{"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/340","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":340,"pages_in_order":375,"rows_per_page":100,"rows":[33901,34000],"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/339","next":"/method/softmax/papers/341","papers":[{"paper":"/paper/interbert-vision-and-language-interaction-for","slug":"interbert-vision-and-language-interaction-for","title":"InterBERT: Vision-and-Language Interaction for Multi-modal Pretraining","date":"2020-03-30","arxiv_id":"2003.13198","n_code_links":0,"syntology":null},{"paper":null,"slug":"learning-contextualized-sentence","title":"Learning Contextualized Sentence Representations for Document-Level Neural Machine Translation","date":"2020-03-30","arxiv_id":"2003.13205","n_code_links":0,"syntology":null},{"paper":"/paper/nukebert-a-pre-trained-language-model-for-low","slug":"nukebert-a-pre-trained-language-model-for-low","title":"NukeBERT: A Pre-trained language model for Low Resource Nuclear Domain","date":"2020-03-30","arxiv_id":"2003.13821","n_code_links":1,"syntology":null},{"paper":"/paper/sign-language-transformers-joint-end-to-end","slug":"sign-language-transformers-joint-end-to-end","title":"Sign Language Transformers: Joint End-to-end Sign Language Recognition and Translation","date":"2020-03-30","arxiv_id":"2003.13830","n_code_links":2,"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":["neccam/slt"],"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":["listed","official"]}}},{"paper":null,"slug":"weakly-supervised-land-classification-for","title":"Weakly-supervised land classification for coastal zone based on deep convolutional neural networks by incorporating dual-polarimetric characteristics into training dataset","date":"2020-03-30","arxiv_id":"2003.13648","n_code_links":0,"syntology":null},{"paper":"/paper/abstractive-text-summarization-based-on","slug":"abstractive-text-summarization-based-on","title":"Abstractive Text Summarization based on Language Model Conditioning and Locality Modeling","date":"2020-03-29","arxiv_id":"2003.13027","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: 1 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","official":{"repos":["axenov/BERT-Summ-OpenNMT"],"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/bert-fine-tuning-for-arabic-text","slug":"bert-fine-tuning-for-arabic-text","title":"BERT Fine-tuning For Arabic Text Summarization","date":"2020-03-29","arxiv_id":"2004.14135","n_code_links":1,"syntology":{"ran":8,"of":8,"n_ran_checked":8,"n_instrument":0,"unverified":0,"pointer_only":5,"phrase":"8 ran (of which 0 constructed an object rather than computing a result; 8 with no instrument failure: 0 honoured, 1 violated, 7 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","official":{"repos":["mukhtar-algezoli/Arabic_PreSumm"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":8,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"defect-segmentation-mapping-tunnel-lining","title":"Defect segmentation: Mapping tunnel lining internal defects with ground penetrating radar data using a convolutional neural network","date":"2020-03-29","arxiv_id":"2003.13120","n_code_links":0,"syntology":null},{"paper":"/paper/meta-fine-tuning-neural-language-models-for","slug":"meta-fine-tuning-neural-language-models-for","title":"Meta Fine-Tuning Neural Language Models for Multi-Domain Text Mining","date":"2020-03-29","arxiv_id":"2003.13003","n_code_links":2,"syntology":null},{"paper":"/paper/recursive-non-autoregressive-graph-to-graph","slug":"recursive-non-autoregressive-graph-to-graph","title":"Recursive Non-Autoregressive Graph-to-Graph Transformer for Dependency Parsing with Iterative Refinement","date":"2020-03-29","arxiv_id":"2003.13118","n_code_links":1,"syntology":null},{"paper":null,"slug":"user-generated-data-achilles-heel-of-bert","title":"Noisy