Yongjun Hong

Computer Vision and Pattern Recognition · 2017–2020

A researcher in the Science corpus: 3 papers; trusts 957 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 245 citations into it
• Artificial Intelligence — 192 citations into it
• Generative Models — 170 citations into it
• Representation & Self-Supervised Learning — 38 citations into it
• Statistical and Nonlinear Physics — 34 citations into it
• Reinforcement Learning — 30 citations into it
• Meta & Continual Learning — 28 citations into it
• Signal Processing — 27 citations into it

Whom Yongjun Hong trusts overall

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

1. Geoffrey E. Hinton
2. Alex Krizhevsky
3. Diederik P. Kingma
4. Yoshua Bengio
5. Yann LeCun
6. Kaiming He
7. Ilya Sutskever
8. Ian J. Goodfellow
9. Karen Simonyan
10. Alex Graves

Who trusts Yongjun Hong overall

1. Garrett Wilson
2. Alexia Jolicoeur-Martineau
3. Arvid Lundervold
4. Timothée Lesort
5. Martin Mundt
6. Sam Powers

Papers from Yongjun Hong's perspective

Ordered by how strongly the researchers Yongjun Hong 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. Long Short-Term Memory
5. Gradient-based learning applied to document recognition
6. Attention is All you Need (Neural Information Processing Systems)
7. ImageNet: A large-scale hierarchical image database
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. ImageNet Large Scale Visual Recognition Challenge
10. GENERATIVE ADVERSARIAL NETS

Their papers

• A Wholistic View of Continual Learning with Deep Neural Networks: Forgotten Lessons and the Bridge to Active and Open World Learning (2020) — 183 citations
• How Generative Adversarial Networks and Their Variants Work (2017) — 184 citations
• How Generative Adversarial Networks and Its Variants Work : An Overview of GAN (2017)