{"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/dense-connections/papers/222","list_of":"/method/dense-connections","method":"Dense Connections","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":222,"pages_in_order":293,"rows_per_page":100,"rows":[22101,22200],"of":29230,"counts":{"archive_papers_tagged":29230,"with_a_code_link":12972,"where_syntology_ran_a_sample":3929,"not_listed_spam_title":0,"listed":29230,"listed_where_code_ran":3929,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":3303,"every_run_a_failure_of_syntologys_instrument":626,"listed_with_a_run_with_no_instrument_failure":3303,"listed_every_run_a_failure_of_syntologys_instrument":626,"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/dense-connections","prev":"/method/dense-connections/papers/221","next":"/method/dense-connections/papers/223","papers":[{"paper":null,"slug":"endtimes-at-semeval-2021-task-7-detecting-and","title":"EndTimes at SemEval-2021 Task 7: Detecting and Rating Humor and Offense with BERT and Ensembles","date":"2021-08-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"es-just-at-semeval-2021-task-7-detecting-and","title":"ES-JUST at SemEval-2021 Task 7: Detecting and Rating Humor and Offensive Text Using Deep Learning","date":"2021-08-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"explaining-contextualization-in-language","title":"Explaining Contextualization in Language Models using Visual Analytics","date":"2021-08-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":"/paper/explanations-for-commonsenseqa-new-dataset","slug":"explanations-for-commonsenseqa-new-dataset","title":"Explanations for CommonsenseQA: New Dataset and Models","date":"2021-08-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"exploring-listwise-evidence-reasoning-with-t5","title":"Exploring Listwise Evidence Reasoning with T5 for Fact Verification","date":"2021-08-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"fast-and-accurate-neural-machine-translation","title":"Fast and Accurate Neural Machine Translation with Translation Memory","date":"2021-08-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"generating-master-faces-for-dictionary","title":"Generating Master Faces for Dictionary Attacks with a Network-Assisted Latent Space Evolution","date":"2021-08-01","arxiv_id":"2108.01077","n_code_links":0,"syntology":null},{"paper":null,"slug":"ghost-at-semeval-2021-task-5-is-explanation","title":"GHOST at SemEval-2021 Task 5: Is explanation all you need?","date":"2021-08-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"ghostbert-generate-more-features-with-cheap","title":"GhostBERT: Generate More Features with Cheap Operations for BERT","date":"2021-08-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"grenzlinie-at-semeval-2021-task-7-detecting","title":"Grenzlinie at SemEval-2021 Task 7: Detecting and Rating Humor and Offense","date":"2021-08-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"gulu-at-semeval-2021-task-7-detecting-and","title":"Gulu at SemEval-2021 Task 7: Detecting and Rating Humor and Offense","date":"2021-08-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":"/paper/gx-at-semeval-2021-task-2-bert-with-lemma","slug":"gx-at-semeval-2021-task-2-bert-with-lemma","title":"GX at SemEval-2021 Task 2: BERT with Lemma Information for MCL-WiC Task","date":"2021-08-01","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":null,"slug":"hamiltondinggg-at-semeval-2021-task-5","title":"HamiltonDinggg at SemEval-2021 Task 5: Investigating Toxic Span Detection using RoBERTa Pre-training","date":"2021-08-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"how-effective-is-bert-without-word-ordering","title":"How effective is BERT without word ordering? Implications for language understanding and data privacy","date":"2021-08-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"how-many-layers-and-why-an-analysis-of-the","title":"How Many Layers and Why? An Analysis of the Model Depth in Transformers","date":"2021-08-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"hub-at-semeval-2021-task-1-fusion-of-sentence","title":"hub at SemEval-2021 Task 1: Fusion of Sentence and Word Frequency to Predict Lexical Complexity","date":"2021-08-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"hub-at-semeval-2021-task-2-word-meaning","title":"hub at SemEval-2021 Task 2: Word Meaning Similarity Prediction Model