{"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":"/task/specificity/papers/5","list_of":"/task/specificity","task":"Specificity","archive":{"snapshot":"2025-07-28"},"key_notes":{"n_ran_checked":"legacy name, kept unchanged so existing readers do not break: it counts the samples that ran with no instrument failure (honoured, violated, and ran with no contract checked); it does not mean a contract was checked, and the pages print it as 'K with no instrument failure', not 'K checked'","n_constructed":"a sub-count of the samples that ran, never subtracted from them and never a failure: an executed sample whose run returned an instance of its own class (fixture_out_type equals the entry name): the run built an object and did not compute a result (Syntology's RAN record, counts.constructed)"},"syntology_read_at":"2026-09-28T10:30:06+00:00","order":"archive","order_definition":"repositories listed in the archive (most first), then date (newest first), then slug","page":5,"pages_in_order":21,"rows_per_page":100,"rows":[401,500],"of":2094,"counts":{"archive_papers_tagged":2094,"with_a_code_link":504,"where_syntology_ran_a_sample":76,"not_listed_spam_title":0,"listed":2094,"listed_where_code_ran":76,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":65,"every_run_a_failure_of_syntologys_instrument":11,"listed_with_a_run_with_no_instrument_failure":65,"listed_every_run_a_failure_of_syntologys_instrument":11,"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":"/task/specificity","prev":"/task/specificity/papers/4","next":"/task/specificity/papers/6","papers":[{"url":"/paper/characterizing-english-variation-across","slug":"characterizing-english-variation-across","title":"Characterizing English Variation across Social Media Communities with BERT","date":"2021-02-12","arxiv_id":"2102.06820","repositories_listed":1,"syntology":null},{"url":"/paper/aura-net-robust-segmentation-of-phase","slug":"aura-net-robust-segmentation-of-phase","title":"aura-net : robust segmentation of phase-contrast microscopy images with few annotations","date":"2021-02-02","arxiv_id":"2102.01389","repositories_listed":1,"syntology":null},{"url":"/paper/covid-net-ct-2-enhanced-deep-neural-networks","slug":"covid-net-ct-2-enhanced-deep-neural-networks","title":"COVID-Net CT-2: Enhanced Deep Neural Networks for Detection of COVID-19 from Chest CT Images Through Bigger, More Diverse Learning","date":"2021-01-19","arxiv_id":"2101.07433","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_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","sample_list":"/paper/covid-net-ct-2-enhanced-deep-neural-networks#ran","syntology_url":"https://syntology.ai/paper/2101.07433","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2101.07433"}},"official":{"repos":["haydengunraj/COVIDNet-CT"],"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"]}}},{"url":"/paper/towards-facilitating-empathic-conversations","slug":"towards-facilitating-empathic-conversations","title":"Towards Facilitating Empathic Conversations in Online Mental Health Support: A Reinforcement Learning Approach","date":"2021-01-19","arxiv_id":"2101.07714","repositories_listed":1,"syntology":null},{"url":"/paper/efficient-and-visualizable-convolutional","slug":"efficient-and-visualizable-convolutional","title":"Efficient and Visualizable Convolutional Neural Networks for COVID-19 Classification Using Chest CT","date":"2020-12-22","arxiv_id":"2012.11860","repositories_listed":1,"syntology":null},{"url":"/paper/manual-clustering-and-spatial-arrangement-of","slug":"manual-clustering-and-spatial-arrangement-of","title":"Manual Clustering and Spatial Arrangement of Verbs for Multilingual Evaluation and Typology Analysis","date":"2020-12-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/exploring-the-effect-of-image-enhancement","slug":"exploring-the-effect-of-image-enhancement","title":"Exploring the Effect of Image Enhancement Techniques on COVID-19 Detection using Chest X-rays Images","date":"2020-11-25","arxiv_id":"2012.02238","repositories_listed":1,"syntology":null},{"url":"/paper/deep-libra-artificial-intelligence-method-for","slug":"deep-libra-artificial-intelligence-method-for","title":"Deep-LIBRA: Artificial intelligence method for robust quantification of breast density with independent validation in breast cancer risk assessment","date":"2020-11-13","arxiv_id":"2011.08001","repositories_listed":1,"syntology":null},{"url":"/paper/deconstruct-to-reconstruct-a-configurable","slug":"deconstruct-to-reconstruct-a-configurable","title":"Deconstruct to Reconstruct a Configurable Evaluation Metric for Open-Domain Dialogue Systems","date":"2020-11-01","arxiv_id":"2011.00483","repositories_listed":1,"syntology":{"n":7,"n_ran":5,"n_constructed":0,"n_ran_checked":5,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_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) · 2 unverified","sample_list":"/paper/deconstruct-to-reconstruct-a-configurable#ran","syntology_url":"https://syntology.ai/paper/2011.00483","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2011.00483"}},"official":{"repos":["vitouphy/usl_dialogue_metric"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/covid-fact-a-fully-automated-capsule-network","slug":"covid-fact-a-fully-automated-capsule-network","title":"COVID-FACT: A Fully-Automated Capsule Network-based Framework for Identification of COVID-19 Cases from Chest CT scans","date":"2020-10-30","arxiv_id":"2010.16041","repositories_listed":1,"syntology":null},{"url":"/paper/ct-caps-feature-extraction-based-automated","slug":"ct-caps-feature-extraction-based-automated","title":"CT-CAPS: Feature Extraction-based Automated Framework for COVID-19 Disease Identification from Chest CT Scans using Capsule Networks","date":"2020-10-30","arxiv_id":"2010.16043","repositories_listed":1,"syntology":null},{"url":"/paper/learning-to-represent-action-values-as-a-1","slug":"learning-to-represent-action-values-as-a-1","title":"Learning to Represent Action Values as a Hypergraph on the Action Vertices","date":"2020-10-28","arxiv_id":"2010.14680","repositories_listed":1,"syntology":{"n":5,"n_ran":4,"n_constructed":0,"n_ran_checked":4,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_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) · 1 unverified","sample_list":"/paper/learning-to-represent-action-values-as-a-1#ran","syntology_url":"https://syntology.ai/paper/2010.14680","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2010.14680"}},"official":{"repos":["atavakol/action-hypergraph-networks"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/neural-code-completion-with-anonymized","slug":"neural-code-completion-with-anonymized","title":"On the Embeddings of Variables in Recurrent Neural Networks for Source Code","date":"2020-10-23","arxiv_id":"2010.12693","repositories_listed":1,"syntology":null},{"url":"/paper/elaborative-simplification-content-addition","slug":"elaborative-simplification-content-addition","title":"Elaborative Simplification: Content Addition and Explanation Generation in Text Simplification","date":"2020-10-20","arxiv_id":"2010.10035","repositories_listed":1,"syntology":null},{"url":"/paper/pay-attention-to-the-cough-early-diagnosis-of","slug":"pay-attention-to-the-cough-early-diagnosis-of","title":"Pay Attention to the cough: Early Diagnosis of COVID-19 using Interpretable Symptoms Embeddings with Cough Sound Signal Processing","date":"2020-10-06","arxiv_id":"2010.02417","repositories_listed":1,"syntology":null},{"url":"/paper/can-automatic-post-editing-improve-nmt","slug":"can-automatic-post-editing-improve-nmt","title":"Can Automatic Post-Editing Improve NMT?","date":"2020-09-30","arxiv_id":"2009.14395","repositories_listed":1,"syntology":null},{"url":"/paper/statistical-control-for-spatio-temporal-meg","slug":"statistical-control-for-spatio-temporal-meg","title":"Statistical control for spatio-temporal MEG/EEG source imaging with desparsified multi-task Lasso","date":"2020-09-29","arxiv_id":"2009.14310","repositories_listed":1,"syntology":{"n":2,"n_ran":0,"n_constructed":0,"n_ran_checked":0,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"0 ran · 2 unverified","sample_list":"/paper/statistical-control-for-spatio-temporal-meg#ran","syntology_url":"https://syntology.ai/paper/2009.14310","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2009.14310"}},"official":{"repos":["ja-che/hidimstat"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":2,"ran_from_kinds":[]}}},{"url":"/paper/podsumm-podcast-audio-summarization","slug":"podsumm-podcast-audio-summarization","title":"PodSumm -- Podcast Audio Summarization","date":"2020-09-22","arxiv_id":"2009.10315","repositories_listed":1,"syntology":null},{"url":"/paper/accelerating-covid-19-differential-diagnosis","slug":"accelerating-covid-19-differential-diagnosis","title":"Accelerating COVID-19 Differential Diagnosis with Explainable Ultrasound Image Analysis","date":"2020-09-13","arxiv_id":"2009.06116","repositories_listed":1,"syntology":null},{"url":"/paper/task-specific-objectives-of-pre-trained","slug":"task-specific-objectives-of-pre-trained","title":"Dialogue-adaptive Language Model Pre-training From Quality Estimation","date":"2020-09-10","arxiv_id":"2009.04984","repositories_listed":1,"syntology":null},{"url":"/paper/learning-to-balance-specificity-and","slug":"learning-to-balance-specificity-and","title":"Learning to Balance Specificity and Invariance for In and Out of Domain Generalization","date":"2020-08-28","arxiv_id":"2008.12839","repositories_listed":1,"syntology":null},{"url":"/paper/perla-a-conversational-agent-for-depression","slug":"perla-a-conversational-agent-for-depression","title":"Perla: A Conversational Agent for Depression Screening in Digital Ecosystems. Design, Implementation and Validation","date":"2020-08-28","arxiv_id":"2008.12875","repositories_listed":1,"syntology":null},{"url":"/paper/from-connectomic-to-task-evoked-fingerprints-1","slug":"from-connectomic-to-task-evoked-fingerprints-1","title":"From Connectomic to Task-evoked Fingerprints: Individualized Prediction of Task Contrasts fromResting-state Functional Connectivity","date":"2020-08-07","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/antibody-watch-text-mining-antibody","slug":"antibody-watch-text-mining-antibody","title":"Antibody Watch: Text Mining Antibody Specificity from the Literature","date":"2020-08-05","arxiv_id":"2008.01937","repositories_listed":1,"syntology":null},{"url":"/paper/fully-automated-and-standardized-segmentation","slug":"fully-automated-and-standardized-segmentation","title":"Fully Automated and Standardized Segmentation of Adipose Tissue Compartments by Deep Learning in Three-dimensional Whole-body MRI of Epidemiological Cohort Studies","date":"2020-08-05","arxiv_id":"2008.02251","repositories_listed":1,"syntology":null},{"url":"/paper/trojaning-language-models-for-fun-and-profit","slug":"trojaning-language-models-for-fun-and-profit","title":"Trojaning Language Models for Fun and Profit","date":"2020-08-01","arxiv_id":"2008.00312","repositories_listed":1,"syntology":null},{"url":"/paper/an-uncertainty-aware-transfer-learning-based","slug":"an-uncertainty-aware-transfer-learning-based","title":"An Uncertainty-aware Transfer Learning-based Framework for Covid-19 Diagnosis","date":"2020-07-26","arxiv_id":"2007.14846","repositories_listed":1,"syntology":null},{"url":"/paper/unsupervised-multi-target-domain-adaptation-2","slug":"unsupervised-multi-target-domain-adaptation-2","title":"Unsupervised Multi-Target Domain Adaptation Through Knowledge Distillation","date":"2020-07-14","arxiv_id":"2007.07077","repositories_listed":1,"syntology":null},{"url":"/paper/non-image-data-classification-with","slug":"non-image-data-classification-with","title":"Classification with 2-D Convolutional Neural Networks for breast cancer diagnosis","date":"2020-07-07","arxiv_id":"2007.03218","repositories_listed":1,"syntology":null},{"url":"/paper/progressive-cluster-purification-for-1","slug":"progressive-cluster-purification-for-1","title":"Progressive Cluster Purification for Unsupervised Feature Learning","date":"2020-07-06","arxiv_id":"2007.02577","repositories_listed":1,"syntology":null},{"url":"/paper/a-unified-technique-for-entropy-enhancement","slug":"a-unified-technique-for-entropy-enhancement","title":"A Unified Technique for Entropy Enhancement Based Diabetic Retinopathy Detection Using Hybrid Neural Network","date":"2020-07-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/a-comparative-study-on-early-detection-of","slug":"a-comparative-study-on-early-detection-of","title":"Advance