Hwanjun Song

Artificial Intelligence · 2019–2023

A researcher in the Science corpus: 6 papers; trusts 359 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 254 citations into it
• Computer Vision and Pattern Recognition — 124 citations into it
• Optimization & Training — 73 citations into it
• Safety, Robustness & Interpretability — 63 citations into it
• Representation & Self-Supervised Learning — 37 citations into it
• Evaluation & Benchmarks — 25 citations into it
• Safety Research — 22 citations into it
• Management Science and Operations Research — 15 citations into it

Whom Hwanjun Song trusts overall

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

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

Who trusts Hwanjun Song overall

1. Steven Euijong Whang
2. Sheng Liu
3. Pan Zhang
4. Haiyang Zhang
5. Jieyu Zhang
6. Minseok Kim
7. Yuji Roh
8. Jiacheng Ye
9. Jae-Gil Lee
10. Jihwan Bang

Papers from Hwanjun Song's perspective

Ordered by how strongly the researchers Hwanjun Song 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. ImageNet: A large-scale hierarchical image database
5. Long Short-Term Memory
6. Gradient-based learning applied to document recognition
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
• One Size Fits All for Semantic Shifts: Adaptive Prompt Tuning for Continual Learning (2023) — 7 citations
• Online Continual Learning on a Contaminated Data Stream with Blurry Task Boundaries (2022) — 61 citations
• Data collection and quality challenges in deep learning: a data-centric AI perspective (2021) — 559 citations
• Robust Learning by Self-Transition for Handling Noisy Labels (2020) — 46 citations
• SELFIE: Refurbishing Unclean Samples for Robust Deep Learning (2019) — 467 citations