{"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/adam/papers/227","list_of":"/method/adam","method":"Adam","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":227,"pages_in_order":244,"rows_per_page":100,"rows":[22601,22700],"of":24390,"counts":{"archive_papers_tagged":24390,"with_a_code_link":10944,"where_syntology_ran_a_sample":3424,"not_listed_spam_title":0,"listed":24390,"listed_where_code_ran":3424,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":2899,"every_run_a_failure_of_syntologys_instrument":525,"listed_with_a_run_with_no_instrument_failure":2899,"listed_every_run_a_failure_of_syntologys_instrument":525,"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/adam","prev":"/method/adam/papers/226","next":"/method/adam/papers/228","papers":[{"paper":"/paper/bert-fine-tuning-for-arabic-text","slug":"bert-fine-tuning-for-arabic-text","title":"BERT Fine-tuning For Arabic Text Summarization","date":"2020-03-29","arxiv_id":"2004.14135","n_code_links":1,"syntology":{"ran":8,"of":8,"n_ran_checked":8,"n_instrument":0,"unverified":0,"pointer_only":5,"phrase":"8 ran (of which 0 constructed an object rather than computing a result; 8 with no instrument failure: 0 honoured, 1 violated, 7 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","official":{"repos":["mukhtar-algezoli/Arabic_PreSumm"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":8,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":"/paper/meta-fine-tuning-neural-language-models-for","slug":"meta-fine-tuning-neural-language-models-for","title":"Meta Fine-Tuning Neural Language Models for Multi-Domain Text Mining","date":"2020-03-29","arxiv_id":"2003.13003","n_code_links":2,"syntology":null},{"paper":"/paper/recursive-non-autoregressive-graph-to-graph","slug":"recursive-non-autoregressive-graph-to-graph","title":"Recursive Non-Autoregressive Graph-to-Graph Transformer for Dependency Parsing with Iterative Refinement","date":"2020-03-29","arxiv_id":"2003.13118","n_code_links":1,"syntology":null},{"paper":null,"slug":"user-generated-data-achilles-heel-of-bert","title":"Noisy Text Data: Achilles' Heel of BERT","date":"2020-03-29","arxiv_id":"2003.12932","n_code_links":0,"syntology":null},{"paper":null,"slug":"actor-transformers-for-group-activity","title":"Actor-Transformers for Group Activity Recognition","date":"2020-03-28","arxiv_id":"2003.12737","n_code_links":0,"syntology":null},{"paper":"/paper/hin-hierarchical-inference-network-for","slug":"hin-hierarchical-inference-network-for","title":"HIN: Hierarchical Inference Network for Document-Level Relation Extraction","date":"2020-03-28","arxiv_id":"2003.12754","n_code_links":0,"syntology":null},{"paper":"/paper/obstacle-avoidance-and-navigation-utilizing","slug":"obstacle-avoidance-and-navigation-utilizing","title":"Obstacle Avoidance and Navigation Utilizing Reinforcement Learning with Reward Shaping","date":"2020-03-28","arxiv_id":"2003.12863","n_code_links":1,"syntology":null},{"paper":"/paper/variational-transformers-for-diverse-response","slug":"variational-transformers-for-diverse-response","title":"Variational Transformers for Diverse Response Generation","date":"2020-03-28","arxiv_id":"2003.12738","n_code_links":2,"syntology":{"ran":3,"of":4,"n_ran_checked":3,"n_instrument":0,"unverified":1,"pointer_only":0,"phrase":"3 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; 0 where Syntology's instrument failed) · 1 unverified","official":{"repos":["zlinao/Variational-Transformer"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":"/paper/cycle-text-to-image-gan-with-bert","slug":"cycle-text-to-image-gan-with-bert","title":"Cycle Text-To-Image GAN with BERT","date":"2020-03-26","arxiv_id":"2003.12137","n_code_links":4,"syntology":null},{"paper":"/paper/hit-detector-hierarchical-trinity","slug":"hit-detector-hierarchical-trinity","title":"Hit-Detector: Hierarchical Trinity Architecture Search for Object Detection","date":"2020-03-26","arxiv_id":"2003.11818","n_code_links":1,"syntology":null},{"paper":null,"slug":"strokecoder-path-based-image-generation-from","title":"StrokeCoder: