{"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/residual-connection/papers/253","list_of":"/method/residual-connection","method":"Residual Connection","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":253,"pages_in_order":285,"rows_per_page":100,"rows":[25201,25300],"of":28401,"counts":{"archive_papers_tagged":28401,"with_a_code_link":12847,"where_syntology_ran_a_sample":3897,"not_listed_spam_title":0,"listed":28401,"listed_where_code_ran":3897,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":3291,"every_run_a_failure_of_syntologys_instrument":606,"listed_with_a_run_with_no_instrument_failure":3291,"listed_every_run_a_failure_of_syntologys_instrument":606,"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/residual-connection","prev":"/method/residual-connection/papers/252","next":"/method/residual-connection/papers/254","papers":[{"paper":null,"slug":"acoustic-anomaly-detection-for-machine-sounds","title":"Acoustic Anomaly Detection for Machine Sounds based on Image Transfer Learning","date":"2020-06-05","arxiv_id":"2006.03429","n_code_links":0,"syntology":null},{"paper":null,"slug":"an-overview-of-neural-network-compression","title":"An Overview of Neural Network Compression","date":"2020-06-05","arxiv_id":"2006.03669","n_code_links":0,"syntology":null},{"paper":null,"slug":"autohas-differentiable-hyper-parameter-and","title":"AutoHAS: Efficient Hyperparameter and Architecture Search","date":"2020-06-05","arxiv_id":"2006.03656","n_code_links":0,"syntology":null},{"paper":"/paper/black-box-explanation-of-object-detectors-via","slug":"black-box-explanation-of-object-detectors-via","title":"Black-box Explanation of Object Detectors via Saliency Maps","date":"2020-06-05","arxiv_id":"2006.03204","n_code_links":2,"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":null}},{"paper":"/paper/deberta-decoding-enhanced-bert-with","slug":"deberta-decoding-enhanced-bert-with","title":"DeBERTa: Decoding-enhanced BERT with Disentangled Attention","date":"2020-06-05","arxiv_id":"2006.03654","n_code_links":14,"syntology":{"ran":4,"of":13,"n_ran_checked":3,"n_instrument":1,"unverified":9,"pointer_only":3,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 2 honoured, 1 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 9 unverified","official":{"repos":["microsoft/DeBERTa"],"state":"official: no sample here; runs from other or unrecorded repositories","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed","named_in_paper","unlocated"]}}},{"paper":"/paper/dilated-convolutions-with-lateral-inhibitions","slug":"dilated-convolutions-with-lateral-inhibitions","title":"Dilated Convolutions with Lateral Inhibitions for Semantic Image Segmentation","date":"2020-06-05","arxiv_id":"2006.03708","n_code_links":1,"syntology":null},{"paper":"/paper/funnel-transformer-filtering-out-sequential","slug":"funnel-transformer-filtering-out-sequential","title":"Funnel-Transformer: Filtering out Sequential Redundancy for Efficient Language Processing","date":"2020-06-05","arxiv_id":"2006.03236","n_code_links":3,"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":["laiguokun/Funnel-Transformer"],"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/gmat-global-memory-augmentation-for","slug":"gmat-global-memory-augmentation-for","title":"GMAT: Global Memory Augmentation for Transformers","date":"2020-06-05","arxiv_id":"2006.03274","n_code_links":1,"syntology":null},{"paper":"/paper/masked-language-modeling-for-proteins-via","slug":"masked-language-modeling-for-proteins-via","title":"Masked Language Modeling for Proteins via Linearly Scalable Long-Context Transformers","date":"2020-06-05","arxiv_id":"2006.03555","n_code_links":1,"syntology":null},{"paper":null,"slug":"segmentation-of-surgical-instruments-for","title":"Segmentation of Surgical Instruments for Minimally-Invasive Robot-Assisted Procedures Using Generative Deep Neural Networks","date":"2020-06-05","arxiv_id":"2006.03486","n_code_links":0,"syntology":null},{"paper":null,"slug":"udpipe-at-evalatin-2020-contextualized-1","title":"UDPipe at EvaLatin 2020: Contextualized Embeddings and Treebank Embeddings","date":"2020-06-05","arxiv_id":"2006.03687","n_code_links":0,"syntology":null},{"paper":"/paper/visual-transformers-token-based-image","slug":"visual-transformers-token-based-image","title":"Visual Transformers: Token-based Image Representation and Processing for Computer Vision","date":"2020-06-05","arxiv_id":"2006.03677","n_code_links":8,"syntology":{"ran":6,"of":6,"n_ran_checked":6,"n_instrument":0,"unverified":0,"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) · 0 unverified","official":null}},{"paper":null,"slug":"end-to-end-speech-translation-with-knowledge-1","title":"End-to-End Speech-Translation with Knowledge Distillation: FBK@IWSLT2020","date":"2020-06-04","arxiv_id":"2006.02965","n_code_links":0,"syntology":null},{"paper":"/paper/the-sofc-exp-corpus-and-neural-approaches-to","slug":"the-sofc-exp-corpus-and-neural-approaches-to","title":"The SOFC-Exp Corpus and Neural Approaches to Information Extraction in the Materials Science Domain","date":"2020-06-04","arxiv_id":"2006.03039","n_code_links":1,"syntology":null},{"paper":"/paper/adaptive-checkpoint-adjoint-method-for","slug":"adaptive-checkpoint-adjoint-method-for","title":"Adaptive Checkpoint Adjoint Method for Gradient Estimation in Neural ODE","date":"2020-06-03","arxiv_id":"2006.02493","n_code_links":2,"syntology":null},{"paper":"/paper/automatic-text-summarization-of-covid-19","slug":"automatic-text-summarization-of-covid-19","title":"Automatic Text Summarization of COVID-19 Medical Research Articles using BERT and GPT-2","date":"2020-06-03","arxiv_id":"2006.01997","n_code_links":1,"syntology":null},{"paper":null,"slug":"efficient-refinements-on-yolov3-for-real-time","title":"Deep Learning Methods for Real-time Detection and Analysis of Wagner Ulcer Classification System","date":"2020-06-03","arxiv_id":"2006.02322","n_code_links":0,"syntology":null},{"paper":null,"slug":"a-pairwise-probe-for-understanding-bert-fine","title":"A Pairwise Probe for Understanding BERT Fine-Tuning on Machine Reading Comprehension","date":"2020-06-02","arxiv_id":"2006.01346","n_code_links":0,"syntology":null},{"paper":"/paper/bert-based-multilingual-machine-comprehension","slug":"bert-based-multilingual-machine-comprehension","title":"BERT Based Multilingual Machine Comprehension in English and Hindi","date":"2020-06-02","arxiv_id":"2006.01432","n_code_links":2,"syntology":null},{"paper":"/paper/exploring-cross-sentence-contexts-for-named","slug":"exploring-cross-sentence-contexts-for-named","title":"Exploring Cross-sentence Contexts for Named Entity Recognition with BERT","date":"2020-06-02","arxiv_id":"2006.01563","n_code_links":1,"syntology":{"ran":6,"of":7,"n_ran_checked":5,"n_instrument":1,"unverified":1,"pointer_only":1,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 0 violated, 5 with no contract checked; 1 where Syntology's instrument failed) · 1 unverified","official":{"repos":["jouniluoma/bert-ner-cmv"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":1,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"grafted-network-for-person-re-identification","title":"Grafted network for person re-identification","date":"2020-06-02","arxiv_id":"2006.01967","n_code_links":0,"syntology":null},{"paper":null,"slug":"interpretation-of-resnet-by-visualization-of","title":"Interpretation of ResNet by Visualization of Preferred Stimulus in Receptive