Lei Wang
Artificial Intelligence · with Weinan · 2016–2018
A researcher in the Science corpus: 2 papers; trusts 34 researchers and is trusted by 23 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 18 citations into it; #6,878 of 35,345 by trust here, #19,873 by citations
- Computer Vision and Pattern Recognition — 16 citations into it
- Generative Models — 15 citations into it
- Probabilistic & Bayesian Methods — 13 citations into it
- Statistical and Nonlinear Physics — 12 citations into it
- Model Architectures — 8 citations into it
- Optimization & Training — 6 citations into it
- Signal Processing — 5 citations into it
Whom Lei Wang trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Iroon Polytechniou
- Cédric Villani
- Diederik P. Kingma
- Thomas Vojta
- Laurent Dinh
- Danilo Jimenez Rezende
- Kaiming He
- Geoffrey E. Hinton
- Yoshua Bengio
- Alex Krizhevsky
Who trusts Lei Wang overall
- Sebastian Johann Wetzel
- Vedran Dunjko
- Jing Liu
- Ribana Roscher
- Hans J. Briegel
- Giuseppe Carleo
- Yogesh Kumar Dwivedi
- Haidong Yuan
- Bastian Bohn
- Dong-Ling Deng
Papers from Lei Wang's perspective
Ordered by how strongly the researchers Lei Wang trusts endorse each paper. Same corpus, a different order for every researcher.
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- ImageNet classification with deep convolutional neural networks
- Gradient-based learning applied to document recognition
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Long Short-Term Memory
- Attention is All you Need (Neural Information Processing Systems)
- Learning Multiple Layers of Features from Tiny Images
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
- ImageNet: A large-scale hierarchical image database
- ImageNet Large Scale Visual Recognition Challenge
Their papers
- Discovering phase transitions with unsupervised learning (2016) — 509 citations
- Monge-Ampère Flow for Generative Modeling (2018) — 76 citations