Chin-Wei Huang

Artificial Intelligence · with Courville · 2017–2021

A researcher in the Science corpus: 5 papers; trusts 533 researchers and is trusted by 24 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 117 citations into it; #27,579 of 35,345 by trust here, #17,412 by citations
• Computer Vision and Pattern Recognition — 109 citations into it; #17,010 of 21,575 by trust here, #15,162 by citations
• Generative Models — 95 citations into it; #647 of 1,956 by trust here, #644 by citations
• Probabilistic & Bayesian Methods — 71 citations into it; #1,315 of 1,525 by trust here, #921 by citations
• Statistical and Nonlinear Physics — 29 citations into it
• Optimization & Training — 20 citations into it
• Model Architectures — 18 citations into it; #1,013 of 2,278 by trust here, #1,131 by citations
• Signal Processing — 18 citations into it

Whom Chin-Wei Huang trusts overall

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

1. Diederik P. Kingma
2. Alex Krizhevsky
3. Geoffrey E. Hinton
4. Radford M. Neal
5. Yoshua Bengio
6. Tim Salimans
7. Danilo Jimenez Rezende
8. Max Welling
9. Yann LeCun
10. Kaiming He

Who trusts Chin-Wei Huang overall

1. Nicola De Cao
2. Ricky T. Q. Chen
3. Arash Vahdat
4. Qinsheng Zhang
5. Antoine Wehenkel
6. Matthias Bauer
7. Zachary M. Ziegler
8. Dongjun Kim
9. Zhenyi Wang
10. Michael S. Albergo

Papers from Chin-Wei Huang's perspective

Ordered by how strongly the researchers Chin-Wei Huang 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. Learning Multiple Layers of Features from Tiny Images
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. GENERATIVE ADVERSARIAL NETS

Their papers

• Bayesian Hypernetworks (2017) — 157 citations
• Learnable Explicit Density for Continuous Latent Space and Variational Inference (2017) — 28 citations
• A Variational Perspective on Diffusion-Based Generative Models and Score Matching (2021) — 239 citations
• Augmented Normalizing Flows: Bridging the Gap Between Generative Flows and Latent Variable Models (2020) — 99 citations
• Neural Autoregressive Flows (2018) — 495 citations