Kai F. Goebel

Control and Systems Engineering · 2020–2021

A researcher in the Science corpus: 2 papers; trusts 13 researchers and is trusted by 2 researchers here, as inferred from the citation graph.

Subfields

• Control and Systems Engineering — 21 citations into it; #7,055 of 8,257 by trust here, #6,083 by citations
• Artificial Intelligence — 7 citations into it
• Mechanical Engineering — 7 citations into it
• Safety, Risk, Reliability and Quality — 5 citations into it; #975 of 1,220 by trust here, #865 by citations

Whom Kai F. Goebel trusts overall

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

1. Xiang Li
2. Manuel Arias Chao
3. Diederik P. Kingma
4. Xavier Glorot
5. Simon Justin Julier
6. John L. Crassidis

Who trusts Kai F. Goebel overall

1. Manuel Arias Chao

Papers from Kai F. Goebel's perspective

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

1. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
2. Understanding the difficulty of training deep feedforward neural networks
3. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
4. ImageNet classification with deep convolutional neural networks
5. Long Short-Term Memory
6. Gradient-based learning applied to document recognition
7. Attention is All you Need (Neural Information Processing Systems)
8. Going deeper with convolutions
9. Learning Multiple Layers of Features from Tiny Images
10. Remaining useful life estimation in prognostics using deep convolution neural networks (Reliability Engineering & System Safety)

Their papers

• Aircraft Engine Run-to-Failure Dataset under Real Flight Conditions for Prognostics and Diagnostics (2021) — 312 citations
• Fusing Physics-based and Deep Learning Models for Prognostics (2020) — 388 citations