Ming-Tse Lu

Computer Vision and Pattern Recognition · 2023–2024

A researcher in the Science corpus: 2 papers; trusts 64 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

Whom Ming-Tse Lu trusts overall

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

  1. Diederik P. Kingma
  2. Geoffrey E. Hinton
  3. Jimmy Ba
  4. Kaiming He
  5. Alex Krizhevsky
  6. Yoshua Bengio
  7. David C. Minnen
  8. Yann LeCun
  9. Jonathan Ho
  10. Johannes Ballé

Papers from Ming-Tse Lu's perspective

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

Their papers