Fields","date":"2020-06-02","arxiv_id":"2006.01645","n_code_links":0,"syntology":null},{"paper":"/paper/on-the-predictive-power-of-neural-language","slug":"on-the-predictive-power-of-neural-language","title":"On the Predictive Power of Neural Language Models for Human Real-Time Comprehension Behavior","date":"2020-06-02","arxiv_id":"2006.01912","n_code_links":1,"syntology":null},{"paper":null,"slug":"position-masking-for-language-models","title":"Position Masking for Language Models","date":"2020-06-02","arxiv_id":"2006.05676","n_code_links":0,"syntology":null},{"paper":"/paper/question-answering-on-scholarly-knowledge","slug":"question-answering-on-scholarly-knowledge","title":"Question Answering on Scholarly Knowledge Graphs","date":"2020-06-02","arxiv_id":"2006.01527","n_code_links":0,"syntology":null},{"paper":"/paper/subjective-question-answering-deciphering-the","slug":"subjective-question-answering-deciphering-the","title":"Subjective Question Answering: Deciphering the inner workings of Transformers in the realm of subjectivity","date":"2020-06-02","arxiv_id":"2006.08342","n_code_links":1,"syntology":null},{"paper":null,"slug":"wikibert-models-deep-transfer-learning-for","title":"WikiBERT models: deep transfer learning for many languages","date":"2020-06-02","arxiv_id":"2006.01538","n_code_links":0,"syntology":null},{"paper":"/paper/a-u-net-based-discriminator-for-generative-1","slug":"a-u-net-based-discriminator-for-generative-1","title":"A U-Net Based Discriminator for Generative Adversarial Networks","date":"2020-06-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":"/paper/adahessian-an-adaptive-second-order-optimizer","slug":"adahessian-an-adaptive-second-order-optimizer","title":"ADAHESSIAN: An Adaptive Second Order Optimizer for Machine Learning","date":"2020-06-01","arxiv_id":"2006.00719","n_code_links":4,"syntology":{"ran":5,"of":11,"n_ran_checked":4,"n_instrument":1,"unverified":6,"pointer_only":1,"phrase":"5 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; 1 where Syntology's instrument failed) · 6 unverified","official":{"repos":["amirgholami/adahessian"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":5,"ran_from_kinds":["listed","official"]}}},{"paper":null,"slug":"an-effective-contextual-language-modeling","title":"An Effective Contextual Language Modeling Framework for Speech Summarization with Augmented Features","date":"2020-06-01","arxiv_id":"2006.01189","n_code_links":0,"syntology":null},{"paper":null,"slug":"an-end-to-end-edge-aggregation-network-for","title":"An End-to-End Edge Aggregation Network for Moving Object Segmentation","date":"2020-06-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"approche-de-g-en-eration-de-r-eponse-a-base","title":"Approche de g\\'en\\'eration de r\\'eponse \\`a base de transformers (Transformer based approach for answer generation)","date":"2020-06-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":"/paper/bert-based-ensembles-for-modeling-disclosure","slug":"bert-based-ensembles-for-modeling-disclosure","title":"BERT-based Ensembles for Modeling Disclosure and Support in Conversational Social Media Text","date":"2020-06-01","arxiv_id":"2006.01222","n_code_links":0,"syntology":null},{"paper":null,"slug":"bwcnn-blink-to-word-a-real-time-convolutional","title":"BWCNN: Blink to Word, a Real-Time Convolutional Neural Network Approach","date":"2020-06-01","arxiv_id":"2006.01232","n_code_links":0,"syntology":null},{"paper":"/paper/camouflaged-object-detection","slug":"camouflaged-object-detection","title":"Camouflaged Object Detection","date":"2020-06-01","arxiv_id":null,"n_code_links":2,"syntology":null},{"paper":"/paper/context-based-transformer-models-for-answer","slug":"context-based-transformer-models-for-answer","title":"Context-based Transformer Models for Answer Sentence