Text Data: Achilles' Heel of BERT","date":"2020-03-29","arxiv_id":"2003.12932","n_code_links":0,"syntology":null},{"paper":null,"slug":"actor-transformers-for-group-activity","title":"Actor-Transformers for Group Activity Recognition","date":"2020-03-28","arxiv_id":"2003.12737","n_code_links":0,"syntology":null},{"paper":"/paper/cakes-channel-wise-automatic-kernel-shrinking","slug":"cakes-channel-wise-automatic-kernel-shrinking","title":"CAKES: Channel-wise Automatic KErnel Shrinking for Efficient 3D Networks","date":"2020-03-28","arxiv_id":"2003.12798","n_code_links":1,"syntology":null},{"paper":"/paper/cross-domain-detection-via-graph-induced","slug":"cross-domain-detection-via-graph-induced","title":"Cross-domain Detection via Graph-induced Prototype Alignment","date":"2020-03-28","arxiv_id":"2003.12849","n_code_links":1,"syntology":null},{"paper":"/paper/hin-hierarchical-inference-network-for","slug":"hin-hierarchical-inference-network-for","title":"HIN: Hierarchical Inference Network for Document-Level Relation Extraction","date":"2020-03-28","arxiv_id":"2003.12754","n_code_links":0,"syntology":null},{"paper":"/paper/variational-transformers-for-diverse-response","slug":"variational-transformers-for-diverse-response","title":"Variational Transformers for Diverse Response Generation","date":"2020-03-28","arxiv_id":"2003.12738","n_code_links":2,"syntology":{"ran":3,"of":4,"n_ran_checked":3,"n_instrument":0,"unverified":1,"pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 1 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","official":{"repos":["zlinao/Variational-Transformer"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"algorithm-based-fault-tolerance-for","title":"FT-CNN: Algorithm-Based Fault Tolerance for Convolutional Neural Networks","date":"2020-03-27","arxiv_id":"2003.12203","n_code_links":0,"syntology":null},{"paper":null,"slug":"da-nas-data-adapted-pruning-for-efficient","title":"DA-NAS: Data Adapted Pruning for Efficient Neural Architecture Search","date":"2020-03-27","arxiv_id":"2003.12563","n_code_links":0,"syntology":null},{"paper":null,"slug":"imac-in-memory-multi-bit-multiplication","title":"IMAC: In-memory multi-bit Multiplication andACcumulation in 6T SRAM Array","date":"2020-03-27","arxiv_id":"2003.12558","n_code_links":0,"syntology":null},{"paper":"/paper/milenas-efficient-neural-architecture-search","slug":"milenas-efficient-neural-architecture-search","title":"MiLeNAS: Efficient Neural Architecture Search via Mixed-Level Reformulation","date":"2020-03-27","arxiv_id":"2003.12238","n_code_links":1,"syntology":null},{"paper":null,"slug":"on-the-evaluation-of-prohibited-item","title":"On the Evaluation of Prohibited Item Classification and Detection in Volumetric 3D Computed Tomography Baggage Security Screening Imagery","date":"2020-03-27","arxiv_id":"2003.12625","n_code_links":0,"syntology":null},{"paper":"/paper/are-labels-necessary-for-neural-architecture","slug":"are-labels-necessary-for-neural-architecture","title":"Are Labels Necessary for Neural Architecture Search?","date":"2020-03-26","arxiv_id":"2003.12056","n_code_links":2,"syntology":{"ran":2,"of":2,"n_ran_checked":1,"n_instrument":1,"unverified":0,"pointer_only":0,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 1 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","official":{"repos":["facebookresearch/unnas"],"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":null,"slug":"classification-of-the-chinese-handwritten","title":"Classification of Chinese Handwritten Numbers with Labeled Projective Dictionary Pair Learning","date":"2020-03-26","arxiv_id":"2003.11700","n_code_links":0,"syntology":null},{"paper":"/paper/cycle-text-to-image-gan-with-bert","slug":"cycle-text-to-image-gan-with-bert","title":"Cycle