Young-Sun Joo

Artificial Intelligence · 2020–2022

A researcher in the Science corpus: 2 papers; trusts 5 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 29 citations into it
• Signal Processing — 27 citations into it
• Speech & Audio — 23 citations into it
• Generative Models — 16 citations into it
• Model Architectures — 5 citations into it

Whom Young-Sun Joo trusts overall

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

1. Diederik P. Kingma
2. Naihan Li
3. Geoffrey E. Hinton
4. Jimmy Ba

Papers from Young-Sun Joo's perspective

Ordered by how strongly the researchers Young-Sun Joo 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. GENERATIVE ADVERSARIAL NETS
3. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
4. ImageNet classification with deep convolutional neural networks
5. Attention is All you Need (Neural Information Processing Systems)
6. WaveNet: A Generative Model for Raw Audio
7. Long Short-Term Memory
8. Gradient-based learning applied to document recognition
9. Improved Techniques for Training GANs
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Avocodo: Generative Adversarial Network for Artifact-free Vocoder (2022) — 45 citations
• Speaking Speed Control of End-to-End Speech Synthesis using Sentence-Level Conditioning (2020) — 19 citations