Xun Zheng

Artificial Intelligence · 2013–2019

A researcher in the Science corpus: 5 papers; trusts 37 researchers and is trusted by 21 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 69 citations into it; #9,153 of 35,345 by trust here, #7,858 by citations
• Probabilistic & Bayesian Methods — 27 citations into it
• Optimization & Training — 23 citations into it
• Statistics and Probability — 22 citations into it
• Causal Inference & Discovery — 16 citations into it
• Management Science and Operations Research — 13 citations into it
• Computational Mechanics — 12 citations into it
• Signal Processing — 12 citations into it

Whom Xun Zheng trusts overall

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

1. L. Eon Bottou
2. Diederik P. Kingma
3. Geoffrey E. Hinton
4. Jan Peters
5. David Maxwell Chickering
6. Yurii E. Nesterov
7. Yoshua Bengio
8. Bernhard Schölkopf
9. Peter Buhlmann
10. Tom L. Burr

Who trusts Xun Zheng overall

1. Ignavier Ng
2. Philippe Brouillard
3. Gonçalo Faria
4. Sébastien Lachapelle
5. Lars Lorch
6. Shengyu Zhu
7. Roxana Pamfil
8. Jonas Rothfuss
9. Tristan Deleu
10. Matthew James Vowels

Papers from Xun Zheng's perspective

Ordered by how strongly the researchers Xun Zheng 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. Learning Multiple Layers of Features from Tiny Images
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• DAGs with NO TEARS: Continuous Optimization for Structure Learning (2018) — 1,439 citations
• Petuum: A New Platform for Distributed Machine Learning on Big Data (2013) — 526 citations
• On Model Parallelization and Scheduling Strategies for Distributed Machine Learning (2014) — 129 citations
• Learning Sparse Nonparametric DAGs (2019) — 339 citations
• State Space LSTM Models with Particle MCMC Inference (2017) — 47 citations