Dongmin Park

Artificial Intelligence · 2019–2023

A researcher in the Science corpus: 4 papers; trusts 160 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 151 citations into it
• Computer Vision and Pattern Recognition — 86 citations into it
• Optimization & Training — 47 citations into it
• Representation & Self-Supervised Learning — 33 citations into it
• Safety, Robustness & Interpretability — 28 citations into it
• Model Architectures — 8 citations into it
• Statistics and Probability — 8 citations into it

Whom Dongmin Park trusts overall

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

1. Alex Krizhevsky
2. Geoffrey E. Hinton
3. Yoshua Bengio
4. Ian J. Goodfellow
5. Yann LeCun
6. Ilya Sutskever
7. Diederik P. Kingma
8. Radford M. Neal
9. Kaiming He
10. Christian Szegedy

Who trusts Dongmin Park overall

1. Dong Yin
2. Liangzu Peng
3. Mozhgan Pourkeshavarz
4. Shixiang Tang
5. Chang-Bin Zhang
6. Mehrdad Farajtabar
7. Paris V. Giampouras
8. Guoying Zhao
9. Ang Li
10. Dapeng Chen

Papers from Dongmin Park's perspective

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

1. ImageNet classification with deep convolutional neural networks
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. ImageNet: A large-scale hierarchical image database
5. Gradient-based learning applied to document recognition
6. Long Short-Term Memory
7. Attention is All you Need (Neural Information Processing Systems)
8. Learning Multiple Layers of Features from Tiny Images
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Learning From Noisy Labels With Deep Neural Networks: A Survey (2020) — 1,409 citations
• Continual Learning by Asymmetric Loss Approximation With Single-Side Overestimation (2019) — 48 citations
• One Size Fits All for Semantic Shifts: Adaptive Prompt Tuning for Continual Learning (2023) — 7 citations
• Robust Learning by Self-Transition for Handling Noisy Labels (2020) — 46 citations