Kexin Yi

Computer Vision and Pattern Recognition · 2018–2020

A researcher in the Science corpus: 3 papers; trusts 286 researchers and is trusted by 8 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 108 citations into it; #10,053 of 21,575 by trust here, #4,750 by citations
• Artificial Intelligence — 93 citations into it; #15,933 of 35,345 by trust here, #6,720 by citations
• Natural Language Processing — 25 citations into it
• Model Architectures — 14 citations into it
• Reinforcement Learning — 13 citations into it
• Generative Models — 6 citations into it

Whom Kexin Yi trusts overall

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

1. Kaiming He
2. Geoffrey E. Hinton
3. Diederik P. Kingma
4. Yoshua Bengio
5. Ross B. Girshick
6. Alex Krizhevsky
7. Ilya Sutskever
8. Sepp Hochreiter
9. Yann LeCun
10. Ronald J. Williams

Who trusts Kexin Yi overall

1. Zhenfang Chen
2. Jiayuan Mao
3. Chuang Gan
4. Josh Tenenbaum
5. Yining Hong
6. Bo Wu
7. Mingyu Ding
8. Jiajun Wu
9. Drew A. Hudson
10. Ben Zion Vatashsky

Papers from Kexin Yi's perspective

Ordered by how strongly the researchers Kexin Yi 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. Long Short-Term Memory
5. Attention is All you Need (Neural Information Processing Systems)
6. Gradient-based learning applied to document recognition
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. Going deeper with convolutions

Their papers

• Neural-Symbolic VQA: Disentangling Reasoning from Vision and Language Understanding (2018) — 717 citations
• CLEVRER: CoLlision Events for Video REpresentation and Reasoning (2019) — 645 citations
• Visual Grounding of Learned Physical Models (2020) — 90 citations