Zhisheng Xiao

Generative Models · 2019–2025

A researcher in the Science corpus: 5 papers; trusts 777 researchers and is trusted by 15 researchers here, as inferred from the citation graph.

Subfields

• Generative Models — 185 citations into it; #1,000 of 1,956 by trust here, #1,382 by citations
• Computer Vision and Pattern Recognition — 181 citations into it; #7,349 of 21,575 by trust here, #7,499 by citations
• Artificial Intelligence — 99 citations into it; #20,974 of 35,345 by trust here, #19,439 by citations
• Probabilistic & Bayesian Methods — 64 citations into it
• Statistical and Nonlinear Physics — 44 citations into it
• Signal Processing — 31 citations into it; #3,309 of 7,572 by trust here, #2,221 by citations
• Optimization & Training — 17 citations into it
• Computer Graphics and Computer-Aided Design — 13 citations into it

Whom Zhisheng Xiao trusts overall

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

1. Diederik P. Kingma
2. Geoffrey E. Hinton
3. Alex Krizhevsky
4. Kaiming He
5. Yoshua Bengio
6. Radford M. Neal
7. Karen Simonyan
8. Yann LeCun
9. Ruslan Salakhutdinov
10. Ilya Sutskever

Who trusts Zhisheng Xiao overall

1. Robin Rombach
2. Patrick Esser
3. Arash Vahdat
4. Yeqing Lin
5. Michael S. Albergo
6. Xingchao Liu
7. Songxiang Liu
8. Tianwei Yin
9. Karsten Kreis
10. Axel Sauer

Papers from Zhisheng Xiao's perspective

Ordered by how strongly the researchers Zhisheng Xiao 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. Attention is All you Need (Neural Information Processing Systems)
5. Long Short-Term Memory
6. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Learning Multiple Layers of Features from Tiny Images
10. GENERATIVE ADVERSARIAL NETS

Their papers

• Tackling the Generative Learning Trilemma with Denoising Diffusion GANs (2021) — 766 citations
• VAEBM: A Symbiosis between Variational Autoencoders and Energy-based Models (2020) — 142 citations
• Generative Latent Flow: A Framework for Non-adversarial Image Generation (2019) — 15 citations
• Exponential Tilting of Generative Models: Improving Sample Quality by Training and Sampling from Latent Energy (2020) — 8 citations
• Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities (2025) — 3,609 citations