Jun Zhu

Statistics and Probability · with Liu · 2019

A researcher in the Science corpus: 1 paper; trusts 14 researchers and is trusted by 1 researcher here, as inferred from the citation graph.

Subfields

• Statistics and Probability — 11 citations into it; #12,730 of 12,932 by trust here, #11,487 by citations
• Probabilistic & Bayesian Methods — 10 citations into it
• Applied Mathematics — 9 citations into it
• Artificial Intelligence — 7 citations into it
• Geometry and Topology — 6 citations into it
• Mathematical Physics — 5 citations into it

Whom Jun Zhu trusts overall

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

1. Felix Otto
2. Michael Betancourt
3. Luigi Ambrosio
4. Max Welling
5. Mark A. Girolami
6. Gareth O. Roberts
7. Tianqi Chen
8. Radford M. Neal

Who trusts Jun Zhu overall

1. Zhenyi Wang

Papers from Jun Zhu's perspective

Ordered by how strongly the researchers Jun Zhu trusts endorse each paper. Same corpus, a different order for every researcher.

1. Bayesian Learning via Stochastic Gradient Langevin Dynamics
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. Riemann manifold Langevin and Hamiltonian Monte Carlo methods
4. On the importance of initialization and momentum in deep learning
5. ImageNet classification with deep convolutional neural networks
6. Hybrid Monte Carlo
7. Learning Multiple Layers of Features from Tiny Images
8. Gradient-based learning applied to document recognition
9. Handbook of Markov Chain Monte Carlo
10. Attention is All you Need (Neural Information Processing Systems)

Their papers

• Understanding MCMC Dynamics as Flows on the Wasserstein Space (2019) — 23 citations