Warning Methodologies for COVID-19 using Chest X-Ray Images","date":"2020-06-07","arxiv_id":"2006.05332","repositories_listed":1,"syntology":null},{"url":"/paper/longitudinal-high-throughput-tcr-repertoire","slug":"longitudinal-high-throughput-tcr-repertoire","title":"Longitudinal high-throughput TCR repertoire profiling reveals the dynamics of T cell memory formation after mild COVID-19 infection","date":"2020-05-17","arxiv_id":"2005.08290","repositories_listed":1,"syntology":null},{"url":"/paper/classification-of-arrhythmia-by-using-deep","slug":"classification-of-arrhythmia-by-using-deep","title":"Classification of Arrhythmia by Using Deep Learning with 2-D ECG Spectral Image Representation","date":"2020-05-14","arxiv_id":"2005.06902","repositories_listed":1,"syntology":null},{"url":"/paper/multi-task-pre-training-of-deep-neural","slug":"multi-task-pre-training-of-deep-neural","title":"Multi-task pre-training of deep neural networks for digital pathology","date":"2020-05-05","arxiv_id":"2005.02561","repositories_listed":1,"syntology":null},{"url":"/paper/explain-your-move-understanding-agent-actions","slug":"explain-your-move-understanding-agent-actions","title":"Explain Your Move: Understanding Agent Actions Using Focused Feature Saliency","date":"2020-05-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/stay-hungry-stay-focused-generating","slug":"stay-hungry-stay-focused-generating","title":"Stay Hungry, Stay Focused: Generating Informative and Specific Questions in Information-Seeking Conversations","date":"2020-04-30","arxiv_id":"2004.14530","repositories_listed":1,"syntology":null},{"url":"/paper/machine-learning-methods-for-brain-network","slug":"machine-learning-methods-for-brain-network","title":"Machine Learning Methods for Brain Network Classification: Application to Autism Diagnosis using Cortical Morphological Networks","date":"2020-04-28","arxiv_id":"2004.13321","repositories_listed":1,"syntology":null},{"url":"/paper/ai-augmentation-of-radiologist-performance-in","slug":"ai-augmentation-of-radiologist-performance-in","title":"AI Augmentation of Radiologist Performance in Distinguishing COVID-19 from Pneumonia of Other Etiology on Chest CT","date":"2020-04-27","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/conservative-plane-releasing-for-spatial","slug":"conservative-plane-releasing-for-spatial","title":"Conservative Plane Releasing for Spatial Privacy Protection in Mixed Reality","date":"2020-04-17","arxiv_id":"2004.08029","repositories_listed":1,"syntology":null},{"url":"/paper/jcs-an-explainable-covid-19-diagnosis-system","slug":"jcs-an-explainable-covid-19-diagnosis-system","title":"JCS: An Explainable COVID-19 Diagnosis System by Joint Classification and Segmentation","date":"2020-04-15","arxiv_id":"2004.07054","repositories_listed":1,"syntology":null},{"url":"/paper/can-ai-help-in-screening-viral-and-covid-19","slug":"can-ai-help-in-screening-viral-and-covid-19","title":"Can AI help in screening Viral and COVID-19 pneumonia?","date":"2020-03-29","arxiv_id":"2003.13145","repositories_listed":1,"syntology":null},{"url":"/paper/classification-of-covid-19-in-chest-x-ray","slug":"classification-of-covid-19-in-chest-x-ray","title":"Classification of COVID-19 in chest X-ray images using DeTraC deep convolutional neural network","date":"2020-03-26","arxiv_id":"2003.13815","repositories_listed":1,"syntology":null},{"url":"/paper/deep-learning-based-detection-for-covid-19","slug":"deep-learning-based-detection-for-covid-19","title":"Deep Learning-based Detection for COVID-19 from Chest CT using Weak Label","date":"2020-03-26","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/bulbar-als-detection-based-on-analysis-of","slug":"bulbar-als-detection-based-on-analysis-of","title":"Bulbar ALS Detection Based on Analysis of Voice Perturbation and Vibrato","date":"2020-03-24","arxiv_id":"2003.10806","repositories_listed":1,"syntology":null},{"url":"/paper/deep-learning-approach-to-diabetic","slug":"deep-learning-approach-to-diabetic","title":"Deep Learning Approach to Diabetic Retinopathy Detection","date":"2020-03-03","arxiv_id":"2003.02261","repositories_listed":1,"syntology":null},{"url":"/paper/learning-from-easy-to-complex-adaptive-multi","slug":"learning-from-easy-to-complex-adaptive-multi","title":"Learning from Easy to Complex: Adaptive Multi-curricula Learning for Neural Dialogue Generation","date":"2020-03-02","arxiv_id":"2003.00639","repositories_listed":1,"syntology":null},{"url":"/paper/a-stacking-based-model-for-non-invasive","slug":"a-stacking-based-model-for-non-invasive","title":"A Stacking-Based Model for Non-Invasive Detection of Coronary Heart Disease","date":"2020-02-20","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/artificial-intelligence-distinguishes-covid","slug":"artificial-intelligence-distinguishes-covid","title":"Artificial Intelligence Distinguishes COVID-19 from Community Acquired Pneumonia on Chest CT","date":"2020-02-19","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/deeper-task-specificity-improves-joint-entity","slug":"deeper-task-specificity-improves-joint-entity","title":"Deeper Task-Specificity Improves Joint Entity and Relation Extraction","date":"2020-02-15","arxiv_id":"2002.06424","repositories_listed":1,"syntology":null},{"url":"/paper/generalizing-meanings-from-partners-to","slug":"generalizing-meanings-from-partners-to","title":"Generalizing meanings from partners to populations: Hierarchical inference supports convention formation on networks","date":"2020-02-04","arxiv_id":"2002.01510","repositories_listed":1,"syntology":null},{"url":"/paper/sensitivity-and-specificity-of-a-bayesian","slug":"sensitivity-and-specificity-of-a-bayesian","title":"Sensitivity and specificity of a Bayesian single trial analysis for time varying neural signals","date":"2020-01-30","arxiv_id":"2001.11582","repositories_listed":1,"syntology":null},{"url":"/paper/segmentation-with-residual-attention-u-net","slug":"segmentation-with-residual-attention-u-net","title":"Segmentation with Residual Attention U-Net and an Edge-Enhancement Approach Preserves Cell Shape Features","date":"2020-01-15","arxiv_id":"2001.05548","repositories_listed":1,"syntology":null},{"url":"/paper/knowledge-graph-transfer-network-for-few-shot","slug":"knowledge-graph-transfer-network-for-few-shot","title":"Knowledge Graph Transfer Network for Few-Shot Recognition","date":"2019-11-21","arxiv_id":"1911.09579","repositories_listed":1,"syntology":null},{"url":"/paper/object-based-multi-temporal-and-multi-source","slug":"object-based-multi-temporal-and-multi-source","title":"Object-based multi-temporal and multi-source land cover mapping leveraging hierarchical class relationships","date":"2019-11-20","arxiv_id":"1911.08815","repositories_listed":1,"syntology":null},{"url":"/paper/a-multiple-testing-framework-for-diagnostic","slug":"a-multiple-testing-framework-for-diagnostic","title":"A multiple testing framework for diagnostic accuracy studies with co-primary endpoints","date":"2019-11-08","arxiv_id":"1911.02982","repositories_listed":1,"syntology":null},{"url":"/paper/unsupervised-common-question-generation-from","slug":"unsupervised-common-question-generation-from","title":"Contrastive Multi-document Question Generation","date":"2019-11-08","arxiv_id":"1911.03047","repositories_listed":1,"syntology":null},{"url":"/paper/screening-for-rem-sleep-behaviour-disorder","slug":"screening-for-rem-sleep-behaviour-disorder","title":"Screening for REM Sleep Behaviour Disorder with Minimal Sensors","date":"2019-10-24","arxiv_id":"1910.11702","repositories_listed":1,"syntology":null},{"url":"/paper/trident-segmentation-cnn-a-spatiotemporal","slug":"trident-segmentation-cnn-a-spatiotemporal","title":"Trident Segmentation CNN: A Spatiotemporal Transformation CNN for Punctate White Matter Lesions Segmentation in Preterm Neonates","date":"2019-10-22","arxiv_id":"1910.09773","repositories_listed":1,"syntology":null},{"url":"/paper/deep-ordinal-regression-for-pledge","slug":"deep-ordinal-regression-for-pledge","title":"Deep Ordinal Regression for Pledge Specificity Prediction","date":"2019-08-31","arxiv_id":"1909.00187","repositories_listed":1,"syntology":null},{"url":"/paper/lu-net-an-efficient-network-for-3d-lidar","slug":"lu-net-an-efficient-network-for-3d-lidar","title":"LU-Net: An Efficient Network for 3D LiDAR Point Cloud Semantic Segmentation