{"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/dropout/papers/247","list_of":"/method/dropout","method":"Dropout","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":247,"pages_in_order":275,"rows_per_page":100,"rows":[24601,24700],"of":27472,"counts":{"archive_papers_tagged":27472,"with_a_code_link":12129,"where_syntology_ran_a_sample":3620,"not_listed_spam_title":0,"listed":27472,"listed_where_code_ran":3620,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":3044,"every_run_a_failure_of_syntologys_instrument":576,"listed_with_a_run_with_no_instrument_failure":3044,"listed_every_run_a_failure_of_syntologys_instrument":576,"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/dropout","prev":"/method/dropout/papers/246","next":"/method/dropout/papers/248","papers":[{"paper":"/paper/adversarial-training-for-large-neural","slug":"adversarial-training-for-large-neural","title":"Adversarial Training for Large Neural Language Models","date":"2020-04-20","arxiv_id":"2004.08994","n_code_links":3,"syntology":null},{"paper":"/paper/chexbert-combining-automatic-labelers-and","slug":"chexbert-combining-automatic-labelers-and","title":"CheXbert: Combining Automatic Labelers and Expert Annotations for Accurate Radiology Report Labeling Using BERT","date":"2020-04-20","arxiv_id":"2004.09167","n_code_links":7,"syntology":{"ran":15,"of":17,"n_ran_checked":11,"n_instrument":4,"unverified":2,"pointer_only":3,"phrase":"15 ran (of which 0 constructed an object rather than computing a result; 11 with no instrument failure: 0 honoured, 0 violated, 11 with no contract checked; 4 where Syntology's instrument failed) · 2 unverified","official":{"repos":["stanfordmlgroup/CheXbert"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]}}},{"paper":"/paper/deep-covid-predicting-covid-19-from-chest-x","slug":"deep-covid-predicting-covid-19-from-chest-x","title":"Deep-COVID: Predicting COVID-19 From Chest X-Ray Images Using Deep Transfer Learning","date":"2020-04-20","arxiv_id":"2004.09363","n_code_links":1,"syntology":null},{"paper":"/paper/lsq-improving-low-bit-quantization-through","slug":"lsq-improving-low-bit-quantization-through","title":"LSQ+: Improving low-bit quantization through learnable offsets and better initialization","date":"2020-04-20","arxiv_id":"2004.09576","n_code_links":4,"syntology":{"ran":5,"of":10,"n_ran_checked":4,"n_instrument":1,"unverified":5,"pointer_only":10,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 2 honoured, 0 violated, 2 with no contract checked; 1 where Syntology's instrument failed) · 5 unverified","official":null}},{"paper":"/paper/mpnet-masked-and-permuted-pre-training-for","slug":"mpnet-masked-and-permuted-pre-training-for","title":"MPNet: Masked and Permuted Pre-training for Language Understanding","date":"2020-04-20","arxiv_id":"2004.09297","n_code_links":7,"syntology":{"ran":8,"of":8,"n_ran_checked":5,"n_instrument":3,"unverified":0,"pointer_only":6,"phrase":"8 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; 3 where Syntology's instrument failed) · 0 unverified","official":{"repos":["microsoft/MPNet"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["listed","official"]}}},{"paper":"/paper/stereoset-measuring-stereotypical-bias-in","slug":"stereoset-measuring-stereotypical-bias-in","title":"StereoSet: Measuring stereotypical bias in pretrained language models","date":"2020-04-20","arxiv_id":"2004.09456","n_code_links":3,"syntology":null},{"paper":"/paper/transformer-reasoning-network-for-image-text","slug":"transformer-reasoning-network-for-image-text","title":"Transformer Reasoning Network for Image-Text Matching and Retrieval","date":"2020-04-20","arxiv_id":"2004.09144","n_code_links":1,"syntology":{"ran":12,"of":14,"n_ran_checked":11,"n_instrument":1,"unverified":2,"pointer_only":2,"phrase":"12 ran (of which 0 constructed an object rather than computing a result; 11 with no instrument failure: 0 honoured, 1 violated, 10 with no contract checked; 1 where Syntology's instrument failed) · 2 unverified","official":{"repos":["mesnico/TERN"],"state":"official (archive's flag): 12 ran","n_ran":12,"n_constructed":0,"n_ran_no_instrument_failure":11,"n_unverified":2,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"whaletrans-e2e-whisper-to-natural-speech","title":"End-to-End Whisper to Natural Speech Conversion using Modified Transformer Network","date":"2020-04-20","arxiv_id":"2004.09347","n_code_links":0,"syntology":null},{"paper":"/paper/resnest-split-attention-networks","slug":"resnest-split-attention-networks","title":"ResNeSt: