Jing Lin

Control and Systems Engineering · IEEE transactions on industrial electronics (1982. Print) · 2015–2018

A researcher in the Science corpus: 9 papers; trusts 17 researchers and is trusted by 24 researchers here, as inferred from the citation graph.

Subfields

• Control and Systems Engineering — 71 citations into it; #854 of 8,257 by trust here, #155 by citations
• Mechanical Engineering — 48 citations into it; #3,407 of 8,300 by trust here, #2,785 by citations
• Artificial Intelligence — 38 citations into it
• Mechanics of Materials — 22 citations into it; #1,162 of 6,291 by trust here, #340 by citations
• Signal Processing — 12 citations into it
• Representation & Self-Supervised Learning — 10 citations into it
• Cognitive Neuroscience — 6 citations into it
• Probabilistic & Bayesian Methods — 5 citations into it

Whom Jing Lin trusts overall

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

1. Geoffrey E. Hinton
2. Richard Baraniuk
3. Arnaud Doucet
4. Yoshua Bengio
5. M. Sanjeev Arulampalam
6. Marcos Eduardo Orchard
7. Adam Coates

Who trusts Jing Lin overall

1. Shen Zhang
2. Yaguo Lei
3. Long Wen
4. Xiang Li
5. Haidong Shao
6. Dhiraj Neupane
7. Shibo Zhang
8. Biao Wang
9. Naipeng Li
10. Liang Guo

Papers from Jing Lin's perspective

Ordered by how strongly the researchers Jing Lin 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. This PDF file includes: Materials and Methods
5. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
6. Long Short-Term Memory
7. Learning Multiple Layers of Features from Tiny Images
8. Attention is All you Need (Neural Information Processing Systems)
9. ImageNet: A large-scale hierarchical image database
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
• A probability distribution model of tooth pits for evaluating time-varying mesh stiffness of pitting gears (2018) — 112 citations
• A neural network constructed by deep learning technique and its application to intelligent fault diagnosis of machines (2018) — 491 citations
• Machinery health prognostics: A systematic review from data acquisition to RUL prediction (2018) — 2,251 citations
• A recurrent neural network based health indicator for remaining useful life prediction of bearings (2017) — 1,105 citations
• An underdamped stochastic resonance method with stable-state matching for incipient fault diagnosis of rolling element bearings (2017) — 141 citations
• Deep neural networks: A promising tool for fault characteristic mining and intelligent diagnosis of rotating machinery with massive data (2016) — 1,612 citations
• A Model-Based Method for Remaining Useful Life Prediction of Machinery (2016) — 526 citations
• An Improved Exponential Model for Predicting Remaining Useful Life of Rolling Element Bearings (2015) — 594 citations