{"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/softmax/papers/359","list_of":"/method/softmax","method":"Softmax","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":359,"pages_in_order":375,"rows_per_page":100,"rows":[35801,35900],"of":37443,"counts":{"archive_papers_tagged":37443,"with_a_code_link":15869,"where_syntology_ran_a_sample":4578,"not_listed_spam_title":0,"listed":37443,"listed_where_code_ran":4578,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":3835,"every_run_a_failure_of_syntologys_instrument":743,"listed_with_a_run_with_no_instrument_failure":3835,"listed_every_run_a_failure_of_syntologys_instrument":743,"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/softmax","prev":"/method/softmax/papers/358","next":"/method/softmax/papers/360","papers":[{"paper":null,"slug":"integration-of-text-maps-in-convolutional","title":"Integration of Text-maps in Convolutional Neural Networks for Region Detection among Different Textual Categories","date":"2019-05-26","arxiv_id":"1905.10858","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":"/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":"dynamic-cell-structure-via-recursive","title":"Dynamic Cell Structure via Recursive-Recurrent Neural Networks","date":"2019-05-25","arxiv_id":"1905.10540","n_code_links":0,"syntology":null},{"paper":"/paper/hyperparameter-free-out-of-distribution","slug":"hyperparameter-free-out-of-distribution","title":"Hyperparameter-Free Out-of-Distribution Detection Using Softmax of Scaled Cosine Similarity","date":"2019-05-25","arxiv_id":"1905.10628","n_code_links":1,"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/rethinking-softmax-cross-entropy-loss-for","slug":"rethinking-softmax-cross-entropy-loss-for","title":"Rethinking Softmax Cross-Entropy Loss for Adversarial Robustness","date":"2019-05-25","arxiv_id":"1905.10626","n_code_links":2,"syntology":{"ran":5,"of":6,"n_ran_checked":4,"n_instrument":1,"unverified":1,"pointer_only":1,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 1 where Syntology's instrument failed) · 1 unverified","official":{"repos":["P2333/Max-Mahalanobis-Training"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":1,"ran_from_kinds":["official"]}}},{"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":null,"slug":"a-real-time-tiny-detection-model-for-stem-end","title":"FSD: Feature Skyscraper Detector for Stem End and Blossom End of Navel Orange","date":"2019-05-24","arxiv_id":"1905.09994","n_code_links":0,"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},{"paper":null,"slug":"structured-compression-by-unstructured","title":"Structured Compression by Weight Encryption for Unstructured Pruning and Quantization","date":"2019-05-24","arxiv_id":"1905.10138","n_code_links":0,"syntology":null},{"paper":"/paper/190513306","slug":"190513306","title":"Implicit Background Estimation for Semantic Segmentation","date":"2019-05-23","arxiv_id":"1905.13306","n_code_links":1,"syntology":null},{"paper":"/paper/analyzing-multi-head-self-attention","slug":"analyzing-multi-head-self-attention","title":"Analyzing Multi-Head Self-Attention: Specialized Heads Do the Heavy Lifting, the Rest Can Be Pruned","date":"2019-05-23","arxiv_id":"1905.09418","n_code_links":1,"syntology":null},{"paper":"/paper/multi-class-gaussian-process-classification","slug":"multi-class-gaussian-process-classification","title":"Multi-Class Gaussian Process Classification Made Conjugate: Efficient Inference via Data Augmentation","date":"2019-05-23","arxiv_id":"1905.09670","n_code_links":3,"syntology":null},{"paper":"/paper/network-pruning-via-transformable","slug":"network-pruning-via-transformable","title":"Network Pruning via Transformable Architecture Search","date":"2019-05-23","arxiv_id":"1905.09717","n_code_links":4,"syntology":null},{"paper":null,"slug":"shift-r-cnn-deep-monocular-3d-object","title":"Shift R-CNN: Deep Monocular 3D Object Detection with Closed-Form Geometric Constraints","date":"2019-05-23","arxiv_id":"1905.09970","n_code_links":0,"syntology":null},{"paper":"/paper/spatial-group-wise-enhance-improving-semantic","slug":"spatial-group-wise-enhance-improving-semantic","title":"Spatial Group-wise Enhance: Improving Semantic Feature Learning in Convolutional Networks","date":"2019-05-23","arxiv_id":"1905.09646","n_code_links":3,"syntology":{"ran":2,"of":4,"n_ran_checked":0,"n_instrument":2,"unverified":2,"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) · 2 unverified","official":{"repos":["implus/PytorchInsight"],"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/data-efficient-image-recognition-with","slug":"data-efficient-image-recognition-with","title":"Data-Efficient Image Recognition with Contrastive Predictive Coding","date":"2019-05-22","arxiv_id":"1905.09272","n_code_links":4,"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":null}},{"paper":"/paper/deeper-text-understanding-for-ir-with","slug":"deeper-text-understanding-for-ir-with","title":"Deeper Text Understanding for IR with Contextual Neural Language Modeling","date":"2019-05-22","arxiv_id":"1905.09217","n_code_links":1,"syntology":{"ran":3,"of":7,"n_ran_checked":1,"n_instrument":2,"unverified":4,"pointer_only":2,"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) · 4 unverified","official":{"repos":["AdeDZY/SIGIR19-BERT-IR"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":4,"ran_from_kinds":["official"]}}},{"paper":"/paper/fastspeech-fast-robust-and-controllable-text","slug":"fastspeech-fast-robust-and-controllable-text","title":"FastSpeech: Fast, Robust and Controllable Text to Speech","date":"2019-05-22","arxiv_id":"1905.09263","n_code_links":22,"syntology":{"ran":10,"of":11,"n_ran_checked":7,"n_instrument":3,"unverified":1,"pointer_only":3,"phrase":"10 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 0 honoured, 0 violated, 7 with no contract checked; 3 where Syntology's instrument failed) · 1 unverified","official":null}},{"paper":"/paper/fastspeech-fastrobustand-controllable-text-to","slug":"fastspeech-fastrobustand-controllable-text-to","title":"FastSpeech: Fast,Robustand Controllable Text-to-Speech","date":"2019-05-22","arxiv_id":null,"n_code_links":11,"syntology":null},{"paper":null,"slug":"a-seq-to-seq-transformer-premised-temporal","title":"A Seq-to-Seq Transformer Premised Temporal Convolutional Network for Chinese Word Segmentation","date":"2019-05-21","arxiv_id":"1905.08454","n_code_links":0,"syntology":null},{"paper":"/paper/adaptive-stochastic-natural-gradient-method","slug":"adaptive-stochastic-natural-gradient-method","title":"Adaptive Stochastic Natural Gradient Method for One-Shot Neural Architecture Search","date":"2019-05-21","arxiv_id":"1905.08537","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":["shirakawas/ASNG-NAS"],"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":"generating-logical-forms-from-graph","title":"Generating Logical Forms from Graph Representations of Text and Entities","date":"2019-05-21","arxiv_id":"1905.08407","n_code_links":0,"syntology":null},{"paper":"/paper/lightweight-network-architecture-for-real","slug":"lightweight-network-architecture-for-real","title":"Lightweight Network Architecture for Real-Time Action Recognition","date":"2019-05-21","arxiv_id":"1905.08711","n_code_links":1,"syntology":null},{"paper":"/paper/look-again-at-the-syntax-relational-graph","slug":"look-again-at-the-syntax-relational-graph","title":"Look Again at the Syntax: Relational Graph Convolutional Network for Gendered Ambiguous Pronoun Resolution","date":"2019-05-21","arxiv_id":"1905.08868","n_code_links":1,"syntology":null},{"paper":"/paper/parallel-neural-text-to-speech","slug":"parallel-neural-text-to-speech","title":"Non-Autoregressive Neural Text-to-Speech","date":"2019-05-21","arxiv_id":"1905.08459","n_code_links":2,"syntology":{"ran":6,"of":7,"n_ran_checked":4,"n_instrument":2,"unverified":1,"pointer_only":0,"phrase":"6 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; 2 where Syntology's instrument failed) · 1 unverified","official":null}},{"paper":"/paper/sample-efficient-text-summarization-using-a","slug":"sample-efficient-text-summarization-using-a","title":"Sample Efficient Text Summarization Using a Single Pre-Trained Transformer","date":"2019-05-21","arxiv_id":"1905.08836","n_code_links":2,"syntology":null},{"paper":"/paper/enriching-pre-trained-language-model-with","slug":"enriching-pre-trained-language-model-with","title":"Enriching