{"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/relu/papers/69","list_of":"/method/relu","method":"ReLU","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":69,"pages_in_order":104,"rows_per_page":100,"rows":[6801,6900],"of":10350,"counts":{"archive_papers_tagged":10350,"with_a_code_link":4256,"where_syntology_ran_a_sample":1079,"not_listed_spam_title":0,"listed":10350,"listed_where_code_ran":1079,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":909,"every_run_a_failure_of_syntologys_instrument":170,"listed_with_a_run_with_no_instrument_failure":909,"listed_every_run_a_failure_of_syntologys_instrument":170,"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/relu","prev":"/method/relu/papers/68","next":"/method/relu/papers/70","papers":[{"paper":null,"slug":"line-art-correlation-matching-network-for","title":"Line Art Correlation Matching Feature Transfer Network for Automatic Animation Colorization","date":"2020-04-14","arxiv_id":"2004.06718","n_code_links":0,"syntology":null},{"paper":"/paper/res-cr-net-a-residual-network-with-a-novel","slug":"res-cr-net-a-residual-network-with-a-novel","title":"Res-CR-Net, a residual network with a novel architecture optimized for the semantic segmentation of microscopy images","date":"2020-04-14","arxiv_id":"2004.08246","n_code_links":1,"syntology":null},{"paper":null,"slug":"towards-robust-classification-with-image","title":"Towards Robust Classification with Image Quality Assessment","date":"2020-04-14","arxiv_id":"2004.06288","n_code_links":0,"syntology":null},{"paper":null,"slug":"transfer-learning-with-deep-convolutional","title":"Transfer Learning with Deep Convolutional Neural Network (CNN) for Pneumonia Detection using Chest X-ray","date":"2020-04-14","arxiv_id":"2004.06578","n_code_links":0,"syntology":null},{"paper":"/paper/transformer-based-grapheme-to-phoneme-1","slug":"transformer-based-grapheme-to-phoneme-1","title":"Transformer based Grapheme-to-Phoneme Conversion","date":"2020-04-14","arxiv_id":"2004.06338","n_code_links":1,"syntology":null},{"paper":"/paper/dynamic-r-cnn-towards-high-quality-object","slug":"dynamic-r-cnn-towards-high-quality-object","title":"Dynamic R-CNN: Towards High Quality Object Detection via Dynamic Training","date":"2020-04-13","arxiv_id":"2004.06002","n_code_links":3,"syntology":{"ran":9,"of":18,"n_ran_checked":9,"n_instrument":0,"unverified":9,"pointer_only":1,"phrase":"9 ran (of which 0 constructed an object rather than computing a result; 9 with no instrument failure: 0 honoured, 1 violated, 8 with no contract checked; 0 where Syntology's instrument failed) · 9 unverified","official":{"repos":["hkzhang95/DynamicRCNN"],"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":["listed","official"]}}},{"paper":null,"slug":"multilevel-minimization-for-deep-residual","title":"Multilevel Minimization for Deep Residual Networks","date":"2020-04-13","arxiv_id":"2004.06196","n_code_links":0,"syntology":null},{"paper":null,"slug":"proformer-towards-on-device-lsh-projection","title":"ProFormer: Towards On-Device LSH Projection Based Transformers","date":"2020-04-13","arxiv_id":"2004.05801","n_code_links":0,"syntology":null},{"paper":"/paper/relation-transformer-network","slug":"relation-transformer-network","title":"Relation Transformer Network","date":"2020-04-13","arxiv_id":"2004.06193","n_code_links":1,"syntology":null},{"paper":null,"slug":"spcnet-spatial-preserve-and-content-aware","title":"SPCNet:Spatial Preserve and Content-aware Network for Human Pose Estimation","date":"2020-04-13","arxiv_id":"2004.05834","n_code_links":0,"syntology":null},{"paper":"/paper/topology-of-deep-neural-networks","slug":"topology-of-deep-neural-networks","title":"Topology of deep neural