Chanyoung Park

Artificial Intelligence · AAAI Conference on Artificial Intelligence · 2016–2021

A researcher in the Science corpus: 3 papers; trusts 52 researchers and is trusted by 1 researcher here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 62 citations into it; #34,655 of 35,345 by trust here, #15,866 by citations
• Representation & Self-Supervised Learning — 31 citations into it
• Computer Vision and Pattern Recognition — 21 citations into it
• Information Systems — 21 citations into it; #8,549 of 8,771 by trust here, #3,761 by citations
• Graph Learning — 17 citations into it
• Natural Language Processing — 13 citations into it
• Molecular Biology — 5 citations into it

Whom Chanyoung Park trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Yoshua Bengio
2. Geoffrey E. Hinton
3. Yann LeCun
4. Thomas Kipf
5. Diederik P. Kingma
6. Ralph Linsker
7. Max Welling
8. Petar Velickovic
9. Alex Krizhevsky
10. Ilya Sutskever

Who trusts Chanyoung Park overall

1. Yuning You
2. Xiao Wang
3. Fangda Gu
4. Yichao Lu
5. Xing Su
6. Aminu Da’u
7. Yi Tay
8. Tianlong Chen
9. Chenliang Li
10. Qian Zhang

Papers from Chanyoung Park's perspective

Ordered by how strongly the researchers Chanyoung Park trusts endorse each paper. Same corpus, a different order for every researcher.

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. ImageNet classification with deep convolutional neural networks
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Attention is All you Need (Neural Information Processing Systems)
5. Long Short-Term Memory
6. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
8. Learning Multiple Layers of Features from Tiny Images
9. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• Convolutional Matrix Factorization for Document Context-Aware Recommendation (2016) — 769 citations
• Augmentation-Free Self-Supervised Learning on Graphs (2021) — 299 citations
• Unsupervised Attributed Multiplex Network Embedding (2019) — 341 citations