{"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/274","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":274,"pages_in_order":285,"rows_per_page":100,"rows":[27301,27400],"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/273","next":"/method/residual-connection/papers/275","papers":[{"paper":"/paper/a-simple-and-effective-approach-to-automatic","slug":"a-simple-and-effective-approach-to-automatic","title":"A Simple and Effective Approach to Automatic Post-Editing with Transfer Learning","date":"2019-06-14","arxiv_id":"1906.06253","n_code_links":1,"syntology":{"ran":2,"of":4,"n_ran_checked":2,"n_instrument":0,"unverified":2,"pointer_only":1,"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) · 2 unverified","official":{"repos":["deep-spin/OpenNMT-APE"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":2,"ran_from_kinds":["official"]}}},{"paper":"/paper/fixing-the-train-test-resolution-discrepancy","slug":"fixing-the-train-test-resolution-discrepancy","title":"Fixing the train-test resolution discrepancy","date":"2019-06-14","arxiv_id":"1906.06423","n_code_links":3,"syntology":{"ran":0,"of":2,"n_ran_checked":0,"n_instrument":0,"unverified":2,"pointer_only":2,"phrase":"0 ran · 2 unverified","official":{"repos":["facebookresearch/FixRes"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":2,"ran_from_kinds":[]}}},{"paper":"/paper/image-captioning-transforming-objects-into","slug":"image-captioning-transforming-objects-into","title":"Image Captioning: Transforming Objects into Words","date":"2019-06-14","arxiv_id":"1906.05963","n_code_links":4,"syntology":{"ran":9,"of":10,"n_ran_checked":4,"n_instrument":5,"unverified":1,"pointer_only":3,"phrase":"9 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 1 violated, 3 with no contract checked; 5 where Syntology's instrument failed) · 1 unverified","official":{"repos":["yahoo/object_relation_transformer"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":0,"ran_from_kinds":["official","unlocated"]}}},{"paper":"/paper/model-agnostic-dual-quality-assessment-for","slug":"model-agnostic-dual-quality-assessment-for","title":"Adversarial Robustness Assessment: Why both $L_0$ and $L_\\infty$ Attacks Are Necessary","date":"2019-06-14","arxiv_id":"1906.06026","n_code_links":1,"syntology":null},{"paper":null,"slug":"contrastive-bidirectional-transformer-for","title":"Learning Video Representations using Contrastive Bidirectional Transformer","date":"2019-06-13","arxiv_id":"1906.05743","n_code_links":0,"syntology":null},{"paper":"/paper/contrastive-multiview-coding","slug":"contrastive-multiview-coding","title":"Contrastive Multiview Coding","date":"2019-06-13","arxiv_id":"1906.05849","n_code_links":8,"syntology":{"ran":4,"of":4,"n_ran_checked":2,"n_instrument":2,"unverified":0,"pointer_only":2,"phrase":"4 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; 2 where Syntology's instrument failed) · 0 unverified","official":{"repos":["HobbitLong/CMC"],"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":null,"slug":"lattice-transformer-for-speech-translation","title":"Lattice Transformer for Speech Translation","date":"2019-06-13","arxiv_id":"1906.05551","n_code_links":0,"syntology":null},{"paper":"/paper/learning-instance-occlusion-for-panoptic","slug":"learning-instance-occlusion-for-panoptic","title":"Learning Instance Occlusion for Panoptic Segmentation","date":"2019-06-13","arxiv_id":"1906.05896","n_code_links":1,"syntology":null},{"paper":"/paper/learning-spatio-temporal-representation-with-3","slug":"learning-spatio-temporal-representation-with-3","title":"Learning Spatio-Temporal Representation with Local and Global Diffusion","date":"2019-06-13","arxiv_id":"1906.05571","n_code_links":0,"syntology":null},{"paper":"/paper/stand-alone-self-attention-in-vision-models","slug":"stand-alone-self-attention-in-vision-models","title":"Stand-Alone