Learning to Learn by Jointly Optimizing Neural Architecture and Weights-Reference-Cited by-同舟云学术

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Learning to Learn by Jointly Optimizing Neural Architecture and Weights

Published:2022-06 Issue: Volume: Page:
ISSN:
Container-title:2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
language:
Short-container-title:

Author:

Ding Yadong¹,Wu Yu²,Huang Chengyue¹,Tang Siliang¹,Yang Yi¹,Wei Longhui³,Zhuang Yueting¹,Tian Qi⁴

Affiliation:

1. Zhejiang University

2. Princeton University

3. University of Science and Technology of China

4. Huawei Cloud & AI

Funder

National Key Research and Development Program of China

CKCEST

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9878378/9878366/09878780.pdf?arnumber=9878780

Reference40 articles.

1. Neural architecture search with reinforcement learning;zoph;International Conference on Learning Representations,2017

2. Neural Architecture Search with Random Labels

3. SNAS: stochastic neural architecture search;xie;International Conference on Learning Representations,2019

4. NAS-FCOS: Fast Neural Architecture Search for Object Detection

5. Matching networks for one shot learning;vinyals;Advances in neural information processing systems,2016

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2. Fast and Efficient Local-Search for Genetic Programming Based Loss Function Learning;Proceedings of the Genetic and Evolutionary Computation Conference;2023-07-12

3. Few-shot Molecular Property Prediction via Hierarchically Structured Learning on Relation Graphs;Neural Networks;2023-06

4. Learning Symbolic Model-Agnostic Loss Functions via Meta-Learning;IEEE Transactions on Pattern Analysis and Machine Intelligence;2023

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