{"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/248","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":248,"pages_in_order":285,"rows_per_page":100,"rows":[24701,24800],"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/247","next":"/method/residual-connection/papers/249","papers":[{"paper":null,"slug":"hate-speech-detection-and-racial-bias","title":"Hate Speech Detection and Racial Bias Mitigation in Social Media based on BERT model","date":"2020-08-14","arxiv_id":"2008.06460","n_code_links":0,"syntology":null},{"paper":null,"slug":"language-models-as-few-shot-learner-for-task","title":"Language Models as Few-Shot Learner for Task-Oriented Dialogue Systems","date":"2020-08-14","arxiv_id":"2008.06239","n_code_links":0,"syntology":null},{"paper":"/paper/a-community-powered-search-of-machine","slug":"a-community-powered-search-of-machine","title":"A community-powered search of machine learning strategy space to find NMR property prediction models","date":"2020-08-13","arxiv_id":"2008.05994","n_code_links":1,"syntology":null},{"paper":null,"slug":"adain-switchable-cyclegan-for-efficient","title":"AdaIN-Switchable CycleGAN for Efficient Unsupervised Low-Dose CT Denoising","date":"2020-08-13","arxiv_id":"2008.05753","n_code_links":0,"syntology":null},{"paper":"/paper/andes-at-semeval-2020-task-12-a-jointly","slug":"andes-at-semeval-2020-task-12-a-jointly","title":"ANDES at SemEval-2020 Task 12: A jointly-trained BERT multilingual model for offensive language detection","date":"2020-08-13","arxiv_id":"2008.06408","n_code_links":1,"syntology":null},{"paper":null,"slug":"conv-transformer-transducer-low-latency-low","title":"Conv-Transformer Transducer: Low Latency, Low Frame Rate, Streamable End-to-End Speech Recognition","date":"2020-08-13","arxiv_id":"2008.05750","n_code_links":0,"syntology":null},{"paper":null,"slug":"end-to-end-contextual-perception-and","title":"End-to-end Contextual Perception and Prediction with Interaction Transformer","date":"2020-08-13","arxiv_id":"2008.05927","n_code_links":0,"syntology":null},{"paper":"/paper/enhancing-speech-intelligibility-in-text-to","slug":"enhancing-speech-intelligibility-in-text-to","title":"Enhancing Speech Intelligibility in Text-To-Speech Synthesis using Speaking Style Conversion","date":"2020-08-13","arxiv_id":"2008.05809","n_code_links":1,"syntology":{"ran":3,"of":3,"n_ran_checked":1,"n_instrument":2,"unverified":0,"pointer_only":2,"phrase":"3 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; 2 where Syntology's instrument failed) · 0 unverified","official":null}},{"paper":null,"slug":"large-scale-transfer-learning-for-low","title":"Large-scale Transfer Learning for Low-resource Spoken Language Understanding","date":"2020-08-13","arxiv_id":"2008.05671","n_code_links":0,"syntology":null},{"paper":"/paper/mice-mining-idioms-with-contextual-embeddings","slug":"mice-mining-idioms-with-contextual-embeddings","title":"MICE: Mining Idioms with Contextual Embeddings","date":"2020-08-13","arxiv_id":"2008.05759","n_code_links":1,"syntology":null},{"paper":"/paper/mmm-exploring-conditional-multi-track-music","slug":"mmm-exploring-conditional-multi-track-music","title":"MMM : Exploring Conditional Multi-Track Music Generation with the Transformer","date":"2020-08-13","arxiv_id":"2008.06048","n_code_links":3,"syntology":{"ran":9,"of":9,"n_ran_checked":9,"n_instrument":0,"unverified":0,"pointer_only":0,"phrase":"9 ran (of which 0 constructed an object rather than computing a result; 9 with no instrument failure: 0 honoured, 0 violated, 9 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","official":null}},{"paper":"/paper/powers-of-layers-for-image-to-image","slug":"powers-of-layers-for-image-to-image","title":"Powers of layers for image-to-image translation","date":"2020-08-13","arxiv_id":"2008.05763","n_code_links":0,"syntology":null},{"paper":null,"slug":"compression-of-deep-learning-models-for-text","title":"Compression of Deep Learning Models for Text: A