{"about":{"non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","site":"https://codewithpapers.app","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page","syntology":{"site":"https://syntology.ai","developers":"https://syntology.ai/developers","mcp":{"server":"https://syntology.ai/mcp","transport":"streamable-http","server_card":"https://syntology.ai/.well-known/mcp/server-card.json","auth":{"type":"trial token, no account","trial_token":"https://syntology.ai/api/oauth/trial/token","method":"POST","docs":"https://syntology.ai/developers"}},"have":"https://syntology.ai/api/graph/have?x=<method, arXiv id or title> (free, answers coverage only)","paper_base":"https://syntology.ai/paper/","atlas_base":"https://app.syntology.ai/?focus="},"machine_readable":[{"url":"https://codewithpapers.app/llms.txt","what":"the machine catalog: every machine-readable file, counted"},{"url":"https://codewithpapers.app/index/manifest.json","what":"paper-to-code index by arXiv id, with Syntology's counts"},{"url":"https://codewithpapers.app/search/manifest.json","what":"site search index (titles, authors) and its files"},{"url":"https://codewithpapers.app/download","what":"bulk files: Syntology's layer, described there"},{"url":"https://codewithpapers.app/build_manifest.json","what":"the build record: inputs, counts, exclusions, probes"}]},"url":"/method/dense-connections/papers/281","list_of":"/method/dense-connections","method":"Dense Connections","archive":{"snapshot":"2025-07-28"},"syntology_read_at":"2026-09-28T10:30:06+00:00","order":"archive","order_definition":"date (newest first), then slug","page":281,"pages_in_order":293,"rows_per_page":100,"rows":[28001,28100],"of":29230,"counts":{"archive_papers_tagged":29230,"with_a_code_link":12972,"where_syntology_ran_a_sample":3929,"not_listed_spam_title":0,"listed":29230,"listed_where_code_ran":3929,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":3303,"every_run_a_failure_of_syntologys_instrument":626,"listed_with_a_run_with_no_instrument_failure":3303,"listed_every_run_a_failure_of_syntologys_instrument":626,"filter":{"states":["a run with no instrument failure","any run, instrument failures included"],"default":"a run with no instrument failure","note":"on the 'only where code ran' pages the default hides, in the browser, the rows where every run was a failure of Syntology's instrument; the second state shows them again. Rows are hidden, never re-ordered; these twins list every row"}},"definition":"distinct papers the archive tags; 'where Syntology ran a sample' counts papers with at least one harvested sample that ran, which is not a correctness claim"},"first_page":"/method/dense-connections","prev":"/method/dense-connections/papers/280","next":"/method/dense-connections/papers/282","papers":[{"paper":"/paper/neural-legal-judgment-prediction-in-english","slug":"neural-legal-judgment-prediction-in-english","title":"Neural Legal Judgment Prediction in English","date":"2019-06-05","arxiv_id":"1906.02059","n_code_links":0,"syntology":null},{"paper":null,"slug":"probabilistic-hypergraph-grammars-for","title":"Probabilistic hypergraph grammars for efficient molecular optimization","date":"2019-06-05","arxiv_id":"1906.01845","n_code_links":0,"syntology":null},{"paper":null,"slug":"single-camera-basketball-tracker-through-pose","title":"Single-Camera Basketball Tracker through Pose and Semantic Feature Fusion","date":"2019-06-05","arxiv_id":"1906.02042","n_code_links":0,"syntology":null},{"paper":null,"slug":"visual-confusion-label-tree-for-image","title":"Visual Confusion Label Tree For Image Classification","date":"2019-06-05","arxiv_id":"1906.02012","n_code_links":0,"syntology":null},{"paper":null,"slug":"cross-domain-cascaded-deep-feature","title":"Cross-Domain Cascaded Deep Feature Translation","date":"2019-06-04","arxiv_id":"1906.01526","n_code_links":0,"syntology":null},{"paper":null,"slug":"exploiting-sentential-context-for-neural","title":"Exploiting