Z. Liu

Artificial Intelligence · with Wang · 2003–2019

A researcher in the Science corpus: 8 papers; trusts 343 researchers and is trusted by 31 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 116 citations into it; #7,780 of 35,345 by trust here, #8,007 by citations
• Computer Vision and Pattern Recognition — 107 citations into it; #11,766 of 21,575 by trust here, #7,852 by citations
• Reinforcement Learning — 45 citations into it; #1,728 of 2,242 by trust here, #1,371 by citations
• Probabilistic & Bayesian Methods — 33 citations into it
• Optimization & Training — 31 citations into it
• Generative Models — 23 citations into it
• Model Architectures — 20 citations into it
• Statistics and Probability — 17 citations into it; #3,428 of 12,932 by trust here, #3,703 by citations

Whom Z. Liu trusts overall

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

1. Geoffrey E. Hinton
2. Kaiming He
3. Diederik P. Kingma
4. Alex Krizhevsky
5. Yoshua Bengio
6. Radford M. Neal
7. Ross B. Girshick
8. Yann LeCun
9. Aapo Hyvärinen
10. Ilya Sutskever

Who trusts Z. Liu overall

1. Weiyang Liu
2. George Dimitri Konidaris
3. James Harrison
4. Annie Xie
5. Rongmei Lin
6. Michael Arbel
7. Chi Zhang
8. Yang Song
9. Christopher F. H. Nam
10. Gaurav Sharma

Papers from Z. Liu's perspective

Ordered by how strongly the researchers Z. Liu 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. ImageNet: A large-scale hierarchical image database
5. Gradient-based learning applied to document recognition
6. Attention is All you Need (Neural Information Processing Systems)
7. Long Short-Term Memory
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Going deeper with convolutions
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• One-Shot Learning for Semantic Segmentation (2017) — 897 citations
• Exponential Family Estimation via Adversarial Dynamics Embedding (2019) — 58 citations
• On‐line inference for multiple changepoint problems (2007) — 367 citations
• Decoupled Networks (2018) — 87 citations
• Learning towards Minimum Hyperspherical Energy (2018) — 172 citations
• SBEED: Convergent Reinforcement Learning with Nonlinear Function Approximation (2017) — 305 citations
• CEPC-SPPC Preliminary Conceptual Design Report. 1. Physics and Detector (2015) — 208 citations
• Capacity, delay and mobility in wireless ad-hoc networks (2003) — 374 citations