Survey","date":"2020-08-12","arxiv_id":"2008.05221","n_code_links":0,"syntology":null},{"paper":"/paper/evaluating-the-impact-of-knowledge-graph","slug":"evaluating-the-impact-of-knowledge-graph","title":"Evaluating the Impact of Knowledge Graph Context on Entity Disambiguation Models","date":"2020-08-12","arxiv_id":"2008.05190","n_code_links":1,"syntology":null},{"paper":"/paper/facial-expression-recognition-under-partial","slug":"facial-expression-recognition-under-partial","title":"Facial Expression Recognition Under Partial Occlusion from Virtual Reality Headsets based on Transfer Learning","date":"2020-08-12","arxiv_id":"2008.05563","n_code_links":1,"syntology":null},{"paper":"/paper/fine-grained-visual-textual-alignment-for","slug":"fine-grained-visual-textual-alignment-for","title":"Fine-grained Visual Textual Alignment for Cross-Modal Retrieval using Transformer Encoders","date":"2020-08-12","arxiv_id":"2008.05231","n_code_links":1,"syntology":{"ran":14,"of":16,"n_ran_checked":13,"n_instrument":1,"unverified":2,"pointer_only":2,"phrase":"14 ran (of which 0 constructed an object rather than computing a result; 13 with no instrument failure: 0 honoured, 1 violated, 12 with no contract checked; 1 where Syntology's instrument failed) · 2 unverified","official":{"repos":["mesnico/TERAN"],"state":"official (archive's flag): 14 ran","n_ran":14,"n_constructed":0,"n_ran_no_instrument_failure":13,"n_unverified":2,"ran_from_kinds":["official"]}}},{"paper":"/paper/logodet-3k-a-large-scale-image-dataset-for","slug":"logodet-3k-a-large-scale-image-dataset-for","title":"LogoDet-3K: A Large-Scale Image Dataset for Logo Detection","date":"2020-08-12","arxiv_id":"2008.05359","n_code_links":1,"syntology":null},{"paper":null,"slug":"variance-reduced-language-pretraining-via-a","title":"Variance-reduced Language Pretraining via a Mask Proposal Network","date":"2020-08-12","arxiv_id":"2008.05333","n_code_links":0,"syntology":null},{"paper":null,"slug":"modeling-prosodic-phrasing-with-multi-task","title":"Modeling Prosodic Phrasing with Multi-Task Learning in Tacotron-based TTS","date":"2020-08-11","arxiv_id":"2008.05284","n_code_links":0,"syntology":null},{"paper":null,"slug":"spectrum-and-prosody-conversion-for-cross","title":"Spectrum and Prosody Conversion for Cross-lingual Voice Conversion with CycleGAN","date":"2020-08-11","arxiv_id":"2008.04562","n_code_links":0,"syntology":null},{"paper":null,"slug":"beyond-lexical-a-semantic-retrieval-framework","title":"Beyond Lexical: A Semantic Retrieval Framework for Textual SearchEngine","date":"2020-08-10","arxiv_id":"2008.03917","n_code_links":0,"syntology":null},{"paper":null,"slug":"deep-learning-based-human-detection-for-uavs","title":"Deep Learning-based Human Detection for UAVs with Optical and Infrared Cameras: System and Experiments","date":"2020-08-10","arxiv_id":"2008.04197","n_code_links":0,"syntology":null},{"paper":"/paper/do-ideas-have-shape-plato-s-theory-of-forms","slug":"do-ideas-have-shape-plato-s-theory-of-forms","title":"Do ideas have shape? Idea registration as the continuous limit of artificial neural networks","date":"2020-08-10","arxiv_id":"2008.03920","n_code_links":1,"syntology":null},{"paper":null,"slug":"does-bert-solve-commonsense-task-via","title":"On Commonsense Cues in BERT for Solving Commonsense Tasks","date":"2020-08-10","arxiv_id":"2008.03945","n_code_links":0,"syntology":null},{"paper":"/paper/firebert-hardening-bert-based-classifiers","slug":"firebert-hardening-bert-based-classifiers","title":"FireBERT: Hardening BERT-based classifiers against adversarial attack","date":"2020-08-10","arxiv_id":"2008.04203","n_code_links":1,"syntology":null},{"paper":null,"slug":"ganbert-generative-adversarial-networks-with","title":"GANBERT: Generative Adversarial Networks with Bidirectional Encoder Representations from Transformers for MRI to PET synthesis","date":"2020-08-10","arxiv_id":"2008.04393","n_code_links":0,"syntology":null},{"paper":"/paper/kr-bert-a-small-scale-korean-specific","slug":"kr-bert-a-small-scale-korean-specific","title":"KR-BERT: A Small-Scale Korean-Specific Language Model","date":"2020-08-10","arxiv_id":"2008.03979","n_code_links":1,"syntology":null},{"paper":null,"slug":"navigating-language-models-with-synthetic","title":"Navigating Human Language Models with Synthetic Agents","date":"2020-08-10","arxiv_id":"2008.04162","n_code_links":0,"syntology":null},{"paper":null,"slug":"diet-snn-direct-input-encoding-with-leakage","title":"DIET-SNN: Direct Input Encoding With Leakage and Threshold Optimization in Deep Spiking Neural Networks","date":"2020-08-09","arxiv_id":"2008.03658","n_code_links":0,"syntology":null},{"paper":"/paper/distilling-the-knowledge-of-bert-for-sequence","slug":"distilling-the-knowledge-of-bert-for-sequence","title":"Distilling the Knowledge of BERT for Sequence-to-Sequence ASR","date":"2020-08-09","arxiv_id":"2008.03822","n_code_links":1,"syntology":null},{"paper":"/paper/fast-and-accurate-neural-crf-constituency-1","slug":"fast-and-accurate-neural-crf-constituency-1","title":"Fast and Accurate Neural CRF Constituency Parsing","date":"2020-08-09","arxiv_id":"2008.03736","n_code_links":2,"syntology":{"ran":3,"of":3,"n_ran_checked":2,"n_instrument":1,"unverified":0,"pointer_only":0,"phrase":"3 ran (of which 2 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","official":{"repos":["yzhangcs/crfpar"],"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":["found_in_text","official"]}}},{"paper":"/paper/spatiotemporal-contrastive-video","slug":"spatiotemporal-contrastive-video","title":"Spatiotemporal Contrastive Video Representation Learning","date":"2020-08-09","arxiv_id":"2008.03800","n_code_links":4,"syntology":null},{"paper":"/paper/speedyspeech-efficient-neural-speech","slug":"speedyspeech-efficient-neural-speech","title":"SpeedySpeech: Efficient Neural Speech Synthesis","date":"2020-08-09","arxiv_id":"2008.03802","n_code_links":3,"syntology":{"ran":1,"of":1,"n_ran_checked":0,"n_instrument":1,"unverified":0,"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) · 0 unverified","official":{"repos":["janvainer/speedyspeech"],"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":["official"]}}},{"paper":"/paper/unsupervised-feature-learning-by-cross-level","slug":"unsupervised-feature-learning-by-cross-level","title":"Unsupervised Feature Learning by Cross-Level Instance-Group Discrimination","date":"2020-08-09","arxiv_id":"2008.03813","n_code_links":2,"syntology":{"ran":6,"of":8,"n_ran_checked":4,"n_instrument":2,"unverified":2,"pointer_only":1,"phrase":"6 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; 2 where Syntology's instrument failed) · 2 unverified","official":{"repos":["frank-xwang/CLD-UnsupervisedLearning"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":2,"ran_from_kinds":["official"]}}},{"paper":"/paper/audio-spoofing-verification-using-deep","slug":"audio-spoofing-verification-using-deep","title":"Audio Spoofing Verification using Deep Convolutional Neural Networks by Transfer Learning","date":"2020-08-08","arxiv_id":"2008.03464","n_code_links":1,"syntology":null},{"paper":null,"slug":"towards-lossless-binary-convolutional-neural","title":"Towards Lossless Binary Convolutional Neural Networks Using Piecewise Approximation","date":"2020-08-08","arxiv_id":"2008.03520","n_code_links":0,"syntology":null},{"paper":null,"slug":"unravelling-small-sample-size-problems-in-the","title":"Unravelling Small Sample Size Problems in the Deep Learning World","date":"2020-08-08","arxiv_id":"2008.03522","n_code_links":0,"syntology":null},{"paper":null,"slug":"using-pspnet-and-unet-to-analyze-the-internal","title":"Exploring the parameter reusability of CNN","date":"2020-08-08","arxiv_id":"2008.03411","n_code_links":0,"syntology":null},{"paper":"/paper/pretraining-techniques-for-sequence-to","slug":"pretraining-techniques-for-sequence-to","title":"Pretraining Techniques for Sequence-to-Sequence Voice