Based on RoBERTa and Word Frequency","date":"2021-08-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"hub-at-semeval-2021-task-7-fusion-of-albert","title":"hub at SemEval-2021 Task 7: Fusion of ALBERT and Word Frequency Information Detecting and Rating Humor and Offense","date":"2021-08-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"iitk-lcp-at-semeval-2021-task-1-1","title":"IITK@LCP at SemEval-2021 Task 1: Classification for Lexical Complexity Regression Task","date":"2021-08-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":"/paper/issues-with-entailment-based-zero-shot-text","slug":"issues-with-entailment-based-zero-shot-text","title":"Issues with Entailment-based Zero-shot Text Classification","date":"2021-08-01","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":null,"slug":"jct-at-semeval-2021-task-1-context-aware","title":"JCT at SemEval-2021 Task 1: Context-aware Representation for Lexical Complexity Prediction","date":"2021-08-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"just-blue-at-semeval-2021-task-1-predicting","title":"JUST-BLUE at SemEval-2021 Task 1: Predicting Lexical Complexity using BERT and RoBERTa Pre-trained Language Models","date":"2021-08-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"kuileixi-a-chinese-open-ended-text-adventure","title":"KuiLeiXi: a Chinese Open-Ended Text Adventure Game","date":"2021-08-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":"/paper/lasor-learning-accurate-3d-human-pose-and","slug":"lasor-learning-accurate-3d-human-pose-and","title":"LASOR: Learning Accurate 3D Human Pose and Shape Via Synthetic Occlusion-Aware Data and Neural Mesh Rendering","date":"2021-08-01","arxiv_id":"2108.00351","n_code_links":1,"syntology":{"ran":7,"of":9,"n_ran_checked":5,"n_instrument":2,"unverified":2,"pointer_only":0,"phrase":"7 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; 2 where Syntology's instrument failed) · 2 unverified","official":{"repos":["iGame-Lab/LASOR"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":2,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"lecun-at-semeval-2021-task-6-detecting","title":"LeCun at SemEval-2021 Task 6: Detecting Persuasion Techniques in Text Using Ensembled Pretrained Transformers and Data Augmentation","date":"2021-08-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"leebert-learned-early-exit-for-bert-with","title":"LeeBERT: Learned Early Exit for BERT with cross-level optimization","date":"2021-08-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"liori-at-semeval-2021-task-8-ask-transformer","title":"LIORI at SemEval-2021 Task 8: Ask Transformer for measurements","date":"2021-08-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"lotus-at-semeval-2021-task-2-combination-of","title":"Lotus at SemEval-2021 Task 2: Combination of BERT and Paraphrasing for English Word Sense Disambiguation","date":"2021-08-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"measure-and-evaluation-of-semantic-divergence","title":"Measure and Evaluation of Semantic Divergence across Two Languages","date":"2021-08-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"measuring-and-improving-bert-s-mathematical-1","title":"Measuring and Improving BERT's Mathematical Abilities by Predicting the Order of Reasoning.","date":"2021-08-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"medai-at-semeval-2021-task-5-start-to-end","title":"MedAI at SemEval-2021 Task 5: Start-to-end Tagging Framework for Toxic Spans Detection","date":"2021-08-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"mind-at-semeval-2021-task-6-propaganda","title":"MinD at SemEval-2021 Task 6: Propaganda Detection using Transfer Learning and Multimodal Fusion","date":"2021-08-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"modeling-task-aware-mimo-cardinality-for","title":"Modeling Task-Aware MIMO Cardinality for Efficient Multilingual Neural Machine Translation","date":"2021-08-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":"/paper/more-than-text-multi-modal-chinese-word","slug":"more-than-text-multi-modal-chinese-word","title":"More than Text: Multi-modal Chinese Word Segmentation","date":"2021-08-01","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":null,"slug":"multi-head-highly-parallelized-lstm-decoder","title":"Multi-Head Highly Parallelized LSTM Decoder for Neural Machine