Self-Attention in Vision Models","date":"2019-06-13","arxiv_id":"1906.05909","n_code_links":8,"syntology":{"ran":2,"of":3,"n_ran_checked":1,"n_instrument":1,"unverified":1,"pointer_only":1,"phrase":"2 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; 1 where Syntology's instrument failed) · 1 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":null,"slug":"telephonetic-making-neural-language-models","title":"Telephonetic: Making Neural Language Models Robust to ASR and Semantic Noise","date":"2019-06-13","arxiv_id":"1906.05678","n_code_links":0,"syntology":null},{"paper":"/paper/temporal-transformer-networks-joint-learning-1","slug":"temporal-transformer-networks-joint-learning-1","title":"Temporal Transformer Networks: Joint Learning of Invariant and Discriminative Time Warping","date":"2019-06-13","arxiv_id":"1906.05947","n_code_links":1,"syntology":null},{"paper":"/paper/a-multiscale-visualization-of-attention-in","slug":"a-multiscale-visualization-of-attention-in","title":"A Multiscale Visualization of Attention in the Transformer Model","date":"2019-06-12","arxiv_id":"1906.05714","n_code_links":3,"syntology":{"ran":3,"of":3,"n_ran_checked":0,"n_instrument":3,"unverified":0,"pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 3 where Syntology's instrument failed) · 0 unverified","official":{"repos":["jessevig/bertviz"],"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":"assisted-excitation-of-activations-a-learning-1","title":"Assisted Excitation of Activations: A Learning Technique to Improve Object Detectors","date":"2019-06-12","arxiv_id":"1906.05388","n_code_links":0,"syntology":null},{"paper":"/paper/neural-arabic-question-answering","slug":"neural-arabic-question-answering","title":"Neural Arabic Question Answering","date":"2019-06-12","arxiv_id":"1906.05394","n_code_links":1,"syntology":{"ran":6,"of":7,"n_ran_checked":5,"n_instrument":1,"unverified":1,"pointer_only":2,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 2 honoured, 0 violated, 3 with no contract checked; 1 where Syntology's instrument failed) · 1 unverified","official":{"repos":["husseinmozannar/SOQAL"],"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":"parameterized-structured-pruning-for-deep","title":"Parameterized Structured Pruning for Deep Neural Networks","date":"2019-06-12","arxiv_id":"1906.05180","n_code_links":0,"syntology":null},{"paper":"/paper/synthetic-qa-corpora-generation-with","slug":"synthetic-qa-corpora-generation-with","title":"Synthetic QA Corpora Generation with Roundtrip Consistency","date":"2019-06-12","arxiv_id":"1906.05416","n_code_links":4,"syntology":null},{"paper":null,"slug":"cuedwmt19ewclms","title":"Cued@wmt19:ewc&lms","date":"2019-06-11","arxiv_id":"1906.05447","n_code_links":0,"syntology":null},{"paper":"/paper/explicit-disentanglement-of-appearance-and","slug":"explicit-disentanglement-of-appearance-and","title":"Explicit Disentanglement of Appearance and Perspective in Generative Models","date":"2019-06-11","arxiv_id":"1906.11881","n_code_links":1,"syntology":null},{"paper":null,"slug":"from-fully-supervised-to-zero-shot-settings","title":"From Fully Supervised to Zero Shot Settings for Twitter Hashtag Recommendation","date":"2019-06-11","arxiv_id":"1906.04914","n_code_links":0,"syntology":null},{"paper":"/paper/graph-convolutional-transformer-learning-the","slug":"graph-convolutional-transformer-learning-the","title":"Learning the Graphical Structure of Electronic Health Records with Graph Convolutional Transformer","date":"2019-06-11","arxiv_id":"1906.04716","n_code_links":2,"syntology":null},{"paper":"/paper/lightweight-and-efficient-neural-natural","slug":"lightweight-and-efficient-neural-natural","title":"Lightweight