Conversion","date":"2020-08-07","arxiv_id":"2008.03088","n_code_links":2,"syntology":null},{"paper":null,"slug":"semeval-2020-task-10-emphasis-selection-for","title":"SemEval-2020 Task 10: Emphasis Selection for Written Text in Visual Media","date":"2020-08-07","arxiv_id":"2008.03274","n_code_links":0,"syntology":null},{"paper":"/paper/aschern-at-semeval-2020-task-11-it-takes","slug":"aschern-at-semeval-2020-task-11-it-takes","title":"aschern at SemEval-2020 Task 11: It Takes Three to Tango: RoBERTa, CRF, and Transfer Learning","date":"2020-08-06","arxiv_id":"2008.02837","n_code_links":1,"syntology":null},{"paper":"/paper/convbert-improving-bert-with-span-based","slug":"convbert-improving-bert-with-span-based","title":"ConvBERT: Improving BERT with Span-based Dynamic Convolution","date":"2020-08-06","arxiv_id":"2008.02496","n_code_links":8,"syntology":null},{"paper":"/paper/detext-a-deep-text-ranking-framework-with","slug":"detext-a-deep-text-ranking-framework-with","title":"DeText: A Deep Text Ranking Framework with BERT","date":"2020-08-06","arxiv_id":"2008.02460","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":["linkedin/detext"],"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/question-and-answer-test-train-overlap-in","slug":"question-and-answer-test-train-overlap-in","title":"Question and Answer Test-Train Overlap in Open-Domain Question Answering Datasets","date":"2020-08-06","arxiv_id":"2008.02637","n_code_links":1,"syntology":{"ran":3,"of":3,"n_ran_checked":2,"n_instrument":1,"unverified":0,"pointer_only":3,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 1 honoured, 1 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","official":{"repos":["facebookresearch/QA-Overlap"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"structured-convolutions-for-efficient-neural","title":"Structured Convolutions for Efficient Neural Network Design","date":"2020-08-06","arxiv_id":"2008.02454","n_code_links":0,"syntology":null},{"paper":null,"slug":"6veclm-language-modeling-in-vector-space-for","title":"6VecLM: Language Modeling in Vector Space for IPv6 Target Generation","date":"2020-08-05","arxiv_id":"2008.02213","n_code_links":0,"syntology":null},{"paper":"/paper/continuous-in-depth-neural-networks","slug":"continuous-in-depth-neural-networks","title":"Continuous-in-Depth Neural Networks","date":"2020-08-05","arxiv_id":"2008.02389","n_code_links":4,"syntology":{"ran":4,"of":6,"n_ran_checked":0,"n_instrument":4,"unverified":2,"pointer_only":6,"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) · 2 unverified","official":{"repos":["afqueiruga/ContinuousNet"],"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/designing-the-business-conversation-corpus-1","slug":"designing-the-business-conversation-corpus-1","title":"Designing the Business Conversation Corpus","date":"2020-08-05","arxiv_id":"2008.01940","n_code_links":1,"syntology":null},{"paper":"/paper/i-aid-identifying-actionable-information-from","slug":"i-aid-identifying-actionable-information-from","title":"I-AID: Identifying Actionable Information from Disaster-related Tweets","date":"2020-08-04","arxiv_id":"2008.13544","n_code_links":1,"syntology":null},{"paper":"/paper/learning-from-a-complementary-label-source","slug":"learning-from-a-complementary-label-source","title":"Learning from a Complementary-label Source Domain: Theory and Algorithms","date":"2020-08-04","arxiv_id":"2008.01454","n_code_links":1,"syntology":{"ran":3,"of":4,"n_ran_checked":1,"n_instrument":2,"unverified":1,"pointer_only":4,"phrase":"3 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; 2 where Syntology's instrument failed) · 1 unverified","official":{"repos":["Yiyang98/BFUDA"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":1,"ran_from_kinds":["official"]}}},{"paper":"/paper/nlpdove-at-semeval-2020-task-12-improving","slug":"nlpdove-at-semeval-2020-task-12-improving","title":"NLPDove at SemEval-2020 Task 12: Improving Offensive Language Detection with Cross-lingual