Split-Attention Networks","date":"2020-04-19","arxiv_id":"2004.08955","n_code_links":36,"syntology":{"ran":28,"of":48,"n_ran_checked":25,"n_instrument":3,"unverified":20,"pointer_only":23,"phrase":"28 ran (of which 0 constructed an object rather than computing a result; 25 with no instrument failure: 0 honoured, 0 violated, 25 with no contract checked; 3 where Syntology's instrument failed) · 20 unverified","official":{"repos":["zhanghang1989/ResNeSt"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":1,"ran_from_kinds":["listed","official"]}}},{"paper":"/paper/driftnet-aggressive-driving-behavior","slug":"driftnet-aggressive-driving-behavior","title":"DriftNet: Aggressive Driving Behavior Classification using 3D EfficientNet Architecture","date":"2020-04-18","arxiv_id":"2004.11970","n_code_links":1,"syntology":null},{"paper":"/paper/enhancing-pharmacovigilance-with-drug-reviews","slug":"enhancing-pharmacovigilance-with-drug-reviews","title":"Enhancing Pharmacovigilance with Drug Reviews and Social Media","date":"2020-04-18","arxiv_id":"2004.08731","n_code_links":1,"syntology":null},{"paper":"/paper/motion-segmentation-using-frequency-domain","slug":"motion-segmentation-using-frequency-domain","title":"Motion Segmentation using Frequency Domain Transformer Networks","date":"2020-04-18","arxiv_id":"2004.08638","n_code_links":1,"syntology":null},{"paper":null,"slug":"single-step-adversarial-training-with-dropout","title":"Single-step Adversarial training with Dropout Scheduling","date":"2020-04-18","arxiv_id":"2004.08628","n_code_links":0,"syntology":null},{"paper":null,"slug":"enriching-the-transformer-with-linguistic-and","title":"Enriching the Transformer with Linguistic Factors for Low-Resource Machine Translation","date":"2020-04-17","arxiv_id":"2004.08053","n_code_links":0,"syntology":null},{"paper":"/paper/etc-encoding-long-and-structured-data-in","slug":"etc-encoding-long-and-structured-data-in","title":"ETC: Encoding Long and Structured Inputs in Transformers","date":"2020-04-17","arxiv_id":"2004.08483","n_code_links":2,"syntology":{"ran":1,"of":1,"n_ran_checked":0,"n_instrument":1,"unverified":0,"pointer_only":1,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","official":{"repos":["google-research/google-research"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]}}},{"paper":"/paper/fast-and-accurate-deep-bidirectional-language","slug":"fast-and-accurate-deep-bidirectional-language","title":"Fast and Accurate Deep Bidirectional Language Representations for Unsupervised Learning","date":"2020-04-17","arxiv_id":"2004.08097","n_code_links":1,"syntology":{"ran":0,"of":1,"n_ran_checked":0,"n_instrument":0,"unverified":1,"pointer_only":0,"phrase":"0 ran · 1 unverified","official":{"repos":["joongbo/tta"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":[]}}},{"paper":"/paper/highway-transformer-self-gating-enhanced-self","slug":"highway-transformer-self-gating-enhanced-self","title":"Highway Transformer: Self-Gating Enhanced Self-Attentive Networks","date":"2020-04-17","arxiv_id":"2004.08178","n_code_links":1,"syntology":{"ran":4,"of":9,"n_ran_checked":4,"n_instrument":0,"unverified":5,"pointer_only":0,"phrase":"4 ran (of which 4 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 0 where Syntology's instrument failed) · 5 unverified; every one of the 4 samples that ran constructed an object rather than computing a result","official":{"repos":["cyk1337/Highway-Transformer"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":4,"n_ran_no_instrument_failure":4,"n_unverified":5,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"learning-to-rank-with-bert-in-tf-ranking","title":"Learning-to-Rank with BERT in TF-Ranking","date":"2020-04-17","arxiv_id":"2004.08476","n_code_links":0,"syntology":null},{"paper":null,"slug":"litedensenet-a-lightweight-network-for","title":"LiteDenseNet: A Lightweight Network for Hyperspectral Image Classification","date":"2020-04-17","arxiv_id":"2004.08112","n_code_links":0,"syntology":null},{"paper":null,"slug":"too-many-claims-to-fact-check-prioritizing","title":"Too Many Claims to Fact-Check: Prioritizing Political Claims Based on Check-Worthiness","date":"2020-04-17","arxiv_id":"2004.08166","n_code_links":0,"syntology":null},{"paper":"/paper/transform-and-tell-entity-aware-news-image","slug":"transform-and-tell-entity-aware-news-image","title":"Transform and Tell: Entity-Aware News Image