Path-Based Image Generation from Single Examples using Transformers","date":"2020-03-26","arxiv_id":"2003.11958","n_code_links":0,"syntology":null},{"paper":"/paper/tldr-token-loss-dynamic-reweighting-for","slug":"tldr-token-loss-dynamic-reweighting-for","title":"TLDR: Token Loss Dynamic Reweighting for Reducing Repetitive Utterance Generation","date":"2020-03-26","arxiv_id":"2003.11963","n_code_links":1,"syntology":{"ran":15,"of":15,"n_ran_checked":14,"n_instrument":1,"unverified":0,"pointer_only":15,"phrase":"15 ran (of which 0 constructed an object rather than computing a result; 14 with no instrument failure: 0 honoured, 0 violated, 14 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","official":{"repos":["ShaojieJiang/tldr"],"state":"official (archive's flag): 15 ran","n_ran":15,"n_constructed":0,"n_ran_no_instrument_failure":14,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":"/paper/generalizing-spatial-transformers-to","slug":"generalizing-spatial-transformers-to","title":"Generalizing Spatial Transformers to Projective Geometry with Applications to 2D/3D Registration","date":"2020-03-24","arxiv_id":"2003.10987","n_code_links":1,"syntology":null},{"paper":null,"slug":"multi-agent-reinforcement-learning-for-1","title":"Multi-Agent Reinforcement Learning for Problems with Combined Individual and Team Reward","date":"2020-03-24","arxiv_id":"2003.10598","n_code_links":0,"syntology":null},{"paper":"/paper/electra-pre-training-text-encoders-as-1","slug":"electra-pre-training-text-encoders-as-1","title":"ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators","date":"2020-03-23","arxiv_id":"2003.10555","n_code_links":19,"syntology":{"ran":31,"of":40,"n_ran_checked":18,"n_instrument":13,"unverified":9,"pointer_only":10,"phrase":"31 ran (of which 7 constructed an object rather than computing a result; 18 with no instrument failure: 2 honoured, 2 violated, 14 with no contract checked; 13 where Syntology's instrument failed) · 9 unverified","official":{"repos":["google-research/electra"],"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/evolutionary-population-curriculum-for-1","slug":"evolutionary-population-curriculum-for-1","title":"Evolutionary Population Curriculum for Scaling Multi-Agent Reinforcement Learning","date":"2020-03-23","arxiv_id":"2003.10423","n_code_links":1,"syntology":{"ran":3,"of":3,"n_ran_checked":1,"n_instrument":2,"unverified":0,"pointer_only":3,"phrase":"3 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; 2 where Syntology's instrument failed) · 0 unverified","official":{"repos":["qian18long/epciclr2020"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":"/paper/pairwise-multi-class-document-classification","slug":"pairwise-multi-class-document-classification","title":"Pairwise Multi-Class Document Classification for Semantic Relations between Wikipedia Articles","date":"2020-03-22","arxiv_id":"2003.09881","n_code_links":4,"syntology":null},{"paper":null,"slug":"a-new-regret-analysis-for-adam-type","title":"A new regret analysis for Adam-type algorithms","date":"2020-03-21","arxiv_id":"2003.09729","n_code_links":0,"syntology":null},{"paper":null,"slug":"accelerating-deep-reinforcement-learning-with","title":"Accelerating Deep Reinforcement Learning With the Aid of Partial Model: Energy-Efficient Predictive Video Streaming","date":"2020-03-21","arxiv_id":"2003.09708","n_code_links":0,"syntology":null},{"paper":null,"slug":"analyzing-word-translation-of-transformer","title":"Probing Word Translations in the Transformer and Trading Decoder for Encoder Layers","date":"2020-03-21","arxiv_id":"2003.09586","n_code_links":0,"syntology":null},{"paper":null,"slug":"event-based-control-for-online-training-of","title":"Event-Based Control for Online Training of Neural Networks","date":"2020-03-20","arxiv_id":"2003.09503","n_code_links":0,"syntology":null},{"paper":"/paper/tnt-kid-transformer-based-neural-tagger-for","slug":"tnt-kid-transformer-based-neural-tagger-for","title":"TNT-KID: Transformer-based Neural Tagger for Keyword