{"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/cosine-annealing/papers/38","list_of":"/method/cosine-annealing","method":"Cosine Annealing","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":38,"pages_in_order":40,"rows_per_page":100,"rows":[3701,3800],"of":3965,"counts":{"archive_papers_tagged":3965,"with_a_code_link":1734,"where_syntology_ran_a_sample":627,"not_listed_spam_title":0,"listed":3965,"listed_where_code_ran":627,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":513,"every_run_a_failure_of_syntologys_instrument":114,"listed_with_a_run_with_no_instrument_failure":513,"listed_every_run_a_failure_of_syntologys_instrument":114,"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/cosine-annealing","prev":"/method/cosine-annealing/papers/37","next":"/method/cosine-annealing/papers/39","papers":[{"paper":"/paper/increasing-learning-efficiency-of-self","slug":"increasing-learning-efficiency-of-self","title":"Increasing Learning Efficiency of Self-Attention Networks through Direct Position Interactions, Learnable Temperature, and Convoluted Attention","date":"2020-12-01","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":"/paper/tablegpt-few-shot-table-to-text-generation","slug":"tablegpt-few-shot-table-to-text-generation","title":"TableGPT: Few-shot Table-to-Text Generation with Table Structure Reconstruction and Content Matching","date":"2020-12-01","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":null,"slug":"ui-at-semeval-2020-task-4-commonsense","title":"UI at SemEval-2020 Task 4: Commonsense Validation and Explanation by Exploiting Contradiction","date":"2020-12-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"generative-pre-training-for-paraphrase","title":"Generative Pre-training for Paraphrase Generation by Representing and Predicting Spans in Exemplars","date":"2020-11-29","arxiv_id":"2011.14344","n_code_links":0,"syntology":null},{"paper":null,"slug":"automatic-detection-of-cardiac-chambers-using","title":"Automatic Detection of Cardiac Chambers Using an Attention-based YOLOv4 Framework from Four-chamber View of Fetal Echocardiography","date":"2020-11-26","arxiv_id":"2011.13096","n_code_links":0,"syntology":null},{"paper":"/paper/an-efficient-and-scalable-deep-learning","slug":"an-efficient-and-scalable-deep-learning","title":"An Efficient and Scalable Deep Learning Approach for Road Damage Detection","date":"2020-11-18","arxiv_id":"2011.09577","n_code_links":2,"syntology":null},{"paper":null,"slug":"do-fine-tuned-commonsense-language-models","title":"Do Fine-tuned Commonsense Language Models Really Generalize?","date":"2020-11-18","arxiv_id":"2011.09159","n_code_links":0,"syntology":null},{"paper":null,"slug":"a-comparative-approach-on-detecting-multi","title":"A comparative approach on detecting multi-lingual and multi-oriented text in natural scene images","date":"2020-11-17","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"attention-mechanism-transformers-bert-and-gpt","title":"Attention Mechanism, Transformers, BERT, and GPT: Tutorial and Survey","date":"2020-11-17","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":"/paper/scaled-yolov4-scaling-cross-stage-partial","slug":"scaled-yolov4-scaling-cross-stage-partial","title":"Scaled-YOLOv4: Scaling Cross Stage Partial Network","date":"2020-11-16","arxiv_id":"2011.08036","n_code_links":41,"syntology":{"ran":1,"of":1,"n_ran_checked":0,"n_instrument":1,"unverified":0,"pointer_only":1,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","official":{"repos":["WongKinYiu/ScaledYOLOv4"],"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":["unlocated"]}}},{"paper":"/paper/real-time-polyp-detection-localisation-and","slug":"real-time-polyp-detection-localisation-and","title":"Real-Time Polyp Detection, Localization and Segmentation in Colonoscopy Using Deep