Captioning","date":"2020-04-17","arxiv_id":"2004.08070","n_code_links":1,"syntology":{"ran":2,"of":2,"n_ran_checked":1,"n_instrument":1,"unverified":0,"pointer_only":2,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 1 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","official":{"repos":["alasdairtran/transform-and-tell"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":"/paper/understanding-the-difficulty-of-training","slug":"understanding-the-difficulty-of-training","title":"Understanding the Difficulty of Training Transformers","date":"2020-04-17","arxiv_id":"2004.08249","n_code_links":2,"syntology":{"ran":4,"of":5,"n_ran_checked":3,"n_instrument":1,"unverified":1,"pointer_only":0,"phrase":"4 ran (of which 2 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 1 where Syntology's instrument failed) · 1 unverified","official":{"repos":["LiyuanLucasLiu/Transforemr-Clinic"],"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":["listed","official"]}}},{"paper":"/paper/cross-lingual-contextualized-topic-models","slug":"cross-lingual-contextualized-topic-models","title":"Cross-lingual Contextualized Topic Models with Zero-shot Learning","date":"2020-04-16","arxiv_id":"2004.07737","n_code_links":2,"syntology":{"ran":6,"of":7,"n_ran_checked":6,"n_instrument":0,"unverified":1,"pointer_only":0,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 0 honoured, 0 violated, 6 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","official":{"repos":["MilaNLProc/contextualized-topic-models"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":1,"ran_from_kinds":["listed","official"]}}},{"paper":"/paper/non-autoregressive-machine-translation-with-1","slug":"non-autoregressive-machine-translation-with-1","title":"Non-Autoregressive Machine Translation with Latent Alignments","date":"2020-04-16","arxiv_id":"2004.07437","n_code_links":2,"syntology":{"ran":1,"of":2,"n_ran_checked":0,"n_instrument":1,"unverified":1,"pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 1 unverified","official":null}},{"paper":"/paper/the-right-tool-for-the-job-matching-model-and","slug":"the-right-tool-for-the-job-matching-model-and","title":"The Right Tool for the Job: Matching Model and Instance Complexities","date":"2020-04-16","arxiv_id":"2004.07453","n_code_links":1,"syntology":{"ran":5,"of":8,"n_ran_checked":2,"n_instrument":3,"unverified":3,"pointer_only":2,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 1 honoured, 0 violated, 1 with no contract checked; 3 where Syntology's instrument failed) · 3 unverified","official":{"repos":["allenai/sledgehammer"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":3,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"towards-instance-level-parser-selection-for","title":"Towards Instance-Level Parser Selection for Cross-Lingual Transfer of Dependency Parsers","date":"2020-04-16","arxiv_id":"2004.07642","n_code_links":0,"syntology":null},{"paper":"/paper/video-face-manipulation-detection-through","slug":"video-face-manipulation-detection-through","title":"Video Face Manipulation Detection Through Ensemble of CNNs","date":"2020-04-16","arxiv_id":"2004.07676","n_code_links":3,"syntology":{"ran":1,"of":3,"n_ran_checked":0,"n_instrument":1,"unverified":2,"pointer_only":3,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 2 unverified","official":{"repos":["polimi-ispl/icpr2020dfdc"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]}}},{"paper":"/paper/an-evaluation-of-dnn-architectures-for-page","slug":"an-evaluation-of-dnn-architectures-for-page","title":"An Evaluation of DNN Architectures for Page Segmentation of Historical Newspapers","date":"2020-04-15","arxiv_id":"2004.07317","n_code_links":1,"syntology":null},{"paper":"/paper/coreferential-reasoning-learning-for-language","slug":"coreferential-reasoning-learning-for-language","title":"Coreferential Reasoning Learning for Language Representation","date":"2020-04-15","arxiv_id":"2004.06870","n_code_links":2,"syntology":null},{"paper":"/paper/document-level-representation-learning-using","slug":"document-level-representation-learning-using","title":"SPECTER: Document-level Representation Learning using Citation-informed Transformers","date":"2020-04-15","arxiv_id":"2004.07180","n_code_links":5,"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":["allenai/scidocs","allenai/specter"],"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":"/paper/entities-as-experts-sparse-memory-access-with","slug":"entities-as-experts-sparse-memory-access-with","title":"Entities