Yotaro Katayama

Generative Models · 2021

A researcher in the Science corpus: 1 paper; trusts 21 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Generative Models — 26 citations into it
• Computer Vision and Pattern Recognition — 21 citations into it
• Artificial Intelligence — 14 citations into it
• Statistical and Nonlinear Physics — 10 citations into it
• Probabilistic & Bayesian Methods — 6 citations into it

Whom Yotaro Katayama trusts overall

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

1. Yang Song
2. Diederik P. Kingma
3. Jonathan Ho
4. Geoffrey E. Hinton
5. B.D.O. Anderson
6. Bernt Øksendal
7. Taye Samuel Faniran

Papers from Yotaro Katayama's perspective

Ordered by how strongly the researchers Yotaro Katayama 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. Attention is All you Need (Neural Information Processing Systems)
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Gradient-based learning applied to document recognition
6. ImageNet: A large-scale hierarchical image database
7. Long Short-Term Memory
8. Learning Multiple Layers of Features from Tiny Images
9. ImageNet Large Scale Visual Recognition Challenge
10. GENERATIVE ADVERSARIAL NETS

Their papers

• Itô-Taylor Sampling Scheme for Denoising Diffusion Probabilistic Models using Ideal Derivatives (2021) — 22 citations