Selection","date":"2020-06-01","arxiv_id":"2006.01285","n_code_links":1,"syntology":null},{"paper":null,"slug":"conversational-machine-comprehension-a","title":"Conversational Machine Comprehension: a Literature Review","date":"2020-06-01","arxiv_id":"2006.00671","n_code_links":0,"syntology":null},{"paper":"/paper/deceiving-computers-in-reverse-turing-test","slug":"deceiving-computers-in-reverse-turing-test","title":"Deceiving computers in Reverse Turing Test through Deep Learning","date":"2020-06-01","arxiv_id":"2006.11373","n_code_links":2,"syntology":null},{"paper":"/paper/distilling-image-dehazing-with-heterogeneous","slug":"distilling-image-dehazing-with-heterogeneous","title":"Distilling Image Dehazing With Heterogeneous Task Imitation","date":"2020-06-01","arxiv_id":null,"n_code_links":2,"syntology":null},{"paper":"/paper/emergence-of-separable-manifolds-in-deep","slug":"emergence-of-separable-manifolds-in-deep","title":"Emergence of Separable Manifolds in Deep Language Representations","date":"2020-06-01","arxiv_id":"2006.01095","n_code_links":1,"syntology":null},{"paper":null,"slug":"etude-des-variations-s-emantiques-a-travers","title":"\\'Etude des variations s\\'emantiques \\`a travers plusieurs dimensions (Studying semantic variations through several dimensions )","date":"2020-06-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"few-shot-learning-of-part-specific","title":"Few-Shot Learning of Part-Specific Probability Space for 3D Shape Segmentation","date":"2020-06-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"how-does-noise-help-robustness-explanation","title":"How Does Noise Help Robustness? Explanation and Exploration under the Neural SDE Framework","date":"2020-06-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":"/paper/image-search-with-text-feedback-by","slug":"image-search-with-text-feedback-by","title":"Image Search With Text Feedback by Visiolinguistic Attention Learning","date":"2020-06-01","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":"/paper/interactive-object-segmentation-with-inside","slug":"interactive-object-segmentation-with-inside","title":"Interactive Object Segmentation With Inside-Outside Guidance","date":"2020-06-01","arxiv_id":null,"n_code_links":2,"syntology":null},{"paper":null,"slug":"introduction-d-informations-s-emantiques-dans","title":"Introduction d'informations s\\'emantiques dans un syst\\`eme de reconnaissance de la parole (Despite spectacular advances in recent years, the Automatic Speech Recognition (ASR) systems still make mistakes, especially in noisy environments)","date":"2020-06-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"les-mod-eles-de-langue-contextuels-camembert","title":"Les mod\\`eles de langue contextuels Camembert pour le fran\\ccais : impact de la taille et de l'h\\'et\\'erog\\'en\\'eit\\'e des donn\\'ees d'entrainement (C AMEM BERT Contextual Language Models for French: Impact of Training Data Size and Heterogeneity )","date":"2020-06-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":"/paper/low-rank-compression-of-neural-nets-learning","slug":"low-rank-compression-of-neural-nets-learning","title":"Low-Rank Compression of Neural Nets: Learning the Rank of Each Layer","date":"2020-06-01","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":null,"slug":"neural-architecture-search-with-reinforce-and","title":"Hyperparameter optimization with REINFORCE and Transformers","date":"2020-06-01","arxiv_id":"2006.00939","n_code_links":0,"syntology":null},{"paper":"/paper/online-versus-offline-nmt-quality-an-in-depth","slug":"online-versus-offline-nmt-quality-an-in-depth","title":"Online Versus Offline NMT Quality: An In-depth Analysis on English-German and