as Experts: Sparse Memory Access with Entity Supervision","date":"2020-04-15","arxiv_id":"2004.07202","n_code_links":1,"syntology":{"ran":11,"of":14,"n_ran_checked":11,"n_instrument":0,"unverified":3,"pointer_only":14,"phrase":"11 ran (of which 0 constructed an object rather than computing a result; 11 with no instrument failure: 0 honoured, 0 violated, 11 with no contract checked; 0 where Syntology's instrument failed) · 3 unverified","official":null}},{"paper":null,"slug":"lambert-language-and-action-learning-using","title":"lamBERT: Language and Action Learning Using Multimodal BERT","date":"2020-04-15","arxiv_id":"2004.07093","n_code_links":0,"syntology":null},{"paper":"/paper/sentiment-analysis-of-yelp-reviews-a","slug":"sentiment-analysis-of-yelp-reviews-a","title":"Sentiment Analysis of Yelp Reviews: A Comparison of Techniques and Models","date":"2020-04-15","arxiv_id":"2004.13851","n_code_links":1,"syntology":null},{"paper":"/paper/tod-bert-pre-trained-natural-language","slug":"tod-bert-pre-trained-natural-language","title":"TOD-BERT: Pre-trained Natural Language Understanding for Task-Oriented Dialogue","date":"2020-04-15","arxiv_id":"2004.06871","n_code_links":1,"syntology":{"ran":4,"of":13,"n_ran_checked":4,"n_instrument":0,"unverified":9,"pointer_only":13,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 0 where Syntology's instrument failed) · 9 unverified","official":{"repos":["jasonwu0731/ToD-BERT"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":9,"ran_from_kinds":["official"]}}},{"paper":"/paper/training-with-quantization-noise-for-extreme","slug":"training-with-quantization-noise-for-extreme","title":"Training with Quantization Noise for Extreme Model Compression","date":"2020-04-15","arxiv_id":"2004.07320","n_code_links":4,"syntology":null},{"paper":"/paper/a-simple-yet-strong-pipeline-for-hotpotqa","slug":"a-simple-yet-strong-pipeline-for-hotpotqa","title":"A Simple Yet Strong Pipeline for HotpotQA","date":"2020-04-14","arxiv_id":"2004.06753","n_code_links":0,"syntology":null},{"paper":"/paper/analysis-of-social-media-data-using","slug":"analysis-of-social-media-data-using","title":"Analysis of Social Media Data using Multimodal Deep Learning for Disaster Response","date":"2020-04-14","arxiv_id":"2004.11838","n_code_links":1,"syntology":null},{"paper":"/paper/deep-learning-models-for-multilingual-hate","slug":"deep-learning-models-for-multilingual-hate","title":"Deep Learning Models for Multilingual Hate Speech Detection","date":"2020-04-14","arxiv_id":"2004.06465","n_code_links":3,"syntology":null},{"paper":"/paper/palm-pre-training-an-autoencoding","slug":"palm-pre-training-an-autoencoding","title":"PALM: Pre-training an Autoencoding&Autoregressive Language Model for Context-conditioned Generation","date":"2020-04-14","arxiv_id":"2004.07159","n_code_links":2,"syntology":null},{"paper":null,"slug":"standardizing-and-benchmarking-crisis-related","title":"CrisisBench: Benchmarking Crisis-related Social Media Datasets for Humanitarian Information Processing","date":"2020-04-14","arxiv_id":"2004.06774","n_code_links":0,"syntology":null},{"paper":null,"slug":"transfer-learning-with-deep-convolutional","title":"Transfer Learning with Deep Convolutional Neural Network (CNN) for Pneumonia Detection using Chest X-ray","date":"2020-04-14","arxiv_id":"2004.06578","n_code_links":0,"syntology":null},{"paper":"/paper/transformer-based-grapheme-to-phoneme-1","slug":"transformer-based-grapheme-to-phoneme-1","title":"Transformer based Grapheme-to-Phoneme Conversion","date":"2020-04-14","arxiv_id":"2004.06338","n_code_links":1,"syntology":null},{"paper":"/paper/what-s-so-special-about-bert-s-layers-a","slug":"what-s-so-special-about-bert-s-layers-a","title":"What's so special about BERT's layers? A closer look at the NLP pipeline in monolingual and multilingual models","date":"2020-04-14","arxiv_id":"2004.06499","n_code_links":2,"syntology":{"ran":2,"of":2,"n_ran_checked":0,"n_instrument":2,"unverified":0,"pointer_only":2,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","official":{"repos":["wietsedv/bertje"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"cascade-neural-ensemble-for-identifying","title":"Cascade Neural Ensemble for Identifying Scientifically Sound Articles","date":"2020-04-13","arxiv_id":"2004.06222","n_code_links":0,"syntology":null},{"paper":null,"slug":"improving-scholarly-knowledge-representation","title":"Improving