Learning","date":"2020-11-15","arxiv_id":"2011.07631","n_code_links":1,"syntology":null},{"paper":"/paper/debatesum-a-large-scale-argument-mining-and","slug":"debatesum-a-large-scale-argument-mining-and","title":"DebateSum: A large-scale argument mining and summarization dataset","date":"2020-11-14","arxiv_id":"2011.07251","n_code_links":3,"syntology":{"ran":1,"of":1,"n_ran_checked":0,"n_instrument":1,"unverified":0,"pointer_only":1,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","official":{"repos":["Hellisotherpeople/DebateSum","Hellisotherpeople/debate2vec","arvind-balaji/debate-cards"],"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/real-time-object-detection-method-based-on","slug":"real-time-object-detection-method-based-on","title":"Real-time object detection method based on improved YOLOv4-tiny","date":"2020-11-09","arxiv_id":"2011.04244","n_code_links":1,"syntology":null},{"paper":"/paper/adapting-a-language-model-for-controlled","slug":"adapting-a-language-model-for-controlled","title":"Adapting a Language Model for Controlled Affective Text Generation","date":"2020-11-08","arxiv_id":"2011.04000","n_code_links":1,"syntology":{"ran":2,"of":3,"n_ran_checked":1,"n_instrument":1,"unverified":1,"pointer_only":3,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 1 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 1 unverified","official":{"repos":["ishikasingh/Affective-text-gen"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":1,"ran_from_kinds":["official","unlocated"]}}},{"paper":"/paper/semi-supervised-low-resource-style-transfer","slug":"semi-supervised-low-resource-style-transfer","title":"Semi-Supervised Low-Resource Style Transfer of Indonesian Informal to Formal Language with Iterative Forward-Translation","date":"2020-11-06","arxiv_id":"2011.03286","n_code_links":1,"syntology":null},{"paper":null,"slug":"improving-rnn-transducer-with-normalized","title":"Improving RNN transducer with normalized jointer network","date":"2020-11-03","arxiv_id":"2011.01576","n_code_links":0,"syntology":null},{"paper":"/paper/tabular-transformers-for-modeling","slug":"tabular-transformers-for-modeling","title":"Tabular Transformers for Modeling Multivariate Time Series","date":"2020-11-03","arxiv_id":"2011.01843","n_code_links":1,"syntology":{"ran":0,"of":2,"n_ran_checked":0,"n_instrument":0,"unverified":2,"pointer_only":0,"phrase":"0 ran · 2 unverified","official":{"repos":["IBM/TabFormer"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":2,"ran_from_kinds":[]}}},{"paper":null,"slug":"the-amazing-world-of-neural-language","title":"The Amazing World of Neural Language Generation","date":"2020-11-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":"/paper/visually-grounded-planning-without-vision-1","slug":"visually-grounded-planning-without-vision-1","title":"Visually-Grounded Planning without Vision: Language Models Infer Detailed Plans from High-level Instructions","date":"2020-11-01","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":null,"slug":"topic-preserving-synthetic-news-generation-an","title":"Topic-Preserving Synthetic News Generation: An Adversarial Deep Reinforcement Learning Approach","date":"2020-10-30","arxiv_id":"2010.16324","n_code_links":0,"syntology":null},{"paper":null,"slug":"unsupervised-paraphrase-generation-via","title":"Unsupervised Paraphrasing with Pretrained Language Models","date":"2020-10-24","arxiv_id":"2010.12885","n_code_links":0,"syntology":null},{"paper":"/paper/lightseq-a-high-performance-inference-library","slug":"lightseq-a-high-performance-inference-library","title":"LightSeq: A High Performance Inference Library for Transformers","date":"2020-10-23","arxiv_id":"2010.13887","n_code_links":1,"syntology":null},{"paper":null,"slug":"topic-modeling-with-contextualized-word","title":"Topic Modeling with Contextualized Word Representation