Translation","date":"2021-08-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"mvp-bert-multi-vocab-pre-training-for-chinese","title":"MVP-BERT: Multi-Vocab Pre-training for Chinese BERT","date":"2021-08-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"neural-metaphor-detection-with-visibility","title":"Neural Metaphor Detection with Visibility Embeddings","date":"2021-08-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"nlpiitr-at-semeval-2021-task-6-roberta-model","title":"NLPIITR at SemEval-2021 Task 6: RoBERTa Model with Data Augmentation for Persuasion Techniques Detection","date":"2021-08-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"nlyticsfkie-at-semeval-2021-task-6-detection","title":"NLyticsFKIE at SemEval-2021 Task 6: Detection of Persuasion Techniques In Texts And Images","date":"2021-08-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"nmt5-is-parallel-data-still-relevant-for-pre-1","title":"nmT5 - Is parallel data still relevant for pre-training massively multilingual language models?","date":"2021-08-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"on-the-differences-between-bert-and-mt","title":"On the differences between BERT and MT encoder spaces and how to address them in translation tasks","date":"2021-08-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"paw-at-semeval-2021-task-2-multilingual-and","title":"PAW at SemEval-2021 Task 2: Multilingual and Cross-lingual Word-in-Context Disambiguation : Exploring Cross Lingual Transfer, Augmentations and Adversarial Training","date":"2021-08-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"pingan-omini-sinitic-at-semeval-2021-task-4","title":"PINGAN Omini-Sinitic at SemEval-2021 Task 4:Reading Comprehension of Abstract Meaning","date":"2021-08-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":"/paper/plome-pre-training-with-misspelled-knowledge","slug":"plome-pre-training-with-misspelled-knowledge","title":"PLOME: Pre-training with Misspelled Knowledge for Chinese Spelling Correction","date":"2021-08-01","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":null,"slug":"point-disambiguate-and-copy-incorporating","title":"Point, Disambiguate and Copy: Incorporating Bilingual Dictionaries for Neural Machine Translation","date":"2021-08-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"pral-a-tailored-pre-training-model-for-task","title":"PRAL: A Tailored Pre-Training Model for Task-Oriented Dialog Generation","date":"2021-08-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"predicting-pragmatic-discourse-features-in","title":"Predicting pragmatic discourse features in the language of adults with autism spectrum disorder","date":"2021-08-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"raw-c-relatedness-of-ambiguous-words-in-1","title":"RAW-C: Relatedness of Ambiguous Words in Context (A New Lexical Resource for English)","date":"2021-08-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"rem-efficient-semi-automated-real-time","title":"REM: Efficient Semi-Automated Real-Time Moderation of Online Forums","date":"2021-08-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"rg-pa-at-semeval-2021-task-1-a-contextual","title":"RG PA at SemEval-2021 Task 1: A Contextual Attention-based Model with RoBERTa for Lexical Complexity Prediction","date":"2021-08-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"sarcasmdet-at-semeval-2021-task-7-detect","title":"SarcasmDet at SemEval-2021 Task 7: Detect Humor and Offensive based on Demographic Factors using RoBERTa Pre-trained Model","date":"2021-08-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":"/paper/sefamerve-arge-at-semeval-2021-task-5-toxic","slug":"sefamerve-arge-at-semeval-2021-task-5-toxic","title":"Sefamerve ARGE at SemEval-2021 Task 5: Toxic Spans Detection Using Segmentation Based 1-D Convolutional Neural Network Model","date":"2021-08-01","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":null,"slug":"skoltechnlp-at-semeval-2021-task-5-leveraging","title":"SkoltechNLP at SemEval-2021 Task 5: Leveraging Sentence-level Pre-training for Toxic Span Detection","date":"2021-08-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"stanford-mlab-at-semeval-2021-task-8-48-hours","title":"Stanford MLab at SemEval-2021 Task 8: 48 Hours Is All You Need","date":"2021-08-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"stretch-vst-getting-flexible-with-visual","title":"Stretch-VST: Getting