Na Lu

Artificial Intelligence · with Jia · 2016–2018

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

Subfields

• Artificial Intelligence — 19 citations into it
• Computer Vision and Pattern Recognition — 7 citations into it
• Representation & Self-Supervised Learning — 6 citations into it

Whom Na Lu trusts overall

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

1. Yoshua Bengio
2. Geoffrey E. Hinton
3. Hugo Larochelle
4. Ruslan Salakhutdinov
5. Alex Krizhevsky
6. Yann LeCun
7. Radford M. Neal
8. Simon Osindero
9. Diederik P. Kingma
10. Ilya Sutskever

Who trusts Na Lu overall

1. Mufti Mahmud
2. Guanghai Dai
3. M. S. Kaiser
4. Oliver Faust
5. Natasha M. J. Padfield
6. Ping Wang
7. Vernon J. Lawhern
8. M. Rashid
9. Amir Hussain
10. Jun Zhou

Papers from Na Lu's perspective

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

1. ImageNet classification with deep convolutional neural networks
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Gradient-based learning applied to document recognition
5. Long Short-Term Memory
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. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• A Deep Learning Scheme for Motor Imagery Classification based on Restricted Boltzmann Machines (2017) — 375 citations
• Deep normalized convolutional neural network for imbalanced fault classification of machinery and its understanding via visualization (2018) — 547 citations
• Deep neural networks: A promising tool for fault characteristic mining and intelligent diagnosis of rotating machinery with massive data (2016) — 1,612 citations