Jiaxin Shi

Artificial Intelligence · with Zhu · 2016–2019

A researcher in the Science corpus: 7 papers; trusts 664 researchers and is trusted by 34 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 190 citations into it; #5,449 of 35,345 by trust here, #9,275 by citations
• Computer Vision and Pattern Recognition — 139 citations into it; #9,546 of 21,575 by trust here, #6,795 by citations
• Probabilistic & Bayesian Methods — 136 citations into it; #914 of 1,525 by trust here, #770 by citations
• Generative Models — 59 citations into it
• Statistics and Probability — 52 citations into it
• Statistical and Nonlinear Physics — 36 citations into it; #1,482 of 10,127 by trust here, #6,421 by citations
• Optimization & Training — 28 citations into it
• Signal Processing — 18 citations into it

Whom Jiaxin Shi trusts overall

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

1. Diederik P. Kingma
2. Radford M. Neal
3. Geoffrey E. Hinton
4. Yoshua Bengio
5. Max Welling
6. Zoubin Ghahramani
7. Alex Krizhevsky
8. Qiang Liu
9. Yann LeCun
10. Alex Graves

Who trusts Jiaxin Shi overall

1. Yang Song
2. Chao Ma
3. David Pfau
4. Artem Sobolev
5. Guodong Zhang
6. Jun Zhu
7. Sam Bond-Taylor
8. Yingzhen Li
9. Shakir Mohamed
10. David R. Burt

Papers from Jiaxin Shi's perspective

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

Their papers

• Sliced Score Matching: A Scalable Approach to Density and Score Estimation (2019) — 535 citations
• Functional Variational Bayesian Neural Networks (2019) — 274 citations
• A Spectral Approach to Gradient Estimation for Implicit Distributions (2018) — 102 citations
• Towards Better Analysis of Deep Convolutional Neural Networks (2016) — 514 citations
• ZhuSuan: A Library for Bayesian Deep Learning (2017) — 43 citations
• Kernel Implicit Variational Inference (2017) — 59 citations
• Implicit Variational Inference with Kernel Density Ratio Fitting (2017) — 3 citations