{"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/240","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":240,"pages_in_order":244,"rows_per_page":100,"rows":[23901,24000],"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/239","next":"/method/adam/papers/241","papers":[{"paper":"/paper/faq-retrieval-using-query-question-similarity","slug":"faq-retrieval-using-query-question-similarity","title":"FAQ Retrieval using Query-Question Similarity and BERT-Based Query-Answer Relevance","date":"2019-05-08","arxiv_id":"1905.02851","n_code_links":1,"syntology":null},{"paper":null,"slug":"photometric-transformer-networks-and-label","title":"Photometric Transformer Networks and Label Adjustment for Breast Density Prediction","date":"2019-05-08","arxiv_id":"1905.02906","n_code_links":0,"syntology":null},{"paper":"/paper/rwth-asr-systems-for-librispeech-hybrid-vs","slug":"rwth-asr-systems-for-librispeech-hybrid-vs","title":"RWTH ASR Systems for LibriSpeech: Hybrid vs Attention -- w/o Data Augmentation","date":"2019-05-08","arxiv_id":"1905.03072","n_code_links":2,"syntology":null},{"paper":"/paper/sadam-a-variant-of-adam-for-strongly-convex","slug":"sadam-a-variant-of-adam-for-strongly-convex","title":"SAdam: A Variant of Adam for Strongly Convex Functions","date":"2019-05-08","arxiv_id":"1905.02957","n_code_links":1,"syntology":null},{"paper":"/paper/unified-language-model-pre-training-for","slug":"unified-language-model-pre-training-for","title":"Unified Language Model Pre-training for Natural Language Understanding and Generation","date":"2019-05-08","arxiv_id":"1905.03197","n_code_links":9,"syntology":null},{"paper":"/paper/a-modular-deep-learning-approach-for-extreme","slug":"a-modular-deep-learning-approach-for-extreme","title":"Taming Pretrained Transformers for Extreme Multi-label Text Classification","date":"2019-05-07","arxiv_id":"1905.02331","n_code_links":2,"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":["OctoberChang/X-Transformer"],"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/mass-masked-sequence-to-sequence-pre-training","slug":"mass-masked-sequence-to-sequence-pre-training","title":"MASS: Masked Sequence to Sequence Pre-training for Language Generation","date":"2019-05-07","arxiv_id":"1905.02450","n_code_links":7,"syntology":null},{"paper":"/paper/anonymized-bert-an-augmentation-approach-to","slug":"anonymized-bert-an-augmentation-approach-to","title":"Anonymized BERT: An Augmentation Approach to the Gendered Pronoun Resolution Challenge","date":"2019-05-06","arxiv_id":"1905.01780","n_code_links":1,"syntology":null},{"paper":"/paper/pog-personalized-outfit-generation-for","slug":"pog-personalized-outfit-generation-for","title":"POG: Personalized Outfit Generation for Fashion Recommendation at Alibaba iFashion","date":"2019-05-06","arxiv_id":"1905.01866","n_code_links":1,"syntology":null},{"paper":null,"slug":"investigating-the-successes-and-failures-of","title":"Investigating the Successes and Failures of BERT for Passage Re-Ranking","date":"2019-05-05","arxiv_id":"1905.01758","n_code_links":0,"syntology":null},{"paper":"/paper/deep-residual-reinforcement-learning","slug":"deep-residual-reinforcement-learning","title":"Deep Residual Reinforcement Learning","date":"2019-05-03","arxiv_id":"1905.01072","n_code_links":1,"syntology":null},{"paper":"/paper/collaborative-evolutionary-reinforcement","slug":"collaborative-evolutionary-reinforcement","title":"Collaborative Evolutionary Reinforcement Learning","date":"2019-05-02","arxiv_id":"1905.00976","n_code_links":1,"syntology":{"ran":5,"of":5,"n_ran_checked":5,"n_instrument":0,"unverified":0,"pointer_only":0,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 0 violated, 5 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","official":{"repos":["intelai/cerl"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"a-unified-theory-of-adaptive-stochastic-1","title":"A unified theory of adaptive stochastic gradient descent as Bayesian filtering","date":"2019-05-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"accelerating-first-order-optimization","title":"Accelerating