Piotr Teterwak

Computer Vision and Pattern Recognition · 2019–2021

A researcher in the Science corpus: 3 papers; trusts 362 researchers and is trusted by 1 researcher here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 101 citations into it; #20,562 of 21,575 by trust here, #3,929 by citations
• Artificial Intelligence — 65 citations into it; #33,830 of 35,345 by trust here, #5,600 by citations
• Representation & Self-Supervised Learning — 35 citations into it; #2,981 of 3,145 by trust here, #755 by citations
• Generative Models — 25 citations into it
• Model Architectures — 14 citations into it
• Media Technology — 9 citations into it
• Safety, Robustness & Interpretability — 9 citations into it
• Computer Graphics and Computer-Aided Design — 8 citations into it

Whom Piotr Teterwak 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. Christian Szegedy
6. Yoshua Bengio
7. Yann LeCun
8. Ross B. Girshick
9. Ilya Sutskever
10. Karen Simonyan

Who trusts Piotr Teterwak overall

1. Chieh Hubert Lin
2. Mahmoud Assran
3. Yen-Chi Cheng
4. Xin Dong
5. Enrico Fini
6. Gyuhak Kim
7. Olivia Wiles
8. Katherine Lee
9. Lucas Caccia
10. Jongheon Jeong

Papers from Piotr Teterwak's perspective

Ordered by how strongly the researchers Piotr Teterwak 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. ImageNet: A large-scale hierarchical image database
5. Attention is All you Need (Neural Information Processing Systems)
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. Learning Multiple Layers of Features from Tiny Images

Their papers

• Supervised Contrastive Learning (2020) — 6,562 citations
• Boundless: Generative Adversarial Networks for Image Extension (2019) — 131 citations
• Understanding invariance via feedforward inversion of discriminatively trained classifiers (2021) — 11 citations