{"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/few-shot-learning/papers/12","list_of":"/task/few-shot-learning","task":"Few-Shot Learning","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":12,"pages_in_order":30,"rows_per_page":100,"rows":[1101,1200],"of":2964,"counts":{"archive_papers_tagged":2964,"with_a_code_link":1297,"where_syntology_ran_a_sample":373,"not_listed_spam_title":0,"listed":2964,"listed_where_code_ran":373,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":306,"every_run_a_failure_of_syntologys_instrument":67,"listed_with_a_run_with_no_instrument_failure":306,"listed_every_run_a_failure_of_syntologys_instrument":67,"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/few-shot-learning","prev":"/task/few-shot-learning/papers/11","next":"/task/few-shot-learning/papers/13","papers":[{"url":"/paper/data-efficient-classification-of-radio","slug":"data-efficient-classification-of-radio","title":"Data-Efficient Classification of Radio Galaxies","date":"2020-11-26","arxiv_id":"2011.13311","repositories_listed":1,"syntology":null},{"url":"/paper/how-well-do-self-supervised-models-transfer","slug":"how-well-do-self-supervised-models-transfer","title":"How Well Do Self-Supervised Models Transfer?","date":"2020-11-26","arxiv_id":"2011.13377","repositories_listed":1,"syntology":{"n":5,"n_ran":4,"n_constructed":0,"n_ran_checked":1,"n_instrument":3,"n_unverified":1,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"4 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; 3 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/how-well-do-self-supervised-models-transfer#ran","syntology_url":"https://syntology.ai/paper/2011.13377","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2011.13377"}},"official":{"repos":["linusericsson/ssl-transfer"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/ifss-net-interactive-few-shot-siamese-network","slug":"ifss-net-interactive-few-shot-siamese-network","title":"IFSS-Net: Interactive Few-Shot Siamese Network for Faster Muscle Segmentation and Propagation in Volumetric Ultrasound","date":"2020-11-26","arxiv_id":"2011.13246","repositories_listed":1,"syntology":null},{"url":"/paper/match-them-up-visually-explainable-few-shot","slug":"match-them-up-visually-explainable-few-shot","title":"Match Them Up: Visually Explainable Few-shot Image Classification","date":"2020-11-25","arxiv_id":"2011.12527","repositories_listed":1,"syntology":null},{"url":"/paper/close-category-generalization","slug":"close-category-generalization","title":"Probing Predictions on OOD Images via Nearest Categories","date":"2020-11-17","arxiv_id":"2011.08485","repositories_listed":1,"syntology":null},{"url":"/paper/zero-shot-learning-for-relation-extraction","slug":"zero-shot-learning-for-relation-extraction","title":"Zero-shot Relation Classification from Side Information","date":"2020-11-13","arxiv_id":"2011.07126","repositories_listed":1,"syntology":null},{"url":"/paper/self-supervised-learning-from-contrastive","slug":"self-supervised-learning-from-contrastive","title":"Self-Supervised Learning from Contrastive Mixtures for Personalized Speech Enhancement","date":"2020-11-06","arxiv_id":"2011.03426","repositories_listed":1,"syntology":null},{"url":"/paper/investigating-novel-verb-learning-in-bert","slug":"investigating-novel-verb-learning-in-bert","title":"Investigating Novel Verb Learning in BERT: Selectional Preference Classes and Alternation-Based Syntactic Generalization","date":"2020-11-04","arxiv_id":"2011.02417","repositories_listed":1,"syntology":null},{"url":"/paper/supervised-contrastive-learning-for-pre-1","slug":"supervised-contrastive-learning-for-pre-1","title":"Supervised Contrastive Learning for Pre-trained Language Model Fine-tuning","date":"2020-11-03","arxiv_id":"2011.01403","repositories_listed":1,"syntology":null},{"url":"/paper/regularization-of-distinct-strategies-for","slug":"regularization-of-distinct-strategies-for","title":"Regularization of Distinct Strategies for Unsupervised Question Generation","date":"2020-11-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/discriminative-nearest-neighbor-few-shot","slug":"discriminative-nearest-neighbor-few-shot","title":"Discriminative Nearest Neighbor Few-Shot Intent Detection by Transferring Natural Language Inference","date":"2020-10-25","arxiv_id":"2010.13009","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/discriminative-nearest-neighbor-few-shot#ran","syntology_url":"https://syntology.ai/paper/2010.13009","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2010.13009"}},"official":{"repos":["salesforce/DNNC-few-shot-intent"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/few-shot-learning-for-decoding-brain-signals","slug":"few-shot-learning-for-decoding-brain-signals","title":"Few-shot