first order optimization algorithms","date":"2019-05-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"cem-rl-combining-evolutionary-and-gradient","title":"CEM-RL: Combining evolutionary and gradient-based methods for policy search","date":"2019-05-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"combining-adaptive-algorithms-and","title":"Combining adaptive algorithms and hypergradient method: a performance and robustness study","date":"2019-05-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":"/paper/discourse-representation-structure-parsing-1","slug":"discourse-representation-structure-parsing-1","title":"Discourse Representation Structure Parsing with Recurrent Neural Networks and the Transformer Model","date":"2019-05-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"graph-transformer","title":"Graph Transformer","date":"2019-05-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"learning-agents-with-prioritization-and","title":"Learning agents with prioritization and parameter noise in continuous state and action space","date":"2019-05-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":"/paper/multi-agent-dual-learning","slug":"multi-agent-dual-learning","title":"Multi-Agent Dual Learning","date":"2019-05-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"optimistic-acceleration-for-optimization","title":"Optimistic Acceleration for Optimization","date":"2019-05-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"padam-closing-the-generalization-gap-of","title":"Padam: Closing the Generalization Gap of Adaptive Gradient Methods in Training Deep Neural Networks","date":"2019-05-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"robustness-and-equivariance-of-neural","title":"Robustness and Equivariance of Neural Networks","date":"2019-05-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"total-style-transfer-with-a-single-feed","title":"Total Style Transfer with a Single Feed-Forward Network","date":"2019-05-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"towards-a-better-understanding-of-vector","title":"Towards a better understanding of Vector Quantized Autoencoders","date":"2019-05-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"transformer-xl-language-modeling-with-longer","title":"Transformer-XL: Language Modeling with Longer-Term Dependency","date":"2019-05-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"very-deep-self-attention-networks-for-end-to","title":"Very Deep Self-Attention Networks for End-to-End Speech Recognition","date":"2019-04-30","arxiv_id":"1904.13377","n_code_links":0,"syntology":null},{"paper":"/paper/unsupervised-data-augmentation-1","slug":"unsupervised-data-augmentation-1","title":"Unsupervised Data Augmentation for Consistency Training","date":"2019-04-29","arxiv_id":"1904.12848","n_code_links":20,"syntology":{"ran":30,"of":52,"n_ran_checked":22,"n_instrument":8,"unverified":22,"pointer_only":17,"phrase":"30 ran (of which 3 constructed an object rather than computing a result; 22 with no instrument failure: 0 honoured, 0 violated, 22 with no contract checked; 8 where Syntology's instrument failed) · 22 unverified","official":{"repos":["google-research/uda"],"state":"official (archive's flag): 10 ran","n_ran":10,"n_constructed":0,"n_ran_no_instrument_failure":10,"n_unverified":14,"ran_from_kinds":["listed","official"]}}},{"paper":null,"slug":"softmax-optimizations-for-intel-xeon","title":"Softmax Optimizations for Intel Xeon Processor-based Platforms","date":"2019-04-28","arxiv_id":"1904.12380","n_code_links":0,"syntology":null},{"paper":"/paper/transformers-with-convolutional-context-for","slug":"transformers-with-convolutional-context-for","title":"Transformers with convolutional context for ASR","date":"2019-04-26","arxiv_id":"1904.11660","n_code_links":4,"syntology":{"ran":8,"of":8,"n_ran_checked":8,"n_instrument":0,"unverified":0,"pointer_only":1,"phrase":"8 ran (of which 0 constructed an object rather than computing a result; 8 with no instrument failure: 