Flexible With Visual Stories","date":"2021-08-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"synchronous-syntactic-attention-for","title":"Synchronous Syntactic Attention for Transformer Neural Machine Translation","date":"2021-08-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":"/paper/taming-pre-trained-language-models-with-n","slug":"taming-pre-trained-language-models-with-n","title":"Taming Pre-trained Language Models with N-gram Representations for Low-Resource Domain Adaptation","date":"2021-08-01","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":null,"slug":"team-kgp-at-semeval-2021-task-7-a-deep-neural","title":"Team\\_KGP at SemEval-2021 Task 7: A Deep Neural System to Detect Humor and Offense with Their Ratings in the Text Data","date":"2021-08-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"tgea-an-error-annotated-dataset-and-benchmark","title":"TGEA: An Error-Annotated Dataset and Benchmark Tasks for TextGeneration from Pretrained Language Models","date":"2021-08-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"to-pos-tag-or-not-to-pos-tag-the-impact-of","title":"To POS Tag or Not to POS Tag: The Impact of POS Tags on Morphological Learning in Low-Resource Settings","date":"2021-08-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"transformer-based-deep-imitation-learning-for","title":"Transformer-based deep imitation learning for dual-arm robot manipulation","date":"2021-08-01","arxiv_id":"2108.00385","n_code_links":0,"syntology":null},{"paper":null,"slug":"transformer-based-map-matching-with-model","title":"Transformer-based Map Matching Model with Limited Ground-Truth Data using Transfer-Learning Approach","date":"2021-08-01","arxiv_id":"2108.00439","n_code_links":0,"syntology":null},{"paper":null,"slug":"transformer-encoder-gru-t-e-gru-for-chinese","title":"Transformer-Encoder-GRU (T-E-GRU) for Chinese Sentiment Analysis on Chinese Comment Text","date":"2021-08-01","arxiv_id":"2108.00400","n_code_links":0,"syntology":null},{"paper":null,"slug":"unleash-gpt-2-power-for-event-detection","title":"Unleash GPT-2 Power for Event Detection","date":"2021-08-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":"/paper/unsupervised-extractive-summarization-based","slug":"unsupervised-extractive-summarization-based","title":"Unsupervised Extractive Summarization-Based Representations for Accurate and Explainable Collaborative Filtering","date":"2021-08-01","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":null,"slug":"uor-at-semeval-2021-task-4-using-pre-trained","title":"UoR at SemEval-2021 Task 4: Using Pre-trained BERT Token Embeddings for Question Answering of Abstract Meaning","date":"2021-08-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"uor-at-semeval-2021-task-7-utilizing-pre","title":"UoR at SemEval-2021 Task 7: Utilizing Pre-trained DistilBERT Model and Multi-scale CNN for Humor Detection","date":"2021-08-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"utfpr-at-semeval-2021-task-1-complexity","title":"UTFPR at SemEval-2021 Task 1: Complexity Prediction by Combining BERT Vectors and Classic Features","date":"2021-08-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"verb-metaphor-detection-via-contextual","title":"Verb Metaphor Detection via Contextual Relation Learning","date":"2021-08-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"ynu-hpcc-at-semeval-2021-task-11-using-a-bert","title":"YNU-HPCC at SemEval-2021 Task 11: Using a BERT Model to Extract Contributions from NLP Scholarly Articles","date":"2021-08-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":"/paper/ynu-hpcc-at-semeval-2021-task-5-using-a","slug":"ynu-hpcc-at-semeval-2021-task-5-using-a","title":"YNU-HPCC at SemEval-2021 Task 5: Using a Transformer-based Model with Auxiliary Information for Toxic Span Detection","date":"2021-08-01","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":null,"slug":"ynu-hpcc-at-semeval-2021-task-6-combining","title":"YNU-HPCC at SemEval-2021 Task 6: Combining ALBERT and Text-CNN for Persuasion Detection in Texts and Images","date":"2021-08-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":"/paper/youngsheldon-at-semeval-2021-task-5-fine","slug":"youngsheldon-at-semeval-2021-task-5-fine","title":"YoungSheldon at SemEval-2021 Task 5: Fine-tuning Pre-trained Language Models for Toxic Spans Detection using Token classification