Pre-trained Language Model with Entity Information for Relation Classification","date":"2019-05-20","arxiv_id":"1905.08284","n_code_links":6,"syntology":{"ran":7,"of":8,"n_ran_checked":4,"n_instrument":3,"unverified":1,"pointer_only":0,"phrase":"7 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; 3 where Syntology's instrument failed) · 1 unverified","official":null}},{"paper":null,"slug":"label-mapping-neural-networks-with-response","title":"Label Mapping Neural Networks with Response Consolidation for Class Incremental Learning","date":"2019-05-20","arxiv_id":"1905.07835","n_code_links":0,"syntology":null},{"paper":null,"slug":"multimodal-transformer-with-multi-view-visual","title":"Multimodal Transformer with Multi-View Visual Representation for Image Captioning","date":"2019-05-20","arxiv_id":"1905.07841","n_code_links":0,"syntology":null},{"paper":"/paper/paperrobot-incremental-draft-generation-of","slug":"paperrobot-incremental-draft-generation-of","title":"PaperRobot: Incremental Draft Generation of Scientific Ideas","date":"2019-05-20","arxiv_id":"1905.07870","n_code_links":2,"syntology":null},{"paper":"/paper/adaptive-attention-span-in-transformers","slug":"adaptive-attention-span-in-transformers","title":"Adaptive Attention Span in Transformers","date":"2019-05-19","arxiv_id":"1905.07799","n_code_links":8,"syntology":null},{"paper":"/paper/hellaswag-can-a-machine-really-finish-your","slug":"hellaswag-can-a-machine-really-finish-your","title":"HellaSwag: Can a Machine Really Finish Your Sentence?","date":"2019-05-19","arxiv_id":"1905.07830","n_code_links":2,"syntology":{"ran":4,"of":6,"n_ran_checked":4,"n_instrument":0,"unverified":2,"pointer_only":4,"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":"learning-to-memorize-in-neural-task-oriented","title":"Learning to Memorize in Neural Task-Oriented Dialogue Systems","date":"2019-05-19","arxiv_id":"1905.07687","n_code_links":0,"syntology":null},{"paper":null,"slug":"u-net-based-multi-instance-video-object","title":"U-Net Based Multi-instance Video Object Segmentation","date":"2019-05-19","arxiv_id":"1905.07826","n_code_links":0,"syntology":null},{"paper":"/paper/bertsel-answer-selection-with-pre-trained","slug":"bertsel-answer-selection-with-pre-trained","title":"BERTSel: Answer Selection with Pre-trained Models","date":"2019-05-18","arxiv_id":"1905.07588","n_code_links":1,"syntology":null},{"paper":"/paper/multinomial-distribution-learning-for","slug":"multinomial-distribution-learning-for","title":"Multinomial Distribution Learning for Effective Neural Architecture Search","date":"2019-05-18","arxiv_id":"1905.07529","n_code_links":1,"syntology":null},{"paper":"/paper/a-deep-learning-approach-to-detecting-volcano","slug":"a-deep-learning-approach-to-detecting-volcano","title":"A deep learning approach to detecting volcano deformation from satellite imagery using synthetic datasets","date":"2019-05-17","arxiv_id":"1905.07286","n_code_links":1,"syntology":null},{"paper":"/paper/autodispnet-improving-disparity-estimation","slug":"autodispnet-improving-disparity-estimation","title":"AutoDispNet: Improving Disparity Estimation With AutoML","date":"2019-05-17","arxiv_id":"1905.07443","n_code_links":1,"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":"/paper/be-your-own-teacher-improve-the-performance","slug":"be-your-own-teacher-improve-the-performance","title":"Be Your Own Teacher: Improve the Performance of Convolutional Neural Networks via Self Distillation","date":"2019-05-17","arxiv_id":"1905.08094","n_code_links":1,"syntology":null},{"paper":"/paper/deepswarm-optimising-convolutional-neural","slug":"deepswarm-optimising-convolutional-neural","title":"DeepSwarm: Optimising Convolutional Neural Networks using Swarm Intelligence","date":"2019-05-17","arxiv_id":"1905.07350","n_code_links":1,"syntology":null},{"paper":"/paper/ernie-enhanced-language-representation-with","slug":"ernie-enhanced-language-representation-with","title":"ERNIE: Enhanced Language Representation with Informative