0 honoured, 0 violated, 8 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","official":null}},{"paper":null,"slug":"low-memory-neural-network-training-a","title":"Low-Memory Neural Network Training: A Technical Report","date":"2019-04-24","arxiv_id":"1904.10631","n_code_links":0,"syntology":null},{"paper":"/paper/190410509","slug":"190410509","title":"Generating Long Sequences with Sparse Transformers","date":"2019-04-23","arxiv_id":"1904.10509","n_code_links":7,"syntology":{"ran":5,"of":6,"n_ran_checked":4,"n_instrument":1,"unverified":1,"pointer_only":0,"phrase":"5 ran (of which 4 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":{"repos":["openai/sparse_attention"],"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/190501969","slug":"190501969","title":"Poly-encoders: Transformer Architectures and Pre-training Strategies for Fast and Accurate Multi-sentence Scoring","date":"2019-04-22","arxiv_id":"1905.01969","n_code_links":7,"syntology":null},{"paper":"/paper/dynamic-past-and-future-for-neural-machine","slug":"dynamic-past-and-future-for-neural-machine","title":"Dynamic Past and Future for Neural Machine Translation","date":"2019-04-21","arxiv_id":"1904.09646","n_code_links":1,"syntology":null},{"paper":null,"slug":"model-compression-with-multi-task-knowledge","title":"Model Compression with Multi-Task Knowledge Distillation for Web-scale Question Answering System","date":"2019-04-21","arxiv_id":"1904.09636","n_code_links":0,"syntology":null},{"paper":"/paper/190409380","slug":"190409380","title":"Repurposing Entailment for Multi-Hop Question Answering Tasks","date":"2019-04-20","arxiv_id":"1904.09380","n_code_links":4,"syntology":null},{"paper":"/paper/190409408","slug":"190409408","title":"Language Models with Transformers","date":"2019-04-20","arxiv_id":"1904.09408","n_code_links":1,"syntology":{"ran":2,"of":2,"n_ran_checked":1,"n_instrument":1,"unverified":0,"pointer_only":2,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","official":{"repos":["cgraywang/gluon-nlp-1"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":"/paper/190409324","slug":"190409324","title":"Mask-Predict: Parallel Decoding of Conditional Masked Language Models","date":"2019-04-19","arxiv_id":"1904.09324","n_code_links":2,"syntology":{"ran":2,"of":2,"n_ran_checked":0,"n_instrument":2,"unverified":0,"pointer_only":2,"phrase":"2 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; 2 where Syntology's instrument failed) · 0 unverified","official":{"repos":["facebookresearch/Mask-Predict"],"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":"190501971","title":"An Evaluation of Transfer Learning for Classifying Sales Engagement Emails at Large Scale","date":"2019-04-19","arxiv_id":"1905.01971","n_code_links":0,"syntology":null},{"paper":"/paper/beto-bentz-becas-the-surprising-cross-lingual","slug":"beto-bentz-becas-the-surprising-cross-lingual","title":"Beto, Bentz, Becas: The Surprising Cross-Lingual Effectiveness of BERT","date":"2019-04-19","arxiv_id":"1904.09077","n_code_links":2,"syntology":null},{"paper":"/paper/ernie-enhanced-representation-through","slug":"ernie-enhanced-representation-through","title":"ERNIE: Enhanced Representation through Knowledge Integration","date":"2019-04-19","arxiv_id":"1904.09223","n_code_links":19,"syntology":{"ran":6,"of":7,"n_ran_checked":6,"n_instrument":0,"unverified":1,"pointer_only":0,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 0 honoured, 0 violated, 6 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","official":{"repos":["PaddlePaddle/PaddleNLP"],"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/on-the-convergence-of-adam-and-beyond-1","slug":"on-the-convergence-of-adam-and-beyond-1","title":"On the Convergence of Adam and Beyond","date":"2019-04-19","arxiv_id":"1904.09237","n_code_links":3,"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":null}},{"paper":null,"slug":"unifying-question-answering-and-text","title":"Unifying Question Answering, Text Classification, and Regression via Span