Hong-Goo Kang

Artificial Intelligence · 2017–2023

A researcher in the Science corpus: 6 papers; trusts 189 researchers and is trusted by 13 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 56 citations into it; #9,326 of 35,345 by trust here, #15,004 by citations
• Signal Processing — 54 citations into it; #1,415 of 7,572 by trust here, #1,857 by citations
• Speech & Audio — 33 citations into it
• Computer Vision and Pattern Recognition — 30 citations into it; #15,739 of 21,575 by trust here, #17,757 by citations
• Natural Language Processing — 22 citations into it; #2,785 of 5,688 by trust here, #4,879 by citations
• Generative Models — 11 citations into it
• Model Architectures — 9 citations into it
• Optimization & Training — 8 citations into it

Whom Hong-Goo Kang trusts overall

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

1. Diederik P. Kingma
2. Andrew Zisserman
3. Joon Son Chung
4. Ronald J. Williams
5. Geoffrey E. Hinton
6. Yoshua Bengio
7. Hideki Kawahara
8. Alex Krizhevsky
9. Karen Simonyan
10. Ilya Sutskever

Who trusts Hong-Goo Kang overall

1. Eunwoo Song
2. Ryuichi Yamamoto
3. Min-Jae Hwang
4. Jae-Min Kim
5. Hiroki Kanagawa
6. Joon Son Chung
7. Triantafyllos Afouras

Papers from Hong-Goo Kang's perspective

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

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

Their papers

• Perfect Match: Improved Cross-modal Embeddings for Audio-visual Synchronisation (2018) — 133 citations
• End-to-End Neural Audio Coding in the MDCT Domain (2023) — 5 citations
• Improving LPCNET-Based Text-to-Speech with Linear Prediction-Structured Mixture Density Network (2020) — 14 citations
• Emotional Speech Synthesis with Rich and Granularized Control (2019) — 97 citations
• ExcitNet Vocoder: A Neural Excitation Model for Parametric Speech Synthesis Systems (2018) — 30 citations
• Effective Spectral and Excitation Modeling Techniques for LSTM-RNN-Based Speech Synthesis Systems (2017) — 61 citations