Ha Young Kim

Management Science and Operations Research · 2018–2019

A researcher in the Science corpus: 4 papers; trusts 48 researchers and is trusted by 21 researchers here, as inferred from the citation graph.

Subfields

• Management Science and Operations Research — 47 citations into it; #1,337 of 11,946 by trust here, #4,921 by citations
• Artificial Intelligence — 41 citations into it
• Economics and Econometrics — 22 citations into it; #6,882 of 23,481 by trust here, #17,969 by citations
• Computer Vision and Pattern Recognition — 21 citations into it
• Finance — 20 citations into it; #2,138 of 10,518 by trust here, #6,162 by citations
• Electrical and Electronic Engineering — 17 citations into it; #1,778 of 16,539 by trust here, #12,348 by citations
• Time-Series & Forecasting — 16 citations into it; #53 of 381 by trust here, #190 by citations
• Model Architectures — 15 citations into it

Whom Ha Young Kim trusts overall

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

1. Tim Bollerslev
2. Robert F. Engle
3. Daniel B. Nelson
4. Fulvio Corsi
5. Francis X. Diebold
6. Diederik P. Kingma
7. Torben G. Andersen
8. Geoffrey E. Hinton
9. Kai Chen
10. Kaiming He

Who trusts Ha Young Kim overall

1. Omer Berat Sezer
2. Ze Hu
3. Weiwei Jiang
4. M. Ugur Gudelek
5. Pedro Lara-Benítez
6. Jiayu Qiu
7. Longbing Cao
8. A. Ozbayoglu
9. Yiqi Zhao
10. Mojtaba Nabipour

Papers from Ha Young Kim's perspective

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

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. ImageNet classification with deep convolutional neural networks
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Long Short-Term Memory
5. Gradient-based learning applied to document recognition
6. Attention is All you Need (Neural Information Processing Systems)
7. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
8. Going deeper with convolutions
9. ImageNet: A large-scale hierarchical image database
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• Forecasting the volatility of stock price index: A hybrid model integrating LSTM with multiple GARCH-type models (2018) — 703 citations
• ModAugNet: A new forecasting framework for stock market index value with an overfitting prevention LSTM module and a prediction LSTM module (2018) — 333 citations
• Improving financial trading decisions using deep Q-learning: Predicting the number of shares, action strategies, and transfer learning (2019) — 188 citations
• Forecasting stock prices with a feature fusion LSTM-CNN model using different representations of the same data (2019) — 327 citations