Objective","date":"2021-08-01","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":null,"slug":"zyj-at-semeval-2021-task-7-hahackathon","title":"ZYJ at SemEval-2021 Task 7: HaHackathon: Detecting and Rating Humor and Offense with ALBERT-Based Model","date":"2021-08-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"debiasing-samples-from-online-learning-using","title":"Debiasing Samples from Online Learning Using Bootstrap","date":"2021-07-31","arxiv_id":"2108.00236","n_code_links":0,"syntology":null},{"paper":"/paper/greedy-network-enlarging","slug":"greedy-network-enlarging","title":"Greedy Network Enlarging","date":"2021-07-31","arxiv_id":"2108.00177","n_code_links":1,"syntology":null},{"paper":"/paper/opinion-prediction-with-user-fingerprinting","slug":"opinion-prediction-with-user-fingerprinting","title":"Opinion Prediction with User Fingerprinting","date":"2021-07-31","arxiv_id":"2108.00270","n_code_links":1,"syntology":null},{"paper":"/paper/using-knowledge-embedded-attention-to-augment","slug":"using-knowledge-embedded-attention-to-augment","title":"Using Knowledge-Embedded Attention to Augment Pre-trained Language Models for Fine-Grained Emotion Recognition","date":"2021-07-31","arxiv_id":"2108.00194","n_code_links":1,"syntology":null},{"paper":"/paper/dadagp-a-dataset-of-tokenized-guitarpro-songs","slug":"dadagp-a-dataset-of-tokenized-guitarpro-songs","title":"DadaGP: A Dataset of Tokenized GuitarPro Songs for Sequence Models","date":"2021-07-30","arxiv_id":"2107.14653","n_code_links":1,"syntology":null},{"paper":"/paper/dpt-deformable-patch-based-transformer-for","slug":"dpt-deformable-patch-based-transformer-for","title":"DPT: Deformable Patch-based Transformer for Visual Recognition","date":"2021-07-30","arxiv_id":"2107.14467","n_code_links":1,"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":["CASIA-IVA-Lab/DPT"],"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/emailsum-abstractive-email-thread","slug":"emailsum-abstractive-email-thread","title":"EmailSum: Abstractive Email Thread Summarization","date":"2021-07-30","arxiv_id":"2107.14691","n_code_links":1,"syntology":null},{"paper":null,"slug":"learnable-compression-network-with","title":"Connecting Compression Spaces with Transformer for Approximate Nearest Neighbor Search","date":"2021-07-30","arxiv_id":"2107.14415","n_code_links":0,"syntology":null},{"paper":"/paper/multi-head-self-attention-via-vision","slug":"multi-head-self-attention-via-vision","title":"Multi-Head Self-Attention via Vision Transformer for Zero-Shot Learning","date":"2021-07-30","arxiv_id":"2108.00045","n_code_links":2,"syntology":null},{"paper":"/paper/perceiver-io-a-general-architecture-for","slug":"perceiver-io-a-general-architecture-for","title":"Perceiver IO: A General Architecture for Structured Inputs & Outputs","date":"2021-07-30","arxiv_id":"2107.14795","n_code_links":9,"syntology":{"ran":7,"of":11,"n_ran_checked":6,"n_instrument":1,"unverified":4,"pointer_only":0,"phrase":"7 ran (of which 4 constructed an object rather than computing a result; 6 with no instrument failure: 0 honoured, 0 violated, 6 with no contract checked; 1 where Syntology's instrument failed) · 4 unverified","official":{"repos":["deepmind/deepmind-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/product1m-towards-weakly-supervised-instance","slug":"product1m-towards-weakly-supervised-instance","title":"Product1M: Towards Weakly Supervised Instance-Level Product Retrieval via Cross-modal Pretraining","date":"2021-07-30","arxiv_id":"2107.14572","n_code_links":1,"syntology":null},{"paper":"/paper/pruning-neural-networks-with-interpolative","slug":"pruning-neural-networks-with-interpolative","title":"Model Preserving Compression for Neural Networks","date":"2021-07-30","arxiv_id":"2108.00065","n_code_links":1,"syntology":null},{"paper":null,"slug":"real-time-streaming-perception-system-for","title":"Real-time Streaming Perception System for Autonomous Driving","date":"2021-07-30","arxiv_id":"2107.14388","n_code_links":0,"syntology":null},{"paper":"/paper/structural-guidance-for-transformer-language","slug":"structural-guidance-for-transformer-language","title":"Structural Guidance for Transformer Language Models","date":"2021-07-30","arxiv_id":"2108.00104","n_code_links":1,"syntology":{"ran":11,"of":12,"n_ran_checked":10,"n_instrument":1,"unverified":1,"pointer_only":1,"phrase":"11 ran (of which 0 constructed an