and Efficient Neural Natural Language Processing with Quaternion Networks","date":"2019-06-11","arxiv_id":"1906.04393","n_code_links":1,"syntology":null},{"paper":null,"slug":"table-based-neural-units-fully-quantizing","title":"Table-Based Neural Units: Fully Quantizing Networks for Multiply-Free Inference","date":"2019-06-11","arxiv_id":"1906.04798","n_code_links":0,"syntology":null},{"paper":"/paper/what-does-bert-look-at-an-analysis-of-berts","slug":"what-does-bert-look-at-an-analysis-of-berts","title":"What Does BERT Look At? An Analysis of BERT's Attention","date":"2019-06-11","arxiv_id":"1906.04341","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":["clarkkev/attention-analysis"],"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/associative-convolutional-layers","slug":"associative-convolutional-layers","title":"Associative Convolutional Layers","date":"2019-06-10","arxiv_id":"1906.04309","n_code_links":1,"syntology":null},{"paper":null,"slug":"halalnet-a-deep-neural-network-that","title":"HalalNet: A Deep Neural Network that Classifies the Halalness Slaughtered Chicken from their Images","date":"2019-06-10","arxiv_id":"1906.11893","n_code_links":0,"syntology":null},{"paper":"/paper/learning-to-combine-grammatical-error","slug":"learning-to-combine-grammatical-error","title":"Learning to combine Grammatical Error Corrections","date":"2019-06-10","arxiv_id":"1906.03897","n_code_links":1,"syntology":null},{"paper":null,"slug":"patch-transformer-for-multi-tagging-whole","title":"Patch Transformer for Multi-tagging Whole Slide Histopathology Images","date":"2019-06-10","arxiv_id":"1906.04151","n_code_links":0,"syntology":null},{"paper":null,"slug":"the-university-of-helsinki-submissions-to-the-1","title":"The University of Helsinki submissions to the WMT19 news translation task","date":"2019-06-10","arxiv_id":"1906.04040","n_code_links":0,"syntology":null},{"paper":"/paper/cross-view-semantic-segmentation-for-sensing","slug":"cross-view-semantic-segmentation-for-sensing","title":"Cross-view Semantic Segmentation for Sensing Surroundings","date":"2019-06-09","arxiv_id":"1906.03560","n_code_links":1,"syntology":null},{"paper":"/paper/distilling-object-detectors-with-fine-grained-1","slug":"distilling-object-detectors-with-fine-grained-1","title":"Distilling Object Detectors with Fine-grained Feature Imitation","date":"2019-06-09","arxiv_id":"1906.03609","n_code_links":3,"syntology":{"ran":6,"of":8,"n_ran_checked":6,"n_instrument":0,"unverified":2,"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) · 2 unverified","official":{"repos":["twangnh/Distilling-Object-Detectors"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":2,"ran_from_kinds":["official"]}}},{"paper":"/paper/gendered-pronoun-resolution-using-bert-and-an","slug":"gendered-pronoun-resolution-using-bert-and-an","title":"Gendered Pronoun Resolution using BERT and an extractive question answering formulation","date":"2019-06-09","arxiv_id":"1906.03695","n_code_links":1,"syntology":null},{"paper":null,"slug":"hgc-hierarchical-group-convolution-for-highly","title":"HGC: Hierarchical Group Convolution for Highly Efficient Neural Network","date":"2019-06-09","arxiv_id":"1906.03657","n_code_links":0,"syntology":null},{"paper":null,"slug":"in-situ-cane-toad-recognition","title":"In Situ Cane Toad Recognition","date":"2019-06-09","arxiv_id":"1906.03547","n_code_links":0,"syntology":null},{"paper":null,"slug":"making-asynchronous-stochastic-gradient","title":"Making Asynchronous Stochastic Gradient Descent Work for Transformers","date":"2019-06-08","arxiv_id":"1906.03496","n_code_links":0,"syntology":null},{"paper":"/paper/sentence-centrality-revisited-for","slug":"sentence-centrality-revisited-for","title":"Sentence Centrality Revisited