Extraction","date":"2019-04-19","arxiv_id":"1904.09286","n_code_links":0,"syntology":null},{"paper":"/paper/190408900","slug":"190408900","title":"CornerNet-Lite: Efficient Keypoint Based Object Detection","date":"2019-04-18","arxiv_id":"1904.08900","n_code_links":6,"syntology":{"ran":21,"of":27,"n_ran_checked":21,"n_instrument":0,"unverified":6,"pointer_only":0,"phrase":"21 ran (of which 0 constructed an object rather than computing a result; 21 with no instrument failure: 1 honoured, 0 violated, 20 with no contract checked; 0 where Syntology's instrument failed) · 6 unverified","official":{"repos":["princeton-vl/CornerNet-Lite"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":4,"ran_from_kinds":["listed","official"]}}},{"paper":"/paper/real-time-style-transfer-with-strength","slug":"real-time-style-transfer-with-strength","title":"Real-Time Style Transfer With Strength Control","date":"2019-04-18","arxiv_id":"1904.08643","n_code_links":1,"syntology":null},{"paper":"/paper/centernet-object-detection-with-keypoint","slug":"centernet-object-detection-with-keypoint","title":"CenterNet: Keypoint Triplets for Object Detection","date":"2019-04-17","arxiv_id":"1904.08189","n_code_links":20,"syntology":{"ran":2,"of":11,"n_ran_checked":0,"n_instrument":2,"unverified":9,"pointer_only":4,"phrase":"2 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; 2 where Syntology's instrument failed) · 9 unverified","official":{"repos":["Duankaiwen/CenterNet"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":3,"ran_from_kinds":["listed"]}}},{"paper":"/paper/docbert-bert-for-document-classification","slug":"docbert-bert-for-document-classification","title":"DocBERT: BERT for Document Classification","date":"2019-04-17","arxiv_id":"1904.08398","n_code_links":3,"syntology":{"ran":11,"of":14,"n_ran_checked":11,"n_instrument":0,"unverified":3,"pointer_only":0,"phrase":"11 ran (of which 0 constructed an object rather than computing a result; 11 with no instrument failure: 0 honoured, 0 violated, 11 with no contract checked; 0 where Syntology's instrument failed) · 3 unverified","official":{"repos":["castorini/hedwig"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":1,"ran_from_kinds":["listed","official"]}}},{"paper":null,"slug":"an-empirical-evaluation-of-text","title":"An Empirical Evaluation of Text Representation Schemes on Multilingual Social Web to Filter the Textual Aggression","date":"2019-04-16","arxiv_id":"1904.08770","n_code_links":0,"syntology":null},{"paper":null,"slug":"understanding-the-behaviors-of-bert-in","title":"Understanding the Behaviors of BERT in Ranking","date":"2019-04-16","arxiv_id":"1904.07531","n_code_links":0,"syntology":null},{"paper":"/paper/190407094","slug":"190407094","title":"CEDR: Contextualized Embeddings for Document Ranking","date":"2019-04-15","arxiv_id":"1904.07094","n_code_links":7,"syntology":{"ran":5,"of":7,"n_ran_checked":5,"n_instrument":0,"unverified":2,"pointer_only":2,"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) · 2 unverified","official":{"repos":["Georgetown-IR-Lab/cedr","Georgetown-IR-Lab/contextualized-reps-for-ranking"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":2,"ran_from_kinds":["listed","official"]}}},{"paper":"/paper/improved-precision-and-recall-metric-for","slug":"improved-precision-and-recall-metric-for","title":"Improved Precision and Recall Metric for Assessing Generative Models","date":"2019-04-15","arxiv_id":"1904.06991","n_code_links":10,"syntology":{"ran":30,"of":42,"n_ran_checked":20,"n_instrument":10,"unverified":12,"pointer_only":25,"phrase":"30 ran (of which 12 constructed an object rather than computing a result; 20 with no instrument failure: 5 honoured, 1 violated, 14 with no contract checked; 10 where Syntology's instrument failed) · 12 unverified","official":{"repos":["kynkaat/improved-precision-and-recall-metric"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":1,"n_ran_no_instrument_failure":1,"n_unverified":3,"ran_from_kinds":["listed","official"]}}},{"paper":null,"slug":"improving-human-text-comprehension-through","title":"Improving Human Text