Jae-Min Kim

Artificial Intelligence · 2019–2021

A researcher in the Science corpus: 5 papers; trusts 109 researchers and is trusted by 88 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 97 citations into it; #2,756 of 35,345 by trust here, #5,600 by citations
• Signal Processing — 70 citations into it; #295 of 7,572 by trust here, #557 by citations
• Speech & Audio — 41 citations into it; #91 of 1,076 by trust here, #238 by citations
• Computer Vision and Pattern Recognition — 29 citations into it
• Generative Models — 24 citations into it; #1,293 of 1,956 by trust here, #1,502 by citations
• Natural Language Processing — 24 citations into it
• Optimization & Training — 12 citations into it
• Model Architectures — 9 citations into it

Whom Jae-Min Kim trusts overall

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

1. Diederik P. Kingma
2. Eunwoo Song
3. Geoffrey E. Hinton
4. Tim Salimans
5. Yoshua Bengio
6. Aäron van den Oord
7. Jimmy Ba
8. Alex Krizhevsky
9. Alex Graves
10. Yann LeCun

Who trusts Jae-Min Kim overall

1. Xu Tan
2. Yi Ren
3. Wei Ping
4. Jinglin Liu
5. Eunwoo Song
6. D. Lim
7. Rongjie Huang
8. Sang-gil Lee
9. Ryuichi Yamamoto
10. Po-Chun Hsu

Papers from Jae-Min Kim's perspective

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

1. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. Attention is All you Need (Neural Information Processing Systems)
4. ImageNet classification with deep convolutional neural networks
5. Long Short-Term Memory
6. GENERATIVE ADVERSARIAL NETS
7. Gradient-based learning applied to document recognition
8. ImageNet: A large-scale hierarchical image database
9. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Parallel Wavegan: A Fast Waveform Generation Model Based on Generative Adversarial Networks with Multi-Resolution Spectrogram (2019) — 1,014 citations
• Probability density distillation with generative adversarial networks for high-quality parallel waveform generation (2019) — 58 citations
• Improved Parallel Wavegan Vocoder with Perceptually Weighted Spectrogram Loss (2021) — 18 citations
• TTS-by-TTS: TTS-Driven Data Augmentation for Fast and High-Quality Speech Synthesis (2020) — 36 citations
• Neural Text-to-Speech with a Modeling-by-Generation Excitation Vocoder (2020) — 6 citations