Zhifeng Kong

Generative Models · 2020–2021

A researcher in the Science corpus: 3 papers; trusts 271 researchers and is trusted by 41 researchers here, as inferred from the citation graph.

Subfields

• Generative Models — 65 citations into it; #372 of 1,956 by trust here, #393 by citations
• Artificial Intelligence — 62 citations into it; #9,318 of 35,345 by trust here, #5,638 by citations
• Computer Vision and Pattern Recognition — 60 citations into it; #9,410 of 21,575 by trust here, #11,950 by citations
• Signal Processing — 40 citations into it; #2,115 of 7,572 by trust here, #570 by citations
• Speech & Audio — 24 citations into it
• Statistical and Nonlinear Physics — 21 citations into it
• Probabilistic & Bayesian Methods — 19 citations into it
• Model Architectures — 14 citations into it

Whom Zhifeng Kong trusts overall

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

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

Who trusts Zhifeng Kong overall

1. Jonathan Ho
2. Arash Vahdat
3. Sang-gil Lee
4. Qinsheng Zhang
5. Xu Tan
6. Jinglin Liu
7. Heeseung Kim
8. Daniel Watson
9. Yang Song
10. Lvmin Zhang

Papers from Zhifeng Kong's perspective

Ordered by how strongly the researchers Zhifeng Kong 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. ImageNet: A large-scale hierarchical image database
7. Gradient-based learning applied to document recognition
8. Learning Multiple Layers of Features from Tiny Images
9. GENERATIVE ADVERSARIAL NETS
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• DiffWave: A Versatile Diffusion Model for Audio Synthesis (2020) — 2,005 citations
• On Fast Sampling of Diffusion Probabilistic Models (2021) — 228 citations
• The Expressive Power of a Class of Normalizing Flow Models (2020) — 58 citations