Decoding of Brain Activation Maps","date":"2020-10-23","arxiv_id":"2010.12500","repositories_listed":1,"syntology":null},{"url":"/paper/restoring-negative-information-in-few-shot","slug":"restoring-negative-information-in-few-shot","title":"Restoring Negative Information in Few-Shot Object Detection","date":"2020-10-22","arxiv_id":"2010.11714","repositories_listed":1,"syntology":null},{"url":"/paper/learning-to-learn-variational-semantic-memory","slug":"learning-to-learn-variational-semantic-memory","title":"Learning to Learn Variational Semantic Memory","date":"2020-10-20","arxiv_id":"2010.10341","repositories_listed":1,"syntology":null},{"url":"/paper/alpaca-vs-gp-based-prior-learning-a","slug":"alpaca-vs-gp-based-prior-learning-a","title":"ALPaCA vs. GP-based Prior Learning: A Comparison between two Bayesian Meta-Learning Algorithms","date":"2020-10-15","arxiv_id":"2010.07994","repositories_listed":1,"syntology":null},{"url":"/paper/self-training-for-few-shot-transfer-across-1","slug":"self-training-for-few-shot-transfer-across-1","title":"Self-training for Few-shot Transfer Across Extreme Task Differences","date":"2020-10-15","arxiv_id":"2010.07734","repositories_listed":1,"syntology":{"n":4,"n_ran":4,"n_constructed":0,"n_ran_checked":1,"n_instrument":3,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"4 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; 3 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/self-training-for-few-shot-transfer-across-1#ran","syntology_url":"https://syntology.ai/paper/2010.07734","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2010.07734"}},"official":{"repos":["cpphoo/STARTUP"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/the-tatoeba-translation-challenge-realistic","slug":"the-tatoeba-translation-challenge-realistic","title":"The Tatoeba Translation Challenge -- Realistic Data Sets for Low Resource and Multilingual MT","date":"2020-10-13","arxiv_id":"2010.06354","repositories_listed":1,"syntology":null},{"url":"/paper/dynamic-semantic-matching-and-aggregation","slug":"dynamic-semantic-matching-and-aggregation","title":"Dynamic Semantic Matching and Aggregation Network for Few-shot Intent Detection","date":"2020-10-06","arxiv_id":"2010.02481","repositories_listed":1,"syntology":null},{"url":"/paper/putting-theory-to-work-from-learning-bounds-1","slug":"putting-theory-to-work-from-learning-bounds-1","title":"Improving Few-Shot Learning through Multi-task Representation Learning Theory","date":"2020-10-05","arxiv_id":"2010.01992","repositories_listed":1,"syntology":null},{"url":"/paper/self-training-improves-pre-training-for","slug":"self-training-improves-pre-training-for","title":"Self-training Improves Pre-training for Natural Language Understanding","date":"2020-10-05","arxiv_id":"2010.02194","repositories_listed":1,"syntology":null},{"url":"/paper/self-supervised-few-shot-learning-on-point","slug":"self-supervised-few-shot-learning-on-point","title":"Self-Supervised Few-Shot Learning on Point Clouds","date":"2020-09-29","arxiv_id":"2009.14168","repositories_listed":1,"syntology":{"n":5,"n_ran":4,"n_constructed":3,"n_ran_checked":3,"n_instrument":1,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":5,"phrase":"4 ran (of which 3 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 1 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/self-supervised-few-shot-learning-on-point#ran","syntology_url":"https://syntology.ai/paper/2009.14168","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2009.14168"}},"official":null}},{"url":"/paper/interventional-few-shot-learning","slug":"interventional-few-shot-learning","title":"Interventional Few-Shot Learning","date":"2020-09-28","arxiv_id":"2009.13000","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/interventional-few-shot-learning#ran","syntology_url":"https://syntology.ai/paper/2009.13000","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2009.13000"}},"official":{"repos":["yue-zhongqi/ifsl"],"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-few-shot-learning-approach-for-historical","slug":"a-few-shot-learning-approach-for-historical","title":"A Few-shot Learning Approach for Historical Ciphered Manuscript Recognition","date":"2020-09-26","arxiv_id":"2009.12577","repositories_listed":1,"syntology":null},{"url":"/paper/a-primal-dual-subgradient-approach-for-fair","slug":"a-primal-dual-subgradient-approach-for-fair","title":"A Primal-Dual Subgradient Approachfor Fair Meta Learning","date":"2020-09-26","arxiv_id":"2009.12675","repositories_listed":1,"syntology":null},{"url":"/paper/vector-projection-network-for-few-shot-slot","slug":"vector-projection-network-for-few-shot-slot","title":"Vector