Chang Mook Kang

Artificial Intelligence · 2017–2018

A researcher in the Science corpus: 3 papers; trusts 6 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 13 citations into it
• Computer Vision and Pattern Recognition — 11 citations into it
• Reinforcement Learning — 7 citations into it

Whom Chang Mook Kang trusts overall

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

1. Sebastian Thrun
2. Richard S. Sutton
3. Sepp Hochreiter
4. J. Urgen Schmidhuber
5. Diederik P. Kingma
6. Peter Ondruska
7. Geoffrey E. Hinton
8. Andrew Barto
9. Jan Koutník
10. Yoshua Bengio

Who trusts Chang Mook Kang overall

1. Andrey Rudenko

Papers from Chang Mook Kang's perspective

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

1. ImageNet classification with deep convolutional neural networks
2. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
3. Long Short-Term Memory
4. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
5. Gradient-based learning applied to document recognition
6. Rectified Linear Units Improve Restricted Boltzmann Machines
7. Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation
8. Reinforcement Learning: An Introduction
9. Attention is All you Need (Neural Information Processing Systems)
10. ImageNet: A large-scale hierarchical image database

Their papers

• Probabilistic vehicle trajectory prediction over occupancy grid map via recurrent neural network (2017) — 361 citations
• Sequence-to-Sequence Prediction of Vehicle Trajectory via LSTM Encoder-Decoder Architecture (2018) — 474 citations
• Autonomous braking system via deep reinforcement learning (2017) — 140 citations