{"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/data-augmentation/papers/14","list_of":"/task/data-augmentation","task":"Data Augmentation","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":14,"pages_in_order":84,"rows_per_page":100,"rows":[1301,1400],"of":8378,"counts":{"archive_papers_tagged":8378,"with_a_code_link":3225,"where_syntology_ran_a_sample":692,"not_listed_spam_title":0,"listed":8378,"listed_where_code_ran":692,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":567,"every_run_a_failure_of_syntologys_instrument":125,"listed_with_a_run_with_no_instrument_failure":567,"listed_every_run_a_failure_of_syntologys_instrument":125,"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/data-augmentation","prev":"/task/data-augmentation/papers/13","next":"/task/data-augmentation/papers/15","papers":[{"url":"/paper/dualaug-exploiting-additional-heavy","slug":"dualaug-exploiting-additional-heavy","title":"DualAug: Exploiting Additional Heavy Augmentation with OOD Data Rejection","date":"2023-10-12","arxiv_id":"2310.08139","repositories_listed":1,"syntology":null},{"url":"/paper/adasr-an-adversarial-auto-augmentation","slug":"adasr-an-adversarial-auto-augmentation","title":"ADASR: An Adversarial Auto-Augmentation Framework for Hyperspectral and Multispectral Data Fusion","date":"2023-10-11","arxiv_id":"2310.07255","repositories_listed":1,"syntology":null},{"url":"/paper/line-detection-and-segmentation-of-annual","slug":"line-detection-and-segmentation-of-annual","title":"Line Detection and Segmentation of Annual Crops Using Hybrid Method","date":"2023-10-11","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/revisiting-plasticity-in-visual-reinforcement","slug":"revisiting-plasticity-in-visual-reinforcement","title":"Revisiting Plasticity in Visual Reinforcement Learning: Data, Modules and Training Stages","date":"2023-10-11","arxiv_id":"2310.07418","repositories_listed":1,"syntology":{"n":4,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":4,"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) · 2 unverified","sample_list":"/paper/revisiting-plasticity-in-visual-reinforcement#ran","syntology_url":"https://syntology.ai/paper/2310.07418","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2310.07418"}},"official":{"repos":["Guozheng-Ma/Adaptive-Replay-Ratio"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/self-supervised-representation-learning-from-6","slug":"self-supervised-representation-learning-from-6","title":"Self-supervised Representation Learning From Random Data Projectors","date":"2023-10-11","arxiv_id":"2310.07756","repositories_listed":1,"syntology":{"n":12,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":11,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":12,"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) · 11 unverified","sample_list":"/paper/self-supervised-representation-learning-from-6#ran","syntology_url":"https://syntology.ai/paper/2310.07756","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2310.07756"}},"official":{"repos":["layer6ai-labs/lfr"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":11,"ran_from_kinds":["official"]}}},{"url":"/paper/domain-generalization-by-rejecting-extreme","slug":"domain-generalization-by-rejecting-extreme","title":"Domain Generalization by Rejecting Extreme Augmentations","date":"2023-10-10","arxiv_id":"2310.06670","repositories_listed":1,"syntology":null},{"url":"/paper/drugclip-contrastive-protein-molecule","slug":"drugclip-contrastive-protein-molecule","title":"DrugCLIP: Contrastive Protein-Molecule Representation Learning for Virtual Screening","date":"2023-10-10","arxiv_id":"2310.06367","repositories_listed":1,"syntology":null},{"url":"/paper/no-pitch-left-behind-addressing-gender","slug":"no-pitch-left-behind-addressing-gender","title":"No Pitch Left Behind: Addressing Gender Unbalance in Automatic Speech Recognition through Pitch Manipulation","date":"2023-10-10","arxiv_id":"2310.06590","repositories_listed":1,"syntology":null},{"url":"/paper/revisit-input-perturbation-problems-for-llms","slug":"revisit-input-perturbation-problems-for-llms","title":"Revisit Input Perturbation Problems for LLMs: A Unified Robustness Evaluation Framework for Noisy Slot Filling Task","date":"2023-10-10","arxiv_id":"2310.06504","repositories_listed":1,"syntology":null},{"url":"/paper/query-and-response-augmentation-cannot-help","slug":"query-and-response-augmentation-cannot-help","title":"MuggleMath: Assessing the Impact of Query and Response Augmentation on Math Reasoning","date":"2023-10-09","arxiv_id":"2310.05506","repositories_listed":1,"syntology":null},{"url":"/paper/cross-head-mutual-mean-teaching-for-semi","slug":"cross-head-mutual-mean-teaching-for-semi","title":"Cross-head mutual Mean-Teaching for semi-supervised medical image segmentation","date":"2023-10-08","arxiv_id":"2310.05082","repositories_listed":1,"syntology":null},{"url":"/paper/minprompt-graph-based-minimal-prompt-data","slug":"minprompt-graph-based-minimal-prompt-data","title":"MinPrompt: Graph-based Minimal Prompt Data