Feng Jia

Control and Systems Engineering · 2016–2020

A researcher in the Science corpus: 7 papers; trusts 6 researchers and is trusted by 19 researchers here, as inferred from the citation graph.

Subfields

• Control and Systems Engineering — 108 citations into it; #1,157 of 8,257 by trust here, #172 by citations
• Artificial Intelligence — 64 citations into it
• Computer Vision and Pattern Recognition — 29 citations into it
• Representation & Self-Supervised Learning — 20 citations into it
• Signal Processing — 16 citations into it
• Probabilistic & Bayesian Methods — 6 citations into it

Whom Feng Jia trusts overall

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

1. Geoffrey E. Hinton
2. Yoshua Bengio
3. Alex Krizhevsky
4. Yann LeCun
5. J. Urgen Schmidhuber
6. Richard Baraniuk
7. Ilya Sutskever
8. Andrew Y. Ng
9. Diederik P. Kingma
10. Radford M. Neal

Who trusts Feng Jia overall

1. Shen Zhang
2. Long Wen
3. Xiang Li
4. Dhiraj Neupane
5. Haidong Shao
6. Yaguo Lei
7. Jinyang Jiao
8. Shibo Zhang
9. Xiaoxi Ding
10. Weiting Zhang

Papers from Feng Jia's perspective

Ordered by how strongly the researchers Feng Jia 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. Gradient-based learning applied to document recognition
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Long Short-Term Memory
6. Attention is All you Need (Neural Information Processing Systems)
7. Learning Multiple Layers of Features from Tiny Images
8. ImageNet: A large-scale hierarchical image database
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• An Intelligent Fault Diagnosis Method Using Unsupervised Feature Learning Towards Mechanical Big Data (2016) — 1,047 citations
• Applications of machine learning to machine fault diagnosis: A review and roadmap (2020) — 2,573 citations
• An intelligent fault diagnosis approach based on transfer learning from laboratory bearings to locomotive bearings (2019) — 758 citations
• A neural network constructed by deep learning technique and its application to intelligent fault diagnosis of machines (2018) — 491 citations
• Deep normalized convolutional neural network for imbalanced fault classification of machinery and its understanding via visualization (2018) — 547 citations
• A recurrent neural network based health indicator for remaining useful life prediction of bearings (2017) — 1,105 citations
• Deep neural networks: A promising tool for fault characteristic mining and intelligent diagnosis of rotating machinery with massive data (2016) — 1,612 citations