Yusuke Tsuzuku

Artificial Intelligence · 2018

A researcher in the Science corpus: 2 papers; trusts 112 researchers and is trusted by 15 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 45 citations into it
• Safety, Robustness & Interpretability — 29 citations into it; #904 of 2,251 by trust here, #1,589 by citations
• Computer Vision and Pattern Recognition — 28 citations into it
• Optimization & Training — 16 citations into it; #1,280 of 2,601 by trust here, #1,638 by citations
• Model Architectures — 9 citations into it
• Hardware and Architecture — 6 citations into it
• Signal Processing — 5 citations into it

Whom Yusuke Tsuzuku trusts overall

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

1. Alex Krizhevsky
2. Diederik P. Kingma
3. Geoffrey E. Hinton
4. Kaiming He
5. Christian Szegedy
6. Ilya Sutskever
7. Yoshua Bengio
8. Karen Simonyan
9. Yann LeCun
10. Ross B. Girshick

Who trusts Yusuke Tsuzuku overall

1. Klas Leino
2. Sungyoon Lee
3. Matt Jordan
4. Jeremy M. Cohen
5. Todd P. Huster
6. Gavin Weiguang Ding
7. Yujia Huang
8. Mahyar Fazlyab
9. Mohammad Mohammadi Amiri
10. J. Kolter

Papers from Yusuke Tsuzuku's perspective

Ordered by how strongly the researchers Yusuke Tsuzuku 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. Gradient-based learning applied to document recognition
5. Long Short-Term Memory
6. ImageNet: A large-scale hierarchical image database
7. Attention is All you Need (Neural Information Processing Systems)
8. Learning Multiple Layers of Features from Tiny Images
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Variance-based Gradient Compression for Efficient Distributed Deep Learning (2018) — 91 citations
• Lipschitz-Margin Training: Scalable Certification of Perturbation Invariance for Deep Neural Networks (2018) — 377 citations