Szu-Yu Chou
Computer Vision and Pattern Recognition · 2017–2019
A researcher in the Science corpus: 2 papers; trusts 47 researchers and is trusted by 10 researchers here, as inferred from the citation graph.
Subfields
- Computer Vision and Pattern Recognition — 27 citations into it; #8,371 of 21,575 by trust here, #12,689 by citations
- Generative Models — 19 citations into it; #785 of 1,956 by trust here, #1,382 by citations
- Signal Processing — 16 citations into it; #2,382 of 7,572 by trust here, #3,099 by citations
- Artificial Intelligence — 11 citations into it
- Representation & Self-Supervised Learning — 6 citations into it
Whom Szu-Yu Chou trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Geoffrey E. Hinton
- Alex Graves
- Yoshua Bengio
- Gregory R. Koch
- Oriol Vinyals
- Olof Mogren
- Aäron van den Oord
- Alex Krizhevsky
- Diederik P. Kingma
- Ilya Sutskever
Who trusts Szu-Yu Chou overall
- Yu-Siang Huang
- Hao-Wen Dong
- Yi-Hsuan Yang
- Divya Saxena
- Jean-Pierre Briot
- Gino Brunner
- Wen-Yi Hsiao
- Yukiya Hono
- Jian Wu
- Prafulla Dhariwal
Papers from Szu-Yu Chou's perspective
Ordered by how strongly the researchers Szu-Yu Chou trusts endorse each paper. Same corpus, a different order for every researcher.
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- ImageNet classification with deep convolutional neural networks
- Long Short-Term Memory
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Gradient-based learning applied to document recognition
- Attention is All you Need (Neural Information Processing Systems)
- GENERATIVE ADVERSARIAL NETS
- ImageNet: A large-scale hierarchical image database
- Learning Multiple Layers of Features from Tiny Images
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
Their papers
- MidiNet: A Convolutional Generative Adversarial Network for Symbolic-Domain Music Generation (2017) — 509 citations
- Multi-label Few-shot Learning for Sound Event Recognition (2019) — 30 citations