MetaFun: meta-learning with iterative functional updates
We develop a functional encoder-decoder approach to supervised meta-learning, where labeled data is encoded into an infinite-dimensional functional representation rather than a finite-dimensional one. Furthermore, rather than directly producing the representation, we learn a neural update rule resem...
Asıl Yazarlar: | Xu, J, Ton, J-F, Kim, H, Kosiorek, AR, Teh, YW |
---|---|
Materyal Türü: | Journal article |
Dil: | English |
Baskı/Yayın Bilgisi: |
MLResearch Press
2020
|
Benzer Materyaller
-
MetaFun: unveiling sex-based differences in multiple transcriptomic studies through comprehensive functional meta-analysis
Yazar:: Pablo Malmierca-Merlo, ve diğerleri
Baskı/Yayın Bilgisi: (2024-08-01) -
Correction: MetaFun: unveiling sex-based differences in multiple transcriptomic studies through comprehensive functional meta-analysis
Yazar:: Pablo Malmierca-Merlo, ve diğerleri
Baskı/Yayın Bilgisi: (2024-09-01) -
Noise contrastive meta-learning for conditional density estimation using kernel mean embeddings
Yazar:: Ton, J-F, ve diğerleri
Baskı/Yayın Bilgisi: (2021) -
Set transformer: A framework for attention-based permutation-invariant neural networks
Yazar:: Lee, J, ve diğerleri
Baskı/Yayın Bilgisi: (2019) -
Model updating for fun kart chassis
Yazar:: Mohd Sahril, Mohd Fouzi
Baskı/Yayın Bilgisi: (2008)