Eunwoo Song

Artificial Intelligence · 2017–2022

A researcher in the Science corpus: 9 papers; trusts 152 researchers and is trusted by 101 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 151 citations into it; #2,322 of 35,345 by trust here, #4,722 by citations
• Signal Processing — 111 citations into it; #236 of 7,572 by trust here, #462 by citations
• Speech & Audio — 83 citations into it; #93 of 1,076 by trust here, #238 by citations
• Computer Vision and Pattern Recognition — 37 citations into it
• Generative Models — 36 citations into it; #1,323 of 1,956 by trust here, #1,502 by citations
• Natural Language Processing — 33 citations into it; #3,089 of 5,688 by trust here, #4,879 by citations
• Optimization & Training — 21 citations into it
• Model Architectures — 17 citations into it

Whom Eunwoo Song trusts overall

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

1. Diederik P. Kingma
2. Yoshua Bengio
3. Xavier Glorot
4. Geoffrey E. Hinton
5. Ronald J. Williams
6. Aäron van den Oord
7. Jimmy Ba
8. Tim Salimans
9. Hideki Kawahara
10. Alex Graves

Who trusts Eunwoo Song overall

1. Ryuichi Yamamoto
2. Min-Jae Hwang
3. Xu Tan
4. Yi Ren
5. Jae-Min Kim
6. Wei Ping
7. Jinglin Liu
8. Tao Qin
9. Qiao Tian
10. Hiroki Kanagawa

Papers from Eunwoo Song's perspective

Ordered by how strongly the researchers Eunwoo Song 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. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
3. ImageNet classification with deep convolutional neural networks
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. GENERATIVE ADVERSARIAL NETS
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

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
• HierSpeech: Bridging the Gap between Text and Speech by Hierarchical Variational Inference using Self-supervised Representations for Speech Synthesis (2022) — 81 citations
• Improved Parallel Wavegan Vocoder with Perceptually Weighted Spectrogram Loss (2021) — 18 citations
• Improving LPCNET-Based Text-to-Speech with Linear Prediction-Structured Mixture Density Network (2020) — 14 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
• 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