Cheng-Zhi Anna Huang

Computer Vision and Pattern Recognition · 2014–2020

A researcher in the Science corpus: 7 papers; trusts 50 researchers and is trusted by 8 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 59 citations into it; #10,604 of 21,575 by trust here, #5,565 by citations
• Signal Processing — 45 citations into it; #3,895 of 7,572 by trust here, #1,012 by citations
• Generative Models — 42 citations into it; #1,662 of 1,956 by trust here, #733 by citations
• Artificial Intelligence — 37 citations into it
• Cognitive Neuroscience — 23 citations into it; #8,315 of 10,852 by trust here, #2,435 by citations
• Model Architectures — 18 citations into it
• Probabilistic & Bayesian Methods — 18 citations into it
• Optimization & Training — 7 citations into it

Whom Cheng-Zhi Anna Huang trusts overall

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

1. Geoffrey E. Hinton
2. Hugo Larochelle
3. Yoshua Bengio
4. Paul Smolensky
5. Diederik P. Kingma
6. Ashish Vaswani
7. Kaiming He
8. Gaëtan Hadjeres
9. Alex Krizhevsky
10. J. Urgen Schmidhuber

Who trusts Cheng-Zhi Anna Huang overall

1. Curtis Hawthorne
2. Ian Simon
3. Monica Dinculescu
4. Prafulla Dhariwal
5. Kristy Choi
6. Rewon Child
7. Jesse Engel
8. Yao-Hung Hubert Tsai
9. Adam Roberts
10. Colin Raffel

Papers from Cheng-Zhi Anna Huang's perspective

Ordered by how strongly the researchers Cheng-Zhi Anna 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. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
9. Learning Multiple Layers of Features from Tiny Images
10. GENERATIVE ADVERSARIAL NETS

Their papers

• Music Transformer: Generating Music with Long-Term Structure (2018) — 598 citations
• Enabling Factorized Piano Music Modeling and Generation with the MAESTRO Dataset (2018) — 596 citations
• Counterpoint by Convolution (2019) — 166 citations
• Human-AI Co-Creation in Songwriting (2020) — 109 citations
• Novice-AI Music Co-Creation via AI-Steering Tools for Deep Generative Models (2020) — 326 citations
• An Improved Relative Self-Attention Mechanism for Transformer with Application to Music Generation (2018) — 82 citations
• Active learning of intuitive control knobs for synthesizers using gaussian processes (2014) — 14 citations