Guangzhi Li

Artificial Intelligence · 2019–2021

A researcher in the Science corpus: 2 papers; trusts 14 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 60 citations into it
• Generative Models — 27 citations into it
• Signal Processing — 27 citations into it
• Computer Vision and Pattern Recognition — 22 citations into it
• Probabilistic & Bayesian Methods — 6 citations into it

Whom Guangzhi Li trusts overall

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

1. Diederik P. Kingma
2. Alex Graves
3. Aäron van den Oord
4. Geoffrey E. Hinton
5. Yoshua Bengio
6. Sepp Hochreiter
7. J. Urgen Schmidhuber
8. Alex Krizhevsky
9. Oriol Vinyals
10. Ilya Sutskever

Papers from Guangzhi Li's perspective

Ordered by how strongly the researchers Guangzhi Li trusts endorse each paper. Same corpus, a different order for every researcher.

1. Long Short-Term Memory
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. ImageNet classification with deep convolutional neural networks
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Attention is All you Need (Neural Information Processing Systems)
6. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
7. Gradient-based learning applied to document recognition
8. Auto-Encoding Variational Bayes (International Conference on Learning Representations)
9. ImageNet: A large-scale hierarchical image database
10. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)

Their papers

• VARA-TTS: Non-Autoregressive Text-to-Speech Synthesis based on Very Deep VAE with Residual Attention (2021) — 24 citations
• Maximizing Mutual Information for Tacotron (2019) — 16 citations