Min Jin Chong
Computer Vision and Pattern Recognition · 2019
A researcher in the Science corpus: 2 papers; trusts 210 researchers and is trusted by 4 researchers here, as inferred from the citation graph.
Subfields
- Computer Vision and Pattern Recognition — 39 citations into it; #10,943 of 21,575 by trust here, #17,258 by citations
- Generative Models — 23 citations into it
- Artificial Intelligence — 22 citations into it; #17,450 of 35,345 by trust here, #26,682 by citations
- Safety, Robustness & Interpretability — 8 citations into it
- Optimization & Training — 6 citations into it
- Model Architectures — 5 citations into it
Whom Min Jin Chong trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Alex Krizhevsky
- Diederik P. Kingma
- Geoffrey E. Hinton
- Christian Szegedy
- Kaiming He
- Jimmy Ba
- Yoshua Bengio
- Karen Simonyan
- Alec Radford
- Yann LeCun
Who trusts Min Jin Chong overall
Papers from Min Jin Chong's perspective
Ordered by how strongly the researchers Min Jin Chong 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)
- Gradient-based learning applied to document recognition
- ImageNet: A large-scale hierarchical image database
- Long Short-Term Memory
- Learning Multiple Layers of Features from Tiny Images
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- ImageNet Large Scale Visual Recognition Challenge
Their papers
- Effectively Unbiased FID and Inception Score and Where to Find Them (2019) — 268 citations
- Big but Imperceptible Adversarial Perturbations via Semantic Manipulation (2019) — 28 citations