-modality Semantic Consistency for Unsupervised Domain Adaptation on Hip Joint Bone Segmentation","date":"2020-12-23","arxiv_id":"2012.12570","n_code_links":0,"syntology":null},{"paper":null,"slug":"bkt-lstm-efficient-student-modeling-for","title":"BKT-LSTM: Efficient Student Modeling for knowledge tracing and student performance prediction","date":"2020-12-22","arxiv_id":"2012.12218","n_code_links":0,"syntology":null},{"paper":null,"slug":"domain-adaptation-of-nmt-models-for-english","title":"Domain Adaptation of NMT models for English-Hindi Machine Translation Task at AdapMT ICON 2020","date":"2020-12-22","arxiv_id":"2012.12112","n_code_links":0,"syntology":null},{"paper":"/paper/improved-biomedical-word-embeddings-in-the","slug":"improved-biomedical-word-embeddings-in-the","title":"Improved Biomedical Word Embeddings in the Transformer Era","date":"2020-12-22","arxiv_id":"2012.11808","n_code_links":1,"syntology":null},{"paper":null,"slug":"techtexc-classification-of-technical-texts","title":"TechTexC: Classification of Technical Texts using Convolution and Bidirectional Long Short Term Memory Network","date":"2020-12-21","arxiv_id":"2012.11420","n_code_links":0,"syntology":null},{"paper":null,"slug":"dccrgan-deep-complex-convolution-recurrent","title":"DCCRGAN: Deep Complex Convolution Recurrent Generator Adversarial Network for Speech Enhancement","date":"2020-12-19","arxiv_id":"2012.10732","n_code_links":0,"syntology":null},{"paper":null,"slug":"recent-advances-of-generic-object-detection","title":"Recent Advances of Generic Object Detection with Deep Learning: A Review","date":"2020-12-19","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"detection-and-prediction-of-nutrient","title":"Detection and Prediction of Nutrient Deficiency Stress using Longitudinal Aerial Imagery","date":"2020-12-17","arxiv_id":"2012.09654","n_code_links":0,"syntology":null},{"paper":"/paper/efficient-cnn-lstm-based-image-captioning","slug":"efficient-cnn-lstm-based-image-captioning","title":"Efficient CNN-LSTM based Image Captioning using Neural Network Compression","date":"2020-12-17","arxiv_id":"2012.09708","n_code_links":1,"syntology":null},{"paper":null,"slug":"parallel-wavenet-conditioned-on-vae-latent","title":"Parallel WaveNet conditioned on VAE latent vectors","date":"2020-12-17","arxiv_id":"2012.09703","n_code_links":0,"syntology":null},{"paper":"/paper/sensitive-data-detection-with-high-throughput","slug":"sensitive-data-detection-with-high-throughput","title":"Sensitive Data Detection with High-Throughput Neural Network Models for Financial Institutions","date":"2020-12-17","arxiv_id":"2012.09597","n_code_links":1,"syntology":null},{"paper":"/paper/strokegan-reducing-mode-collapse-in-chinese","slug":"strokegan-reducing-mode-collapse-in-chinese","title":"StrokeGAN: Reducing Mode Collapse in Chinese Font Generation via Stroke Encoding","date":"2020-12-16","arxiv_id":"2012.08687","n_code_links":1,"syntology":null},{"paper":null,"slug":"visually-grounding-instruction-for-history","title":"Visually Grounding Language Instruction for History-Dependent Manipulation","date":"2020-12-16","arxiv_id":"2012.08977","n_code_links":0,"syntology":null},{"paper":null,"slug":"lstm-based-space-occupancy-prediction-towards","title":"LSTM-based Space Occupancy Prediction towards Efficient Building Energy Management","date":"2020-12-15","arxiv_id":"2012.08114","n_code_links":0,"syntology":null},{"paper":null,"slug":"multi-channel-optical-neuromorphic-processor","title":"Multi-channel optical neuromorphic processor for frequency-multiplexed signals","date":"2020-12-15","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":"/paper/contrastive-learning-with-adversarial-2","slug":"contrastive-learning-with-adversarial-2","title":"Contrastive Learning with Adversarial Perturbations for Conditional Text Generation","date":"2020-12-14","arxiv_id":"2012.07280","n_code_links":1,"syntology":{"ran":0,"of":1,"n_ran_checked":0,"n_instrument":0,"unverified":1,"pointer_only":1,"phrase":"0 ran · 1 unverified","official":{"repos":["seanie12/CLAPS"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":[]}}},{"paper":null,"slug":"discriminative-pre-training-for-low-resource","title":"Discriminative