Augmentation for Few-shot Question Answering","date":"2023-10-08","arxiv_id":"2310.05007","repositories_listed":1,"syntology":null},{"url":"/paper/ipmix-label-preserving-data-augmentation-1","slug":"ipmix-label-preserving-data-augmentation-1","title":"IPMix: Label-Preserving Data Augmentation Method for Training Robust Classifiers","date":"2023-10-07","arxiv_id":"2310.04780","repositories_listed":1,"syntology":{"n":17,"n_ran":15,"n_constructed":0,"n_ran_checked":2,"n_instrument":13,"n_unverified":2,"n_honours":1,"n_violates":1,"n_no_contract":0,"n_pointer_only":1,"phrase":"15 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 1 honoured, 1 violated, 0 with no contract checked; 13 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/ipmix-label-preserving-data-augmentation-1#ran","syntology_url":"https://syntology.ai/paper/2310.04780","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2310.04780"}},"official":{"repos":["hzlsaber/IPMix"],"state":"official (archive's flag): 14 ran","n_ran":14,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":2,"ran_from_kinds":["official","unlocated"]}}},{"url":"/paper/offline-imitation-learning-with-variational","slug":"offline-imitation-learning-with-variational","title":"Offline Imitation Learning with Variational Counterfactual Reasoning","date":"2023-10-07","arxiv_id":"2310.04706","repositories_listed":1,"syntology":null},{"url":"/paper/whole-slide-multiple-instance-learning-for","slug":"whole-slide-multiple-instance-learning-for","title":"Whole Slide Multiple Instance Learning for Predicting Axillary Lymph Node Metastasis","date":"2023-10-06","arxiv_id":"2310.04187","repositories_listed":1,"syntology":null},{"url":"/paper/how-good-are-synthetic-medical-images-an","slug":"how-good-are-synthetic-medical-images-an","title":"How Good Are Synthetic Medical Images? An Empirical Study with Lung Ultrasound","date":"2023-10-05","arxiv_id":"2310.03608","repositories_listed":1,"syntology":null},{"url":"/paper/network-alignment-with-transferable-graph","slug":"network-alignment-with-transferable-graph","title":"T-GAE: Transferable Graph Autoencoder for Network Alignment","date":"2023-10-05","arxiv_id":"2310.03272","repositories_listed":1,"syntology":null},{"url":"/paper/a-recipe-for-improved-certifiable-robustness","slug":"a-recipe-for-improved-certifiable-robustness","title":"A Recipe for Improved Certifiable Robustness","date":"2023-10-04","arxiv_id":"2310.02513","repositories_listed":1,"syntology":null},{"url":"/paper/learnable-data-augmentation-for-one-shot","slug":"learnable-data-augmentation-for-one-shot","title":"Learnable Data Augmentation for One-Shot Unsupervised Domain Adaptation","date":"2023-10-03","arxiv_id":"2310.02201","repositories_listed":1,"syntology":null},{"url":"/paper/fetal-bet-brain-extraction-tool-for-fetal-mri","slug":"fetal-bet-brain-extraction-tool-for-fetal-mri","title":"Fetal-BET: Brain Extraction Tool for Fetal MRI","date":"2023-10-02","arxiv_id":"2310.01523","repositories_listed":1,"syntology":null},{"url":"/paper/text-data-augmentation-in-low-resource","slug":"text-data-augmentation-in-low-resource","title":"Synthetic Data Generation in Low-Resource Settings via Fine-Tuning of Large Language Models","date":"2023-10-02","arxiv_id":"2310.01119","repositories_listed":1,"syntology":null},{"url":"/paper/structural-adversarial-objectives-for-self","slug":"structural-adversarial-objectives-for-self","title":"Structural Adversarial Objectives for Self-Supervised Representation Learning","date":"2023-09-30","arxiv_id":"2310.00357","repositories_listed":1,"syntology":null},{"url":"/paper/asynchronous-graph-generators","slug":"asynchronous-graph-generators","title":"Asynchronous Graph Generator","date":"2023-09-29","arxiv_id":"2309.17335","repositories_listed":1,"syntology":null},{"url":"/paper/investigating-shift-equivalence-of","slug":"investigating-shift-equivalence-of","title":"Investigating Shift Equivalence of Convolutional Neural Networks in Industrial Defect Segmentation","date":"2023-09-29","arxiv_id":"2309.16902","repositories_listed":1,"syntology":null},{"url":"/paper/on-the-equivalence-of-graph-convolution-and","slug":"on-the-equivalence-of-graph-convolution-and","title":"On the Equivalence of Graph Convolution and Mixup","date":"2023-09-29","arxiv_id":"2310.00183","repositories_listed":1,"syntology":null},{"url":"/paper/augment-to-interpret-unsupervised-and","slug":"augment-to-interpret-unsupervised-and","title":"Augment to Interpret: Unsupervised and Inherently Interpretable Graph Embeddings","date":"2023-09-28","arxiv_id":"2309.16564","repositories_listed":1,"syntology":null},{"url":"/paper/improving-equivariance-in-state-of-the-art-1","slug":"improving-equivariance-in-state-of-the-art-1","title":"Improving Equivariance in State-of-the-Art Supervised Depth and Normal Predictors","date":"2023-09-28","arxiv_id":"2309.16646","repositories_listed":1,"syntology":null},{"url":"/paper/learning-to-transform-for-generalizable-1","slug":"learning-to-transform-for-generalizable-1","title":"Learning to Transform for Generalizable