Based on End-to-End-Learned 3D Features and U-Net","date":"2019-08-30","arxiv_id":"1908.11656","repositories_listed":1,"syntology":null},{"url":"/paper/deepclean-self-supervised-artefact-rejection","slug":"deepclean-self-supervised-artefact-rejection","title":"DeepClean -- self-supervised artefact rejection for intensive care waveform data using deep generative learning","date":"2019-08-08","arxiv_id":"1908.03129","repositories_listed":1,"syntology":null},{"url":"/paper/annotating-and-analyzing-the-interactions","slug":"annotating-and-analyzing-the-interactions","title":"Annotating and analyzing the interactions between meaning relations","date":"2019-08-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/exploring-large-scale-public-medical-image","slug":"exploring-large-scale-public-medical-image","title":"Exploring large scale public medical image datasets","date":"2019-07-30","arxiv_id":"1907.12720","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":0,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/exploring-large-scale-public-medical-image#ran","syntology_url":"https://syntology.ai/paper/1907.12720","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1907.12720"}},"official":null}},{"url":"/paper/preprocessing-method-for-performance","slug":"preprocessing-method-for-performance","title":"Preprocessing Method for Performance Enhancement in CNN-based STEMI Detection from 12-lead ECG","date":"2019-07-24","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/refined-segmentation-r-cnn-a-two-stage","slug":"refined-segmentation-r-cnn-a-two-stage","title":"Refined-Segmentation R-CNN: A Two-stage Convolutional Neural Network for Punctate White Matter Lesion Segmentation in Preterm Infants","date":"2019-06-24","arxiv_id":"1906.09684","repositories_listed":1,"syntology":null},{"url":"/paper/predicting-confusion-from-eye-tracking-data","slug":"predicting-confusion-from-eye-tracking-data","title":"Predicting Confusion from Eye-Tracking Data with Recurrent Neural Networks","date":"2019-06-19","arxiv_id":"1906.11211","repositories_listed":1,"syntology":null},{"url":"/paper/a-deep-learning-approach-for-automated","slug":"a-deep-learning-approach-for-automated","title":"A deep learning approach for automated detection of geographic atrophy from color fundus photographs","date":"2019-06-07","arxiv_id":"1906.03153","repositories_listed":1,"syntology":null},{"url":"/paper/linguistically-informed-specificity-and","slug":"linguistically-informed-specificity-and","title":"Linguistically-Informed Specificity and Semantic Plausibility for Dialogue Generation","date":"2019-06-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/computer-aided-diagnosis-in-histopathological","slug":"computer-aided-diagnosis-in-histopathological","title":"Computer-aided diagnosis in histopathological images of the endometrium using a convolutional neural network and attention mechanisms","date":"2019-04-24","arxiv_id":"1904.10626","repositories_listed":1,"syntology":null},{"url":"/paper/answer-based-adversarial-training-for","slug":"answer-based-adversarial-training-for","title":"Answer-based Adversarial Training for Generating Clarification Questions","date":"2019-04-04","arxiv_id":"1904.02281","repositories_listed":1,"syntology":null},{"url":"/paper/automatic-diagnosis-of-the-short-duration-12","slug":"automatic-diagnosis-of-the-short-duration-12","title":"Automatic diagnosis of the 12-lead ECG using a deep neural network","date":"2019-04-02","arxiv_id":"1904.01949","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_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","sample_list":"/paper/automatic-diagnosis-of-the-short-duration-12#ran","syntology_url":"https://syntology.ai/paper/1904.01949","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1904.01949"}},"official":{"repos":["antonior92/automatic-ecg-diagnosis"],"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"]}}},{"url":"/paper/when-redundancy-is-rational-a-bayesian","slug":"when-redundancy-is-rational-a-bayesian","title":"When redundancy is useful: A Bayesian approach to 'overinformative' referring