Jing Shang

Artificial Intelligence · 2018

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

• Artificial Intelligence — 9 citations into it
• Model Architectures — 7 citations into it
• Optimization & Training — 7 citations into it

Whom Jing Shang trusts overall

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

1. Paul J. Werbos
2. Diederik P. Kingma

Papers from Jing Shang's perspective

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

1. Long Short-Term Memory
2. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
3. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
4. Dropout: a simple way to prevent neural networks from overfitting
5. Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation
6. ImageNet classification with deep convolutional neural networks
7. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
8. Gradient-based learning applied to document recognition
9. Backpropagation Through Time: What It Does and How to Do It
10. Attention is All you Need (Neural Information Processing Systems)

Their papers

• LSTM-Based EEG Classification in Motor Imagery Tasks (2018) — 322 citations