Scholarly Knowledge Representation: Evaluating BERT-based Models for Scientific Relation Classification","date":"2020-04-13","arxiv_id":"2004.06153","n_code_links":0,"syntology":null},{"paper":"/paper/pretrained-transformers-improve-out-of","slug":"pretrained-transformers-improve-out-of","title":"Pretrained Transformers Improve Out-of-Distribution Robustness","date":"2020-04-13","arxiv_id":"2004.06100","n_code_links":1,"syntology":null},{"paper":null,"slug":"proformer-towards-on-device-lsh-projection","title":"ProFormer: Towards On-Device LSH Projection Based Transformers","date":"2020-04-13","arxiv_id":"2004.05801","n_code_links":0,"syntology":null},{"paper":"/paper/public-self-consciousness-for-endowing","slug":"public-self-consciousness-for-endowing","title":"Will I Sound Like Me? Improving Persona Consistency in Dialogues through Pragmatic Self-Consciousness","date":"2020-04-13","arxiv_id":"2004.05816","n_code_links":1,"syntology":{"ran":2,"of":2,"n_ran_checked":2,"n_instrument":0,"unverified":0,"pointer_only":0,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","official":{"repos":["skywalker023/pragmatic-consistency"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":"/paper/regularizing-meta-learning-via-gradient","slug":"regularizing-meta-learning-via-gradient","title":"Regularizing Meta-Learning via Gradient Dropout","date":"2020-04-13","arxiv_id":"2004.05859","n_code_links":1,"syntology":null},{"paper":"/paper/relation-transformer-network","slug":"relation-transformer-network","title":"Relation Transformer Network","date":"2020-04-13","arxiv_id":"2004.06193","n_code_links":1,"syntology":null},{"paper":null,"slug":"robustly-pre-trained-neural-model-for-direct","title":"Robustly Pre-trained Neural Model for Direct Temporal Relation Extraction","date":"2020-04-13","arxiv_id":"2004.06216","n_code_links":0,"syntology":null},{"paper":null,"slug":"unified-multi-criteria-chinese-word","title":"Unified Multi-Criteria Chinese Word Segmentation with BERT","date":"2020-04-13","arxiv_id":"2004.05808","n_code_links":0,"syntology":null},{"paper":"/paper/amr-parsing-via-graph-sequence-iterative","slug":"amr-parsing-via-graph-sequence-iterative","title":"AMR Parsing via Graph-Sequence Iterative Inference","date":"2020-04-12","arxiv_id":"2004.05572","n_code_links":3,"syntology":{"ran":21,"of":31,"n_ran_checked":16,"n_instrument":5,"unverified":10,"pointer_only":1,"phrase":"21 ran (of which 11 constructed an object rather than computing a result; 16 with no instrument failure: 1 honoured, 1 violated, 14 with no contract checked; 5 where Syntology's instrument failed) · 10 unverified","official":{"repos":["jcyk/AMR-gs"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":2,"ran_from_kinds":["listed","official","unlocated"]}}},{"paper":"/paper/patchattack-a-black-box-texture-based-attack","slug":"patchattack-a-black-box-texture-based-attack","title":"PatchAttack: A Black-box Texture-based Attack with Reinforcement Learning","date":"2020-04-12","arxiv_id":"2004.05682","n_code_links":2,"syntology":{"ran":0,"of":1,"n_ran_checked":0,"n_instrument":0,"unverified":1,"pointer_only":0,"phrase":"0 ran · 1 unverified","official":{"repos":["Chenglin-Yang/PatchAttack"],"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":"pre-training-text-representations-as-meta","title":"Pre-training Text Representations as Meta Learning","date":"2020-04-12","arxiv_id":"2004.05568","n_code_links":0,"syntology":null},{"paper":null,"slug":"relational-learning-between-multiple","title":"Relational Learning between Multiple Pulmonary Nodules via Deep Set Attention Transformers","date":"2020-04-12","arxiv_id":"2004.05640","n_code_links":0,"syntology":null},{"paper":"/paper/towards-an-efficient-deep-learning-model-for","slug":"towards-an-efficient-deep-learning-model-for","title":"Towards an Effective and Efficient Deep Learning Model for COVID-19 Patterns Detection in X-ray Images","date":"2020-04-12","arxiv_id":"2004.05717","n_code_links":2,"syntology":null},{"paper":"/paper/verification-of-deep-convolutional-neural","slug":"verification-of-deep-convolutional-neural","title":"Verification of Deep Convolutional Neural Networks Using ImageStars","date":"2020-04-12","arxiv_id":"2004.05511","n_code_links":2,"syntology":null},{"paper":"/paper/vgcn-bert-augmenting-bert-with-graph","slug":"vgcn-bert-augmenting-bert-with-graph","title":"VGCN-BERT: Augmenting BERT with Graph Embedding for Text