Text-To-Image GAN with BERT","date":"2020-03-26","arxiv_id":"2003.12137","n_code_links":4,"syntology":null},{"paper":"/paper/hit-detector-hierarchical-trinity","slug":"hit-detector-hierarchical-trinity","title":"Hit-Detector: Hierarchical Trinity Architecture Search for Object Detection","date":"2020-03-26","arxiv_id":"2003.11818","n_code_links":1,"syntology":null},{"paper":"/paper/mask-encoding-for-single-shot-instance","slug":"mask-encoding-for-single-shot-instance","title":"Mask Encoding for Single Shot Instance Segmentation","date":"2020-03-26","arxiv_id":"2003.11712","n_code_links":7,"syntology":null},{"paper":"/paper/negative-margin-matters-understanding-margin","slug":"negative-margin-matters-understanding-margin","title":"Negative Margin Matters: Understanding Margin in Few-shot Classification","date":"2020-03-26","arxiv_id":"2003.12060","n_code_links":1,"syntology":null},{"paper":null,"slug":"strokecoder-path-based-image-generation-from","title":"StrokeCoder: Path-Based Image Generation from Single Examples using Transformers","date":"2020-03-26","arxiv_id":"2003.11958","n_code_links":0,"syntology":null},{"paper":"/paper/tldr-token-loss-dynamic-reweighting-for","slug":"tldr-token-loss-dynamic-reweighting-for","title":"TLDR: Token Loss Dynamic Reweighting for Reducing Repetitive Utterance Generation","date":"2020-03-26","arxiv_id":"2003.11963","n_code_links":1,"syntology":{"ran":15,"of":15,"n_ran_checked":14,"n_instrument":1,"unverified":0,"pointer_only":15,"phrase":"15 ran (of which 0 constructed an object rather than computing a result; 14 with no instrument failure: 0 honoured, 0 violated, 14 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","official":{"repos":["ShaojieJiang/tldr"],"state":"official (archive's flag): 15 ran","n_ran":15,"n_constructed":0,"n_ran_no_instrument_failure":14,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"adversarial-multi-binary-neural-network-for","title":"Adversarial Multi-Binary Neural Network for Multi-class Classification","date":"2020-03-25","arxiv_id":"2003.11184","n_code_links":0,"syntology":null},{"paper":null,"slug":"asfd-automatic-and-scalable-face-detector","title":"ASFD: Automatic and Scalable Face Detector","date":"2020-03-25","arxiv_id":"2003.11228","n_code_links":0,"syntology":null},{"paper":"/paper/pipelined-backpropagation-at-scale-training","slug":"pipelined-backpropagation-at-scale-training","title":"Pipelined Backpropagation at Scale: Training Large Models without Batches","date":"2020-03-25","arxiv_id":"2003.11666","n_code_links":0,"syntology":null},{"paper":"/paper/two-stage-discriminative-re-ranking-for-large","slug":"two-stage-discriminative-re-ranking-for-large","title":"Two-stage Discriminative Re-ranking for Large-scale Landmark Retrieval","date":"2020-03-25","arxiv_id":"2003.11211","n_code_links":2,"syntology":{"ran":1,"of":2,"n_ran_checked":0,"n_instrument":1,"unverified":1,"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) · 1 unverified","official":{"repos":["cvdfoundation/google-landmark","lyakaap/Landmark2019-1st-and-3rd-Place-Solution"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":["official"]}}},{"paper":"/paper/generalizing-spatial-transformers-to","slug":"generalizing-spatial-transformers-to","title":"Generalizing Spatial Transformers to Projective Geometry with Applications to 2D/3D Registration","date":"2020-03-24","arxiv_id":"2003.10987","n_code_links":1,"syntology":null},{"paper":"/paper/model-based-asynchronous-hyperparameter","slug":"model-based-asynchronous-hyperparameter","title":"Model-based Asynchronous Hyperparameter and Neural Architecture