Pre-training for Low Resource Title Compression in Conversational Grocery","date":"2020-12-13","arxiv_id":"2012.06943","n_code_links":0,"syntology":null},{"paper":"/paper/simple-copy-paste-is-a-strong-data","slug":"simple-copy-paste-is-a-strong-data","title":"Simple Copy-Paste is a Strong Data Augmentation Method for Instance Segmentation","date":"2020-12-13","arxiv_id":"2012.07177","n_code_links":5,"syntology":{"ran":2,"of":2,"n_ran_checked":1,"n_instrument":1,"unverified":0,"pointer_only":0,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","official":{"repos":["tensorflow/tpu"],"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":"sparta-speaker-profiling-for-arabic-talk","title":"SPARTA: Speaker Profiling for ARabic TAlk","date":"2020-12-13","arxiv_id":"2012.07073","n_code_links":0,"syntology":null},{"paper":"/paper/using-restricted-boltzmann-machines-to-model","slug":"using-restricted-boltzmann-machines-to-model","title":"Using Restricted Boltzmann Machines to Model Molecular Geometries","date":"2020-12-13","arxiv_id":"2012.06984","n_code_links":1,"syntology":null},{"paper":null,"slug":"mapping-the-timescale-organization-of-neural-1","title":"Mapping the Timescale Organization of Neural Language Models","date":"2020-12-12","arxiv_id":"2012.06717","n_code_links":0,"syntology":null},{"paper":null,"slug":"sensenet-neural-keyphrase-generation-with","title":"SenSeNet: Neural Keyphrase Generation with Document Structure","date":"2020-12-12","arxiv_id":"2012.06754","n_code_links":0,"syntology":null},{"paper":null,"slug":"convolutional-lstm-neural-networks-for","title":"Convolutional LSTM Neural Networks for Modeling Wildland Fire Dynamics","date":"2020-12-11","arxiv_id":"2012.06679","n_code_links":0,"syntology":null},{"paper":"/paper/morphology-matters-a-multilingual-language","slug":"morphology-matters-a-multilingual-language","title":"Morphology Matters: A Multilingual Language Modeling Analysis","date":"2020-12-11","arxiv_id":"2012.06262","n_code_links":1,"syntology":null},{"paper":"/paper/option-tracing-beyond-binary-knowledge","slug":"option-tracing-beyond-binary-knowledge","title":"Option Tracing: Beyond Binary Knowledge Tracing","date":"2020-12-11","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":"/paper/se-ecgnet-a-multi-scale-deep-residual-network","slug":"se-ecgnet-a-multi-scale-deep-residual-network","title":"SE-ECGNet: A Multi-scale Deep Residual Network with Squeeze-and-Excitation Module for ECG Signal Classification","date":"2020-12-10","arxiv_id":"2012.05510","n_code_links":1,"syntology":null},{"paper":null,"slug":"explornn-understanding-recurrent-neural","title":"exploRNN: Understanding Recurrent Neural Networks through Visual Exploration","date":"2020-12-09","arxiv_id":"2012.06326","n_code_links":0,"syntology":null},{"paper":"/paper/extracting-outcomes-from-appellate-decisions","slug":"extracting-outcomes-from-appellate-decisions","title":"Extracting Outcomes from Appellate Decisions in US State Courts","date":"2020-12-09","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":"/paper/lipschitz-regularized-cyclegan-for-improving","slug":"lipschitz-regularized-cyclegan-for-improving","title":"Semantically Robust Unpaired Image Translation for Data with Unmatched Semantics Statistics","date":"2020-12-09","arxiv_id":"2012.04932","n_code_links":1,"syntology":null},{"paper":"/paper/recurrence-free-unconstrained-handwritten","slug":"recurrence-free-unconstrained-handwritten","title":"Recurrence-free unconstrained handwritten text recognition using gated fully convolutional network","date":"2020-12-09","arxiv_id":"2012.04961","n_code_links":1,"syntology":null},{"paper":null,"slug":"revisiting-iterative-back-translation-from","title":"Revisiting Iterative Back-Translation from the Perspective of Compositional Generalization","date":"2020-12-08","arxiv_id":"2012.04276","n_code_links":0,"syntology":null},{"paper":null,"slug":"3d-cnn-for-facial-emotion-recognition-in","title":"3D-CNN for Facial Emotion Recognition in