Transfer","date":"2020-08-04","arxiv_id":"2008.01354","n_code_links":1,"syntology":null},{"paper":"/paper/physics-informed-deep-neural-networks-for","slug":"physics-informed-deep-neural-networks-for","title":"Physics-Informed Deep Neural Networks for Transient Electromagnetic Analysis","date":"2020-08-04","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":null,"slug":"select-extract-and-generate-neural-keyphrase","title":"Select, Extract and Generate: Neural Keyphrase Generation with Layer-wise Coverage Attention","date":"2020-08-04","arxiv_id":"2008.01739","n_code_links":0,"syntology":null},{"paper":null,"slug":"taking-notes-on-the-fly-helps-bert-pre","title":"Taking Notes on the Fly Helps BERT Pre-training","date":"2020-08-04","arxiv_id":"2008.01466","n_code_links":0,"syntology":null},{"paper":"/paper/the-jazz-transformer-on-the-front-line","slug":"the-jazz-transformer-on-the-front-line","title":"The Jazz Transformer on the Front Line: Exploring the Shortcomings of AI-composed Music through Quantitative Measures","date":"2020-08-04","arxiv_id":"2008.01307","n_code_links":2,"syntology":{"ran":13,"of":14,"n_ran_checked":13,"n_instrument":0,"unverified":1,"pointer_only":0,"phrase":"13 ran (of which 0 constructed an object rather than computing a result; 13 with no instrument failure: 0 honoured, 0 violated, 13 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","official":{"repos":["slSeanWU/MusDr","slSeanWU/jazz_transformer"],"state":"official (archive's flag): 13 ran","n_ran":13,"n_constructed":0,"n_ran_no_instrument_failure":13,"n_unverified":1,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"two-stage-deep-learning-for-accelerated-3d","title":"Two-Stage Deep Learning for Accelerated 3D Time-of-Flight MRA without Matched Training Data","date":"2020-08-04","arxiv_id":"2008.01362","n_code_links":0,"syntology":null},{"paper":"/paper/a-spectral-energy-distance-for-parallel","slug":"a-spectral-energy-distance-for-parallel","title":"A Spectral Energy Distance for Parallel Speech Synthesis","date":"2020-08-03","arxiv_id":"2008.01160","n_code_links":2,"syntology":{"ran":2,"of":3,"n_ran_checked":2,"n_instrument":0,"unverified":1,"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) · 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":"/paper/encoding-in-style-a-stylegan-encoder-for","slug":"encoding-in-style-a-stylegan-encoder-for","title":"Encoding in Style: a StyleGAN Encoder for Image-to-Image Translation","date":"2020-08-03","arxiv_id":"2008.00951","n_code_links":10,"syntology":{"ran":16,"of":16,"n_ran_checked":13,"n_instrument":3,"unverified":0,"pointer_only":1,"phrase":"16 ran (of which 0 constructed an object rather than computing a result; 13 with no instrument failure: 1 honoured, 0 violated, 12 with no contract checked; 3 where Syntology's instrument failed) · 0 unverified","official":{"repos":["eladrich/pixel2style2pixel"],"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","unlocated"]}}},{"paper":"/paper/improving-one-stage-visual-grounding-by","slug":"improving-one-stage-visual-grounding-by","title":"Improving One-stage Visual Grounding by Recursive Sub-query Construction","date":"2020-08-03","arxiv_id":"2008.01059","n_code_links":1,"syntology":{"ran":20,"of":22,"n_ran_checked":19,"n_instrument":1,"unverified":2,"pointer_only":3,"phrase":"20 ran (of which 0 constructed an object rather than computing a result; 19 with no instrument failure: 1 honoured, 0 violated, 18 with no contract checked; 1 where Syntology's instrument failed) · 2 unverified","official":{"repos":["zyang-ur/ReSC"],"state":"official (archive's flag): 20 ran","n_ran":20,"n_constructed":0,"n_ran_no_instrument_failure":19,"n_unverified":2,"ran_from_kinds":["official"]}}},{"paper":"/paper/late-temporal-modeling-in-3d-cnn","slug":"late-temporal-modeling-in-3d-cnn","title":"Late Temporal Modeling in 3D CNN Architectures with BERT for Action Recognition","date":"2020-08-03","arxiv_id":"2008.01232","n_code_links":2,"syntology":null},{"paper":null,"slug":"lt-helsinki-at-semeval-2020-task-12","title":"LT@Helsinki