Kaicheng Yu

Computer Vision and Pattern Recognition · 2019–2022

A researcher in the Science corpus: 4 papers; trusts 215 researchers and is trusted by 5 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 94 citations into it; #14,269 of 21,575 by trust here, #6,089 by citations
• Artificial Intelligence — 77 citations into it; #23,085 of 35,345 by trust here, #8,525 by citations
• Model Architectures — 25 citations into it
• Optimization & Training — 18 citations into it
• Representation & Self-Supervised Learning — 12 citations into it
• Reinforcement Learning — 8 citations into it
• Efficiency & Compression — 6 citations into it
• Statistical and Nonlinear Physics — 5 citations into it

Whom Kaicheng Yu trusts overall

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

1. Geoffrey E. Hinton
2. Alex Krizhevsky
3. Kaiming He
4. Yoshua Bengio
5. Karen Simonyan
6. Barret Zoph
7. Quoc V. Le
8. Diederik P. Kingma
9. Yann LeCun
10. Ilya Sutskever

Who trusts Kaicheng Yu overall

1. Miao Zhang
2. Arber Zela
3. Linnan Wang
4. Huiqi Li
5. Xuanyi Dong
6. Xiangxiang Chu
7. Martin Wistuba
8. Saining Xie
9. Xiaojun Chang
10. Shirui Pan

Papers from Kaicheng Yu's perspective

Ordered by how strongly the researchers Kaicheng Yu 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. ImageNet: A large-scale hierarchical image database
7. Gradient-based learning applied to document recognition
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. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Evaluating the Search Phase of Neural Architecture Search (2019) — 375 citations
• BEVFusion: A Simple and Robust LiDAR-Camera Fusion Framework (2022) — 712 citations
• Generalized Class Incremental Learning (2020) — 48 citations
• Overcoming Multi-Model Forgetting (2019) — 40 citations