Entities","date":"2019-05-17","arxiv_id":"1905.07129","n_code_links":2,"syntology":{"ran":3,"of":3,"n_ran_checked":2,"n_instrument":1,"unverified":0,"pointer_only":1,"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":{"repos":["thunlp/ERNIE"],"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","unlocated"]}}},{"paper":"/paper/story-ending-prediction-by-transferable-bert","slug":"story-ending-prediction-by-transferable-bert","title":"Story Ending Prediction by Transferable BERT","date":"2019-05-17","arxiv_id":"1905.07504","n_code_links":1,"syntology":null},{"paper":"/paper/transfer-learning-based-detection-of-diabetic","slug":"transfer-learning-based-detection-of-diabetic","title":"Transfer Learning based Detection of Diabetic Retinopathy from Small Dataset","date":"2019-05-17","arxiv_id":"1905.07203","n_code_links":1,"syntology":null},{"paper":"/paper/190506596","slug":"190506596","title":"Joint Source-Target Self Attention with Locality Constraints","date":"2019-05-16","arxiv_id":"1905.06596","n_code_links":2,"syntology":null},{"paper":"/paper/hibert-document-level-pre-training-of","slug":"hibert-document-level-pre-training-of","title":"HIBERT: Document Level Pre-training of Hierarchical Bidirectional Transformers for Document Summarization","date":"2019-05-16","arxiv_id":"1905.06566","n_code_links":0,"syntology":null},{"paper":null,"slug":"latent-universal-task-specific-bert","title":"Latent Universal Task-Specific BERT","date":"2019-05-16","arxiv_id":"1905.06638","n_code_links":0,"syntology":null},{"paper":null,"slug":"trk-cnn-transferable-ranking-cnn-for-image","title":"TRk-CNN: Transferable Ranking-CNN for image classification of glaucoma, glaucoma suspect, and normal eyes","date":"2019-05-16","arxiv_id":"1905.06509","n_code_links":0,"syntology":null},{"paper":null,"slug":"a-deep-learning-based-approach-for-fast-and","title":"A deep-learning-based approach for fast and robust steel surface defects classification","date":"2019-05-15","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":"/paper/a-surprisingly-robust-trick-for-winograd","slug":"a-surprisingly-robust-trick-for-winograd","title":"A Surprisingly Robust Trick for Winograd Schema Challenge","date":"2019-05-15","arxiv_id":"1905.06290","n_code_links":2,"syntology":null},{"paper":"/paper/behavior-sequence-transformer-for-e-commerce","slug":"behavior-sequence-transformer-for-e-commerce","title":"Behavior Sequence Transformer for E-commerce Recommendation in Alibaba","date":"2019-05-15","arxiv_id":"1905.06874","n_code_links":9,"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":"/paper/bert-rediscovers-the-classical-nlp-pipeline","slug":"bert-rediscovers-the-classical-nlp-pipeline","title":"BERT Rediscovers the Classical NLP Pipeline","date":"2019-05-15","arxiv_id":"1905.05950","n_code_links":1,"syntology":null},{"paper":null,"slug":"dynamic-neural-network-channel-execution-for","title":"Dynamic Neural Network Channel Execution for Efficient Training","date":"2019-05-15","arxiv_id":"1905.06435","n_code_links":0,"syntology":null},{"paper":null,"slug":"geometric-losses-for-distributional-learning","title":"Geometric Losses for Distributional Learning","date":"2019-05-15","arxiv_id":"1905.06005","n_code_links":0,"syntology":null},{"paper":"/paper/190505621","slug":"190505621","title":"Style Transformer: Unpaired Text Style Transfer without Disentangled Latent Representation","date":"2019-05-14","arxiv_id":"1905.05621","n_code_links":4,"syntology":{"ran":3,"of":3,"n_ran_checked":0,"n_instrument":3,"unverified":0,"pointer_only":3,"phrase":"3 ran (of which 2 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 3 where Syntology's instrument failed) · 0 unverified","official":{"repos":["fastnlp/nlp-dataset","fastnlp/style-transformer"],"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/190505661","slug":"190505661","title":"Efficient Ladder-style DenseNets for Semantic Segmentation of Large Images","date":"2019-05-14","arxiv_id":"1905.05661","n_code_links":3,"syntology":null},{"paper":"/paper/190602124","slug":"190602124","title":"PatentBERT: Patent Classification with Fine-Tuning a pre-trained BERT Model","date":"2019-05-14","arxiv_id":"1906.02124","n_code_links":1,"syntology":null},{"paper":"/paper/american-sign-language-alphabet-recognition","slug":"american-sign-language-alphabet-recognition","title":"American