Yuichi Yoshida

Artificial Intelligence · 2013–2018

A researcher in the Science corpus: 4 papers; trusts 221 researchers and is trusted by 1,988 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 67 citations into it; #1,088 of 35,345 by trust here, #1,790 by citations
• Optimization & Training — 55 citations into it; #607 of 2,601 by trust here, #1,571 by citations
• Computer Vision and Pattern Recognition — 43 citations into it; #903 of 21,575 by trust here, #1,329 by citations
• Computational Mechanics — 24 citations into it; #1,079 of 10,001 by trust here, #3,652 by citations
• Generative Models — 22 citations into it; #127 of 1,956 by trust here, #166 by citations
• Model Architectures — 16 citations into it
• Statistical and Nonlinear Physics — 16 citations into it
• Computer Networks and Communications — 14 citations into it

Whom Yuichi Yoshida trusts overall

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

1. Alex Krizhevsky
2. Geoffrey E. Hinton
3. Yoshua Bengio
4. Kaiming He
5. James Martens
6. Sepp Hochreiter
7. Ilya Sutskever
8. Yann LeCun
9. Diederik P. Kingma
10. Ian J. Goodfellow

Who trusts Yuichi Yoshida overall

1. Alberto Bietti
2. Charles H. Martin
3. Takeru Miyato
4. Julien Mairal
5. Haifeng Qian
6. Henry Gouk
7. Cem Anil
8. Rui Wang
9. Grégoire Mialon
10. Nikita Dvornik

Papers from Yuichi Yoshida's perspective

Ordered by how strongly the researchers Yuichi Yoshida 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. Long Short-Term Memory
5. Gradient-based learning applied to document recognition
6. Attention is All you Need (Neural Information Processing Systems)
7. ImageNet: A large-scale hierarchical image database
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

• Spectral Normalization for Generative Adversarial Networks (2018) — 5,083 citations
• Spectral Norm Regularization for Improving the Generalizability of Deep Learning (2017) — 415 citations
• Guaranteed Sufficient Decrease for Stochastic Variance Reduced Gradient Optimization (2018) — 9 citations
• Fast exact shortest-path distance queries on large networks by pruned landmark labeling (2013) — 426 citations