Identification","date":"2020-03-20","arxiv_id":"2003.09166","n_code_links":1,"syntology":null},{"paper":"/paper/beheshti-ner-persian-named-entity-recognition","slug":"beheshti-ner-persian-named-entity-recognition","title":"Beheshti-NER: Persian Named Entity Recognition Using BERT","date":"2020-03-19","arxiv_id":"2003.08875","n_code_links":3,"syntology":null},{"paper":null,"slug":"detecting-lane-and-road-markings-at-a","title":"Detecting Lane and Road Markings at A Distance with Perspective Transformer Layers","date":"2020-03-19","arxiv_id":"2003.08550","n_code_links":0,"syntology":null},{"paper":null,"slug":"diversity-density-and-homogeneity","title":"Diversity, Density, and Homogeneity: Quantitative Characteristic Metrics for Text Collections","date":"2020-03-19","arxiv_id":"2003.08529","n_code_links":0,"syntology":null},{"paper":null,"slug":"normalized-and-geometry-aware-self-attention","title":"Normalized and Geometry-Aware Self-Attention Network for Image Captioning","date":"2020-03-19","arxiv_id":"2003.08897","n_code_links":0,"syntology":null},{"paper":"/paper/robust-deep-reinforcement-learning-against","slug":"robust-deep-reinforcement-learning-against","title":"Robust Deep Reinforcement Learning against Adversarial Perturbations on State Observations","date":"2020-03-19","arxiv_id":"2003.08938","n_code_links":4,"syntology":null},{"paper":null,"slug":"temporal-embeddings-and-transformer-models","title":"Temporal Embeddings and Transformer Models for Narrative Text Understanding","date":"2020-03-19","arxiv_id":"2003.08811","n_code_links":0,"syntology":null},{"paper":null,"slug":"the-value-of-text-for-small-business-default","title":"The value of text for small business default prediction: A deep learning approach","date":"2020-03-19","arxiv_id":"2003.08964","n_code_links":0,"syntology":null},{"paper":null,"slug":"scene-text-recognition-via-transformer","title":"Scene Text Recognition via Transformer","date":"2020-03-18","arxiv_id":"2003.08077","n_code_links":0,"syntology":null},{"paper":"/paper/transformer-networks-for-trajectory","slug":"transformer-networks-for-trajectory","title":"Transformer Networks for Trajectory Forecasting","date":"2020-03-18","arxiv_id":"2003.08111","n_code_links":1,"syntology":{"ran":5,"of":6,"n_ran_checked":5,"n_instrument":0,"unverified":1,"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) · 1 unverified","official":{"repos":["FGiuliari/Trajectory-Transformer"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":1,"ran_from_kinds":["official"]}}},{"paper":"/paper/x-stance-a-multilingual-multi-target-dataset","slug":"x-stance-a-multilingual-multi-target-dataset","title":"X-Stance: A Multilingual Multi-Target Dataset for Stance Detection","date":"2020-03-18","arxiv_id":"2003.08385","n_code_links":1,"syntology":null},{"paper":null,"slug":"author2vec-a-framework-for-generating-user","title":"Author2Vec: A Framework for Generating User Embedding","date":"2020-03-17","arxiv_id":"2003.11627","n_code_links":0,"syntology":null},{"paper":"/paper/calibration-of-pre-trained-transformers","slug":"calibration-of-pre-trained-transformers","title":"Calibration of Pre-trained Transformers","date":"2020-03-17","arxiv_id":"2003.07892","n_code_links":1,"syntology":null},{"paper":"/paper/human-activity-recognition-from-wearable","slug":"human-activity-recognition-from-wearable","title":"Human Activity Recognition from Wearable Sensor Data Using Self-Attention","date":"2020-03-17","arxiv_id":"2003.09018","n_code_links":2,"syntology":null},{"paper":"/paper/multi-modal-dense-video-captioning","slug":"multi-modal-dense-video-captioning","title":"Multi-modal Dense Video Captioning","date":"2020-03-17","arxiv_id":"2003.07758","n_code_links":4,"syntology":null},{"paper":"/paper/po-emo-conceptualization-annotation-and","slug":"po-emo-conceptualization-annotation-and","title":"PO-EMO: Conceptualization, Annotation, and Modeling of Aesthetic Emotions in German and English Poetry","date":"2020-03-17","arxiv_id":"2003.07723","n_code_links":1,"syntology":null},{"paper":"/paper/rethinking-batch-normalization-in","slug":"rethinking-batch-normalization-in","title":"PowerNorm: Rethinking Batch Normalization in Transformers","date":"2020-03-17","arxiv_id":"2003.07845","n_code_links":1,"syntology":null},{"paper":null,"slug":"a-survey-on-contextual-embeddings","title":"A Survey on Contextual Embeddings","date":"2020-03-16","arxiv_id":"2003.07278","n_code_links":0,"syntology":null},{"paper":"/paper/cost-sensitive-bert-for-generalisable-1","slug":"cost-sensitive-bert-for-generalisable-1","title":"Cost-Sensitive BERT for Generalisable Sentence Classification with Imbalanced Data","date":"2020-03-16","arxiv_id":"2003.11563","n_code_links":1,"syntology":null},{"paper":"/paper/particle-based-adaptive-discretization-for","slug":"particle-based-adaptive-discretization-for","title":"PFPN: Continuous Control of Physically Simulated Characters using Particle Filtering Policy Network","date":"2020-03-16","arxiv_id":"2003.06959","n_code_links":1,"syntology":null},{"paper":"/paper/trans-blstm-transformer-with-bidirectional","slug":"trans-blstm-transformer-with-bidirectional","title":"TRANS-BLSTM: Transformer with Bidirectional LSTM for Language Understanding","date":"2020-03-16","arxiv_id":"2003.07000","n_code_links":0,"syntology":null},{"paper":"/paper/deep-multi-agent-reinforcement-learning-for","slug":"deep-multi-agent-reinforcement-learning-for","title":"FACMAC: Factored Multi-Agent Centralised Policy Gradients","date":"2020-03-14","arxiv_id":"2003.06709","n_code_links":3,"syntology":{"ran":2,"of":4,"n_ran_checked":1,"n_instrument":1,"unverified":2,"pointer_only":0,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 1 where Syntology's instrument failed) · 2 unverified","official":{"repos":["schroederdewitt/multiagent_mujoco"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":2,"ran_from_kinds":["official"]}}},{"paper":"/paper/document-ranking-with-a-pretrained-sequence","slug":"document-ranking-with-a-pretrained-sequence","title":"Document Ranking with a Pretrained Sequence-to-Sequence Model","date":"2020-03-14","arxiv_id":"2003.06713","n_code_links":2,"syntology":null},{"paper":null,"slug":"finnish-language-modeling-with-deep","title":"Finnish Language Modeling with Deep Transformer Models","date":"2020-03-14","arxiv_id":"2003.11562","n_code_links":0,"syntology":null},{"paper":null,"slug":"biggan-based-bayesian-reconstruction-of","title":"BigGAN-based Bayesian reconstruction of natural images from human brain activity","date":"2020-03-13","arxiv_id":"2003.06105","n_code_links":0,"syntology":null},{"paper":null,"slug":"generating-major-types-of-chinese-classical","title":"Generating Major Types of Chinese Classical Poetry in a Uniformed Framework","date":"2020-03-13","arxiv_id":"2003.11528","n_code_links":0,"syntology":null},{"paper":"/paper/learning-to-encode-position-for-transformer","slug":"learning-to-encode-position-for-transformer","title":"Learning to Encode Position for Transformer with Continuous Dynamical Model","date":"2020-03-13","arxiv_id":"2003.09229","n_code_links":1,"syntology":{"ran":3,"of":6,"n_ran_checked":1,"n_instrument":2,"unverified":3,"pointer_only":6,"phrase":"3 ran (of which 2 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) · 3 unverified","official":null}},{"paper":null,"slug":"data-driven-deep-learning-to-design-pilot-and","title":"Data-Driven Deep Learning to Design Pilot and Channel Estimator For Massive MIMO","date":"2020-03-12","arxiv_id":"2003.05875","n_code_links":0,"syntology":null},{"paper":"/paper/efficient-content-based-sparse-attention-with-1","slug":"efficient-content-based-sparse-attention-with-1","title":"Efficient Content-Based Sparse Attention with Routing Transformers","date":"2020-03-12","arxiv_id":"2003.05997","n_code_links":2,"syntology":{"ran":3,"of":3,"n_ran_checked":3,"n_instrument":0,"unverified":0,"pointer_only":2,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 1 honoured, 2 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","official":null}},{"paper":null,"slug":"optimized-light-weight-convolutional-neural","title":"Optimized Light-Weight Convolutional Neural Networks for Histopathologic Cancer