Sign Language Alphabet Recognition using Deep Learning","date":"2019-05-14","arxiv_id":"1905.05487","n_code_links":0,"syntology":null},{"paper":"/paper/bert-with-history-answer-embedding-for","slug":"bert-with-history-answer-embedding-for","title":"BERT with History Answer Embedding for Conversational Question Answering","date":"2019-05-14","arxiv_id":"1905.05412","n_code_links":1,"syntology":null},{"paper":"/paper/cognitive-graph-for-multi-hop-reading","slug":"cognitive-graph-for-multi-hop-reading","title":"Cognitive Graph for Multi-Hop Reading Comprehension at Scale","date":"2019-05-14","arxiv_id":"1905.05460","n_code_links":2,"syntology":{"ran":7,"of":9,"n_ran_checked":5,"n_instrument":2,"unverified":2,"pointer_only":1,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 1 honoured, 2 violated, 2 with no contract checked; 2 where Syntology's instrument failed) · 2 unverified","official":{"repos":["THUDM/CogQA"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":2,"ran_from_kinds":["listed","official"]}}},{"paper":"/paper/deep-neural-architecture-search-with-deep","slug":"deep-neural-architecture-search-with-deep","title":"Deep Neural Architecture Search with Deep Graph Bayesian Optimization","date":"2019-05-14","arxiv_id":"1905.06159","n_code_links":2,"syntology":null},{"paper":null,"slug":"end-to-end-recognition-system-for-recognizing","title":"End to End Recognition System for Recognizing Offline Unconstrained Vietnamese Handwriting","date":"2019-05-14","arxiv_id":"1905.05381","n_code_links":0,"syntology":null},{"paper":"/paper/how-to-fine-tune-bert-for-text-classification","slug":"how-to-fine-tune-bert-for-text-classification","title":"How to Fine-Tune BERT for Text Classification?","date":"2019-05-14","arxiv_id":"1905.05583","n_code_links":15,"syntology":{"ran":12,"of":18,"n_ran_checked":7,"n_instrument":5,"unverified":6,"pointer_only":5,"phrase":"12 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 1 honoured, 0 violated, 6 with no contract checked; 5 where Syntology's instrument failed) · 6 unverified","official":{"repos":["xuyige/BERT4doc-Classification"],"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":["listed","official"]}}},{"paper":"/paper/sense-vocabulary-compression-through-the","slug":"sense-vocabulary-compression-through-the","title":"Sense Vocabulary Compression through the Semantic Knowledge of WordNet for Neural Word Sense Disambiguation","date":"2019-05-14","arxiv_id":"1905.05677","n_code_links":2,"syntology":null},{"paper":"/paper/sparse-sequence-to-sequence-models","slug":"sparse-sequence-to-sequence-models","title":"Sparse Sequence-to-Sequence Models","date":"2019-05-14","arxiv_id":"1905.05702","n_code_links":1,"syntology":null},{"paper":null,"slug":"190508606","title":"VGG Fine-tuning for Cooking State Recognition","date":"2019-05-13","arxiv_id":"1905.08606","n_code_links":0,"syntology":null},{"paper":null,"slug":"almost-unsupervised-text-to-speech-and","title":"Almost Unsupervised Text to Speech and Automatic Speech Recognition","date":"2019-05-13","arxiv_id":"1905.06791","n_code_links":0,"syntology":null},{"paper":null,"slug":"bayesnas-a-bayesian-approach-for-neural","title":"BayesNAS: A Bayesian Approach for Neural Architecture Search","date":"2019-05-13","arxiv_id":"1905.04919","n_code_links":0,"syntology":null},{"paper":null,"slug":"isbnet-instance-aware-selective-branching","title":"Dynamic Routing Networks","date":"2019-05-13","arxiv_id":"1905.04849","n_code_links":0,"syntology":null},{"paper":"/paper/synchronous-bidirectional-neural-machine","slug":"synchronous-bidirectional-neural-machine","title":"Synchronous Bidirectional Neural Machine Translation","date":"2019-05-13","arxiv_id":"1905.04847","n_code_links":2,"syntology":null},{"paper":"/paper/weakly-supervised-caricature-face-parsing","slug":"weakly-supervised-caricature-face-parsing","title":"Weakly-supervised Caricature Face Parsing through Domain Adaptation","date":"2019-05-13","arxiv_id":"1905.05091","n_code_links":1,"syntology":null},{"paper":"/paper/budgeted-training-rethinking-deep-neural","slug":"budgeted-training-rethinking-deep-neural","title":"Budgeted Training: Rethinking Deep Neural Network Training