Comprehension through Semi-Markov CRF-based Neural Section Title Generation","date":"2019-04-15","arxiv_id":"1904.07142","n_code_links":0,"syntology":null},{"paper":"/paper/personalized-context-aware-re-ranking-for-e","slug":"personalized-context-aware-re-ranking-for-e","title":"Personalized Re-ranking for Recommendation","date":"2019-04-15","arxiv_id":"1904.06813","n_code_links":1,"syntology":null},{"paper":null,"slug":"190412604","title":"Pre-training of Context-aware Item Representation for Next Basket Recommendation","date":"2019-04-14","arxiv_id":"1904.12604","n_code_links":0,"syntology":null},{"paper":"/paper/data-augmentation-for-bert-fine-tuning-in","slug":"data-augmentation-for-bert-fine-tuning-in","title":"Data Augmentation for BERT Fine-Tuning in Open-Domain Question Answering","date":"2019-04-14","arxiv_id":"1904.06652","n_code_links":0,"syntology":null},{"paper":"/paper/rare-words-a-major-problem-for-contextualized","slug":"rare-words-a-major-problem-for-contextualized","title":"Rare Words: A Major Problem for Contextualized Embeddings And How to Fix it by Attentive Mimicking","date":"2019-04-14","arxiv_id":"1904.06707","n_code_links":2,"syntology":{"ran":2,"of":2,"n_ran_checked":1,"n_instrument":1,"unverified":0,"pointer_only":2,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","official":{"repos":["timoschick/am-for-bert","timoschick/one-token-approximation"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":"/paper/an-empirical-study-of-spatial-attention","slug":"an-empirical-study-of-spatial-attention","title":"An Empirical Study of Spatial Attention Mechanisms in Deep Networks","date":"2019-04-11","arxiv_id":"1904.05873","n_code_links":1,"syntology":null},{"paper":"/paper/190501962","slug":"190501962","title":"Harvey Mudd College at SemEval-2019 Task 4: The Clint Buchanan Hyperpartisan News Detector","date":"2019-04-10","arxiv_id":"1905.01962","n_code_links":1,"syntology":null},{"paper":null,"slug":"nlprsrpol-at-semeval-2019-task-6-and-task-5","title":"NLPR@SRPOL at SemEval-2019 Task 6 and Task 5: Linguistically enhanced deep learning offensive sentence classifier","date":"2019-04-10","arxiv_id":"1904.05152","n_code_links":0,"syntology":null},{"paper":"/paper/simple-bert-models-for-relation-extraction","slug":"simple-bert-models-for-relation-extraction","title":"Simple BERT Models for Relation Extraction and Semantic Role Labeling","date":"2019-04-10","arxiv_id":"1904.05255","n_code_links":3,"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":["Impavidity/relogic"],"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/a-unified-model-for-joint-chinese-word","slug":"a-unified-model-for-joint-chinese-word","title":"A Graph-based Model for Joint Chinese Word Segmentation and Dependency Parsing","date":"2019-04-09","arxiv_id":"1904.04697","n_code_links":1,"syntology":null},{"paper":"/paper/jointly-measuring-diversity-and-quality-in","slug":"jointly-measuring-diversity-and-quality-in","title":"Jointly Measuring Diversity and Quality in Text Generation Models","date":"2019-04-08","arxiv_id":"1904.03971","n_code_links":3,"syntology":{"ran":3,"of":6,"n_ran_checked":2,"n_instrument":1,"unverified":3,"pointer_only":1,"phrase":"3 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; 1 where Syntology's instrument failed) · 3 unverified","official":{"repos":["IAmS4n/TextGenerationEvaluationMetrics"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed","official"]}}},{"paper":null,"slug":"identity-preserving-face-recovery-from-1","title":"Identity-preserving Face Recovery from Stylized Portraits","date":"2019-04-07","arxiv_id":"1904.04241","n_code_links":0,"syntology":null},{"paper":null,"slug":"on-the-convergence-proof-of-amsgrad-and-a-new","title":"On the Convergence Proof of AMSGrad and a New Version","date":"2019-04-07","arxiv_id":"1904.03590","n_code_links":0,"syntology":null},{"paper":"/paper/evaluating-coherence-in-dialogue-systems","slug":"evaluating-coherence-in-dialogue-systems","title":"Evaluating Coherence in Dialogue Systems using