Videos","date":"2020-12-07","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"an-autoencoder-wavelet-based-deep-neural","title":"An autoencoder wavelet based deep neural network with attention mechanism for multistep prediction of plant growth","date":"2020-12-07","arxiv_id":"2012.04041","n_code_links":0,"syntology":null},{"paper":null,"slug":"cycleqsm-unsupervised-qsm-deep-learning-using","title":"CycleQSM: Unsupervised QSM Deep Learning using Physics-Informed CycleGAN","date":"2020-12-07","arxiv_id":"2012.03842","n_code_links":0,"syntology":null},{"paper":null,"slug":"kgplm-knowledge-guided-language-model-pre","title":"KgPLM: Knowledge-guided Language Model Pre-training via Generative and Discriminative Learning","date":"2020-12-07","arxiv_id":"2012.03551","n_code_links":0,"syntology":null},{"paper":null,"slug":"using-previous-acoustic-context-to-improve","title":"Using previous acoustic context to improve Text-to-Speech synthesis","date":"2020-12-07","arxiv_id":"2012.03763","n_code_links":0,"syntology":null},{"paper":"/paper/align-gram-rethinking-the-skip-gram-model-for","slug":"align-gram-rethinking-the-skip-gram-model-for","title":"Align-gram : Rethinking the Skip-gram Model for Protein Sequence Analysis","date":"2020-12-06","arxiv_id":"2012.03324","n_code_links":1,"syntology":null},{"paper":"/paper/a-benchmark-dataset-for-understandable","slug":"a-benchmark-dataset-for-understandable","title":"Benchmarking Automated Clinical Language Simplification: Dataset, Algorithm, and Evaluation","date":"2020-12-04","arxiv_id":"2012.02420","n_code_links":1,"syntology":null},{"paper":"/paper/authnet-a-deep-learning-based-authentication","slug":"authnet-a-deep-learning-based-authentication","title":"AuthNet: A Deep Learning based Authentication Mechanism using Temporal Facial Feature Movements","date":"2020-12-04","arxiv_id":"2012.02515","n_code_links":2,"syntology":null},{"paper":"/paper/crop-classification-under-varying-cloud-cover","slug":"crop-classification-under-varying-cloud-cover","title":"Crop Classification under Varying Cloud Cover with Neural Ordinary Differential Equations","date":"2020-12-04","arxiv_id":"2012.02542","n_code_links":1,"syntology":null},{"paper":"/paper/a-journey-in-esn-and-lstm-visualisations-on-a","slug":"a-journey-in-esn-and-lstm-visualisations-on-a","title":"A journey in ESN and LSTM visualisations on a language task","date":"2020-12-03","arxiv_id":"2012.01748","n_code_links":1,"syntology":null},{"paper":"/paper/bengali-abstractive-news-summarization-bans-a","slug":"bengali-abstractive-news-summarization-bans-a","title":"Bengali Abstractive News Summarization(BANS): A Neural Attention Approach","date":"2020-12-03","arxiv_id":"2012.01747","n_code_links":1,"syntology":null},{"paper":"/paper/bert-hlstms-bert-and-hierarchical-lstms-for","slug":"bert-hlstms-bert-and-hierarchical-lstms-for","title":"BERT-hLSTMs: BERT and Hierarchical LSTMs for Visual Storytelling","date":"2020-12-03","arxiv_id":"2012.02128","n_code_links":0,"syntology":null},{"paper":"/paper/deepvideomvs-multi-view-stereo-on-video-with","slug":"deepvideomvs-multi-view-stereo-on-video-with","title":"DeepVideoMVS: Multi-View Stereo on Video with Recurrent Spatio-Temporal Fusion","date":"2020-12-03","arxiv_id":"2012.02177","n_code_links":2,"syntology":null},{"paper":null,"slug":"evolving-character-level-convolutional-neural","title":"Evolving Character-level Convolutional Neural Networks for Text Classification","date":"2020-12-03","arxiv_id":"2012.02223","n_code_links":0,"syntology":null},{"paper":null,"slug":"traffic-surveillance-using-vehicle-license","title":"Traffic Surveillance using Vehicle License Plate Detection and Recognition in Bangladesh","date":"2020-12-03","arxiv_id":"2012.02218","n_code_links":0,"syntology":null},{"paper":null,"slug":"combining-spatial-clustering-with-lstm-speech","title":"Combining Spatial Clustering with LSTM Speech Models for Multichannel Speech Enhancement","date":"2020-12-02","arxiv_id":"2012.03388","n_code_links":0,"syntology":null},{"paper":null,"slug":"comparison-of-attention-based-deep-learning","title":"Comparison of Attention-based Deep Learning Models for EEG