Yuhao Wang

Artificial Intelligence · with Uhler · 2013–2022

A researcher in the Science corpus: 4 papers; trusts 77 researchers and is trusted by 12 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 31 citations into it; #20,029 of 35,345 by trust here, #23,077 by citations
• Molecular Biology — 29 citations into it; #7,066 of 17,773 by trust here, #10,864 by citations
• Computational Theory and Mathematics — 26 citations into it; #8,240 of 12,182 by trust here, #5,954 by citations
• Statistics and Probability — 24 citations into it; #12,923 of 12,932 by trust here, #10,275 by citations
• Causal Inference & Discovery — 22 citations into it; #222 of 364 by trust here, #249 by citations
• Probabilistic & Bayesian Methods — 15 citations into it
• Computer Vision and Pattern Recognition — 6 citations into it
• Computational Biology & Bioinformatics — 5 citations into it

Whom Yuhao Wang trusts overall

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

1. Geoffrey E. Hinton
2. Yoshua Bengio
3. Alain Hauser
4. Tom L. Burr
5. Ruslan Salakhutdinov
6. Max Welling
7. Hugo Larochelle
8. Nicolas Le Roux
9. Peter Buhlmann
10. Radford M. Neal

Who trusts Yuhao Wang overall

1. Philippe Brouillard
2. Mostafa Karimi
3. Maryam Bagherian
4. Kang Li
5. Chandler Squires
6. Karren D. Yang
7. Xing Chen
8. Yunan Luo
9. Sébastien Lachapelle
10. Liuyi Yao

Papers from Yuhao Wang's perspective

Ordered by how strongly the researchers Yuhao 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. Gradient-based learning applied to document recognition
5. Attention is All you Need (Neural Information Processing Systems)
6. Long Short-Term Memory
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. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Permutation-Based Causal Structure Learning with Unknown Intervention Targets (2019) — 110 citations
• Predicting drug-target interactions using restricted Boltzmann machines (2013) — 202 citations
• Permutation-based Causal Inference Algorithms with Interventions (2017) — 169 citations
• Long-term Causal Inference Under Persistent Confounding via Data Combination (2022) — 64 citations