Instance-wise Invariance","date":"2023-09-28","arxiv_id":"2309.16672","repositories_listed":1,"syntology":{"n":4,"n_ran":2,"n_constructed":0,"n_ran_checked":0,"n_instrument":2,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":4,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/learning-to-transform-for-generalizable-1#ran","syntology_url":"https://syntology.ai/paper/2309.16672","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2309.16672"}},"official":{"repos":["sutkarsh/flow_inv"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/satdm-synthesizing-realistic-satellite-image","slug":"satdm-synthesizing-realistic-satellite-image","title":"SatDM: Synthesizing Realistic Satellite Image with Semantic Layout Conditioning using Diffusion Models","date":"2023-09-28","arxiv_id":"2309.16812","repositories_listed":1,"syntology":null},{"url":"/paper/enhancing-sharpness-aware-optimization","slug":"enhancing-sharpness-aware-optimization","title":"Enhancing Sharpness-Aware Optimization Through Variance Suppression","date":"2023-09-27","arxiv_id":"2309.15639","repositories_listed":1,"syntology":{"n":5,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_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) · 2 unverified","sample_list":"/paper/enhancing-sharpness-aware-optimization#ran","syntology_url":"https://syntology.ai/paper/2309.15639","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2309.15639"}},"official":{"repos":["bingcongli/vasso"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/graph-level-representation-learning-with","slug":"graph-level-representation-learning-with","title":"Graph-level Representation Learning with Joint-Embedding Predictive Architectures","date":"2023-09-27","arxiv_id":"2309.16014","repositories_listed":1,"syntology":{"n":11,"n_ran":10,"n_constructed":0,"n_ran_checked":9,"n_instrument":1,"n_unverified":1,"n_honours":2,"n_violates":0,"n_no_contract":7,"n_pointer_only":4,"phrase":"10 ran (of which 0 constructed an object rather than computing a result; 9 with no instrument failure: 2 honoured, 0 violated, 7 with no contract checked; 1 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/graph-level-representation-learning-with#ran","syntology_url":"https://syntology.ai/paper/2309.16014","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2309.16014"}},"official":{"repos":["geriskenderi/graph-jepa"],"state":"official (archive's flag): 10 ran","n_ran":10,"n_constructed":0,"n_ran_no_instrument_failure":9,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/label-augmentation-method-for-medical","slug":"label-augmentation-method-for-medical","title":"Label Augmentation Method for Medical Landmark Detection in Hip Radiograph Images","date":"2023-09-27","arxiv_id":"2309.16066","repositories_listed":1,"syntology":null},{"url":"/paper/boosting-high-resolution-image-classification","slug":"boosting-high-resolution-image-classification","title":"Boosting High Resolution Image Classification with Scaling-up Transformers","date":"2023-09-26","arxiv_id":"2309.15277","repositories_listed":1,"syntology":null},{"url":"/paper/a-novel-geo-localization-method-for-uav-and","slug":"a-novel-geo-localization-method-for-uav-and","title":"A Novel Geo-Localization Method for UAV and Satellite Images Using Cross-View Consistent Attention","date":"2023-09-23","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/finding-order-in-chaos-a-novel-data-1","slug":"finding-order-in-chaos-a-novel-data-1","title":"Finding Order in Chaos: A Novel Data Augmentation Method for Time Series in Contrastive Learning","date":"2023-09-23","arxiv_id":"2309.13439","repositories_listed":1,"syntology":{"n":5,"n_ran":3,"n_constructed":0,"n_ran_checked":0,"n_instrument":3,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":5,"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","sample_list":"/paper/finding-order-in-chaos-a-novel-data-1#ran","syntology_url":"https://syntology.ai/paper/2309.13439","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2309.13439"}},"official":{"repos":["eth-siplab/Finding_Order_in_Chaos"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/order-preserving-consistency-regularization","slug":"order-preserving-consistency-regularization","title":"Order-preserving Consistency Regularization for Domain Adaptation and Generalization","date":"2023-09-23","arxiv_id":"2309.13258","repositories_listed":1,"syntology":null},{"url":"/paper/wikimt-dataset-card","slug":"wikimt-dataset-card","title":"EMelodyGen: Emotion-Conditioned Melody Generation in ABC Notation with the Musical Feature Template","date":"2023-09-23","arxiv_id":"2309.13259","repositories_listed":1,"syntology":null},{"url":"/paper/amplify-attention-based-mixup-for-performance","slug":"amplify-attention-based-mixup-for-performance","title":"AMPLIFY:Attention-based Mixup for Performance Improvement and Label Smoothing in Transformer","date":"2023-09-22","arxiv_id":"2309.12689","repositories_listed":1,"syntology":null},{"url":"/paper/mosaicfusion-diffusion-models-as-data","slug":"mosaicfusion-diffusion-models-as-data","title":"MosaicFusion: Diffusion Models as Data Augmenters for Large Vocabulary Instance Segmentation","date":"2023-09-22","arxiv_id":"2309.13042","repositories_listed":1,"syntology":{"n":6,"n_ran":5,"n_constructed":0,"n_ran_checked":5,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":6,"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","sample_list":"/paper/mosaicfusion-diffusion-models-as-data#ran","syntology_url":"https://syntology.ai/paper/2309.13042","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2309.13042"}},"official":{"repos":["jiahao000/mosaicfusion"],"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"]}}},{"url":"/paper/a-class-weighted-supervised-contrastive","slug":"a-class-weighted-supervised-contrastive","title":"A class-weighted supervised contrastive learning long-tailed bearing fault diagnosis approach using quadratic neural network","date":"2023-09-21","arxiv_id":"2309.11717","repositories_listed":1,"syntology":null},{"url":"/paper/investigating-personalization-methods-in-text","slug":"investigating-personalization-methods-in-text","title":"Investigating Personalization Methods in Text to Music Generation","date":"2023-09-20","arxiv_id":"2309.11140","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":3,"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) · 0 unverified","sample_list":"/paper/investigating-personalization-methods-in-text#ran","syntology_url":"https://syntology.ai/paper/2309.11140","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2309.11140"}},"official":{"repos":["zelaki/DreamSound"],"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"]}}},{"url":"/paper/long-tail-augmented-graph-contrastive","slug":"long-tail-augmented-graph-contrastive","title":"Long-tail Augmented Graph Contrastive Learning for Recommendation","date":"2023-09-20","arxiv_id":"2309.11177","repositories_listed":1,"syntology":null},{"url":"/paper/rethinking-imitation-based-planner-for","slug":"rethinking-imitation-based-planner-for","title":"Rethinking Imitation-based Planner for Autonomous Driving","date":"2023-09-19","arxiv_id":"2309.10443","repositories_listed":1,"syntology":null},{"url":"/paper/sample-adaptive-augmentation-for-point-cloud","slug":"sample-adaptive-augmentation-for-point-cloud","title":"Sample-adaptive Augmentation for Point Cloud Recognition Against Real-world Corruptions","date":"2023-09-19","arxiv_id":"2309.10431","repositories_listed":1,"syntology":{"n":13,"n_ran":11,"n_constructed":0,"n_ran_checked":6,"n_instrument":5,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":6,"n_pointer_only":5,"phrase":"11 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; 5 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/sample-adaptive-augmentation-for-point-cloud#ran","syntology_url":"https://syntology.ai/paper/2309.10431","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2309.10431"}},"official":{"repos":["roywangj/adaptpoint"],"state":"official (archive's flag): 11 ran","n_ran":11,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/contrastive-learning-and-data-augmentation-in","slug":"contrastive-learning-and-data-augmentation-in","title":"Replication: Contrastive Learning and Data Augmentation in Traffic Classification Using a Flowpic Input Representation","date":"2023-09-18","arxiv_id":"2309.09733","repositories_listed":1,"syntology":null},{"url":"/paper/empirical-study-of-mix-based-data","slug":"empirical-study-of-mix-based-data","title":"Empirical Study of Mix-based Data Augmentation Methods in Physiological Time Series Data","date":"2023-09-18","arxiv_id":"2309.09970","repositories_listed":1,"syntology":null},{"url":"/paper/enhancing-visual-perception-in-novel","slug":"enhancing-visual-perception-in-novel","title":"Enhancing Visual Perception in Novel Environments via Incremental Data Augmentation Based on Style Transfer","date":"2023-09-16","arxiv_id":"2309.08851","repositories_listed":1,"syntology":null},{"url":"/paper/improve-deep-forest-with-learnable-layerwise","slug":"improve-deep-forest-with-learnable-layerwise","title":"Improve Deep Forest with Learnable Layerwise Augmentation Policy Schedule","date":"2023-09-16","arxiv_id":"2309.09030","repositories_listed":1,"syntology":null},{"url":"/paper/data-distribution-bottlenecks-in-grounding","slug":"data-distribution-bottlenecks-in-grounding","title":"Data Distribution Bottlenecks in Grounding Language Models to Knowledge Bases","date":"2023-09-15","arxiv_id":"2309.08345","repositories_listed":1,"syntology":null},{"url":"/paper/feddcsr-federated-cross-domain-sequential","slug":"feddcsr-federated-cross-domain-sequential","title":"FedDCSR: Federated Cross-domain Sequential Recommendation via Disentangled Representation Learning","date":"2023-09-15","arxiv_id":"2309.08420","repositories_listed":1,"syntology":null},{"url":"/paper/catfood-counterfactual-augmented-training-for","slug":"catfood-counterfactual-augmented-training-for","title":"CATfOOD: Counterfactual Augmented Training for Improving Out-of-Domain Performance and