Detection","date":"2020-03-12","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":"/paper/hurtful-words-quantifying-biases-in-clinical","slug":"hurtful-words-quantifying-biases-in-clinical","title":"Hurtful Words: Quantifying Biases in Clinical Contextual Word Embeddings","date":"2020-03-11","arxiv_id":"2003.11515","n_code_links":1,"syntology":null},{"paper":null,"slug":"improving-the-backpropagation-algorithm-with","title":"Improving the Backpropagation Algorithm with Consequentialism Weight Updates over Mini-Batches","date":"2020-03-11","arxiv_id":"2003.05164","n_code_links":0,"syntology":null},{"paper":"/paper/investigating-entity-knowledge-in-bert-with-1","slug":"investigating-entity-knowledge-in-bert-with-1","title":"Investigating Entity Knowledge in BERT with Simple Neural End-To-End Entity Linking","date":"2020-03-11","arxiv_id":"2003.05473","n_code_links":1,"syntology":{"ran":4,"of":4,"n_ran_checked":4,"n_instrument":0,"unverified":0,"pointer_only":0,"phrase":"4 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; 0 where Syntology's instrument failed) · 0 unverified","official":{"repos":["samuelbroscheit/entity_knowledge_in_bert"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":"/paper/keyword-attentive-deep-semantic-matching","slug":"keyword-attentive-deep-semantic-matching","title":"Keyword-Attentive Deep Semantic Matching","date":"2020-03-11","arxiv_id":"2003.11516","n_code_links":1,"syntology":null},{"paper":"/paper/online-meta-critic-learning-for-off-policy-1","slug":"online-meta-critic-learning-for-off-policy-1","title":"Online Meta-Critic Learning for Off-Policy Actor-Critic Methods","date":"2020-03-11","arxiv_id":"2003.05334","n_code_links":1,"syntology":{"ran":6,"of":6,"n_ran_checked":6,"n_instrument":0,"unverified":0,"pointer_only":6,"phrase":"6 ran (of which 6 constructed an object rather than computing a result; 6 with no instrument failure: 0 honoured, 0 violated, 6 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified; every one of the 6 samples that ran constructed an object rather than computing a result","official":{"repos":["zwfightzw/Meta-Critic"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":6,"n_ran_no_instrument_failure":6,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":"/paper/efficient-intent-detection-with-dual-sentence","slug":"efficient-intent-detection-with-dual-sentence","title":"Efficient Intent Detection with Dual Sentence Encoders","date":"2020-03-10","arxiv_id":"2003.04807","n_code_links":5,"syntology":{"ran":4,"of":6,"n_ran_checked":4,"n_instrument":0,"unverified":2,"pointer_only":0,"phrase":"4 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; 0 where Syntology's instrument failed) · 2 unverified","official":null}},{"paper":null,"slug":"hybrid-attention-based-transformer-block","title":"Hybrid Attention-Based Transformer Block Model for Distant Supervision Relation Extraction","date":"2020-03-10","arxiv_id":"2003.11518","n_code_links":0,"syntology":null},{"paper":"/paper/rezero-is-all-you-need-fast-convergence-at","slug":"rezero-is-all-you-need-fast-convergence-at","title":"ReZero is All You Need: Fast Convergence at Large Depth","date":"2020-03-10","arxiv_id":"2003.04887","n_code_links":13,"syntology":{"ran":6,"of":6,"n_ran_checked":0,"n_instrument":6,"unverified":0,"pointer_only":0,"phrase":"6 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; 6 where Syntology's instrument failed) · 0 unverified","official":{"repos":["majumderb/rezero"],"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/capacity-of-continuous-channels-with-memory","slug":"capacity-of-continuous-channels-with-memory","title":"Capacity of Continuous Channels with Memory via Directed Information Neural Estimator","date":"2020-03-09","arxiv_id":"2003.04179","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: 2 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","official":null}},{"paper":"/paper/transformation-based-adversarial-video","slug":"transformation-based-adversarial-video","title":"Transformation-based Adversarial Video Prediction on Large-Scale