Shengyu Zhu

Artificial Intelligence · 2015–2019

A researcher in the Science corpus: 3 papers; trusts 89 researchers and is trusted by 6 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 61 citations into it; #19,209 of 35,345 by trust here, #20,377 by citations
• Causal Inference & Discovery — 27 citations into it; #292 of 364 by trust here, #327 by citations
• Probabilistic & Bayesian Methods — 16 citations into it
• Optimization & Training — 14 citations into it
• Statistics and Probability — 14 citations into it
• Computational Theory and Mathematics — 12 citations into it; #9,595 of 12,182 by trust here, #8,294 by citations
• Computer Networks and Communications — 12 citations into it
• Computational Mechanics — 9 citations into it

Whom Shengyu Zhu trusts overall

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

1. Jan Peters
2. Kun Zhang
3. Ronald J. Williams
4. Diederik P. Kingma
5. Tom L. Burr
6. Bernhard Schölkopf
7. David Maxwell Chickering
8. Dominik Janzing
9. Geoffrey E. Hinton
10. Aapo Hyvärinen

Who trusts Shengyu Zhu overall

1. Matthew James Vowels
2. Ignavier Ng
3. Mengyue Yang
4. Nan Rosemary Ke
5. Necati Cihan Camgöz
6. Anis Elgabli
7. Philippe Brouillard
8. Sébastien Lachapelle
9. Zhuangyan Fang
10. Richard Bowden

Papers from Shengyu Zhu's perspective

Ordered by how strongly the researchers Shengyu Zhu 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. Learning Multiple Layers of Features from Tiny Images
9. GENERATIVE ADVERSARIAL NETS
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Causal Discovery with Reinforcement Learning (2019) — 293 citations
• Masked Gradient-Based Causal Structure Learning (2019) — 143 citations
• Quantized consensus ADMM for multi-agent distributed optimization (2015) — 60 citations