Calibration","date":"2023-09-14","arxiv_id":"2309.07822","repositories_listed":1,"syntology":{"n":12,"n_ran":12,"n_constructed":0,"n_ran_checked":12,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":12,"n_pointer_only":0,"phrase":"12 ran (of which 0 constructed an object rather than computing a result; 12 with no instrument failure: 0 honoured, 0 violated, 12 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/catfood-counterfactual-augmented-training-for#ran","syntology_url":"https://syntology.ai/paper/2309.07822","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2309.07822"}},"official":{"repos":["ukplab/catfood"],"state":"official (archive's flag): 12 ran","n_ran":12,"n_constructed":0,"n_ran_no_instrument_failure":12,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/learning-quasi-static-3d-models-of-markerless","slug":"learning-quasi-static-3d-models-of-markerless","title":"Learning Quasi-Static 3D Models of Markerless Deformable Linear Objects for Bimanual Robotic Manipulation","date":"2023-09-14","arxiv_id":"2309.07609","repositories_listed":1,"syntology":null},{"url":"/paper/in-contextual-bias-suppression-for-large","slug":"in-contextual-bias-suppression-for-large","title":"In-Contextual Gender Bias Suppression for Large Language Models","date":"2023-09-13","arxiv_id":"2309.07251","repositories_listed":1,"syntology":null},{"url":"/paper/the-effect-of-data-augmentation-and-3d-cnn","slug":"the-effect-of-data-augmentation-and-3d-cnn","title":"The effect of data augmentation and 3D-CNN depth on Alzheimer's Disease detection","date":"2023-09-13","arxiv_id":"2309.07192","repositories_listed":1,"syntology":null},{"url":"/paper/unbiased-face-synthesis-with-diffusion-models","slug":"unbiased-face-synthesis-with-diffusion-models","title":"Limitations of Face Image Generation","date":"2023-09-13","arxiv_id":"2309.07277","repositories_listed":1,"syntology":null},{"url":"/paper/2309-05951","slug":"2309-05951","title":"Balanced and Explainable Social Media Analysis for Public Health with Large Language Models","date":"2023-09-12","arxiv_id":"2309.05951","repositories_listed":1,"syntology":null},{"url":"/paper/spatial-variation-generation-algorithm-for","slug":"spatial-variation-generation-algorithm-for","title":"Spatial Variation Generation Algorithm for Motor Imagery Data Augmentation: Increasing the Density of Sample Vicinity","date":"2023-09-12","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/towards-better-data-exploitation-in-self","slug":"towards-better-data-exploitation-in-self","title":"Towards Better Data Exploitation in Self-Supervised Monocular Depth Estimation","date":"2023-09-11","arxiv_id":"2309.05254","repositories_listed":1,"syntology":{"n":15,"n_ran":13,"n_constructed":0,"n_ran_checked":11,"n_instrument":2,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":11,"n_pointer_only":2,"phrase":"13 ran (of which 0 constructed an object rather than computing a result; 11 with no instrument failure: 0 honoured, 0 violated, 11 with no contract checked; 2 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/towards-better-data-exploitation-in-self#ran","syntology_url":"https://syntology.ai/paper/2309.05254","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2309.05254"}},"official":{"repos":["LiuJF1226/BDEdepth"],"state":"official (archive's flag): 13 ran","n_ran":13,"n_constructed":0,"n_ran_no_instrument_failure":11,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/boosting-unsupervised-contrastive-learning","slug":"boosting-unsupervised-contrastive-learning","title":"DiffAug: Enhance Unsupervised Contrastive Learning with Domain-Knowledge-Free Diffusion-based Data Augmentation","date":"2023-09-10","arxiv_id":"2309.07909","repositories_listed":1,"syntology":null},{"url":"/paper/audrandaug-random-image-augmentations-for","slug":"audrandaug-random-image-augmentations-for","title":"AudRandAug: Random Image Augmentations for Audio Classification","date":"2023-09-09","arxiv_id":"2309.04762","repositories_listed":1,"syntology":null},{"url":"/paper/data-augmentation-for-conversational-ai","slug":"data-augmentation-for-conversational-ai","title":"Data Augmentation for Conversational AI","date":"2023-09-09","arxiv_id":"2309.04739","repositories_listed":1,"syntology":null},{"url":"/paper/distributional-data-augmentation-methods-for","slug":"distributional-data-augmentation-methods-for","title":"Distributional Data Augmentation Methods for Low Resource Language","date":"2023-09-09","arxiv_id":"2309.04862","repositories_listed":1,"syntology":null},{"url":"/paper/when-to-learn-what-model-adaptive-data","slug":"when-to-learn-what-model-adaptive-data","title":"When to Learn What: Model-Adaptive Data Augmentation Curriculum","date":"2023-09-09","arxiv_id":"2309.04747","repositories_listed":1,"syntology":{"n":24,"n_ran":23,"n_constructed":1,"n_ran_checked":4,"n_instrument":19,"n_unverified":1,"n_honours":1,"n_violates":1,"n_no_contract":2,"n_pointer_only":24,"phrase":"23 ran (of which 1 constructed an object rather than computing a result; 4 with no instrument failure: 1 honoured, 1 violated, 2 with no contract checked; 19 