Sicong Huang

Artificial Intelligence · 2020–2023

A researcher in the Science corpus: 2 papers; trusts 40 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 52 citations into it
• Computer Vision and Pattern Recognition — 35 citations into it
• Generative Models — 27 citations into it
• Evaluation & Benchmarks — 22 citations into it
• Probabilistic & Bayesian Methods — 9 citations into it
• Optimization & Training — 5 citations into it

Whom Sicong Huang trusts overall

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

1. Alex Krizhevsky
2. Radford M. Neal
3. Diederik P. Kingma
4. Geoffrey E. Hinton
5. Yann LeCun
6. Yoshua Bengio
7. Zhou Wang
8. Alec Radford
9. Alexander A. Alemi
10. Jimmy Ba

Who trusts Sicong Huang overall

1. Jonathan Ho
2. Ajay Jain

Papers from Sicong Huang's perspective

Ordered by how strongly the researchers Sicong Huang 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. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• Inverse Scaling: When Bigger Isn't Better (2023) — 235 citations
• Evaluating Lossy Compression Rates of Deep Generative Models (2020) — 30 citations