expressions","date":"2019-03-19","arxiv_id":"1903.08237","repositories_listed":1,"syntology":null},{"url":"/paper/better-than-expert-detection-of-early","slug":"better-than-expert-detection-of-early","title":"Augmenting expert detection of early coronary artery occlusion from 12 lead electrocardiograms using deep learning","date":"2019-03-11","arxiv_id":"1903.04421","repositories_listed":1,"syntology":null},{"url":"/paper/the-fuzzy-roc","slug":"the-fuzzy-roc","title":"The Fuzzy ROC","date":"2019-03-04","arxiv_id":"1903.01868","repositories_listed":1,"syntology":null},{"url":"/paper/deepdrug3d-classification-of-ligand-binding","slug":"deepdrug3d-classification-of-ligand-binding","title":"DeepDrug3D: Classification of ligand-binding pockets in proteins with a convolutional neural network","date":"2019-02-04","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/reducing-variability-in-along-tract-analysis","slug":"reducing-variability-in-along-tract-analysis","title":"Reducing variability in along-tract analysis with diffusion profile realignment","date":"2019-02-04","arxiv_id":"1902.01399","repositories_listed":1,"syntology":null},{"url":"/paper/deep-learning-based-early-detection-and","slug":"deep-learning-based-early-detection-and","title":"Deep Learning based Early Detection and Grading of Diabetic Retinopathy Using Retinal Fundus Images","date":"2018-12-27","arxiv_id":"1812.10595","repositories_listed":1,"syntology":null},{"url":"/paper/depechemood-a-bilingual-emotion-lexicon-built","slug":"depechemood-a-bilingual-emotion-lexicon-built","title":"DepecheMood++: a Bilingual Emotion Lexicon Built Through Simple Yet Powerful Techniques","date":"2018-10-08","arxiv_id":"1810.03660","repositories_listed":1,"syntology":null},{"url":"/paper/the-privacy-policy-landscape-after-the-gdpr","slug":"the-privacy-policy-landscape-after-the-gdpr","title":"The Privacy Policy Landscape After the GDPR","date":"2018-09-22","arxiv_id":"1809.08396","repositories_listed":1,"syntology":null},{"url":"/paper/distinguishing-affixoid-formations-from","slug":"distinguishing-affixoid-formations-from","title":"Distinguishing affixoid formations from compounds","date":"2018-08-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/vfpred-a-fusion-of-signal-processing-and","slug":"vfpred-a-fusion-of-signal-processing-and","title":"VFPred: A Fusion of Signal Processing and Machine Learning techniques in Detecting Ventricular Fibrillation from ECG Signals","date":"2018-07-07","arxiv_id":"1807.02684","repositories_listed":1,"syntology":null},{"url":"/paper/cossmo-predicting-competitive-alternative","slug":"cossmo-predicting-competitive-alternative","title":"COSSMO: predicting competitive alternative splice site selection using deep learning","date":"2018-06-27","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/a-unified-model-with-structured-output-for","slug":"a-unified-model-with-structured-output-for","title":"A Unified Model with Structured Output for Fashion Images Classification","date":"2018-06-25","arxiv_id":"1806.09445","repositories_listed":1,"syntology":null},{"url":"/paper/domain-adapted-word-embeddings-for-improved","slug":"domain-adapted-word-embeddings-for-improved","title":"Domain Adapted Word Embeddings for Improved Sentiment Classification","date":"2018-05-11","arxiv_id":"1805.04576","repositories_listed":1,"syntology":null},{"url":"/paper/an-ontology-based-dialogue-management-system","slug":"an-ontology-based-dialogue-management-system","title":"An Ontology-Based Dialogue Management System for Banking and Finance Dialogue Systems","date":"2018-04-13","arxiv_id":"1804.04838","repositories_listed":1,"syntology":null},{"url":"/paper/temporal-interpolation-via-motion-field","slug":"temporal-interpolation-via-motion-field","title":"Temporal Interpolation via Motion Field Prediction","date":"2018-04-12","arxiv_id":"1804.04440","repositories_listed":1,"syntology":null},{"url":"/paper/learning-protein-constitutive-motifs-from","slug":"learning-protein-constitutive-motifs-from","title":"Learning protein constitutive motifs from sequence data","date":"2018-03-23","arxiv_id":"1803.08718","repositories_listed":1,"syntology":null},{"url":"/paper/deeply-supervised-neural-network-with-short","slug":"deeply-supervised-neural-network-with-short","title":"BTS-DSN: Deeply Supervised Neural Network with Short Connections for Retinal Vessel Segmentation","date":"2018-03-11","arxiv_id":"1803.03963","repositories_listed":1,"syntology":null},{"url":"/paper/selective-classification-via-curve","slug":"selective-classification-via-curve","title":"A General Framework for Abstention Under Label Shift","date":"2018-02-20","arxiv_id":"1802.07024","repositories_listed":1,"syntology":null},{"url":"/paper/sentiment-analysis-by-capsules","slug":"sentiment-analysis-by-capsules","title":"Sentiment Analysis by Capsules","date":"2018-02-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/pure-robust-pupil-detection-for-real-time","slug":"pure-robust-pupil-detection-for-real-time","title":"PuRe: Robust pupil detection for real-time pervasive eye tracking","date":"2017-12-24","arxiv_id":"1712.08900","repositories_listed":1,"syntology":null},{"url":"/paper/physical-epistatic-landscape-of-antibody","slug":"physical-epistatic-landscape-of-antibody","title":"Physical epistatic landscape of antibody binding affinity","date":"2017-12-11","arxiv_id":"1712.04000","repositories_listed":1,"syntology":null},{"url":"/paper/folded-recurrent-neural-networks-for-future","slug":"folded-recurrent-neural-networks-for-future","title":"Folded Recurrent Neural Networks for Future Video Prediction","date":"2017-12-01","arxiv_id":"1712.00311","repositories_listed":1,"syntology":null},{"url":"/paper/on-breast-cancer-detection-an-application-of","slug":"on-breast-cancer-detection-an-application-of","title":"On Breast Cancer Detection: An Application of Machine Learning Algorithms on the Wisconsin Diagnostic Dataset","date":"2017-11-20","arxiv_id":"1711.07831","repositories_listed":1,"syntology":null},{"url":"/paper/neural-network-an1alysis-of-sleep-stages","slug":"neural-network-an1alysis-of-sleep-stages","title":"Neural network an1alysis of sleep stages enables efficient diagnosis of narcolepsy","date":"2017-10-05","arxiv_id":"1710.02094","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":1,"n_instrument":1,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":2,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/neural-network-an1alysis-of-sleep-stages#ran","syntology_url":"https://syntology.ai/paper/1710.02094","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1710.02094"}},"official":{"repos":["stanford-stages/stanford-stages"],"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"]}}},{"url":"/paper/detection-of-inferior-myocardial-infarction","slug":"detection-of-inferior-myocardial-infarction","title":"Detection of Inferior Myocardial Infarction using Shallow Convolutional Neural Networks","date":"2017-10-03","arxiv_id":"1710.01115","repositories_listed":1,"syntology":null},{"url":"/paper/cross-linguistic-differences-and-similarities","slug":"cross-linguistic-differences-and-similarities","title":"Cross-linguistic differences and similarities in image descriptions","date":"2017-07-06","arxiv_id":"1707.01736","repositories_listed":1,"syntology":{"n":3,"n_ran":2,"n_constructed":0,"n_ran_checked":0,"n_instrument":2,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"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) · 1 unverified","sample_list":"/paper/cross-linguistic-differences-and-similarities#ran","syntology_url":"https://syntology.ai/paper/1707.01736","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1707.01736"}},"official":{"repos":["cltl/DutchDescriptions"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/second-order-word-embeddings-from-nearest","slug":"second-order-word-embeddings-from-nearest","title":"Second-Order Word Embeddings from Nearest Neighbor Topological Features","date":"2017-05-23","arxiv_id":"1705.08488","repositories_listed":1,"syntology":null},{"url":"/paper/accurately-and-efficiently-interpreting-human","slug":"accurately-and-efficiently-interpreting-human","title":"Accurately and Efficiently Interpreting Human-Robot Instructions of Varying Granularities","date":"2017-04-21","arxiv_id":"1704.06616","repositories_listed":1,"syntology":null}],"record_sha256":"b24de144ebee453c78efa14d3dd3f2dbb3cabe1ef0c2746e7676f51ba64c6cc5","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}