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/when-to-learn-what-model-adaptive-data#ran","syntology_url":"https://syntology.ai/paper/2309.04747","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2309.04747"}},"official":{"repos":["jackhck/madaug"],"state":"official (archive's flag): 23 ran","n_ran":23,"n_constructed":1,"n_ran_no_instrument_failure":4,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/uq-at-smm4h-2023-alex-for-public-health","slug":"uq-at-smm4h-2023-alex-for-public-health","title":"UQ at #SMM4H 2023: ALEX for Public Health Analysis with Social Media","date":"2023-09-08","arxiv_id":"2309.04213","repositories_listed":1,"syntology":null},{"url":"/paper/tsgbench-time-series-generation-benchmark","slug":"tsgbench-time-series-generation-benchmark","title":"TSGBench: Time Series Generation Benchmark","date":"2023-09-07","arxiv_id":"2309.03755","repositories_listed":1,"syntology":{"n":14,"n_ran":11,"n_constructed":0,"n_ran_checked":11,"n_instrument":0,"n_unverified":3,"n_honours":0,"n_violates":2,"n_no_contract":9,"n_pointer_only":14,"phrase":"11 ran (of which 0 constructed an object rather than computing a result; 11 with no instrument failure: 0 honoured, 2 violated, 9 with no contract checked; 0 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/tsgbench-time-series-generation-benchmark#ran","syntology_url":"https://syntology.ai/paper/2309.03755","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2309.03755"}},"official":{"repos":["yihaoang/tsgbench"],"state":"official (archive's flag): 11 ran","n_ran":11,"n_constructed":0,"n_ran_no_instrument_failure":11,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/community-based-hierarchical-positive","slug":"community-based-hierarchical-positive","title":"Community-Based Hierarchical Positive-Unlabeled (PU) Model Fusion for Chronic Disease Prediction","date":"2023-09-06","arxiv_id":"2309.03386","repositories_listed":1,"syntology":null},{"url":"/paper/generative-data-augmentation-using-llms","slug":"generative-data-augmentation-using-llms","title":"Generative Data Augmentation using LLMs improves Distributional Robustness in Question Answering","date":"2023-09-03","arxiv_id":"2309.06358","repositories_listed":1,"syntology":null},{"url":"/paper/adler-adversarial-training-with-label-error","slug":"adler-adversarial-training-with-label-error","title":"AdLER: Adversarial Training with Label Error Rectification for One-Shot Medical Image Segmentation","date":"2023-09-02","arxiv_id":"2309.00971","repositories_listed":1,"syntology":null},{"url":"/paper/pretraining-representations-for-bioacoustic","slug":"pretraining-representations-for-bioacoustic","title":"Pretraining Representations for Bioacoustic Few-shot Detection using Supervised Contrastive Learning","date":"2023-09-02","arxiv_id":"2309.00878","repositories_listed":1,"syntology":null},{"url":"/paper/a-locality-based-neural-solver-for-optical","slug":"a-locality-based-neural-solver-for-optical","title":"A Locality-based Neural Solver for Optical Motion Capture","date":"2023-09-01","arxiv_id":"2309.00428","repositories_listed":1,"syntology":null},{"url":"/paper/fine-grained-recognition-with-learnable","slug":"fine-grained-recognition-with-learnable","title":"Fine-grained Recognition with Learnable Semantic Data Augmentation","date":"2023-09-01","arxiv_id":"2309.00399","repositories_listed":1,"syntology":{"n":7,"n_ran":4,"n_constructed":0,"n_ran_checked":2,"n_instrument":2,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":7,"phrase":"4 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; 2 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/fine-grained-recognition-with-learnable#ran","syntology_url":"https://syntology.ai/paper/2309.00399","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2309.00399"}},"official":{"repos":["LeapLabTHU/LearnableISDA"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/taken-out-of-context-on-measuring-situational","slug":"taken-out-of-context-on-measuring-situational","title":"Taken out of context: On measuring situational awareness in LLMs","date":"2023-09-01","arxiv_id":"2309.00667","repositories_listed":1,"syntology":{"n":9,"n_ran":9,"n_constructed":0,"n_ran_checked":9,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":9,"n_pointer_only":9,"phrase":"9 ran (of which 0 constructed an object rather than computing a result; 9 with no instrument failure: 0 honoured, 0 violated, 9 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/taken-out-of-context-on-measuring-situational#ran","syntology_url":"https://syntology.ai/paper/2309.00667","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2309.00667"}},"official":{"repos":["asacooperstickland/situational-awareness-evals"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":0,"n_ran_no_instrument_failure":9,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/will-sentiment-analysis-need-subculture-a-new","slug":"will-sentiment-analysis-need-subculture-a-new","title":"Will sentiment analysis need subculture? A new data augmentation