Clusters","date":"2020-10-23","arxiv_id":"2010.12626","n_code_links":0,"syntology":null},{"paper":null,"slug":"developing-real-time-streaming-transformer","title":"Developing Real-time Streaming Transformer Transducer for Speech Recognition on Large-scale Dataset","date":"2020-10-22","arxiv_id":"2010.11395","n_code_links":0,"syntology":null},{"paper":null,"slug":"transferable-graph-optimizers-for-ml","title":"Transferable Graph Optimizers for ML Compilers","date":"2020-10-21","arxiv_id":"2010.12438","n_code_links":0,"syntology":null},{"paper":null,"slug":"performance-of-transfer-learning-model-vs","title":"Performance of Transfer Learning Model vs. Traditional Neural Network in Low System Resource Environment","date":"2020-10-20","arxiv_id":"2011.07962","n_code_links":0,"syntology":null},{"paper":null,"slug":"better-distractions-transformer-based","title":"Better Distractions: Transformer-based Distractor Generation and Multiple Choice Question Filtering","date":"2020-10-19","arxiv_id":"2010.09598","n_code_links":0,"syntology":null},{"paper":null,"slug":"da-transformer-distance-aware-transformer","title":"DA-Transformer: Distance-aware Transformer","date":"2020-10-14","arxiv_id":"2010.06925","n_code_links":0,"syntology":null},{"paper":"/paper/decoding-methods-for-neural-narrative","slug":"decoding-methods-for-neural-narrative","title":"Decoding Methods for Neural Narrative Generation","date":"2020-10-14","arxiv_id":"2010.07375","n_code_links":2,"syntology":null},{"paper":null,"slug":"memformer-the-memory-augmented-transformer-1","title":"Memformer: A Memory-Augmented Transformer for Sequence Modeling","date":"2020-10-14","arxiv_id":"2010.06891","n_code_links":0,"syntology":null},{"paper":null,"slug":"the-workweek-is-the-best-time-to-start-a","title":"The workweek is the best time to start a family -- A Study of GPT-2 Based Claim Generation","date":"2020-10-13","arxiv_id":"2010.06185","n_code_links":0,"syntology":null},{"paper":"/paper/comet-atomic-2020-on-symbolic-and-neural","slug":"comet-atomic-2020-on-symbolic-and-neural","title":"COMET-ATOMIC 2020: On Symbolic and Neural Commonsense Knowledge Graphs","date":"2020-10-12","arxiv_id":"2010.05953","n_code_links":3,"syntology":{"ran":7,"of":7,"n_ran_checked":6,"n_instrument":1,"unverified":0,"pointer_only":2,"phrase":"7 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; 1 where Syntology's instrument failed) · 0 unverified","official":{"repos":["allenai/comet-atomic-2020"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed","official"]}}},{"paper":"/paper/meta-context-transformers-for-domain-specific","slug":"meta-context-transformers-for-domain-specific","title":"Meta-Context Transformers for Domain-Specific Response Generation","date":"2020-10-12","arxiv_id":"2010.05572","n_code_links":1,"syntology":null},{"paper":"/paper/incremental-processing-in-the-age-of-non","slug":"incremental-processing-in-the-age-of-non","title":"Incremental Processing in the Age of Non-Incremental Encoders: An Empirical Assessment of Bidirectional Models for Incremental NLU","date":"2020-10-11","arxiv_id":"2010.05330","n_code_links":1,"syntology":null},{"paper":"/paper/investigating-african-american-vernacular","slug":"investigating-african-american-vernacular","title":"Investigating African-American Vernacular English in Transformer-Based Text Generation","date":"2020-10-06","arxiv_id":"2010.02510","n_code_links":1,"syntology":null},{"paper":"/paper/scene-graph-modification-based-on-natural","slug":"scene-graph-modification-based-on-natural","title":"Scene Graph Modification Based on Natural Language Commands","date":"2020-10-06","arxiv_id":"2010.02591","n_code_links":1,"syntology":null},{"paper":"/paper/genaug-data-augmentation-for-finetuning-text","slug":"genaug-data-augmentation-for-finetuning-text","title":"GenAug: Data Augmentation for Finetuning Text Generators","date":"2020-10-05","arxiv_id":"2010.01794","n_code_links":2,"syntology":null},{"paper":"/paper/inquisitive-question-generation-for-high","slug":"inquisitive-question-generation-for-high","title":"Inquisitive