ICLR 2021 16편의 논문 채택!

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KAIST AI대학원 소속 연구자들의 논문 16편이 ICLR 2021 (the Ninth International Conference on Learning Representations) 학회에 채택되었습니다. ICLR는 세계 최고의 기계학습 학회 중의 하나로, 2021년에는 온라인으로 5월 4일부터 7일까지 진행됩니다.

채택된 논문 목록:

1. Meta-GMVAE: Mixture of Gaussian VAE for Unsupervised Meta-Learning
Dong Bok Lee, Dongchan Min, Seanie Lee, and Sung Ju Hwang (Spotlight Presentation)

2. Minimum Width for Universal Approximation
Sejun Park, Chulhee Yun, Jaeho Lee, and Jinwoo Shin (Spotlight Presentation)

3. Winning the L2RPN Challenge: Power Grid Management via Semi-Markov Afterstate Actor-Critic
Deunsol Yoon, Sunghoon Hong, Byung-Jun Lee, and Kee-Eung Kim (Spotlight Presentation)

4. Accurate Learning of Graph Representations with Graph Multiset Pooling
Jinheon Baek, Minki Kang, and Sung Ju Hwang

5. Monte-Carlo Planning and Learning with Language Action Value Estimates
Youngsoo Jang, Seokin Seo, Jongmin Lee, and Kee-Eung Kim

6. Training GANs with Stronger Augmentations via Contrastive Discriminator
Jongheon Jeong and Jinwoo Shin

7. Federated Semi-Supervised Learning with Inter-Client Consistency & Disjoint Learning
Wonyong Jeong, Jaehong Yoon, Eunho Yang, and Sung Ju Hwang

8. Representation Balancing Offline Model-based Reinforcement Learning
Byung-Jun Lee, Jongmin Lee, and Kee-Eung Kim

9. Rapid Neural Architecture Search by Learning to Generate Graphs from Datasets
Hayeon Lee, Eunyoung Hyung, and Sung Ju Hwang

10. Layer-adaptive Sparsity for the Magnitude-based Pruning
Jaeho Lee, Sejun Park, Sangwoo Mo, Sungsoo Ahn, and Jinwoo Shin

11. i-Mix: A Strategy for Regularizing Contrastive Representation Learning
Kibok Lee, Yian Zhu, Kihyuk Sohn, Chun-Liang Li, Jinwoo Shin, and Honglak Lee

12. Contrastive Learning with Adversarial Perturbations for Conditional Text Generation
Seanie Lee, Dong Bok Lee, and Sung Ju Hwang

13. BOIL: Towards Representation Change for Few-shot Learning
Jaehoon Oh, Hyungjun Yoo, ChangHwan Kim, and Se-Young Yun,

14. Learning to Sample with Local and Global Contexts in Experience Replay Buffer
Youngmin Oh, Kimin Lee, Jinwoo Shin, Eunho Yang, and Sung Ju Hwang

15. FairBatch: Batch Selection for Model Fairness
Yuji Roh, Kangwook Lee, Steven Euijong Whang, and Changho Suh

16. FedMix: Approximation of Mixup under Mean Augmented Federated Learning
Tehrim Yoon, Sumin Shin, Sung Ju Hwang, and Eunho Yang