Seonguk Park
Artificial Intelligence · 2018–2020
A researcher in the Science corpus: 4 papers; trusts 601 researchers and is trusted by 20 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 88 citations into it; #7,923 of 35,345 by trust here, #10,667 by citations
- Computer Vision and Pattern Recognition — 88 citations into it; #6,212 of 21,575 by trust here, #8,021 by citations
- Efficiency & Compression — 37 citations into it; #335 of 1,264 by trust here, #644 by citations
- Model Architectures — 23 citations into it
- Representation & Self-Supervised Learning — 15 citations into it
- Optimization & Training — 14 citations into it
- Signal Processing — 10 citations into it
- Evaluation & Benchmarks — 9 citations into it
Whom Seonguk Park trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Geoffrey E. Hinton
- Kaiming He
- Sergey Zagoruyko
- Yoshua Bengio
- Alex Krizhevsky
- Thomas G. Dietterich
- Yann LeCun
- Diederik P. Kingma
- Ross B. Girshick
- X. Zhang
Who trusts Seonguk Park overall
- Lin Wang
- Jianping Gou
- Kuk-Jin Yoon
- Nojun Kwak
- Baosheng Yu
- Jangho Kim
- Anbang Yao
- Mingi Ji
- Rafael Müller
- Minsoo Kang
Papers from Seonguk Park's perspective
Ordered by how strongly the researchers Seonguk Park trusts endorse each paper. Same corpus, a different order for every researcher.
- ImageNet classification with deep convolutional neural networks
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Long Short-Term Memory
- Attention is All you Need (Neural Information Processing Systems)
- Gradient-based learning applied to document recognition
- ImageNet: A large-scale hierarchical image database
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- ImageNet Large Scale Visual Recognition Challenge
- Going deeper with convolutions
Their papers
- Paraphrasing Complex Network: Network Compression via Factor Transfer (2018) — 652 citations
- Feature-map-level Online Adversarial Knowledge Distillation (2020) — 155 citations
- Feature-Level Ensemble Knowledge Distillation for Aggregating Knowledge from Multiple Networks (2020) — 47 citations
- FEED: Feature-level Ensemble for Knowledge Distillation (2019) — 46 citations