Question Generation for High Level Text Comprehension","date":"2020-10-04","arxiv_id":"2010.01657","n_code_links":1,"syntology":null},{"paper":null,"slug":"examining-the-rhetorical-capacities-of-neural","title":"Examining the rhetorical capacities of neural language models","date":"2020-10-01","arxiv_id":"2010.00153","n_code_links":0,"syntology":null},{"paper":null,"slug":"the-design-and-implementation-of-language","title":"The design and implementation of Language Learning Chatbot with XAI using Ontology and Transfer Learning","date":"2020-09-29","arxiv_id":"2009.13984","n_code_links":0,"syntology":null},{"paper":"/paper/visually-grounded-planning-without-vision","slug":"visually-grounded-planning-without-vision","title":"Visually-Grounded Planning without Vision: Language Models Infer Detailed Plans from High-level Instructions","date":"2020-09-29","arxiv_id":"2009.14259","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":["cognitiveailab/alfred-gpt2"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":"/paper/detecting-soccer-balls-with-reduced-neural","slug":"detecting-soccer-balls-with-reduced-neural","title":"Detecting soccer balls with reduced neural networks: a comparison of multiple architectures under constrained hardware scenarios","date":"2020-09-28","arxiv_id":"2009.13684","n_code_links":1,"syntology":null},{"paper":"/paper/toward-a-thermodynamics-of-meaning","slug":"toward-a-thermodynamics-of-meaning","title":"Toward a Thermodynamics of Meaning","date":"2020-09-24","arxiv_id":"2009.11963","n_code_links":1,"syntology":null},{"paper":null,"slug":"on-data-augmentation-for-extreme-multi-label","title":"On Data Augmentation for Extreme Multi-label Classification","date":"2020-09-22","arxiv_id":"2009.10778","n_code_links":0,"syntology":null},{"paper":null,"slug":"prior-art-search-and-reranking-for-generated","title":"Prior Art Search and Reranking for Generated Patent Text","date":"2020-09-19","arxiv_id":"2009.09132","n_code_links":0,"syntology":null},{"paper":null,"slug":"hierarchical-gpt-with-congruent-transformers","title":"Hierarchical GPT with Congruent Transformers for Multi-Sentence Language Models","date":"2020-09-18","arxiv_id":"2009.08636","n_code_links":0,"syntology":null},{"paper":"/paper/rcnn-for-region-of-interest-detection-in","slug":"rcnn-for-region-of-interest-detection-in","title":"RCNN for Region of Interest Detection in Whole Slide Images","date":"2020-09-16","arxiv_id":"2009.07532","n_code_links":1,"syntology":null},{"paper":"/paper/critical-thinking-for-language-models","slug":"critical-thinking-for-language-models","title":"Critical Thinking for Language Models","date":"2020-09-15","arxiv_id":"2009.07185","n_code_links":1,"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":["debatelab/aacorpus"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":"/paper/dialogue-response-ranking-training-with-large","slug":"dialogue-response-ranking-training-with-large","title":"Dialogue Response Ranking Training with Large-Scale Human Feedback Data","date":"2020-09-15","arxiv_id":"2009.06978","n_code_links":2,"syntology":{"ran":5,"of":6,"n_ran_checked":4,"n_instrument":1,"unverified":1,"pointer_only":0,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 1 where Syntology's instrument failed) · 1 unverified","official":null}},{"paper":"/paper/it-s-not-just-size-that-matters-small","slug":"it-s-not-just-size-that-matters-small","title":"It's Not Just Size That Matters: Small Language Models Are Also Few-Shot Learners","date":"2020-09-15","arxiv_id":"2009.07118","n_code_links":5,"syntology":null},{"paper":null,"slug":"the-radicalization-risks-of-gpt-3-and","title":"The Radicalization Risks of GPT-3 and Advanced Neural Language