Jaehyeon Kim

Artificial Intelligence · 2018–2021

A researcher in the Science corpus: 4 papers; trusts 176 researchers and is trusted by 185 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 73 citations into it; #2,806 of 35,345 by trust here, #4,627 by citations
• Signal Processing — 50 citations into it; #716 of 7,572 by trust here, #778 by citations
• Generative Models — 49 citations into it; #102 of 1,956 by trust here, #224 by citations
• Speech & Audio — 41 citations into it; #95 of 1,076 by trust here, #181 by citations
• Computer Vision and Pattern Recognition — 34 citations into it
• Natural Language Processing — 26 citations into it
• Probabilistic & Bayesian Methods — 14 citations into it
• Model Architectures — 13 citations into it

Whom Jaehyeon Kim trusts overall

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

1. Diederik P. Kingma
2. Aäron van den Oord
3. Alex Graves
4. Geoffrey E. Hinton
5. Yoshua Bengio
6. Laurent Dinh
7. Alex Krizhevsky
8. Oriol Vinyals
9. Ilya Sutskever
10. Danilo Jimenez Rezende

Who trusts Jaehyeon Kim overall

1. Cong Han
2. Xu Tan
3. Yi Ren
4. Rongjie Huang
5. Ziyue Jiang
6. Hubert Siuzdak
7. Nima Mesgarani
8. Edresson Casanova
9. Chenpeng Du
10. Jinglin Liu

Papers from Jaehyeon Kim's perspective

Ordered by how strongly the researchers Jaehyeon 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. Attention is All you Need (Neural Information Processing Systems)
5. Long Short-Term Memory
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. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• FloWaveNet : A Generative Flow for Raw Audio (2018) — 181 citations
• Conditional Variational Autoencoder with Adversarial Learning for End-to-End Text-to-Speech (2021) — 1,342 citations
• Glow-TTS: A Generative Flow for Text-to-Speech via Monotonic Alignment Search (2020) — 637 citations
• HiFi-GAN: Generative Adversarial Networks for Efficient and High Fidelity Speech Synthesis (2020) — 2,760 citations