for Unsupervised Summarization","date":"2019-06-08","arxiv_id":"1906.03508","n_code_links":1,"syntology":null},{"paper":"/paper/simultaneous-classification-and-novelty","slug":"simultaneous-classification-and-novelty","title":"Outlier Exposure with Confidence Control for Out-of-Distribution Detection","date":"2019-06-08","arxiv_id":"1906.03509","n_code_links":1,"syntology":null},{"paper":null,"slug":"analyzing-the-structure-of-attention-in-a","title":"Analyzing the Structure of Attention in a Transformer Language Model","date":"2019-06-07","arxiv_id":"1906.04284","n_code_links":0,"syntology":null},{"paper":"/paper/improving-relation-extraction-by-pre-trained-1","slug":"improving-relation-extraction-by-pre-trained-1","title":"Improving Relation Extraction by Pre-trained Language Representations","date":"2019-06-07","arxiv_id":"1906.03088","n_code_links":1,"syntology":null},{"paper":"/paper/leveraging-bert-for-extractive-text","slug":"leveraging-bert-for-extractive-text","title":"Leveraging BERT for Extractive Text Summarization on Lectures","date":"2019-06-07","arxiv_id":"1906.04165","n_code_links":8,"syntology":null},{"paper":"/paper/selfie-self-supervised-pretraining-for-image","slug":"selfie-self-supervised-pretraining-for-image","title":"Selfie: Self-supervised Pretraining for Image Embedding","date":"2019-06-07","arxiv_id":"1906.02940","n_code_links":1,"syntology":null},{"paper":null,"slug":"attention-is-all-you-need-for-videos-self","title":"Attention is all you need for Videos: Self-attention based Video Summarization using Universal Transformers","date":"2019-06-06","arxiv_id":"1906.02792","n_code_links":0,"syntology":null},{"paper":"/paper/gcdt-a-global-context-enhanced-deep","slug":"gcdt-a-global-context-enhanced-deep","title":"GCDT: A Global Context Enhanced Deep Transition Architecture for Sequence Labeling","date":"2019-06-06","arxiv_id":"1906.02437","n_code_links":1,"syntology":null},{"paper":null,"slug":"playing-the-lottery-with-rewards-and-multiple","title":"Playing the lottery with rewards and multiple languages: lottery tickets in RL and NLP","date":"2019-06-06","arxiv_id":"1906.02768","n_code_links":0,"syntology":null},{"paper":null,"slug":"robust-neural-machine-translation-with-doubly","title":"Robust Neural Machine Translation with Doubly Adversarial Inputs","date":"2019-06-06","arxiv_id":"1906.02443","n_code_links":0,"syntology":null},{"paper":"/paper/syntactically-supervised-transformers-for","slug":"syntactically-supervised-transformers-for","title":"Syntactically Supervised Transformers for Faster Neural Machine Translation","date":"2019-06-06","arxiv_id":"1906.02780","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":{"repos":["dojoteef/synst"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":1,"ran_from_kinds":["official"]}}},{"paper":"/paper/understanding-and-improving-transformer-from","slug":"understanding-and-improving-transformer-from","title":"Understanding and Improving Transformer From a Multi-Particle Dynamic System Point of View","date":"2019-06-06","arxiv_id":"1906.02762","n_code_links":2,"syntology":{"ran":9,"of":10,"n_ran_checked":8,"n_instrument":1,"unverified":1,"pointer_only":1,"phrase":"9 ran (of which 0 constructed an object rather than computing a result; 8 with no instrument failure: 0 honoured, 0 violated, 8 with no contract checked; 1 where Syntology's instrument failed) · 1 unverified","official":{"repos":["zhuohan123/macaron-net"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":0,"n_ran_no_instrument_failure":8,"n_unverified":1,"ran_from_kinds":["official"]}}},{"paper":"/paper/visualizing-and-measuring-the-geometry-of","slug":"visualizing-and-measuring-the-geometry-of","title":"Visualizing and Measuring the Geometry of