Projection Network for Few-shot Slot Tagging in Natural Language Understanding","date":"2020-09-21","arxiv_id":"2009.09568","repositories_listed":1,"syntology":null},{"url":"/paper/few-shot-unsupervised-continual-learning","slug":"few-shot-unsupervised-continual-learning","title":"Few-Shot Unsupervised Continual Learning through Meta-Examples","date":"2020-09-17","arxiv_id":"2009.08107","repositories_listed":1,"syntology":{"n":8,"n_ran":6,"n_constructed":1,"n_ran_checked":5,"n_instrument":1,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":0,"phrase":"6 ran (of which 1 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 0 violated, 5 with no contract checked; 1 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/few-shot-unsupervised-continual-learning#ran","syntology_url":"https://syntology.ai/paper/2009.08107","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2009.08107"}},"official":{"repos":["alessiabertugli/FUSION"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":1,"n_ran_no_instrument_failure":1,"n_unverified":2,"ran_from_kinds":["found_in_text","official"]}}},{"url":"/paper/less-than-one-shot-learning-learning-n","slug":"less-than-one-shot-learning-learning-n","title":"'Less Than One'-Shot Learning: Learning N Classes From M<N Samples","date":"2020-09-17","arxiv_id":"2009.08449","repositories_listed":1,"syntology":null},{"url":"/paper/few-shot-learning-with-lssvm-base-learner-and","slug":"few-shot-learning-with-lssvm-base-learner-and","title":"Few-shot Learning with LSSVM Base Learner and Transductive Modules","date":"2020-09-12","arxiv_id":"2009.05786","repositories_listed":1,"syntology":null},{"url":"/paper/prototype-completion-with-primitive-knowledge","slug":"prototype-completion-with-primitive-knowledge","title":"Prototype Completion with Primitive Knowledge for Few-Shot Learning","date":"2020-09-10","arxiv_id":"2009.04960","repositories_listed":1,"syntology":{"n":13,"n_ran":12,"n_constructed":0,"n_ran_checked":11,"n_instrument":1,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":11,"n_pointer_only":0,"phrase":"12 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; 1 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/prototype-completion-with-primitive-knowledge#ran","syntology_url":"https://syntology.ai/paper/2009.04960","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2009.04960"}},"official":{"repos":["zhangbq-research/Prototype_Completion_for_FSL"],"state":"official (archive's flag): 12 ran","n_ran":12,"n_constructed":0,"n_ran_no_instrument_failure":11,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/proxy-network-for-few-shot-learning","slug":"proxy-network-for-few-shot-learning","title":"Proxy Network for Few Shot Learning","date":"2020-09-09","arxiv_id":"2009.04292","repositories_listed":1,"syntology":null},{"url":"/paper/region-comparison-network-for-interpretable","slug":"region-comparison-network-for-interpretable","title":"Region Comparison Network for Interpretable Few-shot Image Classification","date":"2020-09-08","arxiv_id":"2009.03558","repositories_listed":1,"syntology":null},{"url":"/paper/gpu-based-self-organizing-maps-for-post","slug":"gpu-based-self-organizing-maps-for-post","title":"GPU-based Self-Organizing Maps for Post-Labeled Few-Shot Unsupervised Learning","date":"2020-09-04","arxiv_id":"2009.03665","repositories_listed":1,"syntology":null},{"url":"/paper/example-based-named-entity-recognition","slug":"example-based-named-entity-recognition","title":"Example-Based Named Entity Recognition","date":"2020-08-24","arxiv_id":"2008.10570","repositories_listed":1,"syntology":null},{"url":"/paper/does-maml-really-want-feature-reuse-only","slug":"does-maml-really-want-feature-reuse-only","title":"BOIL: Towards Representation Change for Few-shot Learning","date":"2020-08-20","arxiv_id":"2008.08882","repositories_listed":1,"syntology":{"n":3,"n_ran":1,"n_constructed":1,"n_ran_checked":1,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":3,"phrase":"1 ran (of which 1 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) · 2 unverified; the one sample that ran constructed an object rather than computing a result","sample_list":"/paper/does-maml-really-want-feature-reuse-only#ran","syntology_url":"https://syntology.ai/paper/2008.08882","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2008.08882"}},"official":null}},{"url":"/paper/domain-generalizer-a-few-shot-meta-learning","slug":"domain-generalizer-a-few-shot-meta-learning","title":"Domain Generalizer: A Few-shot Meta Learning Framework for Domain Generalization in Medical Imaging","date":"2020-08-18","arxiv_id":"2008.07724","repositories_listed":1,"syntology":null},{"url":"/paper/few-shot-clustering-for-indoor-occupancy","slug":"few-shot-clustering-for-indoor-occupancy","title":"Few