Models","date":"2020-09-15","arxiv_id":"2009.06807","n_code_links":0,"syntology":null},{"paper":"/paper/gedi-generative-discriminator-guided-sequence","slug":"gedi-generative-discriminator-guided-sequence","title":"GeDi: Generative Discriminator Guided Sequence Generation","date":"2020-09-14","arxiv_id":"2009.06367","n_code_links":3,"syntology":{"ran":6,"of":11,"n_ran_checked":3,"n_instrument":3,"unverified":5,"pointer_only":0,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 1 honoured, 0 violated, 2 with no contract checked; 3 where Syntology's instrument failed) · 5 unverified","official":{"repos":["salesforce/GeDi"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":4,"ran_from_kinds":["listed","official"]}}},{"paper":"/paper/cluster-former-clustering-based-sparse","slug":"cluster-former-clustering-based-sparse","title":"Cluster-Former: Clustering-based Sparse Transformer for Long-Range Dependency Encoding","date":"2020-09-13","arxiv_id":"2009.06097","n_code_links":0,"syntology":null},{"paper":"/paper/yolobile-real-time-object-detection-on-mobile","slug":"yolobile-real-time-object-detection-on-mobile","title":"YOLObile: Real-Time Object Detection on Mobile Devices via Compression-Compilation Co-Design","date":"2020-09-12","arxiv_id":"2009.05697","n_code_links":3,"syntology":null},{"paper":"/paper/unit-test-case-generation-with-transformers","slug":"unit-test-case-generation-with-transformers","title":"Unit Test Case Generation with Transformers and Focal Context","date":"2020-09-11","arxiv_id":"2009.05617","n_code_links":1,"syntology":null},{"paper":"/paper/brain2word-decoding-brain-activity-for","slug":"brain2word-decoding-brain-activity-for","title":"Brain2Word: Decoding Brain Activity for Language Generation","date":"2020-09-10","arxiv_id":"2009.04765","n_code_links":1,"syntology":null},{"paper":"/paper/modern-methods-for-text-generation","slug":"modern-methods-for-text-generation","title":"Modern Methods for Text Generation","date":"2020-09-10","arxiv_id":"2009.04968","n_code_links":2,"syntology":null},{"paper":"/paper/sparsifying-transformer-models-with","slug":"sparsifying-transformer-models-with","title":"Sparsifying Transformer Models with Trainable Representation Pooling","date":"2020-09-10","arxiv_id":"2009.05169","n_code_links":1,"syntology":null},{"paper":"/paper/pay-attention-when-required","slug":"pay-attention-when-required","title":"Pay Attention when Required","date":"2020-09-09","arxiv_id":"2009.04534","n_code_links":2,"syntology":null},{"paper":null,"slug":"black-box-to-white-box-discover-model","title":"Black Box to White Box: Discover Model Characteristics Based on Strategic Probing","date":"2020-09-07","arxiv_id":"2009.03136","n_code_links":0,"syntology":null},{"paper":"/paper/improving-language-generation-with-sentence","slug":"improving-language-generation-with-sentence","title":"Improving Language Generation with Sentence Coherence Objective","date":"2020-09-07","arxiv_id":"2009.06358","n_code_links":1,"syntology":null},{"paper":"/paper/measuring-massive-multitask-language","slug":"measuring-massive-multitask-language","title":"Measuring Massive Multitask Language Understanding","date":"2020-09-07","arxiv_id":"2009.03300","n_code_links":18,"syntology":{"ran":19,"of":26,"n_ran_checked":15,"n_instrument":4,"unverified":7,"pointer_only":1,"phrase":"19 ran (of which 0 constructed an object rather than computing a result; 15 with no instrument failure: 0 honoured, 0 violated, 15 with no contract checked; 4 where Syntology's instrument failed) · 7 unverified","official":{"repos":["hendrycks/test"],"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/comparative-evaluation-of-pretrained-transfer","slug":"comparative-evaluation-of-pretrained-transfer","title":"Comparative Evaluation of Pretrained Transfer Learning Models on Automatic Short Answer Grading","date":"2020-09-02","arxiv_id":"2009.01303","n_code_links":1,"syntology":null},{"paper":null,"slug":"knowledge-efficient-deep-learning-for-natural","title":"Knowledge