Search","date":"2020-03-24","arxiv_id":"2003.10865","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":{"repos":["awslabs/syne-tune"],"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":null,"slug":"diagnosis-of-breast-cancer-using-hybrid","title":"Diagnosis of Breast Cancer Based on Modern Mammography using Hybrid Transfer Learning","date":"2020-03-23","arxiv_id":"2003.13503","n_code_links":0,"syntology":null},{"paper":"/paper/electra-pre-training-text-encoders-as-1","slug":"electra-pre-training-text-encoders-as-1","title":"ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators","date":"2020-03-23","arxiv_id":"2003.10555","n_code_links":19,"syntology":{"ran":31,"of":40,"n_ran_checked":18,"n_instrument":13,"unverified":9,"pointer_only":10,"phrase":"31 ran (of which 7 constructed an object rather than computing a result; 18 with no instrument failure: 2 honoured, 2 violated, 14 with no contract checked; 13 where Syntology's instrument failed) · 9 unverified","official":{"repos":["google-research/electra"],"state":"official: no sample here; runs from other or unrecorded repositories","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed","unlocated"]}}},{"paper":"/paper/inherent-adversarial-robustness-of-deep","slug":"inherent-adversarial-robustness-of-deep","title":"Inherent Adversarial Robustness of Deep Spiking Neural Networks: Effects of Discrete Input Encoding and Non-Linear Activations","date":"2020-03-23","arxiv_id":"2003.10399","n_code_links":1,"syntology":null},{"paper":"/paper/label-noise-types-and-their-effects-on-deep","slug":"label-noise-types-and-their-effects-on-deep","title":"Label Noise Types and Their Effects on Deep Learning","date":"2020-03-23","arxiv_id":"2003.10471","n_code_links":2,"syntology":null},{"paper":"/paper/multi-plateau-ensemble-for-endoscopic","slug":"multi-plateau-ensemble-for-endoscopic","title":"Multi-Plateau Ensemble for Endoscopic Artefact Segmentation and Detection","date":"2020-03-23","arxiv_id":"2003.10129","n_code_links":1,"syntology":null},{"paper":"/paper/multipath-computation-offloading-for-mobile","slug":"multipath-computation-offloading-for-mobile","title":"Multipath Computation Offloading for Mobile Augmented Reality","date":"2020-03-23","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":null,"slug":"bs-nas-broadening-and-shrinking-one-shot-nas","title":"BS-NAS: Broadening-and-Shrinking One-Shot NAS with Searchable Numbers of Channels","date":"2020-03-22","arxiv_id":"2003.09821","n_code_links":0,"syntology":null},{"paper":"/paper/pairwise-multi-class-document-classification","slug":"pairwise-multi-class-document-classification","title":"Pairwise Multi-Class Document Classification for Semantic Relations between Wikipedia Articles","date":"2020-03-22","arxiv_id":"2003.09881","n_code_links":4,"syntology":null},{"paper":null,"slug":"analyzing-word-translation-of-transformer","title":"Probing Word Translations in the Transformer and Trading Decoder for Encoder Layers","date":"2020-03-21","arxiv_id":"2003.09586","n_code_links":0,"syntology":null},{"paper":"/paper/exploring-categorical-regularization-for","slug":"exploring-categorical-regularization-for","title":"Exploring Categorical Regularization for Domain Adaptive Object Detection","date":"2020-03-20","arxiv_id":"2003.09152","n_code_links":1,"syntology":null},{"paper":"/paper/ftt-nas-discovering-fault-tolerant-neural","slug":"ftt-nas-discovering-fault-tolerant-neural","title":"FTT-NAS: Discovering Fault-Tolerant Convolutional Neural Architecture","date":"2020-03-20","arxiv_id":"2003.10375","n_code_links":1,"syntology":null},{"paper":"/paper/tnt-kid-transformer-based-neural-tagger-for","slug":"tnt-kid-transformer-based-neural-tagger-for","title":"TNT-KID: Transformer-based Neural Tagger for Keyword Identification","date":"2020-03-20","arxiv_id":"2003.09166","n_code_links":1,"syntology":null},{"paper":"/paper/beheshti-ner-persian-named-entity-recognition","slug":"beheshti-ner-persian-named-entity-recognition","title":"Beheshti-NER: Persian Named Entity Recognition Using BERT","date":"2020-03-19","arxiv_id":"2003.08875","n_code_links":3,"syntology":null},{"paper":null,"slug":"detecting-lane-and-road-markings-at-a","title":"Detecting Lane and Road Markings at A Distance with Perspective Transformer Layers","date":"2020-03-19","arxiv_id":"2003.08550","n_code_links":0,"syntology":null},{"paper":null,"slug":"diversity-density-and-homogeneity","title":"Diversity, Density, and Homogeneity: Quantitative Characteristic Metrics for Text Collections","date":"2020-03-19","arxiv_id":"2003.08529","n_code_links":0,"syntology":null},{"paper":null,"slug":"exemplar-normalization-for-learning-deep","title":"Exemplar Normalization for Learning Deep Representation","date":"2020-03-19","arxiv_id":"2003.08761","n_code_links":0,"syntology":null},{"paper":"/paper/gan-compression-efficient-architectures-for","slug":"gan-compression-efficient-architectures-for","title":"GAN Compression: Efficient Architectures for Interactive Conditional GANs","date":"2020-03-19","arxiv_id":"2003.08936","n_code_links":1,"syntology":null},{"paper":null,"slug":"layerwise-knowledge-extraction-from-deep","title":"Layerwise Knowledge Extraction from Deep Convolutional Networks","date":"2020-03-19","arxiv_id":"2003.09000","n_code_links":0,"syntology":null},{"paper":"/paper/lifelong-learning-with-searchable-extension","slug":"lifelong-learning-with-searchable-extension","title":"Lifelong Learning with Searchable Extension Units","date":"2020-03-19","arxiv_id":"2003.08559","n_code_links":1,"syntology":null},{"paper":null,"slug":"normalized-and-geometry-aware-self-attention","title":"Normalized and Geometry-Aware Self-Attention Network for Image Captioning","date":"2020-03-19","arxiv_id":"2003.08897","n_code_links":0,"syntology":null},{"paper":null,"slug":"temporal-embeddings-and-transformer-models","title":"Temporal Embeddings and Transformer Models for Narrative Text Understanding","date":"2020-03-19","arxiv_id":"2003.08811","n_code_links":0,"syntology":null},{"paper":null,"slug":"the-value-of-text-for-small-business-default","title":"The value of text for small business default prediction: A deep learning approach","date":"2020-03-19","arxiv_id":"2003.08964","n_code_links":0,"syntology":null},{"paper":null,"slug":"scene-text-recognition-via-transformer","title":"Scene Text Recognition via Transformer","date":"2020-03-18","arxiv_id":"2003.08077","n_code_links":0,"syntology":null},{"paper":"/paper/transformer-networks-for-trajectory","slug":"transformer-networks-for-trajectory","title":"Transformer Networks for Trajectory Forecasting","date":"2020-03-18","arxiv_id":"2003.08111","n_code_links":1,"syntology":{"ran":5,"of":6,"n_ran_checked":5,"n_instrument":0,"unverified":1,"pointer_only":0,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 0 violated, 5 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","official":{"repos":["FGiuliari/Trajectory-Transformer"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":1,"ran_from_kinds":["official"]}}},{"paper":"/paper/tttttackling-winogrande-schemas","slug":"tttttackling-winogrande-schemas","title":"TTTTTackling WinoGrande Schemas","date":"2020-03-18","arxiv_id":"2003.08380","n_code_links":0,"syntology":null},{"paper":"/paper/x-stance-a-multilingual-multi-target-dataset","slug":"x-stance-a-multilingual-multi-target-dataset","title":"X-Stance: A Multilingual Multi-Target Dataset for Stance Detection","date":"2020-03-18","arxiv_id":"2003.08385","n_code_links":1,"syntology":null},{"paper":null,"slug":"author2vec-a-framework-for-generating-user","title":"Author2Vec: A Framework for Generating User