at SemEval-2020 Task 12: Multilingual or language-specific BERT?","date":"2020-08-03","arxiv_id":"2008.00805","n_code_links":0,"syntology":null},{"paper":null,"slug":"making-coherence-out-of-nothing-at-all","title":"Making Coherence Out of Nothing At All: Measuring the Evolution of Gradient Alignment","date":"2020-08-03","arxiv_id":"2008.01217","n_code_links":0,"syntology":null},{"paper":null,"slug":"musicoder-a-universal-music-acoustic-encoder","title":"MusiCoder: A Universal Music-Acoustic Encoder Based on Transformers","date":"2020-08-03","arxiv_id":"2008.00781","n_code_links":0,"syntology":null},{"paper":"/paper/one-model-many-languages-meta-learning-for","slug":"one-model-many-languages-meta-learning-for","title":"One Model, Many Languages: Meta-learning for Multilingual Text-to-Speech","date":"2020-08-03","arxiv_id":"2008.00768","n_code_links":1,"syntology":null},{"paper":null,"slug":"project-to-adapt-domain-adaptation-for-depth","title":"Project to Adapt: Domain Adaptation for Depth Completion from Noisy and Sparse Sensor Data","date":"2020-08-03","arxiv_id":"2008.01034","n_code_links":0,"syntology":null},{"paper":"/paper/resnet50-on-cifar-100-without-transfer","slug":"resnet50-on-cifar-100-without-transfer","title":"ResNet50_on_Cifar_100_Without_Transfer_Learning","date":"2020-08-03","arxiv_id":null,"n_code_links":6,"syntology":null},{"paper":"/paper/rethinking-image-deraining-via-rain-streaks","slug":"rethinking-image-deraining-via-rain-streaks","title":"Rethinking Image Deraining via Rain Streaks and Vapors","date":"2020-08-03","arxiv_id":"2008.00823","n_code_links":1,"syntology":{"ran":3,"of":3,"n_ran_checked":0,"n_instrument":3,"unverified":0,"pointer_only":3,"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":["yluestc/derain"],"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","unlocated"]}}},{"paper":null,"slug":"self-attention-encoding-and-pooling-for","title":"Self-attention encoding and pooling for speaker recognition","date":"2020-08-03","arxiv_id":"2008.01077","n_code_links":0,"syntology":null},{"paper":"/paper/seqdialn-sequential-visual-dialog-networks-in","slug":"seqdialn-sequential-visual-dialog-networks-in","title":"SeqDialN: Sequential Visual Dialog Networks in Joint Visual-Linguistic Representation Space","date":"2020-08-02","arxiv_id":"2008.00397","n_code_links":1,"syntology":null},{"paper":"/paper/the-chess-transformer-mastering-play-using","slug":"the-chess-transformer-mastering-play-using","title":"The Chess Transformer: Mastering Play using Generative Language Models","date":"2020-08-02","arxiv_id":"2008.04057","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/mining-inter-video-proposal-relations-for","slug":"mining-inter-video-proposal-relations-for","title":"Mining Inter-Video Proposal Relations for Video Object Detection","date":"2020-08-01","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":null,"slug":"multi-node-bert-pretraining-cost-efficient","title":"Multi-node Bert-pretraining: Cost-efficient Approach","date":"2020-08-01","arxiv_id":"2008.00177","n_code_links":0,"syntology":null},{"paper":"/paper/stochastic-fine-grained-labeling-of-multi","slug":"stochastic-fine-grained-labeling-of-multi","title":"Stochastic Fine-grained Labeling of Multi-state Sign Glosses for Continuous Sign Language Recognition","date":"2020-08-01","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":"/paper/trojaning-language-models-for-fun-and-profit","slug":"trojaning-language-models-for-fun-and-profit","title":"Trojaning Language Models for Fun and Profit","date":"2020-08-01","arxiv_id":"2008.00312","n_code_links":1,"syntology":null},{"paper":null,"slug":"a-novel-global-spatial-attention-mechanism-in","title":"A Novel Global Spatial Attention Mechanism in Convolutional Neural Network for Medical Image Classification","date":"2020-07-31","arxiv_id":"2007.15897","n_code_links":0,"syntology":null},{"paper":"/paper/domain-specific-language-model-pretraining","slug":"domain-specific-language-model-pretraining","title":"Domain-Specific