German-English","date":"2020-06-01","arxiv_id":"2006.00814","n_code_links":1,"syntology":null},{"paper":null,"slug":"qu-apporte-bert-a-l-analyse-syntaxique-en","title":"Qu'apporte BERT \\`a l'analyse syntaxique en constituants discontinus ? Une suite de tests pour \\'evaluer les pr\\'edictions de structures syntaxiques discontinues en anglais (What does BERT contribute to discontinuous constituency parsing ? A test suite to evaluate discontinuous constituency structure predictions in English)","date":"2020-06-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"r-e-entra-iner-ou-entra-iner-soi-m-eme-strat","title":"R\\'e-entra\\^\\iner ou entra\\^\\iner soi-m\\^eme ? Strat\\'egies de pr\\'e-entra\\^\\inement de BERT en domaine m\\'edical (Re-train or train from scratch ? Pre-training strategies for BERT in the medical domain )","date":"2020-06-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"rdcface-radial-distortion-correction-for-face","title":"RDCFace: Radial Distortion Correction for Face Recognition","date":"2020-06-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":"/paper/residual-squeeze-and-excitation-network-for","slug":"residual-squeeze-and-excitation-network-for","title":"Residual Squeeze-and-Excitation Network for Fast Image Deraining","date":"2020-06-01","arxiv_id":"2006.00757","n_code_links":1,"syntology":null},{"paper":null,"slug":"seggcn-efficient-3d-point-cloud-segmentation","title":"SegGCN: Efficient 3D Point Cloud Segmentation With Fuzzy Spherical Kernel","date":"2020-06-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"tesa-tensor-element-self-attention-via","title":"TESA: Tensor Element Self-Attention via Matricization","date":"2020-06-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":"/paper/unpaired-portrait-drawing-generation-via","slug":"unpaired-portrait-drawing-generation-via","title":"Unpaired Portrait Drawing Generation via Asymmetric Cycle Mapping","date":"2020-06-01","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":null,"slug":"unsupervised-sparse-view-backprojection-via","title":"Unsupervised Sparse-view Backprojection via Convolutional and Spatial Transformer Networks","date":"2020-06-01","arxiv_id":"2006.01658","n_code_links":0,"syntology":null},{"paper":null,"slug":"when-bert-forgets-how-to-pos-amnesic-probing","title":"Amnesic Probing: Behavioral Explanation with Amnesic Counterfactuals","date":"2020-06-01","arxiv_id":"2006.00995","n_code_links":0,"syntology":null},{"paper":null,"slug":"bpgc-at-semeval-2020-task-11-propaganda","title":"BPGC at SemEval-2020 Task 11: Propaganda Detection in News Articles with Multi-Granularity Knowledge Sharing and Linguistic Features based Ensemble Learning","date":"2020-05-31","arxiv_id":"2006.00593","n_code_links":0,"syntology":null},{"paper":null,"slug":"cnrl-at-semeval-2020-task-5-modelling-causal","title":"CNRL at SemEval-2020 Task 5: Modelling Causal Reasoning in Language with Multi-Head Self-Attention Weights based Counterfactual Detection","date":"2020-05-31","arxiv_id":"2006.00609","n_code_links":0,"syntology":null},{"paper":null,"slug":"judge-me-by-my-size-noun-do-you-yodalib-a","title":"\"Judge me by my size (noun), do you?'' YodaLib: A Demographic-Aware Humor Generation Framework","date":"2020-05-31","arxiv_id":"2006.00578","n_code_links":0,"syntology":null},{"paper":null,"slug":"lrg-at-semeval-2020-task-7-assessing-the","title":"LRG at SemEval-2020 Task 7: Assessing the Ability of BERT and Derivative Models to Perform Short-Edits based Humor Grading","date":"2020-05-31","arxiv_id":"2006.00607","n_code_links":0,"syntology":null},{"paper":null,"slug":"neural-entity-linking-a-survey-of-models","title":"Neural Entity Linking: A Survey of Models Based on Deep Learning","date":"2020-05-31","arxiv_id":"2006.00575","n_code_links":0,"syntology":null},{"paper":null,"slug":"blended-multi-modal-deep-convnet-features-for","title":"Blended