Jae Hyun Lim

Generative Models · with Courville · 2017–2021

A researcher in the Science corpus: 2 papers; trusts 182 researchers and is trusted by 715 researchers here, as inferred from the citation graph.

Subfields

• Generative Models — 36 citations into it; #179 of 1,956 by trust here, #644 by citations
• Artificial Intelligence — 30 citations into it
• Computer Vision and Pattern Recognition — 30 citations into it; #1,615 of 21,575 by trust here, #6,954 by citations
• Probabilistic & Bayesian Methods — 18 citations into it
• Statistical and Nonlinear Physics — 14 citations into it
• Numerical Methods & Scientific Computing — 6 citations into it
• Computational Mechanics — 5 citations into it
• Statistics and Probability — 5 citations into it

Whom Jae Hyun Lim trusts overall

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

1. Diederik P. Kingma
2. Alex Krizhevsky
3. Geoffrey E. Hinton
4. Yoshua Bengio
5. Yann LeCun
6. Yang Song
7. Radford M. Neal
8. Individualized Treat
9. Guo-Jun Qi
10. Pascal Vincent

Who trusts Jae Hyun Lim overall

1. Tianfu Wu
2. Axel Sauer
3. Jeff Donahue
4. Takeru Miyato
5. Han Zhang
6. Rithesh Kumar
7. Yongjun Hong
8. Alexia Jolicoeur-Martineau
9. Yoshua Bengio
10. Ming Tao

Papers from Jae Hyun Lim's perspective

Ordered by how strongly the researchers Jae Hyun Lim 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. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Gradient-based learning applied to document recognition
5. Attention is All you Need (Neural Information Processing Systems)
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. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• Geometric GAN (2017) — 581 citations
• A Variational Perspective on Diffusion-Based Generative Models and Score Matching (2021) — 239 citations