shot clustering for indoor occupancy detection with extremely low-quality images from battery free cameras","date":"2020-08-13","arxiv_id":"2008.05654","repositories_listed":1,"syntology":null},{"url":"/paper/an-overview-of-deep-learning-architectures-in","slug":"an-overview-of-deep-learning-architectures-in","title":"An Overview of Deep Learning Architectures in Few-Shot Learning Domain","date":"2020-08-12","arxiv_id":"2008.06365","repositories_listed":1,"syntology":null},{"url":"/paper/learning-to-reason-in-round-based-games-multi","slug":"learning-to-reason-in-round-based-games-multi","title":"Learning to Reason in Round-based Games: Multi-task Sequence Generation for Purchasing Decision Making in First-person Shooters","date":"2020-08-12","arxiv_id":"2008.05131","repositories_listed":1,"syntology":null},{"url":"/paper/cooperative-bi-path-metric-for-few-shot","slug":"cooperative-bi-path-metric-for-few-shot","title":"Cooperative Bi-path Metric for Few-shot Learning","date":"2020-08-10","arxiv_id":"2008.04031","repositories_listed":1,"syntology":null},{"url":"/paper/few-shot-classification-via-adaptive","slug":"few-shot-classification-via-adaptive","title":"Few-shot Classification via Adaptive Attention","date":"2020-08-06","arxiv_id":"2008.02465","repositories_listed":1,"syntology":null},{"url":"/paper/model-agnostic-boundary-adversarial-sampling","slug":"model-agnostic-boundary-adversarial-sampling","title":"Model-Agnostic Boundary-Adversarial Sampling for Test-Time Generalization in Few-Shot learning","date":"2020-08-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/leveraging-bottom-up-and-top-down-attention","slug":"leveraging-bottom-up-and-top-down-attention","title":"Leveraging Bottom-Up and Top-Down Attention for Few-Shot Object Detection","date":"2020-07-23","arxiv_id":"2007.12104","repositories_listed":1,"syntology":null},{"url":"/paper/complementing-representation-deficiency-in","slug":"complementing-representation-deficiency-in","title":"Complementing Representation Deficiency in Few-shot Image Classification: A Meta-Learning Approach","date":"2020-07-21","arxiv_id":"2007.10778","repositories_listed":1,"syntology":null},{"url":"/paper/a-comprehensive-evaluation-of-multi-task","slug":"a-comprehensive-evaluation-of-multi-task","title":"A Comprehensive Evaluation of Multi-task Learning and Multi-task Pre-training on EHR Time-series Data","date":"2020-07-20","arxiv_id":"2007.10185","repositories_listed":1,"syntology":null},{"url":"/paper/few-shot-link-prediction-via-graph-neural","slug":"few-shot-link-prediction-via-graph-neural","title":"Few-shot link prediction via graph neural networks for Covid-19 drug-repurposing","date":"2020-07-20","arxiv_id":"2007.10261","repositories_listed":1,"syntology":null},{"url":"/paper/fuzzy-graph-neural-network-for-few-shot","slug":"fuzzy-graph-neural-network-for-few-shot","title":"Fuzzy Graph Neural Network for Few-Shot Learning","date":"2020-07-19","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/explanation-guided-training-for-cross-domain","slug":"explanation-guided-training-for-cross-domain","title":"Explanation-Guided Training for Cross-Domain Few-Shot Classification","date":"2020-07-17","arxiv_id":"2007.08790","repositories_listed":1,"syntology":null},{"url":"/paper/generalized-many-way-few-shot-video","slug":"generalized-many-way-few-shot-video","title":"Generalized Few-Shot Video Classification with Video Retrieval and Feature Generation","date":"2020-07-09","arxiv_id":"2007.04755","repositories_listed":1,"syntology":null},{"url":"/paper/wandering-within-a-world-online","slug":"wandering-within-a-world-online","title":"Wandering Within a World: Online Contextualized Few-Shot Learning","date":"2020-07-09","arxiv_id":"2007.04546","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/wandering-within-a-world-online#ran","syntology_url":"https://syntology.ai/paper/2007.04546","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2007.04546"}},"official":{"repos":["renmengye/oc-fewshot-public"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/few-shot-one-class-classification-via-meta-1","slug":"few-shot-one-class-classification-via-meta-1","title":"Few-Shot One-Class Classification via Meta-Learning","date":"2020-07-08","arxiv_id":"2007.04146","repositories_listed":1,"syntology":null},{"url":"/paper/online-probabilistic-label-trees","slug":"online-probabilistic-label-trees","title":"Online probabilistic label trees","date":"2020-07-08","arxiv_id":"2007.04451","repositories_listed":1,"syntology":null},{"url":"/paper/predicting-the-accuracy-of-a-few-shot","slug":"predicting-the-accuracy-of-a-few-shot","title":"Predicting the Accuracy of a Few-Shot Classifier","date":"2020-07-08","arxiv_id":"2007.04238","repositories_listed":1,"syntology":{"n":24,"n_ran":19,"n_constructed":0,"n_ran_checked":19,"n_instrument":0,"n_unverified":5,"n_honours":0,"n_violates":0,"n_no_contract":19,"n_pointer_only":0,"phrase":"19 ran (of which 0 constructed an object rather than computing a result; 19 with no instrument failure: 0 honoured, 0 violated, 19 with no contract checked; 0 where Syntology's instrument failed) · 5 unverified","sample_list":"/paper/predicting-the-accuracy-of-a-few-shot#ran","syntology_url":"https://syntology.ai/paper/2007.04238","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2007.04238"}},"official":{"repos":["mbonto/fewshot_generalization"],"state":"official (archive's flag): 19 ran","n_ran":19,"n_constructed":0,"n_ran_no_instrument_failure":19,"n_unverified":5,"ran_from_kinds":["official"]}}},{"url":"/paper/covariate-distribution-aware-meta-learning","slug":"covariate-distribution-aware-meta-learning","title":"Covariate Distribution Aware Meta-learning","date":"2020-07-06","arxiv_id":"2007.02523","repositories_listed":1,"syntology":null},{"url":"/paper/few-shot-microscopy-image-cell-segmentation","slug":"few-shot-microscopy-image-cell-segmentation","title":"Few-Shot Microscopy Image Cell Segmentation","date":"2020-06-29","arxiv_id":"2007.01671","repositories_listed":1,"syntology":null},{"url":"/paper/many-class-few-shot-learning-on-multi","slug":"many-class-few-shot-learning-on-multi","title":"Many-Class Few-Shot Learning on Multi-Granularity Class Hierarchy","date":"2020-06-28","arxiv_id":"2006.15479","repositories_listed":1,"syntology":null},{"url":"/paper/extensively-matching-for-few-shot-learning","slug":"extensively-matching-for-few-shot-learning","title":"Extensively Matching for Few-shot Learning Event Detection","date":"2020-06-17","arxiv_id":"2006.10093","repositories_listed":1,"syntology":null},{"url":"/paper/graph-meta-learning-via-local-subgraphs","slug":"graph-meta-learning-via-local-subgraphs","title":"Graph Meta Learning via Local Subgraphs","date":"2020-06-14","arxiv_id":"2006.07889","repositories_listed":1,"syntology":null},{"url":"/paper/a-transductive-multi-head-model-for-cross","slug":"a-transductive-multi-head-model-for-cross","title":"A Transductive Multi-Head Model for Cross-Domain Few-Shot Learning","date":"2020-06-08","arxiv_id":"2006.11384","repositories_listed":1,"syntology":null},{"url":"/paper/ensemble-model-with-batch-spectral","slug":"ensemble-model-with-batch-spectral","title":"Ensemble Model with Batch Spectral Regularization and Data Blending for Cross-Domain Few-Shot Learning with Unlabeled Data","date":"2020-06-08","arxiv_id":"2006.04323","repositories_listed":1,"syntology":null},{"url":"/paper/multi-view-contrastive-learning-for-online","slug":"multi-view-contrastive-learning-for-online","title":"Multi-view Contrastive Learning for Online Knowledge Distillation","date":"2020-06-07","arxiv_id":"2006.04093","repositories_listed":1,"syntology":null},{"url":"/paper/interpretable-time-series-classification-on","slug":"interpretable-time-series-classification-on","title":"Interpretable Time-series Classification on Few-shot Samples","date":"2020-06-03","arxiv_id":"2006.02031","repositories_listed":1,"syntology":null},{"url":"/paper/adaptive-subspaces-for-few-shot-learning","slug":"adaptive-subspaces-for-few-shot-learning","title":"Adaptive Subspaces for Few-Shot Learning","date":"2020-06-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/attentive-weights-generation-for-few-shot-1","slug":"attentive-weights-generation-for-few-shot-1","title":"Attentive Weights Generation for Few Shot Learning via Information Maximization","date":"2020-06-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/high-order-structure-preserving-graph-neural","slug":"high-order-structure-preserving-graph-neural","title":"High-order structure preserving graph neural network for few-shot learning","date":"2020-05-29","arxiv_id":"2005.14415","repositories_listed":1,"syntology":null},{"url":"/paper/learning-from-context-agnostic-synthetic-data","slug":"learning-from-context-agnostic-synthetic-data","title":"Towards Context-Agnostic Learning Using Synthetic Data","date":"2020-05-29","arxiv_id":"2005.14707","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":3,"n_ran_checked":3,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":0,"phrase":"3 ran (of which 3 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; every one of the 3 samples that ran constructed an object rather than computing a