Entailment","date":"2019-04-06","arxiv_id":"1904.03371","n_code_links":1,"syntology":{"ran":2,"of":2,"n_ran_checked":1,"n_instrument":1,"unverified":0,"pointer_only":1,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","official":{"repos":["nouhadziri/DialogEntailment"],"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","unlocated"]}}},{"paper":"/paper/publicly-available-clinical-bert-embeddings","slug":"publicly-available-clinical-bert-embeddings","title":"Publicly Available Clinical BERT Embeddings","date":"2019-04-06","arxiv_id":"1904.03323","n_code_links":3,"syntology":{"ran":2,"of":2,"n_ran_checked":1,"n_instrument":1,"unverified":0,"pointer_only":2,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","official":{"repos":["EmilyAlsentzer/clinicalBERT"],"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","unlocated"]}}},{"paper":null,"slug":"thisiscompetition-at-semeval-2019-task-9-bert","title":"ThisIsCompetition at SemEval-2019 Task 9: BERT is unstable for out-of-domain samples","date":"2019-04-06","arxiv_id":"1904.03339","n_code_links":0,"syntology":null},{"paper":"/paper/token-level-ensemble-distillation-for","slug":"token-level-ensemble-distillation-for","title":"Token-Level Ensemble Distillation for Grapheme-to-Phoneme Conversion","date":"2019-04-06","arxiv_id":"1904.03446","n_code_links":0,"syntology":null},{"paper":"/paper/um-iuling-at-semeval-2019-task-6-identifying","slug":"um-iuling-at-semeval-2019-task-6-identifying","title":"UM-IU@LING at SemEval-2019 Task 6: Identifying Offensive Tweets Using BERT and SVMs","date":"2019-04-06","arxiv_id":"1904.03450","n_code_links":1,"syntology":null},{"paper":null,"slug":"modeling-recurrence-for-transformer","title":"Modeling Recurrence for Transformer","date":"2019-04-05","arxiv_id":"1904.03092","n_code_links":0,"syntology":null},{"paper":"/paper/unsupervised-domain-adaptation-of","slug":"unsupervised-domain-adaptation-of","title":"Unsupervised Domain Adaptation of Contextualized Embeddings for Sequence Labeling","date":"2019-04-04","arxiv_id":"1904.02817","n_code_links":1,"syntology":null},{"paper":null,"slug":"visualizing-attention-in-transformer-based","title":"Visualizing Attention in Transformer-Based Language Representation Models","date":"2019-04-04","arxiv_id":"1904.02679","n_code_links":0,"syntology":null},{"paper":"/paper/75-languages-1-model-parsing-universal","slug":"75-languages-1-model-parsing-universal","title":"75 Languages, 1 Model: Parsing Universal Dependencies Universally","date":"2019-04-03","arxiv_id":"1904.02099","n_code_links":3,"syntology":{"ran":9,"of":12,"n_ran_checked":9,"n_instrument":0,"unverified":3,"pointer_only":0,"phrase":"9 ran (of which 0 constructed an object rather than computing a result; 9 with no instrument failure: 0 honoured, 0 violated, 9 with no contract checked; 0 where Syntology's instrument failed) · 3 unverified","official":{"repos":["hyperparticle/udify"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":0,"n_ran_no_instrument_failure":9,"n_unverified":3,"ran_from_kinds":["official"]}}},{"paper":"/paper/a-comprehensive-overhaul-of-feature","slug":"a-comprehensive-overhaul-of-feature","title":"A Comprehensive Overhaul of Feature Distillation","date":"2019-04-03","arxiv_id":"1904.01866","n_code_links":2,"syntology":{"ran":4,"of":6,"n_ran_checked":3,"n_instrument":1,"unverified":2,"pointer_only":1,"phrase":"4 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; 1 where Syntology's instrument failed) · 2 unverified","official":{"repos":["clovaai/overhaul-distillation"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":2,"ran_from_kinds":["official"]}}},{"paper":"/paper/bert-post-training-for-review-reading","slug":"bert-post-training-for-review-reading","title":"BERT Post-Training for Review Reading Comprehension and Aspect-based Sentiment Analysis","date":"2019-04-03","arxiv_id":"1904.02232","n_code_links":1,"syntology":{"ran":4,"of":7,"n_ran_checked":3,"n_instrument":1,"unverified":3,"pointer_only":1,"phrase":"4 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; 1 where Syntology's instrument failed) · 3 