Classification","date":"2020-12-02","arxiv_id":"2012.01074","n_code_links":0,"syntology":null},{"paper":null,"slug":"covid-19-cough-classification-using-machine","title":"COVID-19 Cough Classification using Machine Learning and Global Smartphone Recordings","date":"2020-12-02","arxiv_id":"2012.01926","n_code_links":0,"syntology":null},{"paper":null,"slug":"da2-deep-attention-adapter-for-memory","title":"$DA^3$:Dynamic Additive Attention Adaption for Memory-EfficientOn-Device Multi-Domain Learning","date":"2020-12-02","arxiv_id":"2012.01362","n_code_links":0,"syntology":null},{"paper":null,"slug":"enhancement-of-spatial-clustering-based-time","title":"Enhancement of Spatial Clustering-Based Time-Frequency Masks using LSTM Neural Networks","date":"2020-12-02","arxiv_id":"2012.01576","n_code_links":0,"syntology":null},{"paper":null,"slug":"generating-descriptions-for-sequential-images","title":"Generating Descriptions for Sequential Images with Local-Object Attention and Global Semantic Context Modelling","date":"2020-12-02","arxiv_id":"2012.01295","n_code_links":0,"syntology":null},{"paper":null,"slug":"generation-of-annotated-multimodal-ground","title":"Generation of annotated multimodal ground truth datasets for abdominal medical image registration","date":"2020-12-02","arxiv_id":"2012.01582","n_code_links":0,"syntology":null},{"paper":null,"slug":"improved-mvdr-beamforming-using-lstm-speech","title":"Improved MVDR Beamforming Using LSTM Speech Models to Clean Spatial Clustering Masks","date":"2020-12-02","arxiv_id":"2012.02191","n_code_links":0,"syntology":null},{"paper":"/paper/learning-universal-shape-dictionary-for","slug":"learning-universal-shape-dictionary-for","title":"Learning Universal Shape Dictionary for Realtime Instance Segmentation","date":"2020-12-02","arxiv_id":"2012.01050","n_code_links":1,"syntology":null},{"paper":null,"slug":"physics-guided-machine-learning-methods-for","title":"Physics Guided Machine Learning Methods for Hydrology","date":"2020-12-02","arxiv_id":"2012.02854","n_code_links":0,"syntology":null},{"paper":null,"slug":"soc-estimation-of-li-ion-batteries-with","title":"SOC Estimation of Li-ion Batteries with Learning Rate-Optimized Deep Fully Convolutional Network","date":"2020-12-02","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"tan-ntm-topic-attention-networks-for-neural","title":"TAN-NTM: Topic Attention Networks for Neural Topic Modeling","date":"2020-12-02","arxiv_id":"2012.01524","n_code_links":0,"syntology":null},{"paper":"/paper/arabisc-context-sensitive-neural-spelling","slug":"arabisc-context-sensitive-neural-spelling","title":"Arabisc: Context-Sensitive Neural Spelling Checker","date":"2020-12-01","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":null,"slug":"bertatde-at-semeval-2020-task-6-extracting","title":"BERTatDE at SemEval-2020 Task 6: Extracting Term-definition Pairs in Free Text Using Pre-trained Model","date":"2020-12-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"bridge-the-gap-high-level-semantic-planning","title":"Bridge the Gap: High-level Semantic Planning for Image Captioning","date":"2020-12-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":"/paper/classifier-probes-may-just-learn-from-linear","slug":"classifier-probes-may-just-learn-from-linear","title":"Classifier Probes May Just Learn from Linear Context Features","date":"2020-12-01","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":null,"slug":"creation-of-corpus-and-analysis-in-code-mixed","title":"Creation of Corpus and analysis in Code-Mixed Kannada-English Twitter data for Emotion Prediction","date":"2020-12-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"csecu-kde-ma-at-semeval-2020-task-8-a-neural","title":"CSECU\\_KDE\\_MA at SemEval-2020 Task 8: A Neural Attention Model for Memotion Analysis","date":"2020-12-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"cyclegan-without-checkerboard-artifacts-for","title":"CycleGAN without checkerboard artifacts for counter-forensics of fake-image detection","date":"2020-12-01","arxiv_id":"2012.00287","n_code_links":0,"syntology":null},{"paper":null,"slug":"deftpunk-at-semeval-2020-task-6-using-rnn","title":"DeftPunk