Classification","date":"2020-04-12","arxiv_id":"2004.05707","n_code_links":1,"syntology":{"ran":7,"of":9,"n_ran_checked":5,"n_instrument":2,"unverified":2,"pointer_only":2,"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":["Louis-udm/VGCN-BERT"],"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":"/paper/deepsentipers-novel-deep-learning-models","slug":"deepsentipers-novel-deep-learning-models","title":"DeepSentiPers: Novel Deep Learning Models Trained Over Proposed Augmented Persian Sentiment Corpus","date":"2020-04-11","arxiv_id":"2004.05328","n_code_links":1,"syntology":null},{"paper":null,"slug":"detection-of-covid-19-from-chest-x-ray-images","title":"Detection of Covid-19 From Chest X-ray Images Using Artificial Intelligence: An Early Review","date":"2020-04-11","arxiv_id":"2004.05436","n_code_links":0,"syntology":null},{"paper":null,"slug":"end-to-end-chinese-lexical-fusion-recognition","title":"End to End Chinese Lexical Fusion Recognition with Sememe Knowledge","date":"2020-04-11","arxiv_id":"2004.05456","n_code_links":0,"syntology":null},{"paper":"/paper/fda-fourier-domain-adaptation-for-semantic","slug":"fda-fourier-domain-adaptation-for-semantic","title":"FDA: Fourier Domain Adaptation for Semantic Segmentation","date":"2020-04-11","arxiv_id":"2004.05498","n_code_links":3,"syntology":{"ran":2,"of":3,"n_ran_checked":1,"n_instrument":1,"unverified":1,"pointer_only":3,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 1 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 1 unverified","official":{"repos":["YanchaoYang/FDA"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":1,"ran_from_kinds":["official","unlocated"]}}},{"paper":"/paper/lareqa-language-agnostic-answer-retrieval","slug":"lareqa-language-agnostic-answer-retrieval","title":"LAReQA: Language-agnostic answer retrieval from a multilingual pool","date":"2020-04-11","arxiv_id":"2004.05484","n_code_links":1,"syntology":null},{"paper":null,"slug":"an-in-depth-walkthrough-on-evolution-of","title":"An In-depth Walkthrough on Evolution of Neural Machine Translation","date":"2020-04-10","arxiv_id":"2004.04902","n_code_links":0,"syntology":null},{"paper":"/paper/longformer-the-long-document-transformer","slug":"longformer-the-long-document-transformer","title":"Longformer: The Long-Document Transformer","date":"2020-04-10","arxiv_id":"2004.05150","n_code_links":22,"syntology":{"ran":22,"of":35,"n_ran_checked":14,"n_instrument":8,"unverified":13,"pointer_only":5,"phrase":"22 ran (of which 4 constructed an object rather than computing a result; 14 with no instrument failure: 1 honoured, 1 violated, 12 with no contract checked; 8 where Syntology's instrument failed) · 13 unverified","official":{"repos":["allenai/longformer"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":2,"ran_from_kinds":["listed","official"]}}},{"paper":null,"slug":"model-uncertainty-quantification-for-reliable","title":"Model Uncertainty Quantification for Reliable Deep Vision Structural Health Monitoring","date":"2020-04-10","arxiv_id":"2004.05151","n_code_links":0,"syntology":null},{"paper":null,"slug":"simpletran-transferring-pre-trained-sentence","title":"Beyond Fine-tuning: Few-Sample Sentence Embedding Transfer","date":"2020-04-10","arxiv_id":"2004.05119","n_code_links":0,"syntology":null},{"paper":null,"slug":"stacked-convolutional-deep-encoding-network","title":"Stacked Convolutional Deep Encoding Network for Video-Text Retrieval","date":"2020-04-10","arxiv_id":"2004.04959","n_code_links":0,"syntology":null},{"paper":null,"slug":"telling-bert-s-full-story-from-local","title":"Telling BERT's full story: from Local Attention to Global Aggregation","date":"2020-04-10","arxiv_id":"2004.05916","n_code_links":0,"syntology":null},{"paper":"/paper/analysis-on-deeplabv3-performance-for","slug":"analysis-on-deeplabv3-performance-for","title":"Analysis on DeepLabV3+ Performance for Automatic Steel Defects Detection","date":"2020-04-09","arxiv_id":"2004.04822","n_code_links":1,"syntology":null},{"paper":"/paper/bleurt-learning-robust-metrics-for-text","slug":"bleurt-learning-robust-metrics-for-text","title":"BLEURT: Learning Robust Metrics for Text Generation","date":"2020-04-09","arxiv_id":"2004.04696","n_code_links":4,"syntology":{"ran":1,"of":2,"n_ran_checked":0,"n_instrument":1,"unverified":1,"pointer_only":1,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 1 unverified","official":{"repos":["google-research/bleurt"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]}}},{"paper":"/paper/cortical-surface-registration-using","slug":"cortical-surface-registration-using","title":"Cortical