Seungkwon Beack

Computer Vision and Pattern Recognition · 2019–2021

A researcher in the Science corpus: 2 papers; trusts 11 researchers and is trusted by 4 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 22 citations into it; #14,667 of 21,575 by trust here, #14,822 by citations
• Signal Processing — 22 citations into it; #4,916 of 7,572 by trust here, #4,201 by citations
• Speech & Audio — 18 citations into it
• Artificial Intelligence — 11 citations into it

Whom Seungkwon Beack trusts overall

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

1. Geoffrey E. Hinton
2. Daniel Stoller
3. Srihari Kankanahalli
4. Diederik P. Kingma
5. Kaiming He

Who trusts Seungkwon Beack overall

1. Yi-Chiao Wu
2. Xue Jiang

Papers from Seungkwon Beack's perspective

Ordered by how strongly the researchers Seungkwon Beack 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. Gradient-based learning applied to document recognition
5. Long Short-Term Memory
6. Attention is All you Need (Neural Information Processing Systems)
7. Learning Multiple Layers of Features from Tiny Images
8. ImageNet Large Scale Visual Recognition Challenge
9. ImageNet: A large-scale hierarchical image database
10. Going deeper with convolutions

Their papers

• Harp-Net: Hyper-Autoencoded Reconstruction Propagation for Scalable Neural Audio Coding (2021) — 19 citations
• Cascaded Cross-Module Residual Learning towards Lightweight End-to-End Speech Coding (2019) — 43 citations