Jeremiah Zhe Liu

Artificial Intelligence · 2017–2020

A researcher in the Science corpus: 2 papers; trusts 41 researchers and is trusted by 1 researcher here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 60 citations into it
• Computer Vision and Pattern Recognition — 34 citations into it
• Probabilistic & Bayesian Methods — 27 citations into it
• Safety, Robustness & Interpretability — 21 citations into it
• Model Architectures — 12 citations into it
• Optimization & Training — 10 citations into it
• Representation & Self-Supervised Learning — 9 citations into it
• Radiology, Nuclear Medicine and Imaging — 8 citations into it; #2,621 of 3,265 by trust here, #1,444 by citations

Whom Jeremiah Zhe Liu trusts overall

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

1. Geoffrey E. Hinton
2. Alex Krizhevsky
3. Yoshua Bengio
4. Yann LeCun
5. Diederik P. Kingma
6. David John Cameron MacKay
7. Radford M. Neal
8. Kaiming He
9. Ilya Sutskever
10. Andrey Malinin

Who trusts Jeremiah Zhe Liu overall

1. Stanislav Fort
2. Jie Jessie Ren
3. Arvid Lundervold
4. Jishnu Mukhoti
5. Jong Chul Ye
6. Umang Bhatt
7. Yang Song
8. Greg Zaharchuk
9. Liyue Shen
10. Ahmed Hosny

Papers from Jeremiah Zhe Liu's perspective

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

Their papers

• Simple and Principled Uncertainty Estimation with Deterministic Deep Learning via Distance Awareness (2020) — 573 citations
• Image reconstruction by domain-transform manifold learning (2017) — 1,685 citations