Jungil Kong

Artificial Intelligence · 2020–2021

A researcher in the Science corpus: 3 papers; trusts 140 researchers and is trusted by 161 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 61 citations into it; #3,619 of 35,345 by trust here, #6,468 by citations
• Signal Processing — 46 citations into it; #1,145 of 7,572 by trust here, #1,629 by citations
• Generative Models — 45 citations into it; #112 of 1,956 by trust here, #277 by citations
• Speech & Audio — 36 citations into it; #125 of 1,076 by trust here, #276 by citations
• Computer Vision and Pattern Recognition — 29 citations into it
• Natural Language Processing — 19 citations into it
• Model Architectures — 11 citations into it
• Probabilistic & Bayesian Methods — 9 citations into it

Whom Jungil Kong trusts overall

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

1. Diederik P. Kingma
2. Aäron van den Oord
3. Geoffrey E. Hinton
4. Yoshua Bengio
5. Alex Graves
6. Alex Krizhevsky
7. Wei Ping
8. Oriol Vinyals
9. Karen Simonyan
10. Yann LeCun

Who trusts Jungil Kong overall

1. Cong Han
2. Rongjie Huang
3. Xu Tan
4. Yi Ren
5. Hubert Siuzdak
6. Jinglin Liu
7. Yanqing Liu
8. Songxiang Liu
9. Zhou Zhao
10. Evgeny Kharitonov

Papers from Jungil Kong's perspective

Ordered by how strongly the researchers Jungil Kong 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. GENERATIVE ADVERSARIAL NETS
9. Learning Multiple Layers of Features from Tiny Images
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• 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