Efficient Deep Learning for Natural Language Processing","date":"2020-08-28","arxiv_id":"2008.12878","n_code_links":0,"syntology":null},{"paper":null,"slug":"dave-deriving-automatically-verilog-from","title":"DAVE: Deriving Automatically Verilog from English","date":"2020-08-27","arxiv_id":"2009.01026","n_code_links":0,"syntology":null},{"paper":null,"slug":"discrete-word-embedding-for-logical-natural","title":"Discrete Word Embedding for Logical Natural Language Understanding","date":"2020-08-26","arxiv_id":"2008.11649","n_code_links":0,"syntology":null},{"paper":"/paper/etc-nlg-end-to-end-topic-conditioned-natural","slug":"etc-nlg-end-to-end-topic-conditioned-natural","title":"ETC-NLG: End-to-end Topic-Conditioned Natural Language Generation","date":"2020-08-25","arxiv_id":"2008.10875","n_code_links":1,"syntology":null},{"paper":null,"slug":"dynamics-of-feed-forward-induced-interference","title":"Dynamics of feed forward induced interference training","date":"2020-08-24","arxiv_id":"2008.11111","n_code_links":0,"syntology":null},{"paper":"/paper/explainable-end-to-end-deep-learning-for","slug":"explainable-end-to-end-deep-learning-for","title":"Explainable end-to-end deep learning for diabetic retinopathy detection across multiple datasets","date":"2020-08-20","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"generative-models-are-unsupervised-predictors","title":"Generative Models are Unsupervised Predictors of Page Quality: A Colossal-Scale Study","date":"2020-08-17","arxiv_id":"2008.13533","n_code_links":0,"syntology":null},{"paper":null,"slug":"narrative-interpolation-for-generating-and","title":"Narrative Interpolation for Generating and Understanding Stories","date":"2020-08-17","arxiv_id":"2008.07466","n_code_links":0,"syntology":null},{"paper":null,"slug":"adding-recurrence-to-pretrained-transformers","title":"Adding Recurrence to Pretrained Transformers for Improved Efficiency and Context Size","date":"2020-08-16","arxiv_id":"2008.07027","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":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":"/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":"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/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":"/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":"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":"/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":"/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/pp-yolo-an-effective-and-efficient","slug":"pp-yolo-an-effective-and-efficient","title":"PP-YOLO: An Effective and Efficient Implementation of Object Detector","date":"2020-07-23","arxiv_id":"2007.12099","n_code_links":5,"syntology":null},{"paper":"/paper/generative-pretraining-from-pixels","slug":"generative-pretraining-from-pixels","title":"Generative Pretraining from Pixels","date":"2020-07-17","arxiv_id":null,"n_code_links":4,"syntology":null},{"paper":null,"slug":"deep-transformer-based-data-augmentation-with","title":"Deep Transformer based Data Augmentation with Subword Units for Morphologically Rich Online ASR","date":"2020-07-14","arxiv_id":"2007.06949","n_code_links":0,"syntology":null},{"paper":"/paper/nvae-a-deep-hierarchical-variational","slug":"nvae-a-deep-hierarchical-variational","title":"NVAE: A Deep Hierarchical Variational Autoencoder","date":"2020-07-08","arxiv_id":"2007.03898","n_code_links":10,"syntology":{"ran":26,"of":41,"n_ran_checked":21,"n_instrument":5,"unverified":15,"pointer_only":23,"phrase":"26 ran (of which 15 constructed an object rather than computing a result; 21 with no instrument failure: 3 honoured, 0 violated, 18 with no contract checked; 5 where Syntology's instrument failed) · 15 