P. Kollmeyer

Automotive Engineering · 2018–2020

A researcher in the Science corpus: 4 papers; trusts 25 researchers and is trusted by 3 researchers here, as inferred from the citation graph.

Subfields

• Automotive Engineering — 36 citations into it; #1,954 of 3,081 by trust here, #1,425 by citations
• Electrical and Electronic Engineering — 32 citations into it; #11,123 of 16,539 by trust here, #7,892 by citations
• Control and Systems Engineering — 19 citations into it
• Artificial Intelligence — 12 citations into it
• Model Architectures — 8 citations into it
• Computer Vision and Pattern Recognition — 6 citations into it

Whom P. Kollmeyer trusts overall

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

1. Geoffrey E. Hinton
2. Sepp Hochreiter
3. Yoshua Bengio
4. Diederik P. Kingma
5. Mohammad Charkhgard
6. Yann LeCun
7. Ilya Sutskever
8. J. Urgen Schmidhuber
9. Alex Graves
10. J. C. Álvarez Antón

Who trusts P. Kollmeyer overall

1. Carlos Vidal
2. Yandan Tan
3. Meng Ma
4. Pawel Malysz

Papers from P. Kollmeyer's perspective

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

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

Their papers

• State-of-charge estimation of Li-ion batteries using deep neural networks: A machine learning approach (2018) — 690 citations
• Long Short-Term Memory Networks for Accurate State-of-Charge Estimation of Li-ion Batteries (2018) — 687 citations
• Machine Learning Applied to Electrified Vehicle Battery State of Charge and State of Health Estimation: State-of-the-Art (2020) — 385 citations
• 800-V Electric Vehicle Powertrains: Review and Analysis of Benefits, Challenges, and Future Trends (2020) — 325 citations