Haitao Liu
Artificial Intelligence · with Cai · 2016–2018
A researcher in the Science corpus: 4 papers; trusts 142 researchers and is trusted by 3 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 114 citations into it
- Computational Theory and Mathematics — 72 citations into it; #9,199 of 12,182 by trust here, #6,798 by citations
- Statistics and Probability — 51 citations into it
- Management Science and Operations Research — 44 citations into it
- Probabilistic & Bayesian Methods — 41 citations into it
- Control and Systems Engineering — 25 citations into it
- Statistical and Nonlinear Physics — 19 citations into it
- Computer Vision and Pattern Recognition — 17 citations into it
Whom Haitao Liu trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Mauricio A Álvarez
- Neil D. Lawrence
- Geoffrey E. Hinton
- Matthias W. Seeger
- Andrew Gordon Wilson
- Radford M. Neal
- Christopher K. I. Williams
- Diederik P. Kingma
- Michael I. Jordan
- Carl E. Rasmussen
Who trusts Haitao Liu overall
- Yew-Soon Ong
- Stewart Greenhill
- Félix Musil
- Jianfei Cai
- Santu Rana
- M. J. Willatt
- Xiaobo Shen
- Xilu Wang
Papers from Haitao Liu's perspective
Ordered by how strongly the researchers Haitao Liu trusts endorse each paper. Same corpus, a different order for every researcher.
- ImageNet classification with deep convolutional neural networks
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Long Short-Term Memory
- Gradient-based learning applied to document recognition
- Attention is All you Need (Neural Information Processing Systems)
- Learning Multiple Layers of Features from Tiny Images
- ImageNet: A large-scale hierarchical image database
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
Their papers
- When Gaussian Process Meets Big Data: A Review of Scalable GPs (2018) — 875 citations
- A survey of adaptive sampling for global metamodeling in support of simulation-based complex engineering design (2017) — 421 citations
- Remarks on multi-output Gaussian process regression (2018) — 309 citations
- Optimal Weighted Pointwise Ensemble of Radial Basis Functions with Different Basis Functions (2016) — 47 citations