result","sample_list":"/paper/learning-from-context-agnostic-synthetic-data#ran","syntology_url":"https://syntology.ai/paper/2005.14707","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2005.14707"}},"official":{"repos":["charlesjin/synthetic_data"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":3,"n_ran_no_instrument_failure":3,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/ssm-net-for-plants-disease-identification-in","slug":"ssm-net-for-plants-disease-identification-in","title":"SSM-Net for Plants Disease Identification in Low Data Regime","date":"2020-05-27","arxiv_id":"2005.13140","repositories_listed":1,"syntology":null},{"url":"/paper/span-convert-few-shot-span-extraction-for","slug":"span-convert-few-shot-span-extraction-for","title":"Span-ConveRT: Few-shot Span Extraction for Dialog with Pretrained Conversational Representations","date":"2020-05-18","arxiv_id":"2005.08866","repositories_listed":1,"syntology":null},{"url":"/paper/boosting-on-the-shoulders-of-giants-in","slug":"boosting-on-the-shoulders-of-giants-in","title":"Boosting on the shoulders of giants in quantum device calibration","date":"2020-05-13","arxiv_id":"2005.06194","repositories_listed":1,"syntology":null},{"url":"/paper/soloist-few-shot-task-oriented-dialog-with-a","slug":"soloist-few-shot-task-oriented-dialog-with-a","title":"SOLOIST: Building Task Bots at Scale with Transfer Learning and Machine Teaching","date":"2020-05-11","arxiv_id":"2005.05298","repositories_listed":1,"syntology":{"n":3,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":2,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"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) · 2 unverified","sample_list":"/paper/soloist-few-shot-task-oriented-dialog-with-a#ran","syntology_url":"https://syntology.ai/paper/2005.05298","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2005.05298"}},"official":null}},{"url":"/paper/supervision-and-source-domain-impact-on","slug":"supervision-and-source-domain-impact-on","title":"Supervision and Source Domain Impact on Representation Learning: A Histopathology Case Study","date":"2020-05-10","arxiv_id":"2005.08629","repositories_listed":1,"syntology":null},{"url":"/paper/denoiseg-joint-denoising-and-segmentation","slug":"denoiseg-joint-denoising-and-segmentation","title":"DenoiSeg: Joint Denoising and Segmentation","date":"2020-05-06","arxiv_id":"2005.02987","repositories_listed":1,"syntology":null},{"url":"/paper/harvesting-and-refining-question-answer-pairs","slug":"harvesting-and-refining-question-answer-pairs","title":"Harvesting and Refining Question-Answer Pairs for Unsupervised QA","date":"2020-05-06","arxiv_id":"2005.02925","repositories_listed":1,"syntology":null},{"url":"/paper/towards-fast-adaptation-of-neural","slug":"towards-fast-adaptation-of-neural","title":"Towards Fast Adaptation of Neural Architectures with Meta Learning","date":"2020-05-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/few-shot-learning-for-abstractive-multi","slug":"few-shot-learning-for-abstractive-multi","title":"Few-Shot Learning for Opinion Summarization","date":"2020-04-30","arxiv_id":"2004.14884","repositories_listed":1,"syntology":null},{"url":"/paper/supervised-domain-adaptation-were-we-doing","slug":"supervised-domain-adaptation-were-we-doing","title":"Supervised Domain Adaptation: A Graph Embedding Perspective and a Rectified Experimental Protocol","date":"2020-04-23","arxiv_id":"2004.11262","repositories_listed":1,"syntology":null},{"url":"/paper/meta-meta-classification-for-one-shot","slug":"meta-meta-classification-for-one-shot","title":"Meta-Meta Classification for One-Shot Learning","date":"2020-04-17","arxiv_id":"2004.08083","repositories_listed":1,"syntology":null},{"url":"/paper/few-shot-single-view-3-d-object","slug":"few-shot-single-view-3-d-object","title":"Few-Shot Single-View 3-D Object Reconstruction with Compositional Priors","date":"2020-04-14","arxiv_id":"2004.06302","repositories_listed":1,"syntology":null},{"url":"/paper/unsupervised-few-shot-learning-via","slug":"unsupervised-few-shot-learning-via","title":"Diversity Helps: Unsupervised Few-shot Learning via Distribution Shift-based Data Augmentation","date":"2020-04-13","arxiv_id":"2004.05805","repositories_listed":1,"syntology":{"n":13,"n_ran":11,"n_constructed":0,"n_ran_checked":11,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":11,"n_pointer_only":0,"phrase":"11 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; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/unsupervised-few-shot-learning-via#ran","syntology_url":"https://syntology.ai/paper/2004.05805","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2004.05805"}},"official":{"repos":["WonderSeven/ULDA"],"state":"official (archive's flag): 11 