networks","date":"2020-04-13","arxiv_id":"2004.06093","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":null}},{"paper":"/paper/patchattack-a-black-box-texture-based-attack","slug":"patchattack-a-black-box-texture-based-attack","title":"PatchAttack: A Black-box Texture-based Attack with Reinforcement Learning","date":"2020-04-12","arxiv_id":"2004.05682","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":["Chenglin-Yang/PatchAttack"],"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":"relational-learning-between-multiple","title":"Relational Learning between Multiple Pulmonary Nodules via Deep Set Attention Transformers","date":"2020-04-12","arxiv_id":"2004.05640","n_code_links":0,"syntology":null},{"paper":"/paper/towards-an-efficient-deep-learning-model-for","slug":"towards-an-efficient-deep-learning-model-for","title":"Towards an Effective and Efficient Deep Learning Model for COVID-19 Patterns Detection in X-ray Images","date":"2020-04-12","arxiv_id":"2004.05717","n_code_links":2,"syntology":null},{"paper":"/paper/verification-of-deep-convolutional-neural","slug":"verification-of-deep-convolutional-neural","title":"Verification of Deep Convolutional Neural Networks Using ImageStars","date":"2020-04-12","arxiv_id":"2004.05511","n_code_links":2,"syntology":null},{"paper":null,"slug":"detection-of-covid-19-from-chest-x-ray-images","title":"Detection of Covid-19 From Chest X-ray Images Using Artificial Intelligence: An Early Review","date":"2020-04-11","arxiv_id":"2004.05436","n_code_links":0,"syntology":null},{"paper":"/paper/fda-fourier-domain-adaptation-for-semantic","slug":"fda-fourier-domain-adaptation-for-semantic","title":"FDA: Fourier Domain Adaptation for Semantic Segmentation","date":"2020-04-11","arxiv_id":"2004.05498","n_code_links":3,"syntology":{"ran":2,"of":3,"n_ran_checked":1,"n_instrument":1,"unverified":1,"pointer_only":3,"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) · 1 unverified","official":{"repos":["YanchaoYang/FDA"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":1,"ran_from_kinds":["official","unlocated"]}}},{"paper":null,"slug":"coronet-a-deep-neural-network-for-detection","title":"CoroNet: A deep neural network for detection and diagnosis of COVID-19 from chest x-ray images","date":"2020-04-10","arxiv_id":"2004.04931","n_code_links":0,"syntology":null},{"paper":"/paper/deep-residual-correction-network-for-partial","slug":"deep-residual-correction-network-for-partial","title":"Deep Residual Correction Network for Partial Domain Adaptation","date":"2020-04-10","arxiv_id":"2004.04914","n_code_links":1,"syntology":null},{"paper":"/paper/improved-residual-networks-for-image-and","slug":"improved-residual-networks-for-image-and","title":"Improved Residual Networks for Image and Video Recognition","date":"2020-04-10","arxiv_id":"2004.04989","n_code_links":2,"syntology":{"ran":3,"of":4,"n_ran_checked":1,"n_instrument":2,"unverified":1,"pointer_only":1,"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) · 1 unverified","official":{"repos":["iduta/iresnet"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":1,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"stacked-convolutional-deep-encoding-network","title":"Stacked Convolutional Deep Encoding Network for Video-Text Retrieval","date":"2020-04-10","arxiv_id":"2004.04959","n_code_links":0,"syntology":null},{"paper":null,"slug":"telling-bert-s-full-story-from-local","title":"Telling BERT's full story: from Local Attention to Global Aggregation","date":"2020-04-10","arxiv_id":"2004.05916","n_code_links":0,"syntology":null},{"paper":"/paper/analysis-on-deeplabv3-performance-for","slug":"analysis-on-deeplabv3-performance-for","title":"Analysis on DeepLabV3+ Performance for Automatic Steel Defects