BERT","date":"2019-06-06","arxiv_id":"1906.02715","n_code_links":1,"syntology":null},{"paper":"/paper/butterfly-transform-an-efficient-fft-based","slug":"butterfly-transform-an-efficient-fft-based","title":"Butterfly Transform: An Efficient FFT Based Neural Architecture Design","date":"2019-06-05","arxiv_id":"1906.02256","n_code_links":1,"syntology":null},{"paper":null,"slug":"corn-leaf-detection-using-region-based","title":"Corn leaf detection using Region based convolutional neural network","date":"2019-06-05","arxiv_id":"1906.01900","n_code_links":0,"syntology":null},{"paper":"/paper/detecting-kissing-scenes-in-a-database-of","slug":"detecting-kissing-scenes-in-a-database-of","title":"Detecting Kissing Scenes in a Database of Hollywood Films","date":"2019-06-05","arxiv_id":"1906.01843","n_code_links":1,"syntology":null},{"paper":null,"slug":"farm-land-weed-detection-with-region-based","title":"Farm land weed detection with region-based deep convolutional neural networks","date":"2019-06-05","arxiv_id":"1906.01885","n_code_links":0,"syntology":null},{"paper":null,"slug":"from-balustrades-to-pierre-vinken-looking-for","title":"From Balustrades to Pierre Vinken: Looking for Syntax in Transformer Self-Attentions","date":"2019-06-05","arxiv_id":"1906.01958","n_code_links":0,"syntology":null},{"paper":"/paper/large-scale-multi-label-text-classification-1","slug":"large-scale-multi-label-text-classification-1","title":"Large-Scale Multi-Label Text Classification on EU Legislation","date":"2019-06-05","arxiv_id":"1906.02192","n_code_links":1,"syntology":null},{"paper":"/paper/learning-deep-transformer-models-for-machine","slug":"learning-deep-transformer-models-for-machine","title":"Learning Deep Transformer Models for Machine Translation","date":"2019-06-05","arxiv_id":"1906.01787","n_code_links":2,"syntology":{"ran":3,"of":3,"n_ran_checked":0,"n_instrument":3,"unverified":0,"pointer_only":3,"phrase":"3 ran (of which 3 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 3 where Syntology's instrument failed) · 0 unverified; every one of the 3 samples that ran constructed an object rather than computing a result","official":{"repos":["wangqiangneu/dlcl"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":3,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":"/paper/neural-legal-judgment-prediction-in-english","slug":"neural-legal-judgment-prediction-in-english","title":"Neural Legal Judgment Prediction in English","date":"2019-06-05","arxiv_id":"1906.02059","n_code_links":0,"syntology":null},{"paper":"/paper/neural-sde-stabilizing-neural-ode-networks","slug":"neural-sde-stabilizing-neural-ode-networks","title":"Neural SDE: Stabilizing Neural ODE Networks with Stochastic Noise","date":"2019-06-05","arxiv_id":"1906.02355","n_code_links":1,"syntology":{"ran":4,"of":4,"n_ran_checked":0,"n_instrument":4,"unverified":0,"pointer_only":4,"phrase":"4 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; 4 where Syntology's instrument failed) · 0 unverified","official":{"repos":["xuanqing94/NeuralSDE"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"probabilistic-hypergraph-grammars-for","title":"Probabilistic hypergraph grammars for efficient molecular optimization","date":"2019-06-05","arxiv_id":"1906.01845","n_code_links":0,"syntology":null},{"paper":null,"slug":"exploiting-sentential-context-for-neural","title":"Exploiting Sentential Context for Neural Machine Translation","date":"2019-06-04","arxiv_id":"1906.01268","n_code_links":0,"syntology":null},{"paper":"/paper/how-multilingual-is-multilingual-bert","slug":"how-multilingual-is-multilingual-bert","title":"How multilingual is Multilingual BERT?","date":"2019-06-04","arxiv_id":"1906.01502","n_code_links":3,"syntology":{"ran":2,"of":2,"n_ran_checked":1,"n_instrument":1,"unverified":0,"pointer_only":1,"phrase":"2 