Sungzoon Cho

Artificial Intelligence · 2015–2018

A researcher in the Science corpus: 3 papers; trusts 151 researchers and is trusted by 11 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 34 citations into it; #14,409 of 35,345 by trust here, #29,412 by citations
• Computer Vision and Pattern Recognition — 26 citations into it
• Natural Language Processing — 20 citations into it; #2,671 of 5,688 by trust here, #5,092 by citations
• Model Architectures — 14 citations into it
• Representation & Self-Supervised Learning — 9 citations into it
• Generative Models — 6 citations into it
• Optimization & Training — 6 citations into it

Whom Sungzoon Cho trusts overall

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

1. Diederik P. Kingma
2. Geoffrey E. Hinton
3. Jimmy Ba
4. Alex Graves
5. Sepp Hochreiter
6. Yoshua Bengio
7. Alex Krizhevsky
8. Ilya Sutskever
9. Yann LeCun
10. J. Urgen Schmidhuber

Who trusts Sungzoon Cho overall

1. Paul Bergmann
2. Houssam Zenati
3. Lukas Ruff
4. Izhak Golan
5. Samet Akcay
6. Jamie Ryan Kiros
7. Dilip Ravi Kiran
8. Daehyung Park
9. Michael Fauser
10. Zhikang Liu

Papers from Sungzoon Cho's perspective

Ordered by how strongly the researchers Sungzoon Cho 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. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
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. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• Distance-based Self-Attention Network for Natural Language Inference (2017) — 76 citations
• CRAM: Clued Recurrent Attention Model (2018) — 3 citations
• Variational Autoencoder based Anomaly Detection using Reconstruction Probability (2015) — 1,756 citations