Under Resource Constraints","date":"2019-05-12","arxiv_id":"1905.04753","n_code_links":1,"syntology":null},{"paper":null,"slug":"generative-adversarial-networks-and-3","title":"Generative Adversarial Networks and Conditional Random Fields for Hyperspectral Image Classification","date":"2019-05-12","arxiv_id":"1905.04621","n_code_links":0,"syntology":null},{"paper":null,"slug":"object-detection-in-specific-traffic-scenes","title":"Object Detection in Specific Traffic Scenes using YOLOv2","date":"2019-05-12","arxiv_id":"1905.04740","n_code_links":0,"syntology":null},{"paper":"/paper/video-instance-segmentation","slug":"video-instance-segmentation","title":"Video Instance Segmentation","date":"2019-05-12","arxiv_id":"1905.04804","n_code_links":6,"syntology":{"ran":9,"of":11,"n_ran_checked":8,"n_instrument":1,"unverified":2,"pointer_only":1,"phrase":"9 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; 1 where Syntology's instrument failed) · 2 unverified","official":{"repos":["Epiphqny/VisTR"],"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":"learning-robotic-manipulation-through-visual","title":"Learning Robotic Manipulation through Visual Planning and Acting","date":"2019-05-11","arxiv_id":"1905.04411","n_code_links":0,"syntology":null},{"paper":"/paper/multitask-deep-learning-with-spectral","slug":"multitask-deep-learning-with-spectral","title":"Multitask Deep Learning with Spectral Knowledge for Hyperspectral Image Classification","date":"2019-05-11","arxiv_id":"1905.04535","n_code_links":2,"syntology":null},{"paper":"/paper/190507320","slug":"190507320","title":"EENA: Efficient Evolution of Neural Architecture","date":"2019-05-10","arxiv_id":"1905.07320","n_code_links":1,"syntology":null},{"paper":null,"slug":"densifying-assumed-sparse-tensors-improving","title":"Densifying Assumed-sparse Tensors: Improving Memory Efficiency and MPI Collective Performance during Tensor Accumulation for Parallelized Training of Neural Machine Translation Models","date":"2019-05-10","arxiv_id":"1905.04035","n_code_links":0,"syntology":null},{"paper":null,"slug":"language-modeling-with-deep-transformers","title":"Language Modeling with Deep Transformers","date":"2019-05-10","arxiv_id":"1905.04226","n_code_links":0,"syntology":null},{"paper":null,"slug":"single-path-nas-device-aware-efficient","title":"Single-Path NAS: Device-Aware Efficient ConvNet Design","date":"2019-05-10","arxiv_id":"1905.04159","n_code_links":0,"syntology":null},{"paper":"/paper/using-syntactical-and-logical-forms-to","slug":"using-syntactical-and-logical-forms-to","title":"A logical-based corpus for cross-lingual evaluation","date":"2019-05-10","arxiv_id":"1905.05704","n_code_links":1,"syntology":null},{"paper":null,"slug":"190503288","title":"Advancements in Image Classification using Convolutional Neural Network","date":"2019-05-08","arxiv_id":"1905.03288","n_code_links":0,"syntology":null},{"paper":"/paper/190503381","slug":"190503381","title":"AutoAssist: A Framework to Accelerate Training of Deep Neural Networks","date":"2019-05-08","arxiv_id":"1905.03381","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: 2 honoured, 0 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","official":null}},{"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/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":null,"slug":"efficient-neural-architecture-search-on-low","title":"Efficient Neural Architecture Search on Low-Dimensional Data for OCT Image Segmentation","date":"2019-05-07","arxiv_id":"1905.02590","n_code_links":0,"syntology":null},{"paper":null,"slug":"generalization-ability-of-region-proposal","title":"Generalization ability of region proposal networks for multispectral person detection","date":"2019-05-07","arxiv_id":"1905.02758","n_code_links":0,"syntology":null},{"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":null,"slug":"p2sgrad-refined-gradients-for-optimizing-deep","title":"P2SGrad: Refined Gradients for Optimizing Deep Face Models","date":"2019-05-07","arxiv_id":"1905.02479","n_code_links":0,"syntology":null}],"record_sha256":"6df929bcd5276f8f87ac2496fc4faed1a4568445b31e9309b8f2a92ac8e3265e","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}