Sentential Context for Neural Machine Translation","date":"2019-06-04","arxiv_id":"1906.01268","n_code_links":0,"syntology":null},{"paper":"/paper/how-multilingual-is-multilingual-bert","slug":"how-multilingual-is-multilingual-bert","title":"How multilingual is Multilingual BERT?","date":"2019-06-04","arxiv_id":"1906.01502","n_code_links":3,"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: 0 honoured, 0 violated, 1 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","official":null}},{"paper":null,"slug":"improving-long-distance-slot-carryover-in","title":"Improving Long Distance Slot Carryover in Spoken Dialogue Systems","date":"2019-06-04","arxiv_id":"1906.01149","n_code_links":0,"syntology":null},{"paper":null,"slug":"lattice-based-transformer-encoder-for-neural","title":"Lattice-Based Transformer Encoder for Neural Machine Translation","date":"2019-06-04","arxiv_id":"1906.01282","n_code_links":0,"syntology":null},{"paper":"/paper/open-sesame-getting-inside-berts-linguistic","slug":"open-sesame-getting-inside-berts-linguistic","title":"Open Sesame: Getting Inside BERT's Linguistic Knowledge","date":"2019-06-04","arxiv_id":"1906.01698","n_code_links":1,"syntology":{"ran":13,"of":18,"n_ran_checked":10,"n_instrument":3,"unverified":5,"pointer_only":7,"phrase":"13 ran (of which 0 constructed an object rather than computing a result; 10 with no instrument failure: 4 honoured, 0 violated, 6 with no contract checked; 3 where Syntology's instrument failed) · 5 unverified","official":{"repos":["yongjie-lin/bert-opensesame"],"state":"official (archive's flag): 13 ran","n_ran":13,"n_constructed":0,"n_ran_no_instrument_failure":10,"n_unverified":5,"ran_from_kinds":["official"]}}},{"paper":"/paper/pca-driven-hybrid-network-design-for-enabling","slug":"pca-driven-hybrid-network-design-for-enabling","title":"Constructing Energy-efficient Mixed-precision Neural Networks through Principal Component Analysis for Edge Intelligence","date":"2019-06-04","arxiv_id":"1906.01493","n_code_links":1,"syntology":null},{"paper":"/paper/rthn-a-rnn-transformer-hierarchical-network","slug":"rthn-a-rnn-transformer-hierarchical-network","title":"RTHN: A RNN-Transformer Hierarchical Network for Emotion Cause Extraction","date":"2019-06-04","arxiv_id":"1906.01236","n_code_links":3,"syntology":{"ran":0,"of":4,"n_ran_checked":0,"n_instrument":0,"unverified":4,"pointer_only":4,"phrase":"0 ran · 4 unverified","official":{"repos":["NUSTM/RTHN"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":2,"ran_from_kinds":[]}}},{"paper":"/paper/sequence-tagging-with-contextual-and-non","slug":"sequence-tagging-with-contextual-and-non","title":"Sequence Tagging with Contextual and Non-Contextual Subword Representations: A Multilingual Evaluation","date":"2019-06-04","arxiv_id":"1906.01569","n_code_links":1,"syntology":null},{"paper":"/paper/the-unreasonable-effectiveness-of-transformer","slug":"the-unreasonable-effectiveness-of-transformer","title":"The Unreasonable Effectiveness of Transformer Language Models in Grammatical Error Correction","date":"2019-06-04","arxiv_id":"1906.01733","n_code_links":2,"syntology":null},{"paper":"/paper/transfer-learning-with-intelligent-training","slug":"transfer-learning-with-intelligent-training","title":"Transfer Learning with intelligent training data selection for prediction of Alzheimer's Disease","date":"2019-06-04","arxiv_id":"1906.01160","n_code_links":2,"syntology":null},{"paper":null,"slug":"190600532","title":"Efficient 8-Bit Quantization of Transformer Neural Machine Language Translation Model","date":"2019-06-03","arxiv_id":"1906.00532","n_code_links":0,"syntology":null},{"paper":"/paper/190600668","slug":"190600668","title":"A Closed-form Solution to Universal Style Transfer","date":"2019-06-03","arxiv_id":"1906.00668","n_code_links":3,"syntology":null},{"paper":"/paper/190600675","slug":"190600675","title":"Deeply-supervised