Multi-Modal Deep ConvNet Features for Diabetic Retinopathy Severity Prediction","date":"2020-05-30","arxiv_id":"2006.00197","n_code_links":0,"syntology":null},{"paper":null,"slug":"detecting-problem-statements-in-peer","title":"Detecting Problem Statements in Peer Assessments","date":"2020-05-30","arxiv_id":"2006.04532","n_code_links":0,"syntology":null},{"paper":null,"slug":"a-comparative-study-of-lexical-substitution","title":"A Comparative Study of Lexical Substitution Approaches based on Neural Language Models","date":"2020-05-29","arxiv_id":"2006.00031","n_code_links":0,"syntology":null},{"paper":null,"slug":"automatic-diagnosis-of-pulmonary-embolism","title":"Automatic Diagnosis of Pulmonary Embolism Using an Attention-guided Framework: A Large-scale Study","date":"2020-05-29","arxiv_id":"2006.00074","n_code_links":0,"syntology":null},{"paper":"/paper/dynamic-routing-with-path-diversity-and","slug":"dynamic-routing-with-path-diversity-and","title":"CoDiNet: Path Distribution Modeling with Consistency and Diversity for Dynamic Routing","date":"2020-05-29","arxiv_id":"2005.14439","n_code_links":1,"syntology":null},{"paper":null,"slug":"first-neural-conjecturing-datasets-and","title":"First Neural Conjecturing Datasets and Experiments","date":"2020-05-29","arxiv_id":"2005.14664","n_code_links":0,"syntology":null},{"paper":"/paper/safer-a-structure-free-approach-for-certified","slug":"safer-a-structure-free-approach-for-certified","title":"SAFER: A Structure-free Approach for Certified Robustness to Adversarial Word Substitutions","date":"2020-05-29","arxiv_id":"2005.14424","n_code_links":1,"syntology":{"ran":1,"of":2,"n_ran_checked":1,"n_instrument":0,"unverified":1,"pointer_only":2,"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) · 1 unverified","official":{"repos":["lushleaf/Structure-free-certified-NLP"],"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"]}}},{"paper":"/paper/stance-prediction-for-contemporary-issues","slug":"stance-prediction-for-contemporary-issues","title":"Stance Prediction for Contemporary Issues: Data and Experiments","date":"2020-05-29","arxiv_id":"2006.00052","n_code_links":1,"syntology":null},{"paper":null,"slug":"using-large-pretrained-language-models-for","title":"Using Large Pretrained Language Models for Answering User Queries from Product Specifications","date":"2020-05-29","arxiv_id":"2005.14613","n_code_links":0,"syntology":null},{"paper":null,"slug":"a-normalized-fully-convolutional-approach-to","title":"A Normalized Fully Convolutional Approach to Head and Neck Cancer Outcome Prediction","date":"2020-05-28","arxiv_id":"2005.14017","n_code_links":0,"syntology":null},{"paper":null,"slug":"brief-announcement-on-the-limits-of","title":"Brief Announcement: On the Limits of Parallelizing Convolutional Neural Networks on GPUs","date":"2020-05-28","arxiv_id":"2005.13823","n_code_links":0,"syntology":null},{"paper":"/paper/empirical-evaluation-of-pretraining","slug":"empirical-evaluation-of-pretraining","title":"Empirical Evaluation of Pretraining Strategies for Supervised Entity Linking","date":"2020-05-28","arxiv_id":"2005.14253","n_code_links":0,"syntology":null},{"paper":"/paper/hat-hardware-aware-transformers-for-efficient","slug":"hat-hardware-aware-transformers-for-efficient","title":"HAT: Hardware-Aware Transformers for Efficient Natural Language Processing","date":"2020-05-28","arxiv_id":"2005.14187","n_code_links":4,"syntology":null},{"paper":null,"slug":"knowledge-driven-learning-via-experts-consult","title":"Multimodal Feature Fusion and Knowledge-Driven Learning via Experts Consult for Thyroid Nodule Classification","date":"2020-05-28","arxiv_id":"2005.14117","n_code_links":0,"syntology":null},{"paper":"/paper/language-models-are-few-shot-learners","slug":"language-models-are-few-shot-learners","title":"Language