object rather than computing a result; 10 with no instrument failure: 0 honoured, 1 violated, 9 with no contract checked; 1 where Syntology's instrument failed) · 1 unverified","official":{"repos":["IBM/transformers-struct-guidance"],"state":"official (archive's flag): 11 ran","n_ran":11,"n_constructed":0,"n_ran_no_instrument_failure":10,"n_unverified":1,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"adapting-gpt-gpt-2-and-bert-language-models","title":"Adapting GPT, GPT-2 and BERT Language Models for Speech Recognition","date":"2021-07-29","arxiv_id":"2108.07789","n_code_links":0,"syntology":null},{"paper":"/paper/autotinybert-automatic-hyper-parameter","slug":"autotinybert-automatic-hyper-parameter","title":"AutoTinyBERT: Automatic Hyper-parameter Optimization for Efficient Pre-trained Language Models","date":"2021-07-29","arxiv_id":"2107.13686","n_code_links":1,"syntology":null},{"paper":null,"slug":"convolutional-transformer-based-dual","title":"Convolutional Transformer based Dual Discriminator Generative Adversarial Networks for Video Anomaly Detection","date":"2021-07-29","arxiv_id":"2107.13720","n_code_links":0,"syntology":null},{"paper":"/paper/densely-connected-neural-networks-for","slug":"densely-connected-neural-networks-for","title":"Densely connected neural networks for nonlinear regression","date":"2021-07-29","arxiv_id":"2108.00864","n_code_links":1,"syntology":null},{"paper":null,"slug":"ppt-fusion-pyramid-patch-transformerfor-a","title":"PPT Fusion: Pyramid Patch Transformerfor a Case Study in Image Fusion","date":"2021-07-29","arxiv_id":"2107.13967","n_code_links":0,"syntology":null},{"paper":"/paper/reformer-the-relational-transformer-for-image","slug":"reformer-the-relational-transformer-for-image","title":"ReFormer: The Relational Transformer for Image Captioning","date":"2021-07-29","arxiv_id":"2107.14178","n_code_links":1,"syntology":null},{"paper":"/paper/rethinking-and-improving-relative-position","slug":"rethinking-and-improving-relative-position","title":"Rethinking and Improving Relative Position Encoding for Vision Transformer","date":"2021-07-29","arxiv_id":"2107.14222","n_code_links":1,"syntology":{"ran":7,"of":9,"n_ran_checked":0,"n_instrument":7,"unverified":2,"pointer_only":0,"phrase":"7 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; 7 where Syntology's instrument failed) · 2 unverified","official":{"repos":["microsoft/cream"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":2,"ran_from_kinds":["official"]}}},{"paper":"/paper/self-supervised-transformer-for-multivariate","slug":"self-supervised-transformer-for-multivariate","title":"Self-Supervised Transformer for Sparse and Irregularly Sampled Multivariate Clinical Time-Series","date":"2021-07-29","arxiv_id":"2107.14293","n_code_links":1,"syntology":{"ran":2,"of":2,"n_ran_checked":2,"n_instrument":0,"unverified":0,"pointer_only":0,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","official":{"repos":["sindhura97/STraTS"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"using-perturbed-length-aware-positional","title":"Using Perturbed Length-aware Positional Encoding for Non-autoregressive Neural Machine Translation","date":"2021-07-29","arxiv_id":"2107.13689","n_code_links":0,"syntology":null},{"paper":null,"slug":"a-computer-vision-based-approach-for-driver","title":"A Computer Vision-Based Approach for Driver Distraction Recognition using Deep Learning and Genetic Algorithm Based Ensemble","date":"2021-07-28","arxiv_id":"2107.13355","n_code_links":0,"syntology":null},{"paper":null,"slug":"arabic-aspect-based-sentiment-analysis-using-1","title":"Arabic aspect sentiment polarity classification using BERT","date":"2021-07-28","arxiv_id":"2107.13290","n_code_links":0,"syntology":null},{"paper":"/paper/bi-bimodal-modality-fusion-for-correlation","slug":"bi-bimodal-modality-fusion-for-correlation","title":"Bi-Bimodal Modality Fusion for Correlation-Controlled Multimodal Sentiment Analysis","date":"2021-07-28","arxiv_id":"2107.13669","n_code_links":2,"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: 0 honoured, 1 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","official":{"repos":["declare-lab/multimodal-deep-learning"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]}}}],"record_sha256":"f2dd0945a1c97704182ba06a12d2865576754e02e8caebcde69add3a32768d6e","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}