Ke Li

Computer Vision and Pattern Recognition · with Zhang · 2021–2023

A researcher in the Science corpus: 5 papers; trusts 418 researchers and is trusted by 3 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 234 citations into it; #15,642 of 21,575 by trust here, #7,176 by citations
• Artificial Intelligence — 227 citations into it
• Model Architectures — 27 citations into it
• Representation & Self-Supervised Learning — 25 citations into it
• Generative Models — 21 citations into it; #1,483 of 1,956 by trust here, #1,200 by citations
• Statistical and Nonlinear Physics — 8 citations into it; #6,938 of 10,127 by trust here, #3,010 by citations
• Numerical Methods & Scientific Computing — 5 citations into it

Whom Ke Li trusts overall

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

1. Alex Krizhevsky
2. Geoffrey E. Hinton
3. Kaiming He
4. Diederik P. Kingma
5. Yoshua Bengio
6. Yann LeCun
7. Ross B. Girshick
8. Ilya Sutskever
9. Karen Simonyan
10. Andrew Zisserman

Who trusts Ke Li overall

1. Fan Bao
2. Valentin De Bortoli
3. Chongxuan Li
4. Tero Karras
5. Sam Bond-Taylor
6. Bowen Jing
7. Florinel-Alin Croitoru
8. Cheng Lu

Papers from Ke Li's perspective

Ordered by how strongly the researchers Ke Li 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. Attention is All you Need (Neural Information Processing Systems)
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. ImageNet: A large-scale hierarchical image database
6. Long Short-Term Memory
7. Gradient-based learning applied to document recognition
8. ImageNet Large Scale Visual Recognition Challenge
9. Learning Multiple Layers of Features from Tiny Images
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• Gotta Go Fast When Generating Data with Score-Based Models (2021) — 265 citations
• A survey on multimodal large language models (2023) — 1,509 citations
• PyramidCLIP: Hierarchical Feature Alignment for Vision-language Model Pretraining (2022) — 160 citations
• Evo-ViT: Slow-Fast Token Evolution for Dynamic Vision Transformer (2021) — 301 citations
• MME: A Comprehensive Evaluation Benchmark for Multimodal Large Language Models (2023) — 1,670 citations