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; 1 where Syntology's instrument failed) · 0 unverified","official":null}},{"paper":null,"slug":"improving-long-distance-slot-carryover-in","title":"Improving Long Distance Slot Carryover in Spoken Dialogue Systems","date":"2019-06-04","arxiv_id":"1906.01149","n_code_links":0,"syntology":null},{"paper":null,"slug":"lattice-based-transformer-encoder-for-neural","title":"Lattice-Based Transformer Encoder for Neural Machine Translation","date":"2019-06-04","arxiv_id":"1906.01282","n_code_links":0,"syntology":null},{"paper":"/paper/open-sesame-getting-inside-berts-linguistic","slug":"open-sesame-getting-inside-berts-linguistic","title":"Open Sesame: Getting Inside BERT's Linguistic Knowledge","date":"2019-06-04","arxiv_id":"1906.01698","n_code_links":1,"syntology":{"ran":13,"of":18,"n_ran_checked":10,"n_instrument":3,"unverified":5,"pointer_only":7,"phrase":"13 ran (of which 0 constructed an object rather than computing a result; 10 with no instrument failure: 4 honoured, 0 violated, 6 with no contract checked; 3 where Syntology's instrument failed) · 5 unverified","official":{"repos":["yongjie-lin/bert-opensesame"],"state":"official (archive's flag): 13 ran","n_ran":13,"n_constructed":0,"n_ran_no_instrument_failure":10,"n_unverified":5,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"optimal-unsupervised-domain-translation","title":"Optimal Unsupervised Domain Translation","date":"2019-06-04","arxiv_id":"1906.01292","n_code_links":0,"syntology":null},{"paper":"/paper/pca-driven-hybrid-network-design-for-enabling","slug":"pca-driven-hybrid-network-design-for-enabling","title":"Constructing Energy-efficient Mixed-precision Neural Networks through Principal Component Analysis for Edge Intelligence","date":"2019-06-04","arxiv_id":"1906.01493","n_code_links":1,"syntology":null},{"paper":"/paper/rthn-a-rnn-transformer-hierarchical-network","slug":"rthn-a-rnn-transformer-hierarchical-network","title":"RTHN: A RNN-Transformer Hierarchical Network for Emotion Cause Extraction","date":"2019-06-04","arxiv_id":"1906.01236","n_code_links":3,"syntology":{"ran":0,"of":4,"n_ran_checked":0,"n_instrument":0,"unverified":4,"pointer_only":4,"phrase":"0 ran · 4 unverified","official":{"repos":["NUSTM/RTHN"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":2,"ran_from_kinds":[]}}},{"paper":"/paper/sequence-tagging-with-contextual-and-non","slug":"sequence-tagging-with-contextual-and-non","title":"Sequence Tagging with Contextual and Non-Contextual Subword Representations: A Multilingual Evaluation","date":"2019-06-04","arxiv_id":"1906.01569","n_code_links":1,"syntology":null},{"paper":"/paper/the-unreasonable-effectiveness-of-transformer","slug":"the-unreasonable-effectiveness-of-transformer","title":"The Unreasonable Effectiveness of Transformer Language Models in Grammatical Error Correction","date":"2019-06-04","arxiv_id":"1906.01733","n_code_links":2,"syntology":null},{"paper":null,"slug":"190600532","title":"Efficient 8-Bit Quantization of Transformer Neural Machine Language Translation Model","date":"2019-06-03","arxiv_id":"1906.00532","n_code_links":0,"syntology":null},{"paper":null,"slug":"190600852","title":"Hierarchical Auxiliary Learning","date":"2019-06-03","arxiv_id":"1906.00852","n_code_links":0,"syntology":null},{"paper":"/paper/190600346","slug":"190600346","title":"Pre-training of Graph Augmented Transformers for Medication Recommendation","date":"2019-06-02","arxiv_id":"1906.00346","n_code_links":1,"syntology":{"ran":6,"of":9,"n_ran_checked":5,"n_instrument":1,"unverified":3,"pointer_only":0,"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) · 3 unverified","official":{"repos":["jshang123/G-Bert"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":3,"ran_from_kinds":["official"]}}},{"paper":"/paper/190600446","slug":"190600446","title":"Generating