Detection","date":"2020-04-09","arxiv_id":"2004.04822","n_code_links":1,"syntology":null},{"paper":null,"slug":"capsules-for-biomedical-image-segmentation","title":"Capsules for Biomedical Image Segmentation","date":"2020-04-09","arxiv_id":"2004.04736","n_code_links":0,"syntology":null},{"paper":null,"slug":"centermask-single-shot-instance-segmentation","title":"CenterMask: single shot instance segmentation with point representation","date":"2020-04-09","arxiv_id":"2004.04446","n_code_links":0,"syntology":null},{"paper":"/paper/cortical-surface-registration-using","slug":"cortical-surface-registration-using","title":"Cortical surface registration using unsupervised learning","date":"2020-04-09","arxiv_id":"2004.04617","n_code_links":1,"syntology":null},{"paper":"/paper/instance-aware-context-focused-and-memory","slug":"instance-aware-context-focused-and-memory","title":"Instance-aware, Context-focused, and Memory-efficient Weakly Supervised Object Detection","date":"2020-04-09","arxiv_id":"2004.04725","n_code_links":2,"syntology":{"ran":5,"of":5,"n_ran_checked":5,"n_instrument":0,"unverified":0,"pointer_only":0,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 0 violated, 5 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","official":{"repos":["NVlabs/wetectron"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]}}},{"paper":"/paper/on-optimal-transformer-depth-for-low-resource","slug":"on-optimal-transformer-depth-for-low-resource","title":"On Optimal Transformer Depth for Low-Resource Language Translation","date":"2020-04-09","arxiv_id":"2004.04418","n_code_links":1,"syntology":null},{"paper":null,"slug":"a-deep-learning-approach-for-determining","title":"A Deep Learning Approach for Determining Effects of Tuta Absoluta in Tomato Plants","date":"2020-04-08","arxiv_id":"2004.04023","n_code_links":0,"syntology":null},{"paper":"/paper/adaptive-transformers-in-rl","slug":"adaptive-transformers-in-rl","title":"Adaptive Transformers in RL","date":"2020-04-08","arxiv_id":"2004.03761","n_code_links":1,"syntology":null},{"paper":"/paper/diverse-controllable-and-keyphrase-aware-a","slug":"diverse-controllable-and-keyphrase-aware-a","title":"Diverse, Controllable, and Keyphrase-Aware: A Corpus and Method for News Multi-Headline Generation","date":"2020-04-08","arxiv_id":"2004.03875","n_code_links":1,"syntology":null},{"paper":"/paper/lung-nodule-detection-and-classification-from","slug":"lung-nodule-detection-and-classification-from","title":"Lung nodule detection and classification from Thorax CT-scan using RetinaNet with transfer learning","date":"2020-04-08","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":"/paper/poor-man-s-bert-smaller-and-faster","slug":"poor-man-s-bert-smaller-and-faster","title":"On the Effect of Dropping Layers of Pre-trained Transformer Models","date":"2020-04-08","arxiv_id":"2004.03844","n_code_links":4,"syntology":{"ran":7,"of":8,"n_ran_checked":4,"n_instrument":3,"unverified":1,"pointer_only":1,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 1 honoured, 0 violated, 3 with no contract checked; 3 where Syntology's instrument failed) · 1 unverified","official":{"repos":["hsajjad/transformers"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":0,"ran_from_kinds":["listed","official"]}}},{"paper":null,"slug":"skin-diseases-detection-using-lbp-and-wld-an","title":"Skin Diseases Detection using LBP and WLD- An Ensembling Approach","date":"2020-04-08","arxiv_id":"2004.04122","n_code_links":0,"syntology":null},{"paper":"/paper/the-loss-surfaces-of-neural-networks-with","slug":"the-loss-surfaces-of-neural-networks-with","title":"The Loss Surfaces of Neural Networks with General Activation Functions","date":"2020-04-08","arxiv_id":"2004.03959","n_code_links":1,"syntology":null},{"paper":null,"slug":"bayesian-aggregation-improves-traditional","title":"Bayesian