surface registration using unsupervised learning","date":"2020-04-09","arxiv_id":"2004.04617","n_code_links":1,"syntology":null},{"paper":null,"slug":"gsa-densenet121-covid-19-a-hybrid-deep","title":"GSA-DenseNet121-COVID-19: a Hybrid Deep Learning Architecture for the Diagnosis of COVID-19 Disease based on Gravitational Search Optimization Algorithm","date":"2020-04-09","arxiv_id":"2004.05084","n_code_links":0,"syntology":null},{"paper":null,"slug":"interpretability-analysis-for-named-entity","title":"Interpretability Analysis for Named Entity Recognition to Understand System Predictions and How They Can Improve","date":"2020-04-09","arxiv_id":"2004.04564","n_code_links":0,"syntology":null},{"paper":"/paper/on-optimal-transformer-depth-for-low-resource","slug":"on-optimal-transformer-depth-for-low-resource","title":"On Optimal Transformer Depth for Low-Resource Language Translation","date":"2020-04-09","arxiv_id":"2004.04418","n_code_links":1,"syntology":null},{"paper":"/paper/on-the-language-neutrality-of-pre-trained","slug":"on-the-language-neutrality-of-pre-trained","title":"On the Language Neutrality of Pre-trained Multilingual Representations","date":"2020-04-09","arxiv_id":"2004.05160","n_code_links":1,"syntology":{"ran":4,"of":6,"n_ran_checked":1,"n_instrument":3,"unverified":2,"pointer_only":0,"phrase":"4 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; 3 where Syntology's instrument failed) · 2 unverified","official":{"repos":["jlibovicky/assess-multilingual-bert"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":2,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"sequential-neural-rendering-with-transformer","title":"Sequential View Synthesis with Transformer","date":"2020-04-09","arxiv_id":"2004.04548","n_code_links":0,"syntology":null},{"paper":null,"slug":"a-deep-learning-approach-for-determining","title":"A Deep Learning Approach for Determining Effects of Tuta Absoluta in Tomato Plants","date":"2020-04-08","arxiv_id":"2004.04023","n_code_links":0,"syntology":null},{"paper":"/paper/adaptive-transformers-in-rl","slug":"adaptive-transformers-in-rl","title":"Adaptive Transformers in RL","date":"2020-04-08","arxiv_id":"2004.03761","n_code_links":1,"syntology":null},{"paper":"/paper/dialbert-a-hierarchical-pre-trained-model-for","slug":"dialbert-a-hierarchical-pre-trained-model-for","title":"DialBERT: A Hierarchical Pre-Trained Model for Conversation Disentanglement","date":"2020-04-08","arxiv_id":"2004.03760","n_code_links":1,"syntology":null},{"paper":"/paper/diverse-controllable-and-keyphrase-aware-a","slug":"diverse-controllable-and-keyphrase-aware-a","title":"Diverse, Controllable, and Keyphrase-Aware: A Corpus and Method for News Multi-Headline Generation","date":"2020-04-08","arxiv_id":"2004.03875","n_code_links":1,"syntology":null},{"paper":"/paper/dynabert-dynamic-bert-with-adaptive-width-and","slug":"dynabert-dynamic-bert-with-adaptive-width-and","title":"DynaBERT: Dynamic BERT with Adaptive Width and Depth","date":"2020-04-08","arxiv_id":"2004.04037","n_code_links":3,"syntology":{"ran":5,"of":8,"n_ran_checked":2,"n_instrument":3,"unverified":3,"pointer_only":8,"phrase":"5 ran (of which 1 constructed an object rather than computing a result; 2 with no instrument failure: 1 honoured, 0 violated, 1 with no contract checked; 3 where Syntology's instrument failed) · 3 unverified","official":{"repos":["huawei-noah/Pretrained-Language-Model"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":1,"n_ran_no_instrument_failure":2,"n_unverified":3,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"error-correction-and-extraction-in-request","title":"Error correction and extraction in request dialogs","date":"2020-04-08","arxiv_id":"2004.04243","n_code_links":0,"syntology":null},{"paper":"/paper/exploiting-redundancy-in-pre-trained-language","slug":"exploiting-redundancy-in-pre-trained-language","title":"Analyzing Redundancy in Pretrained Transformer Models","date":"2020-04-08","arxiv_id":"2004.04010","n_code_links":1,"syntology":null},{"paper":null,"slug":"generating-counter-narratives-against-online","title":"Generating Counter Narratives against Online Hate Speech: Data and Strategies","date":"2020-04-08","arxiv_id":"2004.04216","n_code_links":0,"syntology":null},{"paper":"/paper/have-your-text-and-use-it-too-end-to-end","slug":"have-your-text-and-use-it-too-end-to-end","title":"Have Your Text and Use It