Embedding","date":"2020-03-17","arxiv_id":"2003.11627","n_code_links":0,"syntology":null},{"paper":"/paper/breast-cancer-detection-using-convolutional","slug":"breast-cancer-detection-using-convolutional","title":"Breast Cancer Detection Using Convolutional Neural Networks","date":"2020-03-17","arxiv_id":"2003.07911","n_code_links":1,"syntology":null},{"paper":"/paper/calibration-of-pre-trained-transformers","slug":"calibration-of-pre-trained-transformers","title":"Calibration of Pre-trained Transformers","date":"2020-03-17","arxiv_id":"2003.07892","n_code_links":1,"syntology":null},{"paper":null,"slug":"deep-object-detection-based-mitosis-analysis","title":"Deep Object Detection based Mitosis Analysis in Breast Cancer Histopathological Images","date":"2020-03-17","arxiv_id":"2003.08803","n_code_links":0,"syntology":null},{"paper":"/paper/human-activity-recognition-from-wearable","slug":"human-activity-recognition-from-wearable","title":"Human Activity Recognition from Wearable Sensor Data Using Self-Attention","date":"2020-03-17","arxiv_id":"2003.09018","n_code_links":2,"syntology":null},{"paper":"/paper/multi-modal-dense-video-captioning","slug":"multi-modal-dense-video-captioning","title":"Multi-modal Dense Video Captioning","date":"2020-03-17","arxiv_id":"2003.07758","n_code_links":4,"syntology":null},{"paper":"/paper/po-emo-conceptualization-annotation-and","slug":"po-emo-conceptualization-annotation-and","title":"PO-EMO: Conceptualization, Annotation, and Modeling of Aesthetic Emotions in German and English Poetry","date":"2020-03-17","arxiv_id":"2003.07723","n_code_links":1,"syntology":null},{"paper":"/paper/rethinking-batch-normalization-in","slug":"rethinking-batch-normalization-in","title":"PowerNorm: Rethinking Batch Normalization in Transformers","date":"2020-03-17","arxiv_id":"2003.07845","n_code_links":1,"syntology":null},{"paper":null,"slug":"a-survey-on-contextual-embeddings","title":"A Survey on Contextual Embeddings","date":"2020-03-16","arxiv_id":"2003.07278","n_code_links":0,"syntology":null},{"paper":"/paper/cost-sensitive-bert-for-generalisable-1","slug":"cost-sensitive-bert-for-generalisable-1","title":"Cost-Sensitive BERT for Generalisable Sentence Classification with Imbalanced Data","date":"2020-03-16","arxiv_id":"2003.11563","n_code_links":1,"syntology":null},{"paper":null,"slug":"explaining-memorization-and-generalization-a","title":"Weak and Strong Gradient Directions: Explaining Memorization, Generalization, and Hardness of Examples at Scale","date":"2020-03-16","arxiv_id":"2003.07422","n_code_links":0,"syntology":null},{"paper":"/paper/taco-trash-annotations-in-context-for-litter","slug":"taco-trash-annotations-in-context-for-litter","title":"TACO: Trash Annotations in Context for Litter Detection","date":"2020-03-16","arxiv_id":"2003.06975","n_code_links":1,"syntology":{"ran":8,"of":9,"n_ran_checked":8,"n_instrument":0,"unverified":1,"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) · 1 unverified","official":{"repos":["pedropro/TACO"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":8,"n_unverified":1,"ran_from_kinds":["official"]}}},{"paper":"/paper/trans-blstm-transformer-with-bidirectional","slug":"trans-blstm-transformer-with-bidirectional","title":"TRANS-BLSTM: Transformer with Bidirectional LSTM for Language Understanding","date":"2020-03-16","arxiv_id":"2003.07000","n_code_links":0,"syntology":null},{"paper":null,"slug":"analysis-of-softmax-approximation-for-deep","title":"A Simple Probabilistic Method for Deep Classification under Input-Dependent Label Noise","date":"2020-03-15","arxiv_id":"2003.06778","n_code_links":0,"syntology":null},{"paper":null,"slug":"deep-affinity-net-instance-segmentation-via","title":"Deep Affinity Net: Instance Segmentation via Affinity","date":"2020-03-15","arxiv_id":"2003.06849","n_code_links":0,"syntology":null},{"paper":null,"slug":"performance-evaluation-of-advanced-deep","title":"Performance