aggregation improves traditional single image crop classification approaches","date":"2020-04-07","arxiv_id":"2004.03468","n_code_links":0,"syntology":null},{"paper":"/paper/breastscreening-on-the-use-of-multi-modality","slug":"breastscreening-on-the-use-of-multi-modality","title":"BreastScreening: On the Use of Multi-Modality in Medical Imaging Diagnosis","date":"2020-04-07","arxiv_id":"2004.03500","n_code_links":8,"syntology":null},{"paper":"/paper/byte-pair-encoding-is-suboptimal-for-language","slug":"byte-pair-encoding-is-suboptimal-for-language","title":"Byte Pair Encoding is Suboptimal for Language Model Pretraining","date":"2020-04-07","arxiv_id":"2004.03720","n_code_links":1,"syntology":null},{"paper":"/paper/channel-attention-residual-u-net-for-retinal","slug":"channel-attention-residual-u-net-for-retinal","title":"Channel Attention Residual U-Net for Retinal Vessel Segmentation","date":"2020-04-07","arxiv_id":"2004.03702","n_code_links":2,"syntology":null},{"paper":"/paper/dense-residual-network-for-retinal-vessel","slug":"dense-residual-network-for-retinal-vessel","title":"Dense Residual Network for Retinal Vessel Segmentation","date":"2020-04-07","arxiv_id":"2004.03697","n_code_links":2,"syntology":null},{"paper":"/paper/feature-pyramid-grids","slug":"feature-pyramid-grids","title":"Feature Pyramid Grids","date":"2020-04-07","arxiv_id":"2004.03580","n_code_links":1,"syntology":null},{"paper":null,"slug":"iconify-converting-photographs-into-icons","title":"Iconify: Converting Photographs into Icons","date":"2020-04-07","arxiv_id":"2004.03179","n_code_links":0,"syntology":null},{"paper":"/paper/orthant-based-proximal-stochastic-gradient","slug":"orthant-based-proximal-stochastic-gradient","title":"Orthant Based Proximal Stochastic Gradient Method for $\\ell_1$-Regularized Optimization","date":"2020-04-07","arxiv_id":"2004.03639","n_code_links":1,"syntology":null},{"paper":"/paper/probabilistic-spatial-transformers-for","slug":"probabilistic-spatial-transformers-for","title":"Probabilistic Spatial Transformer Networks","date":"2020-04-07","arxiv_id":"2004.03637","n_code_links":1,"syntology":null},{"paper":"/paper/sa-unet-spatial-attention-u-net-for-retinal","slug":"sa-unet-spatial-attention-u-net-for-retinal","title":"SA-UNet: Spatial Attention U-Net for Retinal Vessel Segmentation","date":"2020-04-07","arxiv_id":"2004.03696","n_code_links":4,"syntology":null},{"paper":null,"slug":"super-resolution-of-clinical-ct-volumes-with","title":"Super-resolution of clinical CT volumes with modified CycleGAN using micro CT volumes","date":"2020-04-07","arxiv_id":"2004.03272","n_code_links":0,"syntology":null},{"paper":"/paper/toward-fine-grained-facial-expression","slug":"toward-fine-grained-facial-expression","title":"Toward Fine-grained Facial Expression Manipulation","date":"2020-04-07","arxiv_id":"2004.03132","n_code_links":1,"syntology":null},{"paper":"/paper/transformers-to-learn-hierarchical-contexts","slug":"transformers-to-learn-hierarchical-contexts","title":"Transformers to Learn Hierarchical Contexts in Multiparty Dialogue for Span-based Question Answering","date":"2020-04-07","arxiv_id":"2004.03561","n_code_links":1,"syntology":null},{"paper":null,"slug":"u-net-using-stacked-dilated-convolutions-for","title":"U-Net Using Stacked Dilated Convolutions for Medical Image Segmentation","date":"2020-04-07","arxiv_id":"2004.03466","n_code_links":0,"syntology":null},{"paper":null,"slug":"within-the-lack-of-covid-19-benchmark-dataset","title":"Within the Lack of COVID-19 Benchmark Dataset: A Novel GAN with Deep Transfer Learning for Corona-virus Detection in Chest X-ray