Jiang Bian

Artificial Intelligence · with Wang · 2016–2020

A researcher in the Science corpus: 3 papers; trusts 9 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

Whom Jiang Bian trusts

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

  1. H. Brendan McMahan (in Artificial Intelligence)
  2. Judea Pearl (in Statistics and Probability)
  3. Peter Richtárik (in Artificial Intelligence)
  4. Virginia Smith (in Artificial Intelligence)
  5. Ameet Talwalkar (in Artificial Intelligence)
  6. Ramesh Raskar (in Artificial Intelligence)
  7. Otkrist Gupta (in Artificial Intelligence)
  8. Ananda Theertha Suresh (in Artificial Intelligence)
  9. Martin Takác (in Artificial Intelligence)

Papers from Jiang Bian's perspective

Ordered by how strongly the researchers Jiang Bian trusts endorse each paper. Same corpus, a different order for every researcher.

  1. Distilling the Knowledge in a Neural Network (arXiv.org)
  2. Deep Learning
  3. Gradient-based learning applied to document recognition
  4. ImageNet classification with deep convolutional neural networks
  5. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
  6. Communication-Efficient Learning of Deep Networks from Decentralized Data
  7. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
  8. Deep Compression: Compressing Deep Neural Network with Pruning, Trained Quantization and Huffman Coding
  9. Learning Multiple Layers of Features from Tiny Images
  10. Long Short-Term Memory

Their papers