Knowledge Synergy","date":"2019-06-03","arxiv_id":"1906.00675","n_code_links":1,"syntology":null},{"paper":null,"slug":"analysis-and-improvement-of-adversarial","title":"Analysis and Improvement of Adversarial Training in DQN Agents With Adversarially-Guided Exploration (AGE)","date":"2019-06-03","arxiv_id":"1906.01119","n_code_links":0,"syntology":null},{"paper":null,"slug":"nodedrop-a-condition-for-reducing-network","title":"NodeDrop: A Condition for Reducing Network Size without Effect on Output","date":"2019-06-03","arxiv_id":"1906.01026","n_code_links":0,"syntology":null},{"paper":null,"slug":"rl-based-method-for-benchmarking-the","title":"RL-Based Method for Benchmarking the Adversarial Resilience and Robustness of Deep Reinforcement Learning Policies","date":"2019-06-03","arxiv_id":"1906.01110","n_code_links":0,"syntology":null},{"paper":null,"slug":"sequential-triggers-for-watermarking-of-deep","title":"Sequential Triggers for Watermarking of Deep Reinforcement Learning Policies","date":"2019-06-03","arxiv_id":"1906.01126","n_code_links":0,"syntology":null},{"paper":"/paper/190600346","slug":"190600346","title":"Pre-training of Graph Augmented Transformers for Medication Recommendation","date":"2019-06-02","arxiv_id":"1906.00346","n_code_links":1,"syntology":{"ran":6,"of":9,"n_ran_checked":5,"n_instrument":1,"unverified":3,"pointer_only":0,"phrase":"6 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; 1 where Syntology's instrument failed) · 3 unverified","official":{"repos":["jshang123/G-Bert"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":3,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"190600399","title":"Multi-Objective Pruning for CNNs Using Genetic Algorithm","date":"2019-06-02","arxiv_id":"1906.00399","n_code_links":0,"syntology":null},{"paper":"/paper/190600446","slug":"190600446","title":"Generating Diverse High-Fidelity Images with VQ-VAE-2","date":"2019-06-02","arxiv_id":"1906.00446","n_code_links":15,"syntology":{"ran":5,"of":9,"n_ran_checked":5,"n_instrument":0,"unverified":4,"pointer_only":4,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 1 honoured, 1 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 4 unverified","official":{"repos":["deepmind/sonnet"],"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/190600138","slug":"190600138","title":"Efficient Adaptation of Pretrained Transformers for Abstractive Summarization","date":"2019-06-01","arxiv_id":"1906.00138","n_code_links":2,"syntology":null},{"paper":"/paper/190600214","slug":"190600214","title":"Harnessing Reinforcement Learning for Neural Motion Planning","date":"2019-06-01","arxiv_id":"1906.00214","n_code_links":1,"syntology":null},{"paper":null,"slug":"190600238","title":"Adversarial Generation and Encoding of Nested Texts","date":"2019-06-01","arxiv_id":"1906.00238","n_code_links":0,"syntology":null},{"paper":"/paper/190600295","slug":"190600295","title":"Multimodal Transformer for Unaligned Multimodal Language Sequences","date":"2019-06-01","arxiv_id":"1906.00295","n_code_links":4,"syntology":{"ran":8,"of":17,"n_ran_checked":5,"n_instrument":3,"unverified":9,"pointer_only":10,"phrase":"8 ran (of which 3 constructed an object rather than computing a result; 5 with no instrument failure: 3 honoured, 1 violated, 1 with no contract checked; 3 where Syntology's instrument failed) · 9 unverified","official":{"repos":["yaohungt/Multimodal-Transformer"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":5,"ran_from_kinds":["listed","official","unlocated"]}}},{"paper":null,"slug":"a-bert-based-universal-model-for-both-within","title":"A BERT-based Universal Model for Both Within- and Cross-sentence Clinical Temporal Relation Extraction","date":"2019-06-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":"/paper/a-structural-probe-for-finding-syntax-in-word","slug":"a-structural-probe-for-finding-syntax-in-word","title":"A