Images","date":"2020-04-07","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":"/paper/a-learning-framework-for-n-bit-quantized","slug":"a-learning-framework-for-n-bit-quantized","title":"A Learning Framework for n-bit Quantized Neural Networks toward FPGAs","date":"2020-04-06","arxiv_id":"2004.02396","n_code_links":1,"syntology":null},{"paper":"/paper/a-systematic-analysis-of-morphological","slug":"a-systematic-analysis-of-morphological","title":"A Systematic Analysis of Morphological Content in BERT Models for Multiple Languages","date":"2020-04-06","arxiv_id":"2004.03032","n_code_links":1,"syntology":null},{"paper":"/paper/autotoon-automatic-geometric-warping-for-face","slug":"autotoon-automatic-geometric-warping-for-face","title":"AutoToon: Automatic Geometric Warping for Face Cartoon Generation","date":"2020-04-06","arxiv_id":"2004.02377","n_code_links":1,"syntology":null},{"paper":null,"slug":"cnn2gate-toward-designing-a-general-framework","title":"CNN2Gate: Toward Designing a General Framework for Implementation of Convolutional Neural Networks on FPGA","date":"2020-04-06","arxiv_id":"2004.04641","n_code_links":0,"syntology":null},{"paper":"/paper/evolving-normalization-activation-layers","slug":"evolving-normalization-activation-layers","title":"Evolving Normalization-Activation Layers","date":"2020-04-06","arxiv_id":"2004.02967","n_code_links":8,"syntology":{"ran":3,"of":5,"n_ran_checked":0,"n_instrument":3,"unverified":2,"pointer_only":3,"phrase":"3 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; 3 where Syntology's instrument failed) · 2 unverified","official":null}},{"paper":"/paper/ganspace-discovering-interpretable-gan","slug":"ganspace-discovering-interpretable-gan","title":"GANSpace: Discovering Interpretable GAN Controls","date":"2020-04-06","arxiv_id":"2004.02546","n_code_links":2,"syntology":{"ran":3,"of":4,"n_ran_checked":0,"n_instrument":3,"unverified":1,"pointer_only":0,"phrase":"3 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; 3 where Syntology's instrument failed) · 1 unverified","official":{"repos":["harskish/ganspace"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":["official"]}}},{"paper":"/paper/arbitrary-scale-super-resolution-for-brain","slug":"arbitrary-scale-super-resolution-for-brain","title":"Arbitrary Scale Super-Resolution for Brain MRI Images","date":"2020-04-05","arxiv_id":"2004.02086","n_code_links":1,"syntology":null},{"paper":"/paper/bisenet-v2-bilateral-network-with-guided","slug":"bisenet-v2-bilateral-network-with-guided","title":"BiSeNet V2: Bilateral Network with Guided Aggregation for Real-time Semantic Segmentation","date":"2020-04-05","arxiv_id":"2004.02147","n_code_links":7,"syntology":{"ran":7,"of":10,"n_ran_checked":6,"n_instrument":1,"unverified":3,"pointer_only":5,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 2 honoured, 3 violated, 1 with no contract checked; 1 where Syntology's instrument failed) · 3 unverified","official":null}},{"paper":null,"slug":"condenseunet-a-memory-efficient-condensely","title":"CondenseUNet: A Memory-Efficient Condensely-Connected Architecture for Bi-ventricular Blood Pool and Myocardium Segmentation","date":"2020-04-05","arxiv_id":"2004.02249","n_code_links":0,"syntology":null},{"paper":"/paper/falcon-honest-majority-maliciously-secure","slug":"falcon-honest-majority-maliciously-secure","title":"FALCON: Honest-Majority Maliciously Secure Framework for Private Deep Learning","date":"2020-04-05","arxiv_id":"2004.02229","n_code_links":2,"syntology":null},{"paper":"/paper/feature-quantization-improves-gan-training","slug":"feature-quantization-improves-gan-training","title":"Feature Quantization Improves GAN Training","date":"2020-04-05","arxiv_id":"2004.02088","n_code_links":2,"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: 0 