Shan Yang

Artificial Intelligence · Radiology: Artificial Intelligence · 2016–2022

A researcher in the Science corpus: 12 papers; trusts 205 researchers and is trusted by 15 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 159 citations into it; #11,111 of 35,345 by trust here, #8,999 by citations
• Signal Processing — 101 citations into it; #2,163 of 7,572 by trust here, #1,120 by citations
• Speech & Audio — 78 citations into it
• Generative Models — 54 citations into it
• Computer Vision and Pattern Recognition — 49 citations into it
• Natural Language Processing — 40 citations into it
• Optimization & Training — 11 citations into it
• Representation & Self-Supervised Learning — 10 citations into it

Whom Shan Yang trusts overall

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

1. Diederik P. Kingma
2. Geoffrey E. Hinton
3. Heiga Zen
4. Yoshua Bengio
5. Zhizheng Wu
6. Jimmy Ba
7. Alex Krizhevsky
8. Alex Graves
9. Yuxuan Wang
10. Ilya Sutskever

Who trusts Shan Yang overall

1. Xu Tan
2. Yinjiao Lei
3. Weitong Guo
4. Rui Liu
5. Jian Cong
6. Ryuichi Yamamoto
7. Tao Qin
8. Lei Xie
9. Frank K. Soong
10. Berrak Sisman

Papers from Shan Yang's perspective

Ordered by how strongly the researchers Shan Yang 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. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
3. ImageNet classification with deep convolutional neural networks
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. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
9. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Multi-Band Melgan: Faster Waveform Generation For High-Quality Text-To-Speech (2020) — 238 citations
• TotalSegmentator: Robust Segmentation of 104 Anatomic Structures in CT Images. (2022) — 1,723 citations
• Recent progresses on physics and applications of vanadium dioxide (2018) — 470 citations
• Glow-WaveGAN: Learning Speech Representations from GAN-based Variational Auto-Encoder For High Fidelity Flow-based Speech Synthesis (2021) — 29 citations
• Data Efficient Voice Cloning from Noisy Samples with Domain Adversarial Training (2020) — 33 citations
• Fine-Grained Emotion Strength Transfer, Control and Prediction for Emotional Speech Synthesis (2020) — 66 citations
• Controllable Emotion Transfer For End-to-End Speech Synthesis (2020) — 91 citations
• On the localness modeling for the self-attention based end-to-end speech synthesis (2020) — 32 citations
• Slope stability prediction for circular mode failure using gradient boosting machine approach based on an updated database of case histories (2019) — 314 citations
• Controlling Emotion Strength with Relative Attribute for End-to-End Speech Synthesis (2019) — 57 citations
• Statistical parametric speech synthesis using generative adversarial networks under a multi-task learning framework (2017) — 48 citations
• On the training of DNN-based average voice model for speech synthesis (2016) — 14 citations