Meta-Learning Amidst Heterogeneity and Ambiguity-Reference-Cited by-同舟云学术

Meta-Learning Amidst Heterogeneity and Ambiguity

Author:

Go Kyeongryeol¹^ORCID,Kim Mingyu²,Yun Seyoung²^ORCID

Affiliation:

1. Superb AI Inc., Seoul, South Korea

2. Kim Jaechul Graduate School of AI, Korea Advanced Institute of Science and Technology (KAIST), Seoul, South Korea

Funder

Institute of Information & communications Technology Planning & Evaluation (IITP) grant

Korea government(MSIT), XVoice: Multi-Modal Voice Meta Learning

Center for Applied Research in Artificial Intelligence (CARAI) grant

DAPA and ADD

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Engineering,General Materials Science,General Computer Science,Electrical and Electronic Engineering

Link

Reference78 articles.

1. Transformer neural processes: Uncertainty-aware meta learning via sequence modeling;nguyen;Proc Int Conf Mach Learn,2022

2. Learning hierarchical features from generative models;zhao;arXiv 1702 08396,2017

3. Bayesian context aggregation for neural processes;volpp;Proc Int Conf Learn Represent,2020

4. Pixel recurrent neural networks;van den oord;arXiv 1601 06759,2016

5. Practical conditional neural processes via tractable dependent predictions;markou;arXiv 2203 08775,2022

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