William S. Peebles

Computer Vision and Pattern Recognition · 2019–2022

A researcher in the Science corpus: 5 papers; trusts 837 researchers and is trusted by 247 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 213 citations into it; #2,470 of 21,575 by trust here, #2,836 by citations
• Generative Models — 128 citations into it; #334 of 1,956 by trust here, #317 by citations
• Artificial Intelligence — 107 citations into it; #23,908 of 35,345 by trust here, #12,244 by citations
• Optimization & Training — 41 citations into it
• Computer Graphics and Computer-Aided Design — 25 citations into it
• Model Architectures — 23 citations into it; #746 of 2,278 by trust here, #740 by citations
• Biophysics — 16 citations into it
• Evaluation & Benchmarks — 15 citations into it

Whom William S. Peebles trusts overall

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

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

Who trusts William S. Peebles overall

1. Junsong Chen
2. Diederik P. Kingma
3. Chenlin Meng
4. Enze Xie
5. Aaron Lou
6. Patrick Esser
7. Shanghua Gao
8. Lijun Yu
9. Richard Zhang
10. Alexander C. Li

Papers from William S. Peebles's perspective

Ordered by how strongly the researchers William S. Peebles 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. Attention is All you Need (Neural Information Processing Systems)
5. ImageNet: A large-scale hierarchical image database
6. Long Short-Term Memory
7. Gradient-based learning applied to document recognition
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. ImageNet Large Scale Visual Recognition Challenge
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Semantic photo manipulation with a generative image prior (2019) — 368 citations
• Seeing What a GAN Cannot Generate (2019) — 353 citations
• Scalable Diffusion Models with Transformers (2022) — 6,663 citations
• GAN-Supervised Dense Visual Alignment (2021) — 78 citations
• Learning to Learn with Generative Models of Neural Network Checkpoints (2022) — 100 citations