Structural Probe for Finding Syntax in Word Representations","date":"2019-06-01","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":null,"slug":"autohome-orca-at-semeval-2019-task-8","title":"AUTOHOME-ORCA at SemEval-2019 Task 8: Application of BERT for Fact-Checking in Community Forums","date":"2019-06-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"blcu_nlp-at-semeval-2019-task-8-a-contextual","title":"BLCU\\_NLP at SemEval-2019 Task 8: A Contextual Knowledge-enhanced GPT Model for Fact Checking","date":"2019-06-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"bnu-hkbu-uic-nlp-team-2-at-semeval-2019-task","title":"BNU-HKBU UIC NLP Team 2 at SemEval-2019 Task 6: Detecting Offensive Language Using BERT model","date":"2019-06-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":"/paper/codah-an-adversarially-authored-question","slug":"codah-an-adversarially-authored-question","title":"CODAH: An Adversarially-Authored Question Answering Dataset for Common Sense","date":"2019-06-01","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":null,"slug":"convai-at-semeval-2019-task-6-offensive","title":"ConvAI at SemEval-2019 Task 6: Offensive Language Identification and Categorization with Perspective and BERT","date":"2019-06-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"domlin-at-semeval-2019-task-8-automated-fact","title":"DOMLIN at SemEval-2019 Task 8: Automated Fact Checking exploiting Ratings in Community Question Answering Forums","date":"2019-06-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"ds-at-semeval-2019-task-9-from-suggestion","title":"DS at SemEval-2019 Task 9: From Suggestion Mining with neural networks to adversarial cross-domain classification","date":"2019-06-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":"/paper/embeddia-at-semeval-2019-task-6-detecting","slug":"embeddia-at-semeval-2019-task-6-detecting","title":"Embeddia at SemEval-2019 Task 6: Detecting Hate with Neural Network and Transfer Learning Approaches","date":"2019-06-01","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":null,"slug":"figure-eight-at-semeval-2019-task-3-ensemble","title":"Figure Eight at SemEval-2019 Task 3: Ensemble of Transfer Learning Methods for Contextual Emotion Detection","date":"2019-06-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":"/paper/hltsuda-at-semeval-2019-task-1-ucca-graph-1","slug":"hltsuda-at-semeval-2019-task-1-ucca-graph-1","title":"HLT@SUDA at SemEval-2019 Task 1: UCCA Graph Parsing as Constituent Tree Parsing","date":"2019-06-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"how-well-do-embedding-models-capture-non","title":"How Well Do Embedding Models Capture Non-compositionality? A View from Multiword Expressions","date":"2019-06-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"improving-cuneiform-language-identification","title":"Improving Cuneiform Language Identification with BERT","date":"2019-06-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"kdehateval-at-semeval-2019-task-5-a-neural","title":"KDEHatEval at SemEval-2019 Task 5: A Neural Network Model for Detecting Hate Speech in Twitter","date":"2019-06-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":"/paper/laf-net-locally-adaptive-fusion-networks-for","slug":"laf-net-locally-adaptive-fusion-networks-for","title":"LAF-Net: Locally Adaptive Fusion Networks for Stereo Confidence Estimation","date":"2019-06-01","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":"/paper/learning-roi-transformer-for-oriented-object","slug":"learning-roi-transformer-for-oriented-object","title":"Learning RoI Transformer for Oriented Object Detection in Aerial Images","date":"2019-06-01","arxiv_id":null,"n_code_links":2,"syntology":null},{"paper":null,"slug":"local-detection-of-stereo-occlusion","title":"Local Detection of Stereo Occlusion