at SemEval-2020 Task 6: Using RNN-ensemble for the Sentence Classification.","date":"2020-12-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"dothemath-at-semeval-2020-task-12-deep-neural","title":"DoTheMath at SemEval-2020 Task 12 : Deep Neural Networks with Self Attention for Arabic Offensive Language Detection","date":"2020-12-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"edge-deep-learning-for-neural-implants","title":"Edge Deep Learning for Neural Implants","date":"2020-12-01","arxiv_id":"2012.00307","n_code_links":0,"syntology":null},{"paper":null,"slug":"embeddings-in-natural-language-processing","title":"Embeddings in Natural Language Processing","date":"2020-12-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"emotion-classification-by-jointly-learning-to","title":"Emotion Classification by Jointly Learning to Lexiconize and Classify","date":"2020-12-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"funny3-at-semeval-2020-task-7-humor-detection","title":"Funny3 at SemEval-2020 Task 7: Humor Detection of Edited Headlines with LSTM and TFIDF Neural Network System","date":"2020-12-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":"/paper/grubert-a-gru-based-method-to-fuse-bert","slug":"grubert-a-gru-based-method-to-fuse-bert","title":"GRUBERT: A GRU-Based Method to Fuse BERT Hidden Layers for Twitter Sentiment Analysis","date":"2020-12-01","arxiv_id":null,"n_code_links":2,"syntology":null},{"paper":"/paper/hinglishnlp-at-semeval-2020-task-9-fine-tuned","slug":"hinglishnlp-at-semeval-2020-task-9-fine-tuned","title":"HinglishNLP at SemEval-2020 Task 9: Fine-tuned Language Models for Hinglish Sentiment Detection","date":"2020-12-01","arxiv_id":null,"n_code_links":2,"syntology":null},{"paper":null,"slug":"iiitg-adbu-at-semeval-2020-task-12-comparison","title":"IIITG-ADBU at SemEval-2020 Task 12: Comparison of BERT and BiLSTM in Detecting Offensive Language","date":"2020-12-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"iiitg-adbu-at-semeval-2020-task-8-a","title":"IIITG-ADBU at SemEval-2020 Task 8: A Multimodal Approach to Detect Offensive, Sarcastic and Humorous Memes","date":"2020-12-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"incorporating-inner-word-and-out-word","title":"Incorporating Inner-word and Out-word Features for Mongolian Morphological Segmentation","date":"2020-12-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"interpretable-multi-timescale-models-for","title":"Interpretable multi-timescale models for predicting fMRI responses to continuous natural speech","date":"2020-12-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"ir3218-ui-at-semeval-2020-task-12-emoji","title":"IR3218-UI at SemEval-2020 Task 12: Emoji Effects on Offensive Language IdentifiCation","date":"2020-12-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"last-at-semeval-2020-task-10-finding-tokens","title":"LAST at SemEval-2020 Task 10: Finding Tokens to Emphasise in Short Written Texts with Precomputed Embedding Models and LightGBM","date":"2020-12-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":"/paper/memebusters-at-semeval-2020-task-8-feature","slug":"memebusters-at-semeval-2020-task-8-feature","title":"Memebusters at SemEval-2020 Task 8: Feature Fusion Model for Sentiment Analysis on Memes Using Transfer Learning","date":"2020-12-01","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":null,"slug":"mineriaunam-at-semeval-2020-task-3-predicting","title":"MineriaUNAM at SemEval-2020 Task 3: Predicting Contextual WordSimilarity Using a Centroid Based Approach and Word Embeddings","date":"2020-12-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"mr-based-camera-less-eye-tracking-using-deep","title":"MR-based camera-less eye tracking using deep neural networks","date":"2020-12-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"multitask-easy-first-dependency-parsing","title":"Multitask Easy-First Dependency Parsing: Exploiting Complementarities of Different Dependency Representations","date":"2020-12-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"noise-isn-t-always-negative-countering","title":"Noise Isn't