Data","date":"2020-03-09","arxiv_id":"2003.04035","n_code_links":0,"syntology":null},{"paper":null,"slug":"cross-modal-learning-for-multi-modal-video","title":"Cross-modal Learning for Multi-modal Video Categorization","date":"2020-03-07","arxiv_id":"2003.03501","n_code_links":0,"syntology":null},{"paper":null,"slug":"ttpp-temporal-transformer-with-progressive","title":"TTPP: Temporal Transformer with Progressive Prediction for Efficient Action Anticipation","date":"2020-03-07","arxiv_id":"2003.03530","n_code_links":0,"syntology":null},{"paper":null,"slug":"sensitive-data-detection-and-classification","title":"Sensitive Data Detection and Classification in Spanish Clinical Text: Experiments with BERT","date":"2020-03-06","arxiv_id":"2003.03106","n_code_links":0,"syntology":null},{"paper":"/paper/teaching-temporal-logics-to-neural-networks","slug":"teaching-temporal-logics-to-neural-networks","title":"Teaching Temporal Logics to Neural Networks","date":"2020-03-06","arxiv_id":"2003.04218","n_code_links":2,"syntology":{"ran":14,"of":24,"n_ran_checked":11,"n_instrument":3,"unverified":10,"pointer_only":0,"phrase":"14 ran (of which 0 constructed an object rather than computing a result; 11 with no instrument failure: 2 honoured, 0 violated, 9 with no contract checked; 3 where Syntology's instrument failed) · 10 unverified","official":{"repos":["reactive-systems/deepltl"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":8,"ran_from_kinds":["listed","official"]}}},{"paper":null,"slug":"transfer-learning-for-information-extraction","title":"Transfer Learning for Information Extraction with Limited Data","date":"2020-03-06","arxiv_id":"2003.03064","n_code_links":0,"syntology":null},{"paper":"/paper/augmented-transformer-achieves-97-and-85-for","slug":"augmented-transformer-achieves-97-and-85-for","title":"State-of-the-Art Augmented NLP Transformer models for direct and single-step retrosynthesis","date":"2020-03-05","arxiv_id":"2003.02804","n_code_links":1,"syntology":null},{"paper":null,"slug":"bert-as-a-teacher-contextual-embeddings-for","title":"BERT as a Teacher: Contextual Embeddings for Sequence-Level Reward","date":"2020-03-05","arxiv_id":"2003.02738","n_code_links":0,"syntology":null},{"paper":"/paper/emptransfo-a-multi-head-transformer","slug":"emptransfo-a-multi-head-transformer","title":"EmpTransfo: A Multi-head Transformer Architecture for Creating Empathetic Dialog Systems","date":"2020-03-05","arxiv_id":"2003.02958","n_code_links":1,"syntology":null},{"paper":null,"slug":"hyponli-exploring-the-artificial-patterns-of","title":"HypoNLI: Exploring the Artificial Patterns of Hypothesis-only Bias in Natural Language Inference","date":"2020-03-05","arxiv_id":"2003.02756","n_code_links":0,"syntology":null},{"paper":null,"slug":"on-the-convergence-of-adam-and-adagrad","title":"A Simple Convergence Proof of Adam and Adagrad","date":"2020-03-05","arxiv_id":"2003.02395","n_code_links":0,"syntology":null},{"paper":"/paper/permute-to-train-a-new-dimension-to-training","slug":"permute-to-train-a-new-dimension-to-training","title":"Train-by-Reconnect: Decoupling Locations of Weights from their Values","date":"2020-03-05","arxiv_id":"2003.02570","n_code_links":1,"syntology":{"ran":0,"of":1,"n_ran_checked":0,"n_instrument":0,"unverified":1,"pointer_only":0,"phrase":"0 ran · 1 unverified","official":null}},{"paper":"/paper/recipegpt-generative-pre-training-based","slug":"recipegpt-generative-pre-training-based","title":"RecipeGPT: Generative Pre-training Based Cooking Recipe Generation and Evaluation System","date":"2020-03-05","arxiv_id":"2003.02498","n_code_links":1,"syntology":null},{"paper":null,"slug":"what-the-mask-making-sense-of-language","title":"What the [MASK]? Making Sense of Language-Specific BERT Models","date":"2020-03-05","arxiv_id":"2003.02912","n_code_links":0,"syntology":null},{"paper":null,"slug":"a-study-on-efficiency-accuracy-and-document","title":"A Study on Efficiency, Accuracy and Document Structure for Answer Sentence Selection","date":"2020-03-04","arxiv_id":"2003.02349","n_code_links":0,"syntology":null},{"paper":"/paper/aligntts-efficient-feed-forward-text-to","slug":"aligntts-efficient-feed-forward-text-to","title":"AlignTTS: Efficient Feed-Forward Text-to-Speech System without Explicit Alignment","date":"2020-03-04","arxiv_id":"2003.01950","n_code_links":2,"syntology":{"ran":3,"of":6,"n_ran_checked":1,"n_instrument":2,"unverified":3,"pointer_only":3,"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) · 3 unverified","official":null}},{"paper":"/paper/data-augmentation-using-pre-trained","slug":"data-augmentation-using-pre-trained","title":"Data Augmentation using Pre-trained Transformer Models","date":"2020-03-04","arxiv_id":"2003.02245","n_code_links":4,"syntology":{"ran":6,"of":9,"n_ran_checked":6,"n_instrument":0,"unverified":3,"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) · 3 unverified","official":{"repos":["varinf/TransformersDataAugmentation"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]}}},{"paper":null,"slug":"dynamic-experience-replay","title":"Dynamic Experience Replay","date":"2020-03-04","arxiv_id":"2003.02372","n_code_links":0,"syntology":null},{"paper":"/paper/jiant-a-software-toolkit-for-research-on","slug":"jiant-a-software-toolkit-for-research-on","title":"jiant: A Software Toolkit for Research on General-Purpose Text Understanding Models","date":"2020-03-04","arxiv_id":"2003.02249","n_code_links":6,"syntology":{"ran":6,"of":8,"n_ran_checked":4,"n_instrument":2,"unverified":2,"pointer_only":0,"phrase":"6 ran (of which 3 constructed an object rather than computing a result; 4 with no instrument failure: 1 honoured, 0 violated, 3 with no contract checked; 2 where Syntology's instrument failed) · 2 unverified","official":{"repos":["nyu-mll/jiant"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":3,"n_ran_no_instrument_failure":4,"n_unverified":2,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"kleister-a-novel-task-for-information","title":"Kleister: A novel task for Information Extraction involving Long Documents with Complex Layout","date":"2020-03-04","arxiv_id":"2003.02356","n_code_links":0,"syntology":null},{"paper":"/paper/cluecorpus2020-a-large-scale-chinese-corpus","slug":"cluecorpus2020-a-large-scale-chinese-corpus","title":"CLUECorpus2020: A Large-scale Chinese Corpus for Pre-training Language Model","date":"2020-03-03","arxiv_id":"2003.01355","n_code_links":2,"syntology":null},{"paper":"/paper/contention-window-optimization-in-ieee","slug":"contention-window-optimization-in-ieee","title":"Contention Window Optimization in IEEE 802.11ax Networks with Deep Reinforcement Learning","date":"2020-03-03","arxiv_id":"2003.01492","n_code_links":1,"syntology":null},{"paper":null,"slug":"controllable-time-delay-transformer-for-real","title":"Controllable Time-Delay Transformer for Real-Time Punctuation Prediction and Disfluency Detection","date":"2020-03-03","arxiv_id":"2003.01309","n_code_links":0,"syntology":null},{"paper":"/paper/heterogeneous-graph-transformer","slug":"heterogeneous-graph-transformer","title":"Heterogeneous Graph Transformer","date":"2020-03-03","arxiv_id":"2003.01332","n_code_links":4,"syntology":{"ran":2,"of":6,"n_ran_checked":2,"n_instrument":0,"unverified":4,"pointer_only":3,"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) · 4 unverified","official":{"repos":["acbull/pyHGT"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":4,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"hierarchical-context-enhanced-multi-domain","title":"Hierarchical Context Enhanced Multi-Domain Dialogue System for Multi-domain Task Completion","date":"2020-03-03","arxiv_id":"2003.01338","n_code_links":0,"syntology":null},{"paper":null,"slug":"hybrid-generative-retrieval-transformers-for","title":"Hybrid Generative-Retrieval Transformers for Dialogue Domain Adaptation","date":"2020-03-03","arxiv_id":"2003.01680","n_code_links":0,"syntology":null},{"paper":null,"slug":"meta-embeddings-based-on-self-attention","title":"Meta-Embeddings Based On Self-Attention","date":"2020-03-03","arxiv_id":"2003.01371","n_code_links":0,"syntology":null},{"paper":null,"slug":"transfer-learning-for-context-aware-spoken","title":"Transfer