Muneyoshi Inahara

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 Muneyoshi Inahara 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
8. Alex Krizhevsky
9. Yoshua Bengio
10. Stefano Ermon

Papers from Muneyoshi Inahara's perspective

Ordered by how strongly the researchers Muneyoshi Inahara 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. Long Short-Term Memory
7. ImageNet: A large-scale hierarchical image database
8. Learning Multiple Layers of Features from Tiny Images
9. GENERATIVE ADVERSARIAL NETS
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

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