Toshiki Kataoka

Computer Vision and Pattern Recognition · 2018–2021

A researcher in the Science corpus: 2 papers; trusts 73 researchers and is trusted by 1,777 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 31 citations into it; #971 of 21,575 by trust here, #1,423 by citations
• Artificial Intelligence — 26 citations into it; #1,270 of 35,345 by trust here, #1,924 by citations
• Generative Models — 22 citations into it; #126 of 1,956 by trust here, #166 by citations
• Computational Theory and Mathematics — 20 citations into it
• Optimization & Training — 12 citations into it
• Statistical and Nonlinear Physics — 5 citations into it

Whom Toshiki Kataoka trusts overall

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

1. Diederik P. Kingma
2. Geoffrey E. Hinton
3. Kaiming He
4. Yoshua Bengio
5. Tim Salimans
6. Alex Krizhevsky
7. Martín Arjovsky
8. Yann LeCun
9. L. Eon Bottou
10. Christian Szegedy

Who trusts Toshiki Kataoka overall

1. Yilun Du
2. Tianfu Wu
3. Ngoc-Trung Tran
4. Tero Karras
5. Yoshua Bengio
6. Shiyu Chang
7. Jeff Donahue
8. Alexia Jolicoeur-Martineau
9. Ning Yu
10. Arash Vahdat

Papers from Toshiki Kataoka's perspective

Ordered by how strongly the researchers Toshiki Kataoka 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. 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. ImageNet Large Scale Visual Recognition Challenge
9. Going deeper with convolutions
10. GENERATIVE ADVERSARIAL NETS

Their papers

• Spectral Normalization for Generative Adversarial Networks (2018) — 5,083 citations
• Towards universal neural network potential for material discovery applicable to arbitrary combination of 45 elements (2021) — 376 citations