Evaluation of Advanced Deep Learning Architectures for Offline Handwritten Character Recognition","date":"2020-03-15","arxiv_id":"2003.06794","n_code_links":0,"syntology":null},{"paper":"/paper/vision-dialog-navigation-by-exploring-cross","slug":"vision-dialog-navigation-by-exploring-cross","title":"Vision-Dialog Navigation by Exploring Cross-modal Memory","date":"2020-03-15","arxiv_id":"2003.06745","n_code_links":1,"syntology":null},{"paper":"/paper/document-ranking-with-a-pretrained-sequence","slug":"document-ranking-with-a-pretrained-sequence","title":"Document Ranking with a Pretrained Sequence-to-Sequence Model","date":"2020-03-14","arxiv_id":"2003.06713","n_code_links":2,"syntology":null},{"paper":"/paper/efficient-backbone-search-for-scene-text","slug":"efficient-backbone-search-for-scene-text","title":"AutoSTR: Efficient Backbone Search for Scene Text Recognition","date":"2020-03-14","arxiv_id":"2003.06567","n_code_links":2,"syntology":null},{"paper":null,"slug":"finnish-language-modeling-with-deep","title":"Finnish Language Modeling with Deep Transformer Models","date":"2020-03-14","arxiv_id":"2003.11562","n_code_links":0,"syntology":null},{"paper":null,"slug":"identifying-individual-dogs-in-social-media","title":"Identifying Individual Dogs in Social Media Images","date":"2020-03-14","arxiv_id":"2003.06705","n_code_links":0,"syntology":null},{"paper":null,"slug":"advanced-deep-learning-methodologies-for-skin","title":"Advanced Deep Learning Methodologies for Skin Cancer Classification in Prodromal Stages","date":"2020-03-13","arxiv_id":"2003.06356","n_code_links":0,"syntology":null},{"paper":null,"slug":"biggan-based-bayesian-reconstruction-of","title":"BigGAN-based Bayesian reconstruction of natural images from human brain activity","date":"2020-03-13","arxiv_id":"2003.06105","n_code_links":0,"syntology":null},{"paper":null,"slug":"edge-tailored-perception-fast-inferencing-in","title":"LCP: A Low-Communication Parallelization Method for Fast Neural Network Inference in Image Recognition","date":"2020-03-13","arxiv_id":"2003.06464","n_code_links":0,"syntology":null},{"paper":"/paper/encoder-decoder-based-convolutional-neural-1","slug":"encoder-decoder-based-convolutional-neural-1","title":"Encoder-Decoder Based Convolutional Neural Network with Multi-Scale-Aware Modules for Crowd Counting","date":"2020-03-13","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":null,"slug":"generating-major-types-of-chinese-classical","title":"Generating Major Types of Chinese Classical Poetry in a Uniformed Framework","date":"2020-03-13","arxiv_id":"2003.11528","n_code_links":0,"syntology":null},{"paper":"/paper/learning-to-encode-position-for-transformer","slug":"learning-to-encode-position-for-transformer","title":"Learning to Encode Position for Transformer with Continuous Dynamical Model","date":"2020-03-13","arxiv_id":"2003.09229","n_code_links":1,"syntology":{"ran":3,"of":6,"n_ran_checked":1,"n_instrument":2,"unverified":3,"pointer_only":6,"phrase":"3 ran (of which 2 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 2 where Syntology's instrument failed) · 3 unverified","official":null}},{"paper":null,"slug":"analyzing-visual-representations-in-embodied","title":"Analyzing Visual Representations in Embodied Navigation Tasks","date":"2020-03-12","arxiv_id":"2003.05993","n_code_links":0,"syntology":null},{"paper":"/paper/conditional-convolutions-for-instance","slug":"conditional-convolutions-for-instance","title":"Conditional Convolutions for Instance Segmentation","date":"2020-03-12","arxiv_id":"2003.05664","n_code_links":7,"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":["aim-uofa/AdelaiDet"],"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/efficient-content-based-sparse-attention-with-1","slug":"efficient-content-based-sparse-attention-with-1","title":"Efficient