{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,22]],"date-time":"2026-04-22T21:05:33Z","timestamp":1776891933628,"version":"3.51.2"},"reference-count":34,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Neurocomputing"],"published-print":{"date-parts":[[2026,6]]},"DOI":"10.1016\/j.neucom.2026.133417","type":"journal-article","created":{"date-parts":[[2026,3,21]],"date-time":"2026-03-21T16:26:30Z","timestamp":1774110390000},"page":"133417","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["Modeling task uncertainty for neural processes to meta-learn with fewer tasks"],"prefix":"10.1016","volume":"682","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-4329-2054","authenticated-orcid":false,"given":"Eva","family":"Cherian","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8048-672X","authenticated-orcid":false,"given":"Mrinal","family":"Das","sequence":"additional","affiliation":[]}],"member":"78","reference":[{"key":"10.1016\/j.neucom.2026.133417_bib0005","series-title":"Proceedings of the 34th International Conference on Machine Learning","article-title":"Model-Agnostic Meta-Learning for fast adaptation of deep networks","author":"Finn","year":"2017"},{"key":"10.1016\/j.neucom.2026.133417_bib0010","series-title":"Advances in Neural Information Processing Systems","article-title":"Prototypical networks for few-shot learning","volume":"vol. 30","author":"Snell","year":"2017"},{"key":"10.1016\/j.neucom.2026.133417_bib0015","series-title":"Proceedings of the 35th International Conference on Machine Learning","article-title":"Conditional neural processes","author":"Garnelo","year":"2018"},{"key":"10.1016\/j.neucom.2026.133417_bib0020","author":"Garnelo"},{"key":"10.1016\/j.neucom.2026.133417_bib0025","series-title":"International Conference on Learning Representations","article-title":"Attentive neural processes","author":"Kim","year":"2019"},{"key":"10.1016\/j.neucom.2026.133417_bib0030","series-title":"International Conference on Learning Representations","article-title":"Convolutional conditional neural processes","author":"Gordon","year":"2020"},{"key":"10.1016\/j.neucom.2026.133417_bib0035","series-title":"Advances in Neural Information Processing Systems","article-title":"Meta-Learning requires Meta-Augmentation","volume":"vol. 33","author":"Rajendran","year":"2020"},{"key":"10.1016\/j.neucom.2026.133417_bib0040","series-title":"Proceedings of the 38th International Conference on Machine Learning","article-title":"Data augmentation for Meta-Learning","author":"Ni","year":"2021"},{"key":"10.1016\/j.neucom.2026.133417_bib0045","series-title":"Advances in Neural Information Processing Systems","article-title":"Set-based meta-interpolation for few-task meta-learning","author":"Lee","year":"2022"},{"key":"10.1016\/j.neucom.2026.133417_bib0050","series-title":"International Conference on Learning Representations","article-title":"Meta-Learning with fewer tasks through task interpolation","author":"Yao","year":"2021"},{"key":"10.1016\/j.neucom.2026.133417_bib0055","series-title":"Proceedings of the 38th International Conference on Machine Learning","article-title":"Improving generalization in meta-learning via task augmentation","author":"Yao","year":"2021"},{"key":"10.1016\/j.neucom.2026.133417_bib0060","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2024.111839","article-title":"Multi-task convex combination interpolation for meta-learning with fewer tasks","volume":"296","author":"Tang","year":"2024","journal-title":"Knowl.-Based Syst."},{"key":"10.1016\/j.neucom.2026.133417_bib0065","series-title":"Proceedings of the 40th International Conference on Machine Learning of Proceedings of Machine Learning Research","first-page":"7141","article-title":"Interval bound interpolation for few-shot learning with few tasks","volume":"vol. 202","author":"Datta","year":"2023"},{"key":"10.1016\/j.neucom.2026.133417_bib0070","series-title":"Advances in Neural Information Processing Systems","article-title":"Adversarial task up-sampling for meta-learning","author":"Wu","year":"2022"},{"key":"10.1016\/j.neucom.2026.133417_bib0075","series-title":"International Conference on Learning Representations","article-title":"Density estimation using real NVP","author":"Dinh","year":"2017"},{"key":"10.1016\/j.neucom.2026.133417_bib0080","series-title":"2021 IEEE\/CVF International Conference on Computer Vision (ICCV)","first-page":"6599","article-title":"Semantic perturbations with normalizing flows for improved generalization","author":"Y\u00fcksel","year":"2021"},{"key":"10.1016\/j.neucom.2026.133417_bib0085","series-title":"Advances in Neural Information Processing