unverified","official":null}},{"paper":"/paper/probing-biomedical-embeddings-from-language","slug":"probing-biomedical-embeddings-from-language","title":"Probing Biomedical Embeddings from Language Models","date":"2019-04-03","arxiv_id":"1904.02181","n_code_links":1,"syntology":null},{"paper":"/paper/videobert-a-joint-model-for-video-and","slug":"videobert-a-joint-model-for-video-and","title":"VideoBERT: A Joint Model for Video and Language Representation Learning","date":"2019-04-03","arxiv_id":"1904.01766","n_code_links":3,"syntology":{"ran":1,"of":1,"n_ran_checked":1,"n_instrument":0,"unverified":0,"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) · 0 unverified","official":null}},{"paper":null,"slug":"personalized-cancer-chemotherapy-schedule-a","title":"Personalized Cancer Chemotherapy Schedule: a numerical comparison of performance and robustness in model-based and model-free scheduling methodologies","date":"2019-04-02","arxiv_id":"1904.01200","n_code_links":0,"syntology":null},{"paper":"/paper/reducing-bert-pre-training-time-from-3-days","slug":"reducing-bert-pre-training-time-from-3-days","title":"Large Batch Optimization for Deep Learning: Training BERT in 76 minutes","date":"2019-04-01","arxiv_id":"1904.00962","n_code_links":32,"syntology":{"ran":6,"of":11,"n_ran_checked":6,"n_instrument":0,"unverified":5,"pointer_only":9,"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) · 5 unverified","official":{"repos":["tensorflow/addons"],"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/regional-homogeneity-towards-learning","slug":"regional-homogeneity-towards-learning","title":"Regional Homogeneity: Towards Learning Transferable Universal Adversarial Perturbations Against Defenses","date":"2019-04-01","arxiv_id":"1904.00979","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":["LiYingwei/Regional-Homogeneity"],"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":"/paper/ana-at-semeval-2019-task-3-contextual-emotion","slug":"ana-at-semeval-2019-task-3-contextual-emotion","title":"ANA at SemEval-2019 Task 3: Contextual Emotion detection in Conversations through hierarchical LSTMs and BERT","date":"2019-03-30","arxiv_id":"1904.00132","n_code_links":1,"syntology":null},{"paper":null,"slug":"making-neural-machine-reading-comprehension","title":"Making Neural Machine Reading Comprehension Faster","date":"2019-03-29","arxiv_id":"1904.00796","n_code_links":0,"syntology":null},{"paper":"/paper/towards-knowledge-based-personalized-product","slug":"towards-knowledge-based-personalized-product","title":"Towards Knowledge-Based Personalized Product Description Generation in E-commerce","date":"2019-03-29","arxiv_id":"1903.12457","n_code_links":4,"syntology":null},{"paper":"/paper/distilling-task-specific-knowledge-from-bert","slug":"distilling-task-specific-knowledge-from-bert","title":"Distilling Task-Specific Knowledge from BERT into Simple Neural Networks","date":"2019-03-28","arxiv_id":"1903.12136","n_code_links":4,"syntology":{"ran":9,"of":9,"n_ran_checked":3,"n_instrument":6,"unverified":0,"pointer_only":0,"phrase":"9 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 3 honoured, 0 violated, 0 with no contract checked; 6 where Syntology's instrument failed) · 0 unverified","official":null}},{"paper":"/paper/train-sort-explain-learning-to-diagnose","slug":"train-sort-explain-learning-to-diagnose","title":"Train, Sort, Explain: Learning to Diagnose Translation Models","date":"2019-03-28","arxiv_id":"1903.12017","n_code_links":1,"syntology":null},{"paper":null,"slug":"190410045","title":"Automatic Spelling Correction with Transformer for CTC-based End-to-End Speech Recognition","date":"2019-03-27","arxiv_id":"1904.10045","n_code_links":0,"syntology":null},{"paper":"/paper/scibert-pretrained-contextualized-embeddings","slug":"scibert-pretrained-contextualized-embeddings","title":"SciBERT: A Pretrained Language Model for Scientific Text","date":"2019-03-26","arxiv_id":"1903.10676","n_code_links":6,"syntology":null},{"paper":"/paper/simple-applications-of-bert-for-ad-hoc","slug":"simple-applications-of-bert-for-ad-hoc","title":"Simple