ran","n_ran":11,"n_constructed":0,"n_ran_no_instrument_failure":11,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/meta-learning-in-neural-networks-a-survey","slug":"meta-learning-in-neural-networks-a-survey","title":"Meta-Learning in Neural Networks: A Survey","date":"2020-04-11","arxiv_id":"2004.05439","repositories_listed":1,"syntology":null},{"url":"/paper/ma-3-model-agnostic-adversarial-augmentation","slug":"ma-3-model-agnostic-adversarial-augmentation","title":"MA 3 : Model Agnostic Adversarial Augmentation for Few Shot learning","date":"2020-04-10","arxiv_id":"2004.05100","repositories_listed":1,"syntology":null},{"url":"/paper/long-tail-learning-with-attributes","slug":"long-tail-learning-with-attributes","title":"From Generalized zero-shot learning to long-tail with class descriptors","date":"2020-04-05","arxiv_id":"2004.02235","repositories_listed":1,"syntology":null},{"url":"/paper/learning-to-segment-the-tail","slug":"learning-to-segment-the-tail","title":"Learning to Segment the Tail","date":"2020-04-02","arxiv_id":"2004.00900","repositories_listed":1,"syntology":{"n":4,"n_ran":4,"n_constructed":0,"n_ran_checked":4,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":4,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/learning-to-segment-the-tail#ran","syntology_url":"https://syntology.ai/paper/2004.00900","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2004.00900"}},"official":{"repos":["JoyHuYY1412/LST_LVIS"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/dpgn-distribution-propagation-graph-network","slug":"dpgn-distribution-propagation-graph-network","title":"DPGN: Distribution Propagation Graph Network for Few-shot Learning","date":"2020-03-31","arxiv_id":"2003.14247","repositories_listed":1,"syntology":null},{"url":"/paper/adversarial-feature-hallucination-networks","slug":"adversarial-feature-hallucination-networks","title":"Adversarial Feature Hallucination Networks for Few-Shot Learning","date":"2020-03-30","arxiv_id":"2003.13193","repositories_listed":1,"syntology":null},{"url":"/paper/instance-credibility-inference-for-few-shot","slug":"instance-credibility-inference-for-few-shot","title":"Instance Credibility Inference for Few-Shot Learning","date":"2020-03-26","arxiv_id":"2003.11853","repositories_listed":1,"syntology":null},{"url":"/paper/negative-margin-matters-understanding-margin","slug":"negative-margin-matters-understanding-margin","title":"Negative Margin Matters: Understanding Margin in Few-shot Classification","date":"2020-03-26","arxiv_id":"2003.12060","repositories_listed":1,"syntology":null},{"url":"/paper/selecting-relevant-features-from-a-universal","slug":"selecting-relevant-features-from-a-universal","title":"Selecting Relevant Features from a Multi-domain Representation for Few-shot Classification","date":"2020-03-20","arxiv_id":"2003.09338","repositories_listed":1,"syntology":{"n":10,"n_ran":9,"n_constructed":0,"n_ran_checked":6,"n_instrument":3,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":6,"n_pointer_only":1,"phrase":"9 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; 3 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/selecting-relevant-features-from-a-universal#ran","syntology_url":"https://syntology.ai/paper/2003.09338","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2003.09338"}},"official":{"repos":["dvornikita/SUR"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/domain-adaptive-few-shot-learning","slug":"domain-adaptive-few-shot-learning","title":"Domain-Adaptive Few-Shot Learning","date":"2020-03-19","arxiv_id":"2003.08626","repositories_listed":1,"syntology":null},{"url":"/paper/xtarnet-learning-to-extract-task-adaptive","slug":"xtarnet-learning-to-extract-task-adaptive","title":"XtarNet: Learning to Extract Task-Adaptive Representation for Incremental Few-Shot Learning","date":"2020-03-19","arxiv_id":"2003.08561","repositories_listed":1,"syntology":{"n":5,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":4,"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) · 4 unverified","sample_list":"/paper/xtarnet-learning-to-extract-task-adaptive#ran","syntology_url":"https://syntology.ai/paper/2003.08561","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2003.08561"}},"official":{"repos":["EdwinKim3069/XtarNet"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":4,"ran_from_kinds":["official"]}}},{"url":"/paper/context-transformer-tackling-object-confusion","slug":"context-transformer-tackling-object-confusion","title":"Context-Transformer: Tackling Object Confusion for Few-Shot Detection","date":"2020-03-16","arxiv_id":"2003.07304","repositories_listed":1,"syntology":{"n":3,"n_ran":2,"n_constructed":2,"n_ran_checked":2,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":0,"phrase":"2 ran (of which 