Seung-bin Kim

Artificial Intelligence · 2020–2023

A researcher in the Science corpus: 3 papers; trusts 34 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 103 citations into it
• Signal Processing — 79 citations into it
• Speech & Audio — 67 citations into it
• Generative Models — 19 citations into it
• Computer Vision and Pattern Recognition — 16 citations into it
• Model Architectures — 14 citations into it
• Representation & Self-Supervised Learning — 13 citations into it
• Evaluation & Benchmarks — 6 citations into it

Whom Seung-bin Kim trusts overall

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

1. Yoshua Bengio
2. Geoffrey E. Hinton
3. Joon Son Chung
4. Kaiming He
5. Andrew Zisserman
6. Alex Krizhevsky
7. Diederik P. Kingma
8. Jimmy Ba
9. Arsha Nagrani
10. Mirco Ravanelli

Papers from Seung-bin Kim's perspective

Ordered by how strongly the researchers Seung-bin 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. Attention is All you Need (Neural Information Processing Systems)
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Long Short-Term Memory
6. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• HierSpeech++: Bridging the Gap Between Semantic and Acoustic Representation of Speech by Hierarchical Variational Inference for Zero-Shot Speech Synthesis (2023) — 78 citations
• HierSpeech: Bridging the Gap between Text and Speech by Hierarchical Variational Inference using Self-supervised Representations for Speech Synthesis (2022) — 81 citations
• Improved RawNet with Filter-wise Rescaling for Text-independent Speaker Verification using Raw Waveforms (2020) — 60 citations