unverified","official":{"repos":["NVlabs/NVAE"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":3,"n_ran_no_instrument_failure":6,"n_unverified":14,"ran_from_kinds":["listed","official"]}}},{"paper":"/paper/do-transformers-need-deep-long-range-memory-1","slug":"do-transformers-need-deep-long-range-memory-1","title":"Do Transformers Need Deep Long-Range Memory","date":"2020-07-07","arxiv_id":"2007.03356","n_code_links":1,"syntology":null},{"paper":null,"slug":"the-go-transformer-natural-language-modeling","title":"The Go Transformer: Natural Language Modeling for Game Play","date":"2020-07-07","arxiv_id":"2007.03500","n_code_links":0,"syntology":null},{"paper":null,"slug":"you-autocomplete-me-poisoning-vulnerabilities","title":"You Autocomplete Me: Poisoning Vulnerabilities in Neural Code Completion","date":"2020-07-05","arxiv_id":"2007.02220","n_code_links":0,"syntology":null},{"paper":null,"slug":"on-the-fly-information-retrieval-augmentation-1","title":"On-The-Fly Information Retrieval Augmentation for Language Models","date":"2020-07-03","arxiv_id":"2007.01528","n_code_links":0,"syntology":null},{"paper":null,"slug":"do-transformers-need-deep-long-range-memory","title":"Do Transformers Need Deep Long-Range Memory?","date":"2020-07-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"lstm-and-gpt-2-synthetic-speech-transfer","title":"LSTM and GPT-2 Synthetic Speech Transfer Learning for Speaker Recognition to Overcome Data Scarcity","date":"2020-07-01","arxiv_id":"2007.00659","n_code_links":0,"syntology":null},{"paper":null,"slug":"probing-for-referential-information-in","title":"Probing for Referential Information in Language Models","date":"2020-07-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":"/paper/roles-and-utilization-of-attention-heads-in","slug":"roles-and-utilization-of-attention-heads-in","title":"Roles and Utilization of Attention Heads in Transformer-based Neural Language Models","date":"2020-07-01","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":"/paper/towards-holistic-and-automatic-evaluation-of-1","slug":"towards-holistic-and-automatic-evaluation-of-1","title":"Towards Holistic and Automatic Evaluation of Open-Domain Dialogue Generation","date":"2020-07-01","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":null,"slug":"knowledge-aware-language-model-pretraining","title":"Knowledge-Aware Language Model Pretraining","date":"2020-06-29","arxiv_id":"2007.00655","n_code_links":0,"syntology":null},{"paper":"/paper/progressive-generation-of-long-text","slug":"progressive-generation-of-long-text","title":"Progressive Generation of Long Text with Pretrained Language Models","date":"2020-06-28","arxiv_id":"2006.15720","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":["tanyuqian/progressive-generation"],"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":null,"slug":"mind-the-facts-knowledge-boosted-coherent","title":"Mind The Facts: Knowledge-Boosted Coherent Abstractive Text Summarization","date":"2020-06-27","arxiv_id":"2006.15435","n_code_links":0,"syntology":null},{"paper":null,"slug":"video-grounded-dialogues-with-pretrained-1","title":"Video-Grounded Dialogues with Pretrained Generation Language Models","date":"2020-06-27","arxiv_id":"2006.15319","n_code_links":0,"syntology":null},{"paper":"/paper/inductive-unsupervised-domain-adaptation-for","slug":"inductive-unsupervised-domain-adaptation-for","title":"Inductive Unsupervised Domain Adaptation for Few-Shot Classification via Clustering","date":"2020-06-23","arxiv_id":"2006.12816","n_code_links":1,"syntology":null},{"paper":"/paper/a-qualitative-evaluation-of-language-models","slug":"a-qualitative-evaluation-of-language-models","title":"A Qualitative Evaluation of Language Models on Automatic Question-Answering for COVID-19","date":"2020-06-19","arxiv_id":"2006.10964","n_code_links":1,"syntology":null}],"record_sha256":"17bbc668fb92c44771987e4a322ea3c0735d82065600bf303335da9321d2d047","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}