Boundaries","date":"2019-06-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"ltl-ude-at-semeval-2019-task-6-bert-and-two","title":"LTL-UDE at SemEval-2019 Task 6: BERT and Two-Vote Classification for Categorizing Offensiveness","date":"2019-06-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"mitre-at-semeval-2019-task-5-transfer","title":"MITRE at SemEval-2019 Task 5: Transfer Learning for Multilingual Hate Speech Detection","date":"2019-06-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"multi-task-self-supervised-object-detection","title":"Multi-Task Self-Supervised Object Detection via Recycling of Bounding Box Annotations","date":"2019-06-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"neural-machine-translation-between-myanmar","title":"Neural Machine Translation between Myanmar (Burmese) and Rakhine (Arakanese)","date":"2019-06-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"nuli-at-semeval-2019-task-6-transfer-learning","title":"NULI at SemEval-2019 Task 6: Transfer Learning for Offensive Language Detection using Bidirectional Transformers","date":"2019-06-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"opinion-mining-with-deep-contextualized","title":"Opinion Mining with Deep Contextualized Embeddings","date":"2019-06-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"predicting-global-variations-in-outdoor-pm25","title":"Predicting Global Variations in Outdoor PM2.5 Concentrations using Satellite Images and Deep Convolutional Neural Networks","date":"2019-06-01","arxiv_id":"1906.03975","n_code_links":0,"syntology":null},{"paper":null,"slug":"stance-classification-outcome-prediction-and","title":"Stance Classification, Outcome Prediction, and Impact Assessment: NLP Tasks for Studying Group Decision-Making","date":"2019-06-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"team-howard-beale-at-semeval-2019-task-4","title":"Team Howard Beale at SemEval-2019 Task 4: Hyperpartisan News Detection with BERT","date":"2019-06-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"team-jack-ryder-at-semeval-2019-task-4-using","title":"Team Jack Ryder at SemEval-2019 Task 4: Using BERT Representations for Detecting Hyperpartisan News","date":"2019-06-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":"/paper/team-yeon-zi-at-semeval-2019-task-4","slug":"team-yeon-zi-at-semeval-2019-task-4","title":"Team yeon-zi at SemEval-2019 Task 4: Hyperpartisan News Detection by De-noising Weakly-labeled Data","date":"2019-06-01","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":null,"slug":"the-sally-smedley-hyperpartisan-news-detector","title":"The Sally Smedley Hyperpartisan News Detector at SemEval-2019 Task 4","date":"2019-06-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"using-contextual-representations-for-suicide","title":"Using Contextual Representations for Suicide Risk Assessment from Internet Forums","date":"2019-06-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"zqm-at-semeval-2019-task9-a-single-layer-cnn","title":"ZQM at SemEval-2019 Task9: A Single Layer CNN Based on Pre-trained Model for Suggestion Mining","date":"2019-06-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":"/paper/attention-is-not-all-you-need-for-commonsense","slug":"attention-is-not-all-you-need-for-commonsense","title":"Attention Is (not) All You Need for Commonsense Reasoning","date":"2019-05-31","arxiv_id":"1905.13497","n_code_links":2,"syntology":null},{"paper":"/paper/multiqa-an-empirical-investigation-of","slug":"multiqa-an-empirical-investigation-of","title":"MultiQA: An Empirical Investigation of Generalization and Transfer in Reading Comprehension","date":"2019-05-31","arxiv_id":"1905.13453","n_code_links":1,"syntology":{"ran":3,"of":3,"n_ran_checked":2,"n_instrument":1,"unverified":0,"pointer_only":3,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 1 honoured, 1 