Yu-Siang Huang

Computer Vision and Pattern Recognition · 2020

A researcher in the Science corpus: 2 papers; trusts 62 researchers and is trusted by 3 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 26 citations into it; #15,311 of 21,575 by trust here, #19,310 by citations
• Signal Processing — 21 citations into it; #5,166 of 7,572 by trust here, #6,355 by citations
• Artificial Intelligence — 13 citations into it
• Generative Models — 13 citations into it
• Multimodal Learning — 11 citations into it
• Model Architectures — 10 citations into it
• Cognitive Neuroscience — 7 citations into it; #6,644 of 10,852 by trust here, #9,453 by citations

Whom Yu-Siang Huang trusts overall

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

1. Li-Chia Yang
2. Ashish Vaswani
3. Peter Shaw
4. Curtis Hawthorne
5. Adam Roberts
6. Jakob Uszkoreit
7. Hao-Wen Dong
8. Nitish Shirish Keskar
9. Ian Simon
10. Kristy Choi

Who trusts Yu-Siang Huang overall

1. Stella Biderman
2. Ziyu Wang
3. Kieran Bicheno
4. Yiyi Zhang
5. Cheng-Zhi Anna Huang
6. Leo Gao

Papers from Yu-Siang Huang's perspective

Ordered by how strongly the researchers Yu-Siang 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. Attention is All you Need (Neural Information Processing Systems)
3. ImageNet classification with deep convolutional neural networks
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. GENERATIVE ADVERSARIAL NETS
10. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)

Their papers

• Pop Music Transformer: Generating Music with Rhythm and Harmony (2020) — 40 citations
• Pop Music Transformer: Beat-based Modeling and Generation of Expressive Pop Piano Compositions (2020) — 399 citations