Language Model Pretraining for Biomedical Natural Language Processing","date":"2020-07-31","arxiv_id":"2007.15779","n_code_links":2,"syntology":null},{"paper":"/paper/language-modelling-for-source-code-with","slug":"language-modelling-for-source-code-with","title":"Language Modelling for Source Code with Transformer-XL","date":"2020-07-31","arxiv_id":"2007.15813","n_code_links":1,"syntology":null},{"paper":null,"slug":"model-reduction-of-shallow-cnn-model-for","title":"Model Reduction of Shallow CNN Model for Reliable Deployment of Information Extraction from Medical Reports","date":"2020-07-31","arxiv_id":"2008.01572","n_code_links":0,"syntology":null},{"paper":null,"slug":"object-detection-and-tracking-algorithms-for","title":"Object Detection and Tracking Algorithms for Vehicle Counting: A Comparative Analysis","date":"2020-07-31","arxiv_id":"2007.16198","n_code_links":0,"syntology":null},{"paper":null,"slug":"on-learning-universal-representations-across","title":"On Learning Universal Representations Across Languages","date":"2020-07-31","arxiv_id":"2007.15960","n_code_links":0,"syntology":null},{"paper":"/paper/tweepfake-about-detecting-deepfake-tweets","slug":"tweepfake-about-detecting-deepfake-tweets","title":"TweepFake: about Detecting Deepfake Tweets","date":"2020-07-31","arxiv_id":"2008.00036","n_code_links":1,"syntology":null},{"paper":null,"slug":"deep-multi-view-spatiotemporal-virtual-graph","title":"Deep Multi-View Spatiotemporal Virtual Graph Neural Network for Significant Citywide Ride-hailing Demand Prediction","date":"2020-07-30","arxiv_id":"2007.15189","n_code_links":0,"syntology":null},{"paper":null,"slug":"depressive-drug-abusive-or-informative","title":"Depressive, Drug Abusive, or Informative: Knowledge-aware Study of News Exposure during COVID-19 Outbreak","date":"2020-07-30","arxiv_id":"2007.15209","n_code_links":0,"syntology":null},{"paper":"/paper/instance-selection-for-gans","slug":"instance-selection-for-gans","title":"Instance Selection for GANs","date":"2020-07-30","arxiv_id":"2007.15255","n_code_links":2,"syntology":{"ran":11,"of":19,"n_ran_checked":5,"n_instrument":6,"unverified":8,"pointer_only":19,"phrase":"11 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 5 honoured, 0 violated, 0 with no contract checked; 6 where Syntology's instrument failed) · 8 unverified","official":{"repos":["uoguelph-mlrg/instance_selection_for_gans"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":3,"ran_from_kinds":["listed","official"]}}},{"paper":"/paper/interpretable-contextual-team-aware-item","slug":"interpretable-contextual-team-aware-item","title":"Interpretable Contextual Team-aware Item Recommendation: Application in Multiplayer Online Battle Arena Games","date":"2020-07-30","arxiv_id":"2007.15236","n_code_links":1,"syntology":null},{"paper":"/paper/mkqa-a-linguistically-diverse-benchmark-for","slug":"mkqa-a-linguistically-diverse-benchmark-for","title":"MKQA: A Linguistically Diverse Benchmark for Multilingual Open Domain Question Answering","date":"2020-07-30","arxiv_id":"2007.15207","n_code_links":2,"syntology":{"ran":5,"of":5,"n_ran_checked":5,"n_instrument":0,"unverified":0,"pointer_only":0,"phrase":"5 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; 0 where Syntology's instrument failed) · 0 unverified","official":{"repos":["apple/ml-mkqa"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":"/paper/vocgan-a-high-fidelity-real-time-vocoder-with","slug":"vocgan-a-high-fidelity-real-time-vocoder-with","title":"VocGAN: A High-Fidelity Real-time Vocoder with a Hierarchically-nested Adversarial Network","date":"2020-07-30","arxiv_id":"2007.15256","n_code_links":2,"syntology":null},{"paper":"/paper/what-does-bert-know-about-books-movies-and","slug":"what-does-bert-know-about-books-movies-and","title":"What does BERT know about books, movies and music? Probing BERT for Conversational