Content-Based Sparse Attention with Routing Transformers","date":"2020-03-12","arxiv_id":"2003.05997","n_code_links":2,"syntology":{"ran":3,"of":3,"n_ran_checked":3,"n_instrument":0,"unverified":0,"pointer_only":2,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 1 honoured, 2 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","official":null}},{"paper":"/paper/encoder-decoder-based-convolutional-neural","slug":"encoder-decoder-based-convolutional-neural","title":"Encoder-Decoder Based Convolutional Neural Networks with Multi-Scale-Aware Modules for Crowd Counting","date":"2020-03-12","arxiv_id":"2003.05586","n_code_links":2,"syntology":null},{"paper":null,"slug":"syncgan-using-learnable-class-specific-priors","title":"SynCGAN: Using learnable class specific priors to generate synthetic data for improving classifier performance on cytological images","date":"2020-03-12","arxiv_id":"2003.05712","n_code_links":0,"syntology":null},{"paper":"/paper/equalization-loss-for-long-tailed-object","slug":"equalization-loss-for-long-tailed-object","title":"Equalization Loss for Long-Tailed Object Recognition","date":"2020-03-11","arxiv_id":"2003.05176","n_code_links":1,"syntology":{"ran":9,"of":12,"n_ran_checked":8,"n_instrument":1,"unverified":3,"pointer_only":12,"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) · 3 unverified","official":{"repos":["tztztztztz/eql.detectron2"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":0,"n_ran_no_instrument_failure":8,"n_unverified":3,"ran_from_kinds":["official"]}}},{"paper":"/paper/hurtful-words-quantifying-biases-in-clinical","slug":"hurtful-words-quantifying-biases-in-clinical","title":"Hurtful Words: Quantifying Biases in Clinical Contextual Word Embeddings","date":"2020-03-11","arxiv_id":"2003.11515","n_code_links":1,"syntology":null},{"paper":"/paper/investigating-entity-knowledge-in-bert-with-1","slug":"investigating-entity-knowledge-in-bert-with-1","title":"Investigating Entity Knowledge in BERT with Simple Neural End-To-End Entity Linking","date":"2020-03-11","arxiv_id":"2003.05473","n_code_links":1,"syntology":{"ran":4,"of":4,"n_ran_checked":4,"n_instrument":0,"unverified":0,"pointer_only":0,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","official":{"repos":["samuelbroscheit/entity_knowledge_in_bert"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"kernel-quantization-for-efficient-network","title":"Kernel Quantization for Efficient Network Compression","date":"2020-03-11","arxiv_id":"2003.05148","n_code_links":0,"syntology":null},{"paper":"/paper/keyword-attentive-deep-semantic-matching","slug":"keyword-attentive-deep-semantic-matching","title":"Keyword-Attentive Deep Semantic Matching","date":"2020-03-11","arxiv_id":"2003.11516","n_code_links":1,"syntology":null},{"paper":"/paper/ponas-progressive-one-shot-neural","slug":"ponas-progressive-one-shot-neural","title":"PONAS: Progressive One-shot Neural Architecture Search for Very Efficient Deployment","date":"2020-03-11","arxiv_id":"2003.05112","n_code_links":1,"syntology":null},{"paper":"/paper/softmax-splatting-for-video-frame","slug":"softmax-splatting-for-video-frame","title":"Softmax Splatting for Video Frame Interpolation","date":"2020-03-11","arxiv_id":"2003.05534","n_code_links":2,"syntology":{"ran":0,"of":1,"n_ran_checked":0,"n_instrument":0,"unverified":1,"pointer_only":0,"phrase":"0 ran · 1 unverified","official":{"repos":["sniklaus/softmax-splatting"],"state":"official: not harvested","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":[]}}}],"record_sha256":"c211ace5214fbc69e2019e6b226526ebbe7544328bc9f4f1f2985ccf930d70f1","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}