Learning for Context-Aware Spoken Language Understanding","date":"2020-03-03","arxiv_id":"2003.01305","n_code_links":0,"syntology":null},{"paper":null,"slug":"iterate-averaging-helps-an-alternative","title":"Iterative Averaging in the Quest for Best Test Error","date":"2020-03-02","arxiv_id":"2003.01247","n_code_links":0,"syntology":null},{"paper":"/paper/reinforcement-co-learning-of-deep-and-spiking","slug":"reinforcement-co-learning-of-deep-and-spiking","title":"Reinforcement co-Learning of Deep and Spiking Neural Networks for Energy-Efficient Mapless Navigation with Neuromorphic Hardware","date":"2020-03-02","arxiv_id":"2003.01157","n_code_links":1,"syntology":{"ran":3,"of":3,"n_ran_checked":3,"n_instrument":0,"unverified":0,"pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","official":{"repos":["combra-lab/spiking-ddpg-mapless-navigation"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"transformer","title":"Transformer++","date":"2020-03-02","arxiv_id":"2003.04974","n_code_links":0,"syntology":null},{"paper":"/paper/adaptive-federated-optimization","slug":"adaptive-federated-optimization","title":"Adaptive Federated Optimization","date":"2020-02-29","arxiv_id":"2003.00295","n_code_links":8,"syntology":{"ran":1,"of":2,"n_ran_checked":1,"n_instrument":0,"unverified":1,"pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","official":{"repos":["google-research/federated"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]}}},{"paper":null,"slug":"conjugate-gradient-based-adam-for-stochastic","title":"Conjugate-gradient-based Adam for stochastic optimization and its application to deep learning","date":"2020-02-29","arxiv_id":"2003.00231","n_code_links":0,"syntology":null},{"paper":"/paper/tadam-a-robust-stochastic-gradient-optimizer","slug":"tadam-a-robust-stochastic-gradient-optimizer","title":"TAdam: A Robust Stochastic Gradient Optimizer","date":"2020-02-29","arxiv_id":"2003.00179","n_code_links":3,"syntology":null},{"paper":"/paper/a-u-net-based-discriminator-for-generative","slug":"a-u-net-based-discriminator-for-generative","title":"A U-Net Based Discriminator for Generative Adversarial Networks","date":"2020-02-28","arxiv_id":"2002.12655","n_code_links":3,"syntology":null},{"paper":"/paper/arabert-transformer-based-model-for-arabic","slug":"arabert-transformer-based-model-for-arabic","title":"AraBERT: Transformer-based Model for Arabic Language Understanding","date":"2020-02-28","arxiv_id":"2003.00104","n_code_links":4,"syntology":null},{"paper":"/paper/automatic-perturbation-analysis-on-general","slug":"automatic-perturbation-analysis-on-general","title":"Automatic Perturbation Analysis for Scalable Certified Robustness and Beyond","date":"2020-02-28","arxiv_id":"2002.12920","n_code_links":7,"syntology":{"ran":14,"of":18,"n_ran_checked":13,"n_instrument":1,"unverified":4,"pointer_only":2,"phrase":"14 ran (of which 0 constructed an object rather than computing a result; 13 with no instrument failure: 0 honoured, 2 violated, 11 with no contract checked; 1 where Syntology's instrument failed) · 4 unverified","official":{"repos":["KaidiXu/auto_LiRPA"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]}}},{"paper":null,"slug":"dc-bert-decoupling-question-and-document-for","title":"DC-BERT: Decoupling Question and Document for Efficient Contextual Encoding","date":"2020-02-28","arxiv_id":"2002.12591","n_code_links":0,"syntology":null},{"paper":null,"slug":"exploring-and-distilling-cross-modal","title":"Exploring and Distilling Cross-Modal Information for Image Captioning","date":"2020-02-28","arxiv_id":"2002.12585","n_code_links":0,"syntology":null},{"paper":"/paper/textbrewer-an-open-source-knowledge","slug":"textbrewer-an-open-source-knowledge","title":"TextBrewer: An Open-Source Knowledge Distillation Toolkit for Natural Language Processing","date":"2020-02-28","arxiv_id":"2002.12620","n_code_links":1,"syntology":null}],"record_sha256":"e188efa2a9d18daec8a22b4cb1565362a98a2c5ac92eff5267c87e6bb2ab3009","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}