Systems","article-title":"Non-gaussian Gaussian processes for few-shot regression","author":"Sendera","year":"2021"},{"key":"10.1016\/j.neucom.2026.133417_bib0090","series-title":"Advances in Neural Information Processing Systems","article-title":"Deep sets","volume":"vol. 30","author":"Zaheer","year":"2017"},{"key":"10.1016\/j.neucom.2026.133417_bib0095","series-title":"International Conference on Learning Representations","article-title":"Meta-Learning without memorization","author":"Yin","year":"2019"},{"issue":"3","key":"10.1016\/j.neucom.2026.133417_bib0100","doi-asserted-by":"crossref","first-page":"355","DOI":"10.1023\/A:1007618624809","article-title":"Some PAC-Bayesian theorems","volume":"37","author":"McAllester","year":"1999","journal-title":"Mach. Learn."},{"key":"10.1016\/j.neucom.2026.133417_bib0105","author":"Catoni"},{"key":"10.1016\/j.neucom.2026.133417_bib0110","series-title":"International Conference on Machine Learning","article-title":"A pac-bayesian bound for lifelong learning","author":"Pentina","year":"2014"},{"key":"10.1016\/j.neucom.2026.133417_bib0115","series-title":"International Conference on Machine Learning","article-title":"Meta-learning by adjusting priors based on extended Pac-Bayes theory","author":"Amit","year":"2018"},{"key":"10.1016\/j.neucom.2026.133417_bib0120","series-title":"International Conference on Machine Learning","article-title":"Pacoh: Bayes-optimal meta-learning with pac-guarantees","author":"Rothfuss","year":"2021"},{"key":"10.1016\/j.neucom.2026.133417_bib0125","series-title":"Advances in Neural Information Processing Systems","article-title":"Pac-bayesian theory meets Bayesian inference","author":"Germain","year":"2016"},{"issue":"1","key":"10.1016\/j.neucom.2026.133417_bib0130","doi-asserted-by":"crossref","first-page":"149","DOI":"10.1613\/jair.731","article-title":"A model of inductive bias learning","volume":"12","author":"Baxter","year":"2000","journal-title":"J. Artif. Intell. Res."},{"issue":"2","key":"10.1016\/j.neucom.2026.133417_bib0135","doi-asserted-by":"crossref","first-page":"285","DOI":"10.1111\/j.2517-6161.1991.tb01825.x","article-title":"Procrustes methods in the statistical analysis of shape","volume":"53","author":"Goodall","year":"1991","journal-title":"J. R. Stat. Soc. Ser. B (Methodol.)"},{"issue":"3","key":"10.1016\/j.neucom.2026.133417_bib0140","first-page":"460","article-title":"Multivariate shape analysis","volume":"55","author":"Dryden","year":"1993","journal-title":"Sankhy\u0103: Indian J. Stat, Ser. A"},{"key":"10.1016\/j.neucom.2026.133417_bib0145","series-title":"NIPS-W","article-title":"Automatic differentiation in Pytorch","author":"Paszke","year":"2017"},{"key":"10.1016\/j.neucom.2026.133417_bib0150","series-title":"Proceedings of the 36th International Conference on Machine Learning of Proceedings of Machine Learning Research","first-page":"7693","article-title":"Fast context adaptation via meta-learning","volume":"vol. 97","author":"Zintgraf","year":"2019"},{"key":"10.1016\/j.neucom.2026.133417_bib0155","author":"Zhang"},{"issue":"11","key":"10.1016\/j.neucom.2026.133417_bib0160","doi-asserted-by":"crossref","first-page":"2278","DOI":"10.1109\/5.726791","article-title":"Gradient-based learning applied to document recognition","volume":"86","author":"Lecun","year":"1998","journal-title":"Proc. IEEE"},{"issue":"3","key":"10.1016\/j.neucom.2026.133417_bib0165","article-title":"Stacked ensemble model for accurate crop yield prediction using machine learning techniques","volume":"7","author":"Ramesh","year":"2025","journal-title":"Environ. Res. Commun."},{"key":"10.1016\/j.neucom.2026.133417_bib0170","author":"Jung"}],"container-title":["Neurocomputing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0925231226008143?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0925231226008143?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,4,22]],"date-time":"2026-04-22T20:32:51Z","timestamp":1776889971000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0925231226008143"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,6]]},"references-count":34,"alternative-id":["S0925231226008143"],"URL":"https:\/\/doi.org\/10.1016\/j.neucom.2026.133417","relation":{},"ISSN":["0925-2312"],"issn-type":[{"value":"0925-2312","type":"print"}],"subject":[],"published":{"date-parts":[[2026,6]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Modeling task uncertainty for neural processes to meta-learn with fewer tasks","name":"articletitle","label":"Article Title"},{"value":"Neurocomputing","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.neucom.2026.133417","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"133417"}}