Sung Min Park
Artificial Intelligence · with Ilyas · 2022–2023
A researcher in the Science corpus: 2 papers; trusts 48 researchers and is trusted by 0 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 81 citations into it
- Computer Vision and Pattern Recognition — 37 citations into it
- Safety, Robustness & Interpretability — 27 citations into it
- Evaluation & Benchmarks — 18 citations into it
- Natural Language Processing — 14 citations into it
- Management Science and Operations Research — 11 citations into it
- Representation & Self-Supervised Learning — 10 citations into it
Whom Sung Min Park trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Alex Krizhevsky
- Geoffrey E. Hinton
- Yoshua Bengio
- Kaiming He
- Radford M. Neal
- Diederik P. Kingma
- Yann LeCun
- Vitaly Feldman
- Ilya Sutskever
- Scott M. Lundberg
Who trusts Sung Min Park overall
Papers from Sung Min Park's perspective
Ordered by how strongly the researchers Sung Min Park trusts endorse each paper. Same corpus, a different order for every researcher.
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- ImageNet classification with deep convolutional neural networks
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Attention is All you Need (Neural Information Processing Systems)
- Long Short-Term Memory
- ImageNet: A large-scale hierarchical image database
- Gradient-based learning applied to document recognition
- Learning Multiple Layers of Features from Tiny Images
- ImageNet Large Scale Visual Recognition Challenge
- BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
Their papers
- Datamodels: Predicting Predictions from Training Data (2022) — 215 citations
- TRAK: Attributing Model Behavior at Scale (2023) — 305 citations