Kristian C. Henrickson

Building and Construction · 2018

A researcher in the Science corpus: 2 papers; trusts 24 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Building and Construction — 36 citations into it
• Control and Systems Engineering — 25 citations into it
• Artificial Intelligence — 23 citations into it
• Transportation — 18 citations into it
• Graph Learning — 16 citations into it
• Computer Vision and Pattern Recognition — 7 citations into it
• Signal Processing — 6 citations into it

Whom Kristian C. Henrickson trusts overall

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

1. Thomas Kipf
2. Paul J. Werbos
3. Michaël Defferrard
4. J. V. Lint
5. Ting Yu
6. Xuan Song
7. Xiaolei Ma
8. Max Welling
9. Martin Simonovsky
10. Junbo Zhang

Who trusts Kristian C. Henrickson overall

1. Senzhang Wang
2. Saeed Rahmani
3. B. Khemani
4. David Alexander Tedjopurnomo

Papers from Kristian C. Henrickson's perspective

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

1. Long Short-Term Memory
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. ImageNet classification with deep convolutional neural networks
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Gradient-based learning applied to document recognition
6. Attention is All you Need (Neural Information Processing Systems)
7. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
8. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
9. Learning Multiple Layers of Features from Tiny Images
10. ImageNet: A large-scale hierarchical image database

Their papers

• Traffic Graph Convolutional Recurrent Neural Network: A Deep Learning Framework for Network-Scale Traffic Learning and Forecasting (2018) — 905 citations
• High-Order Graph Convolutional Recurrent Neural Network: A Deep Learning Framework for Network-Scale Traffic Learning and Forecasting (2018) — 82 citations