Jessica Yung

Computer Vision and Pattern Recognition · 2019–2021

A researcher in the Science corpus: 4 papers; trusts 438 researchers and is trusted by 86 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 205 citations into it; #3,505 of 21,575 by trust here, #2,228 by citations
• Artificial Intelligence — 186 citations into it; #10,633 of 35,345 by trust here, #2,944 by citations
• Representation & Self-Supervised Learning — 73 citations into it; #1,680 of 3,145 by trust here, #649 by citations
• Optimization & Training — 52 citations into it
• Evaluation & Benchmarks — 49 citations into it
• Model Architectures — 43 citations into it
• Media Technology — 12 citations into it
• Efficiency & Compression — 9 citations into it

Whom Jessica Yung trusts overall

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

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

Who trusts Jessica Yung overall

1. Xiaohua Zhai
2. Lucas Beyer
3. Mostafa Dehghani
4. Anders Andreassen

Papers from Jessica Yung's perspective

Ordered by how strongly the researchers Jessica Yung 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

• Big Transfer (BiT): General Visual Representation Learning (2019) — 1,392 citations
• MLP-Mixer: An all-MLP Architecture for Vision (2021) — 3,705 citations
• On Robustness and Transferability of Convolutional Neural Networks (2020) — 174 citations
• Large Scale Learning of General Visual Representations for Transfer (2019) — 104 citations