Seongkyu Mun

Artificial Intelligence · 2017–2022

A researcher in the Science corpus: 5 papers; trusts 117 researchers and is trusted by 10 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 52 citations into it; #24,587 of 35,345 by trust here, #23,731 by citations
• Signal Processing — 29 citations into it; #5,092 of 7,572 by trust here, #4,560 by citations
• Computer Vision and Pattern Recognition — 26 citations into it; #12,306 of 21,575 by trust here, #19,310 by citations
• Speech & Audio — 22 citations into it
• Representation & Self-Supervised Learning — 10 citations into it
• Evaluation & Benchmarks — 8 citations into it
• Generative Models — 8 citations into it; #1,197 of 1,956 by trust here, #1,843 by citations
• Model Architectures — 6 citations into it

Whom Seongkyu Mun trusts overall

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

1. Alex Krizhevsky
2. Geoffrey E. Hinton
3. Augustus Odena
4. Andrew Zisserman
5. Sergey Ioffe
6. Alec Radford
7. Yoshua Bengio
8. Kaiming He
9. Diederik P. Kingma
10. Ilya Sutskever

Who trusts Seongkyu Mun overall

1. Ekin Dogus Cubuk
2. Barret Zoph
3. Xiao Wang
4. Quoc V. Le
5. Tong Niu
6. Jonathon Shlens
7. Guo-Jun Qi
8. Golnaz Ghiasi
9. Hee-Soo Heo
10. Mohit Bansal

Papers from Seongkyu Mun's perspective

Ordered by how strongly the researchers Seongkyu Mun trusts endorse each paper. Same corpus, a different order for every researcher.

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

Their papers

• Generative Adversarial Network Based Acoustic Scene Training Set Augmentation and Selection Using SVM Hyper-Plane (2017) — 139 citations
• In defence of metric learning for speaker recognition (2020) — 499 citations
• An Empirical Study on L2 Accents of Cross-lingual Text-to-Speech Systems via Vowel Space (2022)
• Delving into VoxCeleb: environment invariant speaker recognition (2019) — 55 citations
• Generative Adversarial Network Based Acoustic Scene Training Set Augmentation and Selection Using SVM Hyper-Plane (2017)