M. Hua

Artificial Intelligence · 2020

A researcher in the Science corpus: 1 paper; trusts 15 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 5 citations into it
• Health, Toxicology and Mutagenesis — 5 citations into it

Whom M. Hua trusts overall

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

1. Yann LeCun
2. Geoffrey E. Hinton
3. Alex Krizhevsky
4. Yoshua Bengio
5. Alex Smola
6. Ilya Sutskever
7. J. Urgen Schmidhuber
8. Chiou-Jye Huang
9. L. Eon Bottou
10. Bernhard Schölkopf

Papers from M. Hua's perspective

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

1. ImageNet classification with deep convolutional neural networks
2. Gradient-based learning applied to document recognition
3. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
4. Long Short-Term Memory
5. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
6. Attention is All you Need (Neural Information Processing Systems)
7. ImageNet: A large-scale hierarchical image database
8. Learning Multiple Layers of Features from Tiny Images
9. Dropout: a simple way to prevent neural networks from overfitting
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• A Hybrid CNN-LSTM Model for Forecasting Particulate Matter (PM2.5) (2020) — 322 citations