Chin-Cheng Hsu

Artificial Intelligence · 2016–2017

A researcher in the Science corpus: 3 papers; trusts 17 researchers and is trusted by 15 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 24 citations into it; #9,502 of 35,345 by trust here, #9,870 by citations
• Signal Processing — 15 citations into it; #3,971 of 7,572 by trust here, #2,114 by citations
• Representation & Self-Supervised Learning — 6 citations into it
• Generative Models — 5 citations into it; #1,176 of 1,956 by trust here, #1,163 by citations

Whom Chin-Cheng Hsu trusts overall

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

1. Diederik P. Kingma
2. Geoffrey E. Hinton
3. Tomoki Toda
4. Max Welling
5. Hsin-Te Hwang
6. Vinod Nair
7. Yoshua Bengio
8. Hideki Kawahara
9. Linghui Chen
10. Mirjam Wester

Who trusts Chin-Cheng Hsu overall

1. Hirokazu Kameoka
2. Takuhiro Kaneko
3. Junichi Yamagishi
4. Berrak Sisman
5. Xin Eric Wang
6. Mingyang Zhang
7. Wei-Ning Hsu
8. Gustav Eje Henter
9. Yongjun Hong
10. Haizhou Li

Papers from Chin-Cheng Hsu's perspective

Ordered by how strongly the researchers Chin-Cheng Hsu 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. Auto-Encoding Variational Bayes (International Conference on Learning Representations)
8. ImageNet: A large-scale hierarchical image database
9. GENERATIVE ADVERSARIAL NETS
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• Voice Conversion from Unaligned Corpora Using Variational Autoencoding Wasserstein Generative Adversarial Networks (2017) — 326 citations
• Voice conversion from non-parallel corpora using variational auto-encoder (2016) — 319 citations
• Locally Linear Embedding for Exemplar-Based Spectral Conversion (2016) — 37 citations