Applications of BERT for Ad Hoc Document Retrieval","date":"2019-03-26","arxiv_id":"1903.10972","n_code_links":2,"syntology":null},{"paper":"/paper/fine-tune-bert-for-extractive-summarization","slug":"fine-tune-bert-for-extractive-summarization","title":"Fine-tune BERT for Extractive Summarization","date":"2019-03-25","arxiv_id":"1903.10318","n_code_links":12,"syntology":{"ran":5,"of":5,"n_ran_checked":4,"n_instrument":1,"unverified":0,"pointer_only":1,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 1 violated, 3 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","official":{"repos":["nlpyang/BertSum"],"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":"knowledge-driven-encode-retrieve-paraphrase","title":"Knowledge-driven Encode, Retrieve, Paraphrase for Medical Image Report Generation","date":"2019-03-25","arxiv_id":"1903.10122","n_code_links":0,"syntology":null},{"paper":"/paper/on-measuring-social-biases-in-sentence","slug":"on-measuring-social-biases-in-sentence","title":"On Measuring Social Biases in Sentence Encoders","date":"2019-03-25","arxiv_id":"1903.10561","n_code_links":1,"syntology":null},{"paper":"/paper/recognizing-arrow-of-time-in-the-short","slug":"recognizing-arrow-of-time-in-the-short","title":"Recognizing Arrow Of Time In The Short Stories","date":"2019-03-25","arxiv_id":"1903.10548","n_code_links":1,"syntology":null},{"paper":"/paper/utilizing-bert-for-aspect-based-sentiment","slug":"utilizing-bert-for-aspect-based-sentiment","title":"Utilizing BERT for Aspect-Based Sentiment Analysis via Constructing Auxiliary Sentence","date":"2019-03-22","arxiv_id":"1903.09588","n_code_links":8,"syntology":{"ran":19,"of":27,"n_ran_checked":15,"n_instrument":4,"unverified":8,"pointer_only":9,"phrase":"19 ran (of which 0 constructed an object rather than computing a result; 15 with no instrument failure: 3 honoured, 0 violated, 12 with no contract checked; 4 where Syntology's instrument failed) · 8 unverified","official":{"repos":["HSLCY/ABSA-BERT-pair"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":2,"ran_from_kinds":["listed","official"]}}},{"paper":null,"slug":"learning-multi-level-information-for-dialogue","title":"Learning Multi-Level Information for Dialogue Response Selection by Highway Recurrent Transformer","date":"2019-03-21","arxiv_id":"1903.08953","n_code_links":0,"syntology":null},{"paper":null,"slug":"linguistic-knowledge-and-transferability-of","title":"Linguistic Knowledge and Transferability of Contextual Representations","date":"2019-03-21","arxiv_id":"1903.08855","n_code_links":0,"syntology":null},{"paper":"/paper/selective-attention-for-context-aware-neural","slug":"selective-attention-for-context-aware-neural","title":"Selective Attention for Context-aware Neural Machine Translation","date":"2019-03-21","arxiv_id":"1903.08788","n_code_links":1,"syntology":null},{"paper":"/paper/cloze-driven-pretraining-of-self-attention","slug":"cloze-driven-pretraining-of-self-attention","title":"Cloze-driven Pretraining of Self-attention Networks","date":"2019-03-19","arxiv_id":"1903.07785","n_code_links":0,"syntology":null},{"paper":"/paper/neutron-an-implementation-of-the-transformer","slug":"neutron-an-implementation-of-the-transformer","title":"Neutron: An Implementation of the Transformer Translation Model and its Variants","date":"2019-03-18","arxiv_id":"1903.07402","n_code_links":2,"syntology":null},{"paper":null,"slug":"stnreid-deep-convolutional-networks-with","title":"STNReID : Deep Convolutional Networks with Pairwise Spatial Transformer Networks for Partial Person Re-identification","date":"2019-03-17","arxiv_id":"1903.07072","n_code_links":0,"syntology":null},{"paper":null,"slug":"a-deep-patent-landscaping-model-using","title":"Deep Patent Landscaping Model Using Transformer and Graph Embedding","date":"2019-03-14","arxiv_id":"1903.05823","n_code_links":0,"syntology":null}],"record_sha256":"0aff08762db81339dd36bc49c00f62d25ae52b2ad54877dd4d0d22628a7aa077","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}