Cheng Zhang

Artificial Intelligence · with Kjellström · 2017

A researcher in the Science corpus: 3 papers; trusts 251 researchers and is trusted by 2 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 129 citations into it; #25,734 of 35,345 by trust here, #19,018 by citations
• Probabilistic & Bayesian Methods — 88 citations into it
• Computer Vision and Pattern Recognition — 54 citations into it
• Optimization & Training — 43 citations into it
• Statistics and Probability — 37 citations into it
• Generative Models — 28 citations into it
• Management Science and Operations Research — 26 citations into it
• Computational Mechanics — 11 citations into it

Whom Cheng Zhang trusts overall

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

1. David M. Blei
2. Diederik P. Kingma
3. Radford M. Neal
4. Michael I. Jordan
5. Geoffrey E. Hinton
6. L. Eon Bottou
7. Yoshua Bengio
8. Yann LeCun
9. Karen Simonyan
10. Alex Krizhevsky

Who trusts Cheng Zhang overall

1. Dong Yin
2. Florian Schmidt
3. Ashwin Pananjady
4. J. Ash
5. Matthew Riemer
6. Maximilian Lam
7. Vincent Fortuin
8. Jun Han
9. Aodong Li
10. Stephan Mandt

Papers from Cheng Zhang's perspective

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

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. ImageNet classification with deep convolutional neural networks
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

• Balanced Mini-batch Sampling for SGD Using Determinantal Point Processes (2017) — 34 citations
• Advances in Variational Inference (2017) — 815 citations
• Stochastic Learning on Imbalanced Data: Determinantal Point Processes for Mini-batch Diversification (2017) — 58 citations