Guangzhi Sun

Artificial Intelligence · 2020–2023

A researcher in the Science corpus: 3 papers; trusts 14 researchers and is trusted by 2 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 77 citations into it; #32,841 of 35,345 by trust here, #16,166 by citations
• Signal Processing — 31 citations into it; #7,095 of 7,572 by trust here, #2,589 by citations
• Speech & Audio — 30 citations into it; #861 of 1,076 by trust here, #724 by citations
• Computer Vision and Pattern Recognition — 25 citations into it
• Natural Language Processing — 19 citations into it
• Generative Models — 16 citations into it; #1,581 of 1,956 by trust here, #1,382 by citations

Whom Guangzhi Sun trusts overall

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

1. Diederik P. Kingma
2. Ilya Sutskever
3. Geoffrey E. Hinton
4. Oriol Vinyals
5. Yoshua Bengio
6. Aäron van den Oord
7. A. de Cheveigné
8. Wei-Chung Chu
9. Ashish Vaswani
10. Alex Graves

Who trusts Guangzhi Sun overall

1. Chenpeng Du
2. Xu Tan
3. K. Yu
4. Yiwei Guo
5. Isaac Elias
6. Kai Yu
7. Yukiya Hono
8. Chung-Ming Chien
9. Rui Liu
10. Jonathan Shen

Papers from Guangzhi Sun's perspective

Ordered by how strongly the researchers Guangzhi Sun 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. Attention is All you Need (Neural Information Processing Systems)
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Long Short-Term Memory
6. Gradient-based learning applied to document recognition
7. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
8. ImageNet: A large-scale hierarchical image database
9. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)

Their papers

• Fully-Hierarchical Fine-Grained Prosody Modeling For Interpretable Speech Synthesis (2020) — 131 citations
• SALMONN: Towards Generic Hearing Abilities for Large Language Models (2023) — 644 citations
• Generating Diverse and Natural Text-to-Speech Samples Using a Quantized Fine-Grained VAE and Autoregressive Prosody Prior (2020) — 96 citations