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","official":{"repos":["alontalmor/multiqa"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":"/paper/sequence-modeling-of-temporal-credit","slug":"sequence-modeling-of-temporal-credit","title":"Sequence Modeling of Temporal Credit Assignment for Episodic Reinforcement Learning","date":"2019-05-31","arxiv_id":"1905.13420","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/what-does-a-car-ssette-tape-tell","slug":"what-does-a-car-ssette-tape-tell","title":"Audio Caption in a Car Setting with a Sentence-Level Loss","date":"2019-05-31","arxiv_id":"1905.13448","n_code_links":1,"syntology":null},{"paper":null,"slug":"a-simple-but-effective-method-to-incorporate","title":"A Simple but Effective Method to Incorporate Multi-turn Context with BERT for Conversational Machine Comprehension","date":"2019-05-30","arxiv_id":"1905.12848","n_code_links":0,"syntology":null},{"paper":"/paper/deepshift-towards-multiplication-less-neural","slug":"deepshift-towards-multiplication-less-neural","title":"DeepShift: Towards Multiplication-Less Neural Networks","date":"2019-05-30","arxiv_id":"1905.13298","n_code_links":1,"syntology":null},{"paper":"/paper/hierarchical-transformers-for-multi-document","slug":"hierarchical-transformers-for-multi-document","title":"Hierarchical Transformers for Multi-Document Summarization","date":"2019-05-30","arxiv_id":"1905.13164","n_code_links":1,"syntology":{"ran":6,"of":7,"n_ran_checked":5,"n_instrument":1,"unverified":1,"pointer_only":2,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 1 violated, 4 with no contract checked; 1 where Syntology's instrument failed) · 1 unverified","official":{"repos":["nlpyang/hiersumm"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":1,"ran_from_kinds":["official"]}}},{"paper":"/paper/isaid-a-large-scale-dataset-for-instance","slug":"isaid-a-large-scale-dataset-for-instance","title":"iSAID: A Large-scale Dataset for Instance Segmentation in Aerial Images","date":"2019-05-30","arxiv_id":"1905.12886","n_code_links":3,"syntology":{"ran":1,"of":1,"n_ran_checked":1,"n_instrument":0,"unverified":0,"pointer_only":1,"phrase":"1 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; 0 where Syntology's instrument failed) · 0 unverified","official":{"repos":["CAPTAIN-WHU/iSAID_Devkit"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":"/paper/memory-driven-mixed-low-precision","slug":"memory-driven-mixed-low-precision","title":"Memory-Driven Mixed Low Precision Quantization For Enabling Deep Network Inference On Microcontrollers","date":"2019-05-30","arxiv_id":"1905.13082","n_code_links":2,"syntology":{"ran":4,"of":4,"n_ran_checked":1,"n_instrument":3,"unverified":0,"pointer_only":4,"phrase":"4 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; 3 where Syntology's instrument failed) · 0 unverified","official":{"repos":["mrusci/training-mixed-precision-quantized-networks"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"unbabels-submission-to-the-wmt2019-ape-shared","title":"Unbabel's Submission to the WMT2019 APE Shared Task: BERT-based Encoder-Decoder for Automatic Post-Editing","date":"2019-05-30","arxiv_id":"1905.13068","n_code_links":0,"syntology":null},{"paper":null,"slug":"unsupervised-classification-of-street","title":"Unsupervised Classification of Street Architectures Based on InfoGAN","date":"2019-05-30","arxiv_id":"1905.12844","n_code_links":0,"syntology":null},{"paper":"/paper/a-generalized-framework-of-sequence","slug":"a-generalized-framework-of-sequence","title":"A Generalized Framework of Sequence Generation with Application to Undirected Sequence Models","date":"2019-05-29","arxiv_id":"1905.12790","n_code_links":1,"syntology":{"ran":3,"of":3,"n_ran_checked":1,"n_instrument":2,"unverified":0,"pointer_only":1,"phrase":"3 ran (of which 2 constructed an object rather than computing a result; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","official":{"repos":["nyu-dl/dl4mt-seqgen"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":1,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official","unlocated"]}}},{"paper":"/paper/coherent-semantic-attention-for-image","slug":"coherent-semantic-attention-for-image","title":"Coherent Semantic Attention for Image Inpainting","date":"2019-05-29","arxiv_id":"1905.12384","n_code_links":3,"syntology":null},{"paper":"/paper/emergence-of-object-segmentation-in-perturbed","slug":"emergence-of-object-segmentation-in-perturbed","title":"Emergence of Object Segmentation in Perturbed Generative Models","date":"2019-05-29","arxiv_id":"1905.12663","n_code_links":1,"syntology":null},{"paper":"/paper/path-augmented-graph-transformer-network","slug":"path-augmented-graph-transformer-network","title":"Path-Augmented Graph Transformer Network","date":"2019-05-29","arxiv_id":"1905.12712","n_code_links":2,"syntology":{"ran":8,"of":8,"n_ran_checked":8,"n_instrument":0,"unverified":0,"pointer_only":0,"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":{"repos":["benatorc/PA-Graph-Transformer"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":0,"ran_from_kinds":["listed","official"]}}},{"paper":"/paper/towards-better-substitution-based-word-sense","slug":"towards-better-substitution-based-word-sense","title":"Towards better substitution-based word sense induction","date":"2019-05-29","arxiv_id":"1905.12598","n_code_links":2,"syntology":null},{"paper":"/paper/efficientnet-rethinking-model-scaling-for","slug":"efficientnet-rethinking-model-scaling-for","title":"EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks","date":"2019-05-28","arxiv_id":"1905.11946","n_code_links":144,"syntology":{"ran":198,"of":302,"n_ran_checked":157,"n_instrument":41,"unverified":104,"pointer_only":113,"phrase":"198 ran (of which 73 constructed an object rather than computing a result; 157 with no instrument failure: 26 honoured, 2 violated, 129 with no contract checked; 41 where Syntology's instrument failed) · 104 unverified","official":{"repos":["tensorflow/tpu"],"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/greedy-infomax-for-biologically-plausible","slug":"greedy-infomax-for-biologically-plausible","title":"Putting An End to End-to-End: Gradient-Isolated Learning of Representations","date":"2019-05-28","arxiv_id":"1905.11786","n_code_links":1,"syntology":{"ran":3,"of":4,"n_ran_checked":3,"n_instrument":0,"unverified":1,"pointer_only":1,"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":["loeweX/Greedy_InfoMax"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":1,"ran_from_kinds":["official"]}}},{"paper":"/paper/interpreting-and-improving-natural-language","slug":"interpreting-and-improving-natural-language","title":"Interpreting and improving natural-language processing (in machines) with natural language-processing (in the brain)","date":"2019-05-28","arxiv_id":"1905.11833","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":{"repos":["mtoneva/brain_language_nlp"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":[]}}},{"paper":null,"slug":"learning-distant-cause-and-effect-using-only","title":"Learning distant cause and effect using only local and immediate credit assignment","date":"2019-05-28","arxiv_id":"1905.11589","n_code_links":0,"syntology":null},{"paper":"/paper/oicsr-out-in-channel-sparsity-regularization-1","slug":"oicsr-out-in-channel-sparsity-regularization-1","title":"OICSR: Out-In-Channel Sparsity Regularization for Compact Deep Neural Networks","date":"2019-05-28","arxiv_id":"1905.11664","n_code_links":1,"syntology":null},{"paper":"/paper/combating-adversarial-misspellings-with","slug":"combating-adversarial-misspellings-with","title":"Combating Adversarial Misspellings with Robust Word Recognition","date":"2019-05-27","arxiv_id":"1905.11268","n_code_links":3,"syntology":null},{"paper":null,"slug":"compositional-pre-training-for-neural","title":"Compositional