Xiaobing Liu

Artificial Intelligence · with Chen · 2016–2020

A researcher in the Science corpus: 5 papers; trusts 12 researchers and is trusted by 4 researchers here, as inferred from the citation graph.

Subfields

Whom Xiaobing Liu trusts

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

  1. Quoc V. Le (in Artificial Intelligence)
  2. Christian Szegedy (in Artificial Intelligence)
  3. Yoshua Bengio (in Artificial Intelligence)
  4. Sergey Ioffe (in Artificial Intelligence)
  5. Ilya Sutskever (in Artificial Intelligence)
  6. Barret Zoph (in Artificial Intelligence)
  7. Diederik P. Kingma (in Artificial Intelligence)
  8. Kaiming He (in Computer Vision and Pattern Recognition)
  9. Geoffrey E. Hinton (in Artificial Intelligence)
  10. Shaoqing Ren (in Computer Vision and Pattern Recognition)

Who trusts Xiaobing Liu

  1. Quoc V. Le (in Artificial Intelligence)
  2. Xiangnan He (in Artificial Intelligence)
  3. Richard Socher (in Artificial Intelligence)
  4. Caiming Xiong (in Artificial Intelligence)

Papers from Xiaobing Liu's perspective

Ordered by how strongly the researchers Xiaobing Liu 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. Long Short-Term Memory
  3. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
  4. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
  5. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
  6. Et al
  7. ImageNet classification with deep convolutional neural networks
  8. Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation
  9. Attention is All you Need (Neural Information Processing Systems)
  10. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)

Their papers