Diverse High-Fidelity Images with VQ-VAE-2","date":"2019-06-02","arxiv_id":"1906.00446","n_code_links":15,"syntology":{"ran":5,"of":9,"n_ran_checked":5,"n_instrument":0,"unverified":4,"pointer_only":4,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 1 honoured, 1 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 4 unverified","official":{"repos":["deepmind/sonnet"],"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/190600138","slug":"190600138","title":"Efficient Adaptation of Pretrained Transformers for Abstractive Summarization","date":"2019-06-01","arxiv_id":"1906.00138","n_code_links":2,"syntology":null},{"paper":null,"slug":"190600238","title":"Adversarial Generation and Encoding of Nested Texts","date":"2019-06-01","arxiv_id":"1906.00238","n_code_links":0,"syntology":null},{"paper":"/paper/190600295","slug":"190600295","title":"Multimodal Transformer for Unaligned Multimodal Language Sequences","date":"2019-06-01","arxiv_id":"1906.00295","n_code_links":4,"syntology":{"ran":8,"of":17,"n_ran_checked":5,"n_instrument":3,"unverified":9,"pointer_only":10,"phrase":"8 ran (of which 3 constructed an object rather than computing a result; 5 with no instrument failure: 3 honoured, 1 violated, 1 with no contract checked; 3 where Syntology's instrument failed) · 9 unverified","official":{"repos":["yaohungt/Multimodal-Transformer"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":5,"ran_from_kinds":["listed","official","unlocated"]}}},{"paper":null,"slug":"a-bert-based-universal-model-for-both-within","title":"A BERT-based Universal Model for Both Within- and Cross-sentence Clinical Temporal Relation Extraction","date":"2019-06-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":"/paper/a-structural-probe-for-finding-syntax-in-word","slug":"a-structural-probe-for-finding-syntax-in-word","title":"A Structural Probe for Finding Syntax in Word Representations","date":"2019-06-01","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":null,"slug":"adaptive-transfer-network-for-cross-domain","title":"Adaptive Transfer Network for Cross-Domain Person Re-Identification","date":"2019-06-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"autohome-orca-at-semeval-2019-task-8","title":"AUTOHOME-ORCA at SemEval-2019 Task 8: Application of BERT for Fact-Checking in Community Forums","date":"2019-06-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"blcu_nlp-at-semeval-2019-task-8-a-contextual","title":"BLCU\\_NLP at SemEval-2019 Task 8: A Contextual Knowledge-enhanced GPT Model for Fact Checking","date":"2019-06-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"bnu-hkbu-uic-nlp-team-2-at-semeval-2019-task","title":"BNU-HKBU UIC NLP Team 2 at SemEval-2019 Task 6: Detecting Offensive Language Using BERT model","date":"2019-06-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"cn-hit-mit-at-semeval-2019-task-6-offensive","title":"CN-HIT-MI.T at SemEval-2019 Task 6: Offensive Language Identification Based on BiLSTM with Double Attention","date":"2019-06-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":"/paper/co-occurrent-features-in-semantic","slug":"co-occurrent-features-in-semantic","title":"Co-Occurrent Features in Semantic Segmentation","date":"2019-06-01","arxiv_id":null,"n_code_links":2,"syntology":null},{"paper":"/paper/codah-an-adversarially-authored-question","slug":"codah-an-adversarially-authored-question","title":"CODAH: An Adversarially-Authored Question Answering Dataset for Common Sense","date":"2019-06-01","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":"/paper/collaborative-spatiotemporal-feature-learning","slug":"collaborative-spatiotemporal-feature-learning","title":"Collaborative Spatiotemporal Feature Learning for Video Action Recognition","date":"2019-06-01","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":null,"slug":"convai-at-semeval-2019-task-6-offensive","title":"ConvAI at SemEval-2019 Task 6: Offensive Language Identification and Categorization with Perspective and BERT","date":"2019-06-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"domlin-at-semeval-2019-task-8-automated-fact","title":"DOMLIN at SemEval-2019 Task 8: Automated Fact Checking exploiting Ratings in Community Question Answering Forums","date":"2019-06-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"ds-at-semeval-2019-task-9-from-suggestion","title":"DS at SemEval-2019 Task 9: From Suggestion Mining with neural networks to adversarial cross-domain classification","date":"2019-06-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":"/paper/embeddia-at-semeval-2019-task-6-detecting","slug":"embeddia-at-semeval-2019-task-6-detecting","title":"Embeddia at SemEval-2019 Task 6: Detecting Hate with Neural Network and Transfer Learning Approaches","date":"2019-06-01","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":null,"slug":"figure-eight-at-semeval-2019-task-3-ensemble","title":"Figure Eight at SemEval-2019 Task 3: Ensemble of Transfer Learning Methods for Contextual Emotion Detection","date":"2019-06-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":"/paper/hltsuda-at-semeval-2019-task-1-ucca-graph-1","slug":"hltsuda-at-semeval-2019-task-1-ucca-graph-1","title":"HLT@SUDA at SemEval-2019 Task 1: UCCA Graph Parsing as Constituent Tree Parsing","date":"2019-06-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"how-well-do-embedding-models-capture-non","title":"How Well Do Embedding Models Capture Non-compositionality? A View from Multiword Expressions","date":"2019-06-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"improving-cuneiform-language-identification","title":"Improving Cuneiform Language Identification with BERT","date":"2019-06-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"kdehateval-at-semeval-2019-task-5-a-neural","title":"KDEHatEval at SemEval-2019 Task 5: A Neural Network Model for Detecting Hate Speech in Twitter","date":"2019-06-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":"/paper/laf-net-locally-adaptive-fusion-networks-for","slug":"laf-net-locally-adaptive-fusion-networks-for","title":"LAF-Net: Locally Adaptive Fusion Networks for Stereo Confidence Estimation","date":"2019-06-01","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":"/paper/learning-roi-transformer-for-oriented-object","slug":"learning-roi-transformer-for-oriented-object","title":"Learning RoI Transformer for Oriented Object Detection in Aerial Images","date":"2019-06-01","arxiv_id":null,"n_code_links":2,"syntology":null},{"paper":null,"slug":"ltl-ude-at-semeval-2019-task-6-bert-and-two","title":"LTL-UDE at SemEval-2019 Task 6: BERT and Two-Vote Classification for Categorizing Offensiveness","date":"2019-06-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"mitre-at-semeval-2019-task-5-transfer","title":"MITRE at SemEval-2019 Task 5: Transfer Learning for Multilingual Hate Speech Detection","date":"2019-06-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"multi-task-self-supervised-object-detection","title":"Multi-Task Self-Supervised Object Detection via Recycling of Bounding Box Annotations","date":"2019-06-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"neural-machine-translation-between-myanmar","title":"Neural Machine Translation between Myanmar (Burmese) and Rakhine (Arakanese)","date":"2019-06-01","arxiv_id":null,"n_code_links":0,"syntology":null}],"record_sha256":"35797c033249ad88dbe76c55f697e8ae997e189aa3ff8173a4ad3cd119f092b8","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}