Always Negative: Countering Exposure Bias in Sequence-to-Sequence Inflection Models","date":"2020-12-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"ntuaails-at-semeval-2020-task-11-propaganda","title":"NTUAAILS at SemEval-2020 Task 11: Propaganda Detection and Classification with biLSTMs and ELMo","date":"2020-12-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"pgsg-at-semeval-2020-task-12-bert-lstm-with","title":"PGSG at SemEval-2020 Task 12: BERT-LSTM with Tweets' Pretrained Model and Noisy Student Training Method","date":"2020-12-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"pointing-to-select-a-fast-pointer-lstm-for","title":"Pointing to Select: A Fast Pointer-LSTM for Long Text Classification","date":"2020-12-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"smash-at-semeval-2020-task-7-optimizing-the","title":"Smash at SemEval-2020 Task 7: Optimizing the Hyperparameters of ERNIE 2.0 for Humor Ranking and Rating","date":"2020-12-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":"/paper/solving-math-word-problems-with-multi","slug":"solving-math-word-problems-with-multi","title":"Solving Math Word Problems with Multi-Encoders and Multi-Decoders","date":"2020-12-01","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":null,"slug":"sonal-kumari-at-semeval-2020-task-12-social","title":"Sonal.kumari at SemEval-2020 Task 12: Social Media Multilingual Offensive Text Identification and Categorization Using Neural Network Models","date":"2020-12-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"stronger-baselines-for-grammatical-error-1","title":"Stronger Baselines for Grammatical Error Correction Using a Pretrained Encoder-Decoder Model","date":"2020-12-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"topic-driven-ensemble-for-online-advertising","title":"Topic-driven Ensemble for Online Advertising Generation","date":"2020-12-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"training-linear-finite-state-machines","title":"Training Linear Finite-State Machines","date":"2020-12-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"translation-vs-dialogue-a-comparative","title":"Translation vs. Dialogue: A Comparative Analysis of Sequence-to-Sequence Modeling","date":"2020-12-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"tue-at-semeval-2020-task-1-detecting-semantic","title":"TUE at SemEval-2020 Task 1: Detecting Semantic Change by Clustering Contextual Word Embeddings","date":"2020-12-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"unituebingencl-at-semeval-2020-task-7-humor","title":"UniTuebingenCL at SemEval-2020 Task 7: Humor Detection in News Headlines","date":"2020-12-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"uor-at-semeval-2020-task-4-pre-trained","title":"UoR at SemEval-2020 Task 4: Pre-trained Sentence Transformer Models for Commonsense Validation and Explanation","date":"2020-12-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"ynu-oxz-at-semeval-2020-task-5-detecting","title":"YNU-oxz at SemEval-2020 Task 5: Detecting Counterfactuals Based on Ordered Neurons LSTM and Hierarchical Attention Network","date":"2020-12-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"zyy1510-team-at-semeval-2020-task-9-sentiment","title":"Zyy1510 Team at SemEval-2020 Task 9: Sentiment Analysis for Code-Mixed Social Media Text with Sub-word Level Representations","date":"2020-12-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":"/paper/deep-implicit-templates-for-3d-shape","slug":"deep-implicit-templates-for-3d-shape","title":"Deep Implicit Templates for 3D Shape Representation","date":"2020-11-30","arxiv_id":"2011.14565","n_code_links":1,"syntology":{"ran":3,"of":3,"n_ran_checked":2,"n_instrument":1,"unverified":0,"pointer_only":3,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 2 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","official":{"repos":["ZhengZerong/DeepImplicitTemplates"],"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":["official"]}}}],"record_sha256":"ed1bf28eb9e97b40c76eba98e065b8e87f30189031684fb3f5934f742d44034b","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}