Recommendation","date":"2020-07-30","arxiv_id":"2007.15356","n_code_links":1,"syntology":null},{"paper":"/paper/clarinet-a-one-step-approach-towards-budget","slug":"clarinet-a-one-step-approach-towards-budget","title":"Clarinet: A One-step Approach Towards Budget-friendly Unsupervised Domain Adaptation","date":"2020-07-29","arxiv_id":"2007.14612","n_code_links":1,"syntology":{"ran":3,"of":4,"n_ran_checked":1,"n_instrument":2,"unverified":1,"pointer_only":4,"phrase":"3 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; 2 where Syntology's instrument failed) · 1 unverified","official":{"repos":["Yiyang98/BFUDA"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":1,"ran_from_kinds":["official"]}}},{"paper":"/paper/composer-style-classification-of-piano-sheet","slug":"composer-style-classification-of-piano-sheet","title":"Composer Style Classification of Piano Sheet Music Images Using Language Model Pretraining","date":"2020-07-29","arxiv_id":"2007.14587","n_code_links":1,"syntology":null},{"paper":"/paper/generative-classifiers-as-a-basis-for","slug":"generative-classifiers-as-a-basis-for","title":"Generative Classifiers as a Basis for Trustworthy Image Classification","date":"2020-07-29","arxiv_id":"2007.15036","n_code_links":2,"syntology":{"ran":0,"of":4,"n_ran_checked":0,"n_instrument":0,"unverified":4,"pointer_only":0,"phrase":"0 ran · 4 unverified","official":{"repos":["VLL-HD/trustworthy_GCs"],"state":"official: not harvested","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":[]}}},{"paper":null,"slug":"reliable-tuberculosis-detection-using-chest-x","title":"Reliable Tuberculosis Detection using Chest X-ray with Deep Learning, Segmentation and Visualization","date":"2020-07-29","arxiv_id":"2007.14895","n_code_links":0,"syntology":null},{"paper":"/paper/but-fit-at-semeval-2020-task-5-automatic","slug":"but-fit-at-semeval-2020-task-5-automatic","title":"BUT-FIT at SemEval-2020 Task 5: Automatic detection of counterfactual statements with deep pre-trained language representation models","date":"2020-07-28","arxiv_id":"2007.14128","n_code_links":1,"syntology":null},{"paper":null,"slug":"deep-learning-brasil-nlp-at-semeval-2020-task","title":"Deep Learning Brasil -- NLP at SemEval-2020 Task 9: Overview of Sentiment Analysis of Code-Mixed Tweets","date":"2020-07-28","arxiv_id":"2008.01544","n_code_links":0,"syntology":null},{"paper":null,"slug":"guir-at-semeval-2020-task-12-domain-tuned","title":"GUIR at SemEval-2020 Task 12: Domain-Tuned Contextualized Models for Offensive Language Detection","date":"2020-07-28","arxiv_id":"2007.14477","n_code_links":0,"syntology":null},{"paper":"/paper/improving-results-on-russian-sentiment","slug":"improving-results-on-russian-sentiment","title":"Improving Results on Russian Sentiment Datasets","date":"2020-07-28","arxiv_id":"2007.14310","n_code_links":1,"syntology":null},{"paper":null,"slug":"tensorcoder-dimension-wise-attention-via","title":"TensorCoder: Dimension-Wise Attention via Tensor Representation for Natural Language Modeling","date":"2020-07-28","arxiv_id":"2008.01547","n_code_links":0,"syntology":null},{"paper":null,"slug":"variants-of-bert-random-forests-and-svm","title":"Variants of BERT, Random Forests and SVM approach for Multimodal Emotion-Target Sub-challenge","date":"2020-07-28","arxiv_id":"2007.13928","n_code_links":0,"syntology":null},{"paper":null,"slug":"fedemail-performance-measurement-of-privacy","title":"Evaluation of Federated Learning in Phishing Email Detection","date":"2020-07-27","arxiv_id":"2007.13300","n_code_links":0,"syntology":null},{"paper":"/paper/receptive-field-regularized-cnns-for-music","slug":"receptive-field-regularized-cnns-for-music","title":"Receptive-Field Regularized CNNs for Music Classification and Tagging","date":"2020-07-27","arxiv_id":"2007.13503","n_code_links":1,"syntology":null}],"record_sha256":"f3e5d82475e19bb67c5ed3fdc9a7611dff31f115629916c7e07a0378a6fdbe77","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}