Models are Few-Shot Learners","date":"2020-05-28","arxiv_id":"2005.14165","n_code_links":67,"syntology":{"ran":45,"of":65,"n_ran_checked":40,"n_instrument":5,"unverified":20,"pointer_only":7,"phrase":"45 ran (of which 0 constructed an object rather than computing a result; 40 with no instrument failure: 2 honoured, 1 violated, 37 with no contract checked; 5 where Syntology's instrument failed) · 20 unverified","official":{"repos":["openai/gpt-3"],"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/on-incorporating-structural-information-to","slug":"on-incorporating-structural-information-to","title":"On Incorporating Structural Information to improve Dialogue Response Generation","date":"2020-05-28","arxiv_id":"2005.14315","n_code_links":1,"syntology":null},{"paper":null,"slug":"variational-neural-machine-translation-with","title":"Variational Neural Machine Translation with Normalizing Flows","date":"2020-05-28","arxiv_id":"2005.13978","n_code_links":0,"syntology":null},{"paper":"/paper/causalm-causal-model-explanation-through","slug":"causalm-causal-model-explanation-through","title":"CausaLM: Causal Model Explanation Through Counterfactual Language Models","date":"2020-05-27","arxiv_id":"2005.13407","n_code_links":1,"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":null}},{"paper":"/paper/clocs-contrastive-learning-of-cardiac-signals","slug":"clocs-contrastive-learning-of-cardiac-signals","title":"CLOCS: Contrastive Learning of Cardiac Signals Across Space, Time, and Patients","date":"2020-05-27","arxiv_id":"2005.13249","n_code_links":2,"syntology":{"ran":2,"of":2,"n_ran_checked":1,"n_instrument":1,"unverified":0,"pointer_only":2,"phrase":"2 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; 1 where Syntology's instrument failed) · 0 unverified","official":{"repos":["danikiyasseh/CLOCS"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed","official"]}}},{"paper":"/paper/communication-efficient-distributed-deep-1","slug":"communication-efficient-distributed-deep-1","title":"A Quantitative Survey of Communication Optimizations in Distributed Deep Learning","date":"2020-05-27","arxiv_id":"2005.13247","n_code_links":1,"syntology":null},{"paper":"/paper/general-purpose-user-embeddings-based-on","slug":"general-purpose-user-embeddings-based-on","title":"General-Purpose User Embeddings based on Mobile App Usage","date":"2020-05-27","arxiv_id":"2005.13303","n_code_links":1,"syntology":null},{"paper":"/paper/gsto-gated-scale-transfer-operation-for-multi","slug":"gsto-gated-scale-transfer-operation-for-multi","title":"GSTO: Gated Scale-Transfer Operation for Multi-Scale Feature Learning in Pixel Labeling","date":"2020-05-27","arxiv_id":"2005.13363","n_code_links":1,"syntology":null},{"paper":"/paper/language-representation-models-for-fine","slug":"language-representation-models-for-fine","title":"Language Representation Models for Fine-Grained Sentiment Classification","date":"2020-05-27","arxiv_id":"2005.13619","n_code_links":1,"syntology":null},{"paper":"/paper/network-fusion-for-content-creation-with","slug":"network-fusion-for-content-creation-with","title":"Network-to-Network Translation with Conditional Invertible Neural Networks","date":"2020-05-27","arxiv_id":"2005.13580","n_code_links":1,"syntology":null},{"paper":"/paper/pai-conv-permutable-anisotropic-convolutional","slug":"pai-conv-permutable-anisotropic-convolutional","title":"Permutation Matters: Anisotropic Convolutional Layer for Learning on Point Clouds","date":"2020-05-27","arxiv_id":"2005.13135","n_code_links":1,"syntology":null},{"paper":"/paper/poly-yolo-higher-speed-more-precise-detection","slug":"poly-yolo-higher-speed-more-precise-detection","title":"Poly-YOLO: higher speed, more precise detection and instance segmentation for YOLOv3","date":"2020-05-27","arxiv_id":"2005.13243","n_code_links":2,"syntology":null},{"paper":"/paper/ssm-net-for-plants-disease-identification-in","slug":"ssm-net-for-plants-disease-identification-in","title":"SSM-Net for Plants Disease Identification in Low Data Regime","date":"2020-05-27","arxiv_id":"2005.13140","n_code_links":1,"syntology":null},{"paper":null,"slug":"syntactic-structure-distillation-pretraining","title":"Syntactic Structure Distillation Pretraining For Bidirectional Encoders","date":"2020-05-27","arxiv_id":"2005.13482","n_code_links":0,"syntology":null},{"paper":"/paper/towards-the-infeasibility-of-membership","slug":"towards-the-infeasibility-of-membership","title":"On the Difficulty of Membership Inference Attacks","date":"2020-05-27","arxiv_id":"2005.13702","n_code_links":1,"syntology":null},{"paper":"/paper/transition-based-semantic-dependency-parsing","slug":"transition-based-semantic-dependency-parsing","title":"Transition-based Semantic Dependency Parsing with Pointer Networks","date":"2020-05-27","arxiv_id":"2005.13344","n_code_links":1,"syntology":null},{"paper":"/paper/a-data-driven-approach-for-noise-reduction-in","slug":"a-data-driven-approach-for-noise-reduction-in","title":"A Data-driven Approach for Noise Reduction in Distantly Supervised Biomedical Relation Extraction","date":"2020-05-26","arxiv_id":"2005.12565","n_code_links":1,"syntology":null},{"paper":"/paper/beep-korean-corpus-of-online-news-comments","slug":"beep-korean-corpus-of-online-news-comments","title":"BEEP! Korean Corpus of Online News Comments for Toxic Speech Detection","date":"2020-05-26","arxiv_id":"2005.12503","n_code_links":1,"syntology":null},{"paper":null,"slug":"bert-xml-large-scale-automated-icd-coding","title":"BERT-XML: Large Scale Automated ICD Coding Using BERT Pretraining","date":"2020-05-26","arxiv_id":"2006.03685","n_code_links":0,"syntology":null},{"paper":null,"slug":"comparing-bert-against-traditional-machine","title":"Comparing BERT against traditional machine learning text classification","date":"2020-05-26","arxiv_id":"2005.13012","n_code_links":0,"syntology":null},{"paper":"/paper/end-to-end-object-detection-with-transformers","slug":"end-to-end-object-detection-with-transformers","title":"End-to-End Object Detection with Transformers","date":"2020-05-26","arxiv_id":"2005.12872","n_code_links":37,"syntology":{"ran":70,"of":92,"n_ran_checked":62,"n_instrument":8,"unverified":22,"pointer_only":19,"phrase":"70 ran (of which 45 constructed an object rather than computing a result; 62 with no instrument failure: 2 honoured, 1 violated, 59 with no contract checked; 8 where Syntology's instrument failed) · 22 unverified","official":{"repos":["facebookresearch/detr"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":1,"ran_from_kinds":["listed","official"]}}},{"paper":"/paper/gector-grammatical-error-correction-tag-not","slug":"gector-grammatical-error-correction-tag-not","title":"GECToR -- Grammatical Error Correction: Tag, Not Rewrite","date":"2020-05-26","arxiv_id":"2005.12592","n_code_links":3,"syntology":{"ran":10,"of":12,"n_ran_checked":9,"n_instrument":1,"unverified":2,"pointer_only":0,"phrase":"10 ran (of which 0 constructed an object rather than computing a result; 9 with no instrument failure: 0 honoured, 1 violated, 8 with no contract checked; 1 where Syntology's instrument failed) · 2 unverified","official":{"repos":["grammarly/gector"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":8,"n_unverified":2,"ran_from_kinds":["listed","official"]}}}],"record_sha256":"f102658477f585d41d8b756a95c3e0c25c4d63e5a2ca218e80e8959947c3f240","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}