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","official":{"repos":["YangNaruto/FQ-GAN"],"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":["found_in_text","official"]}}},{"paper":null,"slug":"syntax-driven-iterative-expansion-language","title":"Syntax-driven Iterative Expansion Language Models for Controllable Text Generation","date":"2020-04-05","arxiv_id":"2004.02211","n_code_links":0,"syntology":null},{"paper":null,"slug":"conversational-question-reformulation-via","title":"Conversational Question Reformulation via Sequence-to-Sequence Architectures and Pretrained Language Models","date":"2020-04-04","arxiv_id":"2004.01909","n_code_links":0,"syntology":null},{"paper":null,"slug":"model-based-actor-critic-gan-drl-actor-critic","title":"Model-based actor-critic: GAN (model generator) + DRL (actor-critic) => AGI","date":"2020-04-04","arxiv_id":"2004.04574","n_code_links":0,"syntology":null},{"paper":"/paper/neural-architecture-search-for-lightweight","slug":"neural-architecture-search-for-lightweight","title":"Neural Architecture Search for Lightweight Non-Local Networks","date":"2020-04-04","arxiv_id":"2004.01961","n_code_links":2,"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":["LiYingwei/AutoNL"],"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/objectnet-dataset-reanalysis-and-correction","slug":"objectnet-dataset-reanalysis-and-correction","title":"ObjectNet Dataset: Reanalysis and Correction","date":"2020-04-04","arxiv_id":"2004.02042","n_code_links":1,"syntology":{"ran":1,"of":1,"n_ran_checked":0,"n_instrument":1,"unverified":0,"pointer_only":1,"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":["aliborji/ObjectNetReanalysis"],"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":"optimization-of-image-embeddings-for-few-shot","title":"Optimization of Image Embeddings for Few Shot Learning","date":"2020-04-04","arxiv_id":"2004.02034","n_code_links":0,"syntology":null},{"paper":"/paper/pixel-consensus-voting-for-panoptic","slug":"pixel-consensus-voting-for-panoptic","title":"Pixel Consensus Voting for Panoptic Segmentation","date":"2020-04-04","arxiv_id":"2004.01849","n_code_links":0,"syntology":null},{"paper":"/paper/rational-neural-networks","slug":"rational-neural-networks","title":"Rational neural networks","date":"2020-04-04","arxiv_id":"2004.01902","n_code_links":3,"syntology":{"ran":8,"of":8,"n_ran_checked":7,"n_instrument":1,"unverified":0,"pointer_only":0,"phrase":"8 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 0 honoured, 1 violated, 6 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","official":{"repos":["NBoulle/RationalNets"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"step-sequence-to-sequence-transformer-pre","title":"Pre-training for Abstractive Document Summarization by Reinstating Source Text","date":"2020-04-04","arxiv_id":"2004.01853","n_code_links":0,"syntology":null},{"paper":"/paper/a-fast-fully-octave-convolutional-neural","slug":"a-fast-fully-octave-convolutional-neural","title":"A Fast Fully Octave Convolutional Neural Network for Document Image Segmentation","date":"2020-04-03","arxiv_id":"2004.01317","n_code_links":1,"syntology":null},{"paper":"/paper/cell-segmentation-and-tracking-using-distance","slug":"cell-segmentation-and-tracking-using-distance","title":"Cell Segmentation and Tracking using CNN-Based Distance Predictions and a Graph-Based Matching Strategy","date":"2020-04-03","arxiv_id":"2004.01486","n_code_links":1,"syntology":null},{"paper":"/paper/context-prior-for-scene-segmentation","slug":"context-prior-for-scene-segmentation","title":"Context Prior for Scene Segmentation","date":"2020-04-03","arxiv_id":"2004.01547","n_code_links":2,"syntology":null},{"paper":null,"slug":"deep-transfer-learning-for-texture","title":"Deep Transfer Learning for Texture Classification in Colorectal Cancer Histology","date":"2020-04-03","arxiv_id":"2004.01614","n_code_links":0,"syntology":null},{"paper":"/paper/google-landmarks-dataset-v2-a-large-scale","slug":"google-landmarks-dataset-v2-a-large-scale","title":"Google Landmarks Dataset v2 -- A Large-Scale Benchmark for Instance-Level Recognition and Retrieval","date":"2020-04-03","arxiv_id":"2004.01804","n_code_links":5,"syntology":null},{"paper":"/paper/lidar-based-online-3d-video-object-detection","slug":"lidar-based-online-3d-video-object-detection","title":"LiDAR-based Online 3D Video Object Detection with Graph-based Message Passing and Spatiotemporal Transformer Attention","date":"2020-04-03","arxiv_id":"2004.01389","n_code_links":1,"syntology":null},{"paper":null,"slug":"testing-pre-trained-transformer-models-for","title":"Testing pre-trained Transformer models for Lithuanian news clustering","date":"2020-04-03","arxiv_id":"2004.03461","n_code_links":0,"syntology":null},{"paper":null,"slug":"detection-of-coronavirus-covid-19-associated","title":"Detection of Coronavirus (COVID-19) Associated Pneumonia based on Generative Adversarial Networks and a Fine-Tuned Deep Transfer Learning Model using Chest X-ray Dataset","date":"2020-04-02","arxiv_id":"2004.01184","n_code_links":0,"syntology":null},{"paper":"/paper/knowing-what-where-and-when-to-look-efficient","slug":"knowing-what-where-and-when-to-look-efficient","title":"Knowing What, Where and When to Look: Efficient Video Action Modeling with Attention","date":"2020-04-02","arxiv_id":"2004.01278","n_code_links":0,"syntology":null},{"paper":null,"slug":"distance-and-equivalence-between-finite-state","title":"Distance and Equivalence between Finite State Machines and Recurrent Neural Networks: Computational results","date":"2020-04-01","arxiv_id":"2004.00478","n_code_links":0,"syntology":null},{"paper":null,"slug":"dstc8-avsd-multimodal-semantic-transformer","title":"DSTC8-AVSD: Multimodal Semantic Transformer Network with Retrieval Style Word Generator","date":"2020-04-01","arxiv_id":"2004.08299","n_code_links":0,"syntology":null},{"paper":null,"slug":"manifold-aware-cyclegan-for-high-resolution","title":"Manifold-Aware CycleGAN for High-Resolution Structural-to-DTI Synthesis","date":"2020-04-01","arxiv_id":"2004.00173","n_code_links":0,"syntology":null},{"paper":"/paper/nbdt-neural-backed-decision-trees","slug":"nbdt-neural-backed-decision-trees","title":"NBDT: Neural-Backed Decision Trees","date":"2020-04-01","arxiv_id":"2004.00221","n_code_links":2,"syntology":{"ran":18,"of":29,"n_ran_checked":4,"n_instrument":14,"unverified":11,"pointer_only":0,"phrase":"18 ran (of which 2 constructed an object rather than computing a result; 4 with no instrument failure: 1 honoured, 0 violated, 3 with no contract checked; 14 where Syntology's instrument failed) · 11 unverified","official":{"repos":["alvinwan/neural-backed-decision-trees"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":5,"ran_from_kinds":["listed","official"]}}},{"paper":"/paper/sign-language-translation-with-transformers","slug":"sign-language-translation-with-transformers","title":"Better Sign Language Translation with STMC-Transformer","date":"2020-04-01","arxiv_id":"2004.00588","n_code_links":1,"syntology":{"ran":2,"of":5,"n_ran_checked":2,"n_instrument":0,"unverified":3,"pointer_only":1,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 1 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 3 unverified","official":{"repos":["kayoyin/transformer-slt"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":3,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"tightened-convex-relaxations-for-neural","title":"Tightened Convex Relaxations for Neural Network Robustness