Mingzhe Wang

Artificial Intelligence · 2015–2020

A researcher in the Science corpus: 3 papers; trusts 43 researchers and is trusted by 60 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 49 citations into it; #4,719 of 35,345 by trust here, #3,304 by citations
• Computational Theory and Mathematics — 18 citations into it
• Natural Language Processing — 17 citations into it; #2,466 of 5,688 by trust here, #4,682 by citations
• Representation & Self-Supervised Learning — 17 citations into it; #797 of 3,145 by trust here, #496 by citations
• Graph Learning — 16 citations into it; #148 of 693 by trust here, #85 by citations
• Computer Vision and Pattern Recognition — 13 citations into it; #13,199 of 21,575 by trust here, #13,815 by citations
• Information Systems — 6 citations into it
• Program Analysis & Verification — 6 citations into it

Whom Mingzhe Wang trusts overall

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

1. Daniel Whalen
2. Ronald J. Williams
3. Christian Szegedy
4. Diederik P. Kingma
5. Tomas Mikolov
6. Geoffrey E. Hinton
7. Sarah M. Loos
8. Geoffrey Irving
9. Kshitij Bansal
10. Ilya Sutskever

Who trusts Mingzhe Wang overall

1. Stanislas Polu
2. Yuhuai Wu
3. Kaiyu Yang
4. Ilya Sutskever
5. Justin Johnson
6. Wenda Li
7. William L. Hamilton
8. Albert Qiaochu Jiang
9. Trieu H. Trinh
10. Jesse Michael Han

Papers from Mingzhe Wang's perspective

Ordered by how strongly the researchers Mingzhe Wang 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. 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. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
9. Learning Multiple Layers of Features from Tiny Images
10. Going deeper with convolutions

Their papers

• LINE: Large-scale Information Network Embedding (2015) — 5,660 citations
• Premise Selection for Theorem Proving by Deep Graph Embedding (2017) — 151 citations
• Learning to Prove Theorems by Learning to Generate Theorems (2020) — 59 citations