pre-training for neural semantic parsing","date":"2019-05-27","arxiv_id":"1905.11531","n_code_links":0,"syntology":null},{"paper":"/paper/levenshtein-transformer","slug":"levenshtein-transformer","title":"Levenshtein Transformer","date":"2019-05-27","arxiv_id":"1905.11006","n_code_links":3,"syntology":null},{"paper":null,"slug":"specnet-spectral-domain-convolutional-neural","title":"SpecNet: Spectral Domain Convolutional Neural Network","date":"2019-05-27","arxiv_id":"1905.10915","n_code_links":0,"syntology":null},{"paper":null,"slug":"hashing-based-answer-selection","title":"Hashing based Answer Selection","date":"2019-05-26","arxiv_id":"1905.10718","n_code_links":0,"syntology":null},{"paper":null,"slug":"underwater-fish-detection-with-weak-multi","title":"Underwater Fish Detection with Weak Multi-Domain Supervision","date":"2019-05-26","arxiv_id":"1905.10708","n_code_links":0,"syntology":null},{"paper":null,"slug":"190512726","title":"Prioritized Sequence Experience Replay","date":"2019-05-25","arxiv_id":"1905.12726","n_code_links":0,"syntology":null},{"paper":"/paper/are-sixteen-heads-really-better-than-one","slug":"are-sixteen-heads-really-better-than-one","title":"Are Sixteen Heads Really Better than One?","date":"2019-05-25","arxiv_id":"1905.10650","n_code_links":4,"syntology":null},{"paper":null,"slug":"locality-promoting-representation-learning","title":"Locality-Promoting Representation Learning","date":"2019-05-25","arxiv_id":"1905.10661","n_code_links":0,"syntology":null},{"paper":"/paper/stochastic-shared-embeddings-data-driven","slug":"stochastic-shared-embeddings-data-driven","title":"Stochastic Shared Embeddings: Data-driven Regularization of Embedding Layers","date":"2019-05-25","arxiv_id":"1905.10630","n_code_links":3,"syntology":null},{"paper":null,"slug":"a-call-for-prudent-choice-of-subword-merge","title":"A Call for Prudent Choice of Subword Merge Operations in Neural Machine Translation","date":"2019-05-24","arxiv_id":"1905.10453","n_code_links":0,"syntology":null},{"paper":"/paper/adaptive-symmetric-reward-noising-for","slug":"adaptive-symmetric-reward-noising-for","title":"Adaptive Symmetric Reward Noising for Reinforcement Learning","date":"2019-05-24","arxiv_id":"1905.10144","n_code_links":1,"syntology":null},{"paper":"/paper/additive-noise-annealing-and-approximation","slug":"additive-noise-annealing-and-approximation","title":"Additive Noise Annealing and Approximation Properties of Quantized Neural Networks","date":"2019-05-24","arxiv_id":"1905.10452","n_code_links":1,"syntology":null},{"paper":"/paper/boolq-exploring-the-surprising-difficulty-of","slug":"boolq-exploring-the-surprising-difficulty-of","title":"BoolQ: Exploring the Surprising Difficulty of Natural Yes/No Questions","date":"2019-05-24","arxiv_id":"1905.10044","n_code_links":1,"syntology":null},{"paper":null,"slug":"human-vs-muppet-a-conservative-estimate-of","title":"Human vs. Muppet: A Conservative Estimate of Human Performance on the GLUE Benchmark","date":"2019-05-24","arxiv_id":"1905.10425","n_code_links":0,"syntology":null},{"paper":"/paper/light-weight-retinanet-for-object-detection","slug":"light-weight-retinanet-for-object-detection","title":"Light-Weight RetinaNet for Object Detection","date":"2019-05-24","arxiv_id":"1905.10011","n_code_links":1,"syntology":null},{"paper":null,"slug":"magnetoresistive-ram-for-error-resilient-xnor","title":"Magnetoresistive RAM for error resilient XNOR-Nets","date":"2019-05-24","arxiv_id":"1905.10927","n_code_links":0,"syntology":null},{"paper":null,"slug":"scram-spatially-coherent-randomized-attention","title":"SCRAM: Spatially Coherent Randomized Attention Maps","date":"2019-05-24","arxiv_id":"1905.10308","n_code_links":0,"syntology":null}],"record_sha256":"71b15732a5c6fd9ced1a21a3ae8589fc31c01ffdbd7b931e60c894a68174d8b4","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}