알림
뒤로

AI 논문 추천 자료

[인공지능 논문 Review - 10] TASKNORM: Rethinking Batch Normalization for Meta-Learning

AI 논문

[TASKNORM: Rethinking Batch Normalization for Meta-Learning]

(J. Bronskill et al., ICML-2020)


Batch normalization is an essential component of modern deep learning.

Meta-learning also relies on deep neural networks, so batch normalization needs to be tailored, according to the hierarchical nature of meta-learning.


This paper begins with having a close look at various normalization methods, in the meta-learning framework.

Then it presents “TASKNORM” which normalizes a task with the context set moments in combination with a set of non-transductive secondary moments (instance normalization or layer normalization) computed from the input being normalized. Experiments demonstrate that TASKNORM consistently improves the performance.


논문보기 링크↓

https://arxiv.org/pdf/2003.03284.pdf


[인공지능 논문 Review - 09] Unravelling Meta-Learning: Understanding Feature Representations for Few-Shot Tasks
목록

최승진 석학의 대표적인 논문

1. Juho Lee, Yoonho Lee, Jungtaek Kim, Adam R. Kosiorek, Seungjin Choi, Yee Whye Teh (2019),
"Set transformer: A framework for attention-based permutation-invariant neural networks,"
Proceedings of the Thirty-Sixth International Conference on Machine Learning (ICML-2019),
Long Beach, California, USA, June 9-15, 2019.
(earlier version in preprint arXiv:1810.00825 )

2. Juho Lee, Lancelot James, Seungjin Choi, and François Caron (2019),
"A Bayesian model for sparse graphs with flexible degree distribution and overlapping community structure,"
Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics (AISTATS-2019),
Naha, Okinawa, Japan, April 16-18, 2019. (oral)
(earlier version in preprint arXiv:1810.01778 )

3. Yoonho Lee and Seungjin Choi (2018),
"Gradient-based meta-learning with adaptive layerwise metric and subspace,"
in Proceedings of the Thirty-Fifth International Conference on Machine Learning (ICML-2018),
Stockholm, Sweden, July 10-15, 2018.
(earlier version in preprint arXiv:1810.05558 )

4. Saehoon Kim, Jungtaek Kim, and Seungjin Choi (2018),
"On the optimal bit complexity of circulant binary embedding,"
in Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence (AAAI-2018),

5. Juho Lee, Creighton Heaukulani, Zoubin Ghahramani, Lancelot James, and Seungjin Choi (2017),
"Bayesian inference on random simple graphs with power law degree distributions,"
in Proceedings of the International Conference on Machine Learning (ICML-2017),
Sydney, Australia, August 6-11, 2017.
(earlier version in preprint arXiv:1702.08239 )

6. Saehoon Kim and Seungjin Choi (2017),
"Binary embedding with additive homogeneous kernels,"
in Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence (AAAI-2017),
San Francisco, California, USA, February 4-9, 2017.

7. Juho Lee, Lancelot F. James, and Seungjin Choi (2016),
"Finite-dimensional BFRY priors and variational Bayesian inference for power law models,"
in Advances in Neural Information Processing Systems 29 (NIPS-2016),
Barcelona, Spain, December 5-10, 2016.

8. Suwon Suh and Seungjin Choi (2016),
"Gaussian copula variational autoencoders for mixed data,"
Preprint arXiv:1604.04960, 2016.

9. Yong-Deok Kim, Taewoong Jang, Bohyung Han, and Seungjin Choi (2016),
"Learning to select pre-trained deep representations with Bayesian evidence framework,"
in Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition (CVPR-2016),
Las Vegas, Nevada, USA, June 27-30, 2016. (oral)

10. Juho Lee and Seungjin Choi (2015),
"Tree-guided MCMC inference for normalized random measure mixture models,"
in Advances in Neural Information Processing Systems 28 (NIPS-2015),
Montreal, Canada, December 7-12, 2015.

최승진 석학의 대표적인 논문

1. Juho Lee, Yoonho Lee, Jungtaek Kim, Adam R. Kosiorek, Seungjin Choi, Yee Whye Teh (2019),

"Set transformer: A framework for attention-based permutation-invariant neural networks,"

Proceedings of the Thirty-Sixth International Conference on Machine Learning (ICML-2019),

Long Beach, California, USA, June 9-15, 2019.

(earlier version in preprint arXiv:1810.00825 )


2. Juho Lee, Lancelot James, Seungjin Choi, and François Caron (2019), 

"A Bayesian model for sparse graphs with flexible degree distribution and overlapping community structure,"

Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics (AISTATS-2019),
Naha, Okinawa, Japan, April 16-18, 2019. (oral)
(earlier version in preprint arXiv:1810.01778 )

3. Yoonho Lee and Seungjin Choi (2018), 

"Gradient-based meta-learning with adaptive layerwise metric and subspace,"

in Proceedings of the Thirty-Fifth International Conference on Machine Learning (ICML-2018),
Stockholm, Sweden, July 10-15, 2018.
(earlier version in preprint arXiv:1810.05558 )

4. Saehoon Kim, Jungtaek Kim, and Seungjin Choi (2018), 

"On the optimal bit complexity of circulant binary embedding," 

in Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence (AAAI-2018),
New Orleans, Louisiana, USA, February 2-7, 2018.

5. Juho Lee, Creighton Heaukulani, Zoubin Ghahramani, Lancelot James, and Seungjin Choi (2017),

"Bayesian inference on random simple graphs with power law degree distributions,"

in Proceedings of the International Conference on Machine Learning (ICML-2017),
Sydney, Australia, August 6-11, 2017.
(earlier version in preprint arXiv:1702.08239 )

6. Saehoon Kim and Seungjin Choi (2017),

"Binary embedding with additive homogeneous kernels,"

in Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence (AAAI-2017),
San Francisco, California, USA, February 4-9, 2017.