Haohan Guo
Artificial Intelligence · 2019
A researcher in the Science corpus: 2 papers; trusts 14 researchers and is trusted by 4 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 32 citations into it; #22,069 of 35,345 by trust here, #21,316 by citations
- Speech & Audio — 18 citations into it
- Signal Processing — 17 citations into it; #7,230 of 7,572 by trust here, #4,560 by citations
- Computer Vision and Pattern Recognition — 10 citations into it
- Model Architectures — 6 citations into it
Whom Haohan Guo trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Heiga Zen
- Ronald J. Williams
- Geoffrey E. Hinton
- Keiichi Tokuda
- Yuxuan Wang
- Andrew Rosenberg
- Yoshua Bengio
- Diederik P. Kingma
- Daniel Klein
- Oriol Vinyals
Who trusts Haohan Guo overall
- Zhaoxi Mu
- Xu Tan
- Xinyu Yang
- Xiaochun An
- Rui Liu
- Frank K. Soong
- Yixuan Zhou
- Ying-Cheng Lu
- Sri Karlapati
- Yizhuo Dong
Papers from Haohan Guo's perspective
Ordered by how strongly the researchers Haohan Guo