2 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) · 1 unverified; every one of the 2 samples that ran constructed an object rather than computing a result","sample_list":"/paper/context-transformer-tackling-object-confusion#ran","syntology_url":"https://syntology.ai/paper/2003.07304","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2003.07304"}},"official":{"repos":["Ze-Yang/Context-Transformer"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":2,"n_ran_no_instrument_failure":2,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/starnet-towards-weakly-supervised-few-shot","slug":"starnet-towards-weakly-supervised-few-shot","title":"StarNet: towards Weakly Supervised Few-Shot Object Detection","date":"2020-03-15","arxiv_id":"2003.06798","repositories_listed":1,"syntology":null},{"url":"/paper/tafssl-task-adaptive-feature-sub-space","slug":"tafssl-task-adaptive-feature-sub-space","title":"TAFSSL: Task-Adaptive Feature Sub-Space Learning for few-shot classification","date":"2020-03-14","arxiv_id":"2003.06670","repositories_listed":1,"syntology":{"n":1,"n_ran":0,"n_constructed":0,"n_ran_checked":0,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"0 ran · 1 unverified","sample_list":"/paper/tafssl-task-adaptive-feature-sub-space#ran","syntology_url":"https://syntology.ai/paper/2003.06670","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2003.06670"}},"official":null}},{"url":"/paper/meta-learning-initializations-for-low","slug":"meta-learning-initializations-for-low","title":"Meta-Learning Initializations for Low-Resource Drug Discovery","date":"2020-03-12","arxiv_id":"2003.05996","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/meta-learning-initializations-for-low#ran","syntology_url":"https://syntology.ai/paper/2003.05996","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2003.05996"}},"official":{"repos":["GSK-AI/meta-learning-qsar"],"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/on-the-texture-bias-for-few-shot-cnn","slug":"on-the-texture-bias-for-few-shot-cnn","title":"On the Texture Bias for Few-Shot CNN Segmentation","date":"2020-03-09","arxiv_id":"2003.04052","repositories_listed":1,"syntology":null},{"url":"/paper/pac-bayesian-meta-learning-with-implicit","slug":"pac-bayesian-meta-learning-with-implicit","title":"PAC-Bayes meta-learning with implicit task-specific posteriors","date":"2020-03-05","arxiv_id":"2003.02455","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/pac-bayesian-meta-learning-with-implicit#ran","syntology_url":"https://syntology.ai/paper/2003.02455","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2003.02455"}},"official":{"repos":["cnguyen10/few_shot_meta_learning"],"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/few-shot-learning-on-graphs-via-super-classes-1","slug":"few-shot-learning-on-graphs-via-super-classes-1","title":"Few-Shot Learning on Graphs via Super-Classes based on Graph Spectral Measures","date":"2020-02-27","arxiv_id":"2002.12815","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":2,"n_ran_checked":2,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":3,"phrase":"3 ran (of which 2 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/few-shot-learning-on-graphs-via-super-classes-1#ran","syntology_url":"https://syntology.ai/paper/2002.12815","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2002.12815"}},"official":{"repos":["chauhanjatin10/GraphsFewShot"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":2,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/transductive-few-shot-learning-with-meta","slug":"transductive-few-shot-learning-with-meta","title":"Meta-Learned Confidence for Few-shot Learning","date":"2020-02-27","arxiv_id":"2002.12017","repositories_listed":1,"syntology":null},{"url":"/paper/an-open-set-recognition-and-few-shot-learning","slug":"an-open-set-recognition-and-few-shot-learning","title":"An Open-set Recognition and Few-Shot Learning Dataset for Audio Event Classification in Domestic Environments","date":"2020-02-26","arxiv_id":"2002.11561","repositories_listed":1,"syntology":null},{"url":"/paper/a-structured-prediction-approach-for-2","slug":"a-structured-prediction-approach-for-2","title":"Structured Prediction for Conditional Meta-Learning","date":"2020-02-20","arxiv_id":"2002.08799","repositories_listed":1,"syntology":null},{"url":"/paper/task-augmentation-by-rotating-for-meta","slug":"task-augmentation-by-rotating-for-meta","title":"Task Augmentation by Rotating for Meta-Learning","date":"2020-02-08","arxiv_id":"2003.00804","repositories_listed":1,"syntology":null}],"record_sha256":"7104c8b8f80db46d6b0b53e8b27ba89564e94ddcb6d171686a80bdaea5d5437b","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}