Min Wang

Optimization & Training · with Gao · 2020

A researcher in the Science corpus: 1 paper; trusts 5 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

Whom Min Wang trusts overall

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

  1. James Martens
  2. Alex Krizhevsky
  3. Diederik P. Kingma
  4. Yann LeCun
  5. Karen Simonyan

Papers from Min Wang's perspective

Ordered by how strongly the researchers Min Wang 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. Gradient-based learning applied to document recognition
  3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
  4. Learning Multiple Layers of Features from Tiny Images
  5. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
  6. ImageNet classification with deep convolutional neural networks
  7. Long Short-Term Memory
  8. ImageNet: A large-scale hierarchical image database
  9. Attention is All you Need (Neural Information Processing Systems)
  10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers