Jake Zhao

Artificial Intelligence · 2016–2019

A researcher in the Science corpus: 3 papers; trusts 257 researchers and is trusted by 31 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 47 citations into it; #6,822 of 35,345 by trust here, #2,382 by citations
• Computer Vision and Pattern Recognition — 38 citations into it; #5,654 of 21,575 by trust here, #1,803 by citations
• Natural Language Processing — 29 citations into it; #1,917 of 5,688 by trust here, #3,154 by citations
• Safety, Robustness & Interpretability — 15 citations into it; #1,693 of 2,251 by trust here, #2,180 by citations
• Model Architectures — 10 citations into it
• Evaluation & Benchmarks — 8 citations into it
• Generative Models — 8 citations into it
• Optimization & Training — 6 citations into it

Whom Jake Zhao trusts overall

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

1. Alex Krizhevsky
2. Yoshua Bengio
3. Geoffrey E. Hinton
4. Diederik P. Kingma
5. Yann LeCun
6. Ian J. Goodfellow
7. Christian Szegedy
8. Kyunghyun Cho
9. Ilya Sutskever
10. Kaiming He

Who trusts Jake Zhao overall

1. Emin Orhan
2. Urvashi Khandelwal
3. Vikas Verma
4. Weijia Xu
5. Jiatao Gu
6. William Chan
7. Felix Stahlberg
8. Omer Levy
9. Alex Lamb
10. Chitwan Saharia

Papers from Jake Zhao's perspective

Ordered by how strongly the researchers Jake Zhao 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. Long Short-Term Memory
6. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
8. Learning Multiple Layers of Features from Tiny Images
9. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• End to End Learning for Self-Driving Cars (2016) — 4,658 citations
• Levenshtein Transformer (2019) — 401 citations
• Retrieval-Augmented Convolutional Neural Networks for Improved Robustness against Adversarial Examples (2018) — 20 citations