Certification","date":"2020-04-01","arxiv_id":"2004.00570","n_code_links":0,"syntology":null},{"paper":null,"slug":"a-swiss-german-dictionary-variation-in-speech","title":"A Swiss German Dictionary: Variation in Speech and Writing","date":"2020-03-31","arxiv_id":"2004.00139","n_code_links":0,"syntology":null},{"paper":null,"slug":"attention-based-assisted-excitation-for","title":"Attention-based Assisted Excitation for Salient Object Detection","date":"2020-03-31","arxiv_id":"2003.14194","n_code_links":0,"syntology":null},{"paper":null,"slug":"automated-methods-for-detection-and","title":"Automated Methods for Detection and Classification Pneumonia based on X-Ray Images Using Deep Learning","date":"2020-03-31","arxiv_id":"2003.14363","n_code_links":0,"syntology":null},{"paper":null,"slug":"characterizing-speech-adversarial-examples","title":"Characterizing Speech Adversarial Examples Using Self-Attention U-Net Enhancement","date":"2020-03-31","arxiv_id":"2003.13917","n_code_links":0,"syntology":null},{"paper":"/paper/covid-resnet-a-deep-learning-framework-for","slug":"covid-resnet-a-deep-learning-framework-for","title":"COVID-ResNet: A Deep Learning Framework for Screening of COVID19 from Radiographs","date":"2020-03-31","arxiv_id":"2003.14395","n_code_links":1,"syntology":null},{"paper":null,"slug":"deepsumm-deep-code-summaries-using-neural","title":"DeepSumm -- Deep Code Summaries using Neural Transformer Architecture","date":"2020-03-31","arxiv_id":"2004.00998","n_code_links":0,"syntology":null},{"paper":null,"slug":"diagnosing-covid-19-pneumonia-from-x-ray-and","title":"Diagnosing COVID-19 Pneumonia from X-Ray and CT Images using Deep Learning and Transfer Learning Algorithms","date":"2020-03-31","arxiv_id":"2004.00038","n_code_links":0,"syntology":null},{"paper":null,"slug":"graph-enhanced-representation-learning-for","title":"Graph Enhanced Representation Learning for News Recommendation","date":"2020-03-31","arxiv_id":"2003.14292","n_code_links":0,"syntology":null},{"paper":null,"slug":"learning-oracle-attention-for-high-fidelity","title":"Learning Oracle Attention for High-fidelity Face Completion","date":"2020-03-31","arxiv_id":"2003.13903","n_code_links":0,"syntology":null},{"paper":null,"slug":"radiologist-level-stroke-classification-on","title":"Radiologist-level stroke classification on non-contrast CT scans with Deep U-Net","date":"2020-03-31","arxiv_id":"2003.14287","n_code_links":0,"syntology":null},{"paper":"/paper/spare3d-a-dataset-for-spatial-reasoning-on","slug":"spare3d-a-dataset-for-spatial-reasoning-on","title":"SPARE3D: A Dataset for SPAtial REasoning on Three-View Line Drawings","date":"2020-03-31","arxiv_id":"2003.14034","n_code_links":1,"syntology":null},{"paper":"/paper/towards-lifelong-self-supervision-for","slug":"towards-lifelong-self-supervision-for","title":"Towards Lifelong Self-Supervision For Unpaired Image-to-Image Translation","date":"2020-03-31","arxiv_id":"2004.00161","n_code_links":1,"syntology":null},{"paper":"/paper/x-linear-attention-networks-for-image","slug":"x-linear-attention-networks-for-image","title":"X-Linear Attention Networks for Image Captioning","date":"2020-03-31","arxiv_id":"2003.14080","n_code_links":2,"syntology":null},{"paper":null,"slug":"a-hierarchical-transformer-for-unsupervised","title":"A Hierarchical Transformer for Unsupervised Parsing","date":"2020-03-30","arxiv_id":"2003.13841","n_code_links":0,"syntology":null},{"paper":null,"slug":"ariel-volume-coding-for-sentence-generation","title":"AriEL: volume coding for sentence generation","date":"2020-03-30","arxiv_id":"2003.13600","n_code_links":0,"syntology":null}],"record_sha256":"f47abf4f95d2ffc26d031e3ee8cc5b19a9dd59aecb754da380200b92a23447bc","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}