approach","date":"2023-09-01","arxiv_id":"2309.00178","repositories_listed":1,"syntology":null},{"url":"/paper/dual-decoder-consistency-via-pseudo-labels","slug":"dual-decoder-consistency-via-pseudo-labels","title":"Dual-Decoder Consistency via Pseudo-Labels Guided Data Augmentation for Semi-Supervised Medical Image Segmentation","date":"2023-08-31","arxiv_id":"2308.16573","repositories_listed":1,"syntology":null},{"url":"/paper/the-gender-gap-pipeline-a-gender-aware","slug":"the-gender-gap-pipeline-a-gender-aware","title":"The Gender-GAP Pipeline: A Gender-Aware Polyglot Pipeline for Gender Characterisation in 55 Languages","date":"2023-08-31","arxiv_id":"2308.16871","repositories_listed":1,"syntology":null},{"url":"/paper/interpretability-guided-data-augmentation-for","slug":"interpretability-guided-data-augmentation-for","title":"Interpretability-guided Data Augmentation for Robust Segmentation in Multi-centre Colonoscopy Data","date":"2023-08-30","arxiv_id":"2308.15881","repositories_listed":1,"syntology":null},{"url":"/paper/classification-robustness-to-common-optical","slug":"classification-robustness-to-common-optical","title":"Classification robustness to common optical aberrations","date":"2023-08-29","arxiv_id":"2308.15499","repositories_listed":1,"syntology":{"n":14,"n_ran":12,"n_constructed":0,"n_ran_checked":12,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":12,"n_pointer_only":14,"phrase":"12 ran (of which 0 constructed an object rather than computing a result; 12 with no instrument failure: 0 honoured, 0 violated, 12 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/classification-robustness-to-common-optical#ran","syntology_url":"https://syntology.ai/paper/2308.15499","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2308.15499"}},"official":{"repos":["patmue/classification_robustness"],"state":"official (archive's flag): 12 ran","n_ran":12,"n_constructed":0,"n_ran_no_instrument_failure":12,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/from-smote-to-mixup-for-deep-imbalanced","slug":"from-smote-to-mixup-for-deep-imbalanced","title":"From SMOTE to Mixup for Deep Imbalanced Classification","date":"2023-08-29","arxiv_id":"2308.15457","repositories_listed":1,"syntology":null},{"url":"/paper/heterogeneous-multi-task-gaussian-cox","slug":"heterogeneous-multi-task-gaussian-cox","title":"Heterogeneous Multi-Task Gaussian Cox Processes","date":"2023-08-29","arxiv_id":"2308.15364","repositories_listed":1,"syntology":null},{"url":"/paper/on-the-robustness-of-object-detection-models","slug":"on-the-robustness-of-object-detection-models","title":"On the Robustness of Object Detection Models on Aerial Images","date":"2023-08-29","arxiv_id":"2308.15378","repositories_listed":1,"syntology":null},{"url":"/paper/semi-supervised-learning-for-visual-bird-s","slug":"semi-supervised-learning-for-visual-bird-s","title":"Semi-Supervised Learning for Visual Bird's Eye View Semantic Segmentation","date":"2023-08-28","arxiv_id":"2308.14525","repositories_listed":1,"syntology":null},{"url":"/paper/chatgpt-as-data-augmentation-for","slug":"chatgpt-as-data-augmentation-for","title":"ChatGPT as Data Augmentation for Compositional Generalization: A Case Study in Open Intent Detection","date":"2023-08-25","arxiv_id":"2308.13517","repositories_listed":1,"syntology":null},{"url":"/paper/rella-retrieval-enhanced-large-language","slug":"rella-retrieval-enhanced-large-language","title":"ReLLa: Retrieval-enhanced Large Language Models for Lifelong Sequential Behavior Comprehension in Recommendation","date":"2023-08-22","arxiv_id":"2308.11131","repositories_listed":1,"syntology":{"n":9,"n_ran":8,"n_constructed":0,"n_ran_checked":8,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":8,"n_pointer_only":9,"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","sample_list":"/paper/rella-retrieval-enhanced-large-language#ran","syntology_url":"https://syntology.ai/paper/2308.11131","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2308.11131"}},"official":{"repos":["lavieenrose365/rella"],"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"]}}},{"url":"/paper/revisiting-and-exploring-efficient-fast","slug":"revisiting-and-exploring-efficient-fast","title":"Revisiting and Exploring Efficient Fast Adversarial Training via LAW: Lipschitz Regularization and Auto Weight Averaging","date":"2023-08-22","arxiv_id":"2308.11443","repositories_listed":1,"syntology":null},{"url":"/paper/turning-waste-into-wealth-leveraging-low","slug":"turning-waste-into-wealth-leveraging-low","title":"Turning Waste into Wealth: Leveraging Low-Quality Samples for Enhancing Continuous Conditional Generative Adversarial Networks","date":"2023-08-20","arxiv_id":"2308.10273","repositories_listed":1,"syntology":null},{"url":"/paper/an-empirical-study-of-clip-for-text-based","slug":"an-empirical-study-of-clip-for-text-based","title":"An Empirical Study of CLIP for Text-based Person Search","date":"2023-08-19","arxiv_id":"2308.10045","repositories_listed":1,"syntology":null},{"url":"/paper/aspire-language-guided-augmentation-for","slug":"aspire-language-guided-augmentation-for","title":"ASPIRE: Language-Guided Data Augmentation for Improving Robustness Against Spurious Correlations","date":"2023-08-19","arxiv_id":"2308.10103","repositories_listed":1,"syntology":null},{"url":"/paper/uncertainty-based-quality-assurance-of","slug":"uncertainty-based-quality-assurance-of","title":"Uncertainty-based quality assurance of carotid artery wall segmentation in black-blood MRI","date":"2023-08-18","arxiv_id":"2308.09538","repositories_listed":1,"syntology":null},{"url":"/paper/lesionmix-a-lesion-level-data-augmentation","slug":"lesionmix-a-lesion-level-data-augmentation","title":"LesionMix: A Lesion-Level Data Augmentation Method for Medical Image Segmentation","date":"2023-08-17","arxiv_id":"2308.09026","repositories_listed":1,"syntology":null},{"url":"/paper/advancing-continual-lifelong-learning-in","slug":"advancing-continual-lifelong-learning-in","title":"Advancing continual lifelong learning in neural information retrieval: definition, dataset, framework, and empirical evaluation","date":"2023-08-16","arxiv_id":"2308.08378","repositories_listed":1,"syntology":null},{"url":"/paper/adrmx-additive-disentanglement-of-domain","slug":"adrmx-additive-disentanglement-of-domain","title":"ADRMX: Additive Disentanglement of Domain Features with Remix Loss","date":"2023-08-12","arxiv_id":"2308.06624","repositories_listed":1,"syntology":null},{"url":"/paper/dfm-x-augmentation-by-leveraging-prior","slug":"dfm-x-augmentation-by-leveraging-prior","title":"DFM-X: Augmentation by Leveraging Prior Knowledge of Shortcut Learning","date":"2023-08-12","arxiv_id":"2308.06622","repositories_listed":1,"syntology":null},{"url":"/paper/diverse-data-augmentation-with-diffusions-for","slug":"diverse-data-augmentation-with-diffusions-for","title":"Diverse Data Augmentation with Diffusions for Effective Test-time Prompt Tuning","date":"2023-08-11","arxiv_id":"2308.06038","repositories_listed":1,"syntology":null},{"url":"/paper/pde-refiner-achieving-accurate-long-rollouts","slug":"pde-refiner-achieving-accurate-long-rollouts","title":"PDE-Refiner: Achieving Accurate Long Rollouts with Neural PDE Solvers","date":"2023-08-10","arxiv_id":"2308.05732","repositories_listed":1,"syntology":null},{"url":"/paper/sslrec-a-self-supervised-learning-library-for","slug":"sslrec-a-self-supervised-learning-library-for","title":"SSLRec: A Self-Supervised Learning Framework for Recommendation","date":"2023-08-10","arxiv_id":"2308.05697","repositories_listed":1,"syntology":{"n":4,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":0,"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","sample_list":"/paper/sslrec-a-self-supervised-learning-library-for#ran","syntology_url":"https://syntology.ai/paper/2308.05697","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2308.05697"}},"official":{"repos":["hkuds/sslrec"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/self-supervised-learning-of-rotation","slug":"self-supervised-learning-of-rotation","title":"Self-supervised Learning of Rotation-invariant 3D Point Set Features using Transformer and its Self-distillation","date":"2023-08-09","arxiv_id":"2308.04725","repositories_listed":1,"syntology":null},{"url":"/paper/apbench-a-unified-benchmark-for-availability","slug":"apbench-a-unified-benchmark-for-availability","title":"APBench: A Unified Benchmark for Availability Poisoning Attacks and Defenses","date":"2023-08-07","arxiv_id":"2308.03258","repositories_listed":1,"syntology":{"n":7,"n_ran":5,"n_constructed":0,"n_ran_checked":3,"n_instrument":2,"n_unverified":2,"n_honours":0,"n_violates":1,"n_no_contract":2,"n_pointer_only":4,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 1 violated, 2 with no contract checked; 2 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/apbench-a-unified-benchmark-for-availability#ran","syntology_url":"https://syntology.ai/paper/2308.03258","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2308.03258"}},"official":{"repos":["lafeat/apbench"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/medmine-examining-pre-trained-language-models","slug":"medmine-examining-pre-trained-language-models","title":"MedMine: Examining Pre-trained Language Models on Medication Mining","date":"2023-08-07","arxiv_id":"2308.03629","repositories_listed":1,"syntology":null},{"url":"/paper/explainable-deep-learning-based-solar-flare","slug":"explainable-deep-learning-based-solar-flare","title":"Explainable Deep Learning-based Solar Flare Prediction with post hoc Attention for Operational Forecasting","date":"2023-08-04","arxiv_id":"2308.02682","repositories_listed":1,"syntology":null},{"url":"/paper/generation-of-realistic-synthetic-raw-radar","slug":"generation-of-realistic-synthetic-raw-radar","title":"Generation of Realistic Synthetic Raw Radar Data for Automated Driving Applications using Generative Adversarial Networks","date":"2023-08-04","arxiv_id":"2308.02632","repositories_listed":1,"syntology":null}],"record_sha256":"f8f6a6628f81b96f48cbad01c3bcab6ba4e3061d15b36889bf6c1a6773a926ef","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}