Hong-Min Chu

Computer Vision and Pattern Recognition · 2022–2023

A researcher in the Science corpus: 2 papers; trusts 59 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 48 citations into it
• Generative Models — 37 citations into it
• Artificial Intelligence — 23 citations into it
• Statistical and Nonlinear Physics — 12 citations into it
• Biophysics — 6 citations into it

Whom Hong-Min Chu trusts overall

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

1. Alex Krizhevsky
2. Jonathan Ho
3. Diederik P. Kingma
4. Prafulla Dhariwal
5. Geoffrey E. Hinton
6. Kaiming He
7. Yang Song
8. Alex Nichol
9. Yoshua Bengio
10. Yann LeCun

Papers from Hong-Min Chu's perspective

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

Their papers

• Universal Guidance for Diffusion Models (2023) — 495 citations
• Cold Diffusion: Inverting Arbitrary Image Transforms Without Noise (2022) — 440 citations