Too! End-to-End Neural Data-to-Text Generation with Semantic Fidelity","date":"2020-04-08","arxiv_id":"2004.06577","n_code_links":1,"syntology":null},{"paper":"/paper/improving-bert-with-self-supervised-attention","slug":"improving-bert-with-self-supervised-attention","title":"Improving BERT with Self-Supervised Attention","date":"2020-04-08","arxiv_id":"2004.03808","n_code_links":1,"syntology":null},{"paper":null,"slug":"improving-expressivity-of-graph-neural","title":"Improving Expressivity of Graph Neural Networks","date":"2020-04-08","arxiv_id":"2004.05994","n_code_links":0,"syntology":null},{"paper":null,"slug":"ladabert-lightweight-adaptation-of-bert","title":"LadaBERT: Lightweight Adaptation of BERT through Hybrid Model Compression","date":"2020-04-08","arxiv_id":"2004.04124","n_code_links":0,"syntology":null},{"paper":"/paper/poor-man-s-bert-smaller-and-faster","slug":"poor-man-s-bert-smaller-and-faster","title":"On the Effect of Dropping Layers of Pre-trained Transformer Models","date":"2020-04-08","arxiv_id":"2004.03844","n_code_links":4,"syntology":{"ran":7,"of":8,"n_ran_checked":4,"n_instrument":3,"unverified":1,"pointer_only":1,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 1 honoured, 0 violated, 3 with no contract checked; 3 where Syntology's instrument failed) · 1 unverified","official":{"repos":["hsajjad/transformers"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":0,"ran_from_kinds":["listed","official"]}}},{"paper":"/paper/pre-training-is-a-hot-topic-contextualized","slug":"pre-training-is-a-hot-topic-contextualized","title":"Pre-training is a Hot Topic: Contextualized Document Embeddings Improve Topic Coherence","date":"2020-04-08","arxiv_id":"2004.03974","n_code_links":3,"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":["MilaNLProc/contextualized-topic-models"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]}}},{"paper":"/paper/sciwing-a-software-toolkit-for-scientific","slug":"sciwing-a-software-toolkit-for-scientific","title":"SciWING -- A Software Toolkit for Scientific Document Processing","date":"2020-04-08","arxiv_id":"2004.03807","n_code_links":1,"syntology":null},{"paper":null,"slug":"severing-the-edge-between-before-and-after","title":"Severing the Edge Between Before and After: Neural Architectures for Temporal Ordering of Events","date":"2020-04-08","arxiv_id":"2004.04295","n_code_links":0,"syntology":null},{"paper":null,"slug":"skin-diseases-detection-using-lbp-and-wld-an","title":"Skin Diseases Detection using LBP and WLD- An Ensembling Approach","date":"2020-04-08","arxiv_id":"2004.04122","n_code_links":0,"syntology":null},{"paper":"/paper/are-natural-language-inference-models","slug":"are-natural-language-inference-models","title":"Are Natural Language Inference Models IMPPRESsive? Learning IMPlicature and PRESupposition","date":"2020-04-07","arxiv_id":"2004.03066","n_code_links":1,"syntology":{"ran":2,"of":5,"n_ran_checked":2,"n_instrument":0,"unverified":3,"pointer_only":5,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 1 honoured, 1 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 3 unverified","official":{"repos":["alexwarstadt/data_generation"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":3,"ran_from_kinds":["found_in_text"]}}},{"paper":"/paper/breastscreening-on-the-use-of-multi-modality","slug":"breastscreening-on-the-use-of-multi-modality","title":"BreastScreening: On the Use of Multi-Modality in Medical Imaging Diagnosis","date":"2020-04-07","arxiv_id":"2004.03500","n_code_links":8,"syntology":null},{"paper":"/paper/byte-pair-encoding-is-suboptimal-for-language","slug":"byte-pair-encoding-is-suboptimal-for-language","title":"Byte Pair Encoding is Suboptimal for Language Model Pretraining","date":"2020-04-07","arxiv_id":"2004.03720","n_code_links":1,"syntology":null},{"paper":"/paper/channel-attention-residual-u-net-for-retinal","slug":"channel-attention-residual-u-net-for-retinal","title":"Channel Attention Residual U-Net for Retinal Vessel Segmentation","date":"2020-04-07","arxiv_id":"2004.03702","n_code_links":2,"syntology":null},{"paper":"/paper/evaluating-machines-by-their-real-world","slug":"evaluating-machines-by-their-real-world","title":"TuringAdvice: A Generative and Dynamic Evaluation of Language Use","date":"2020-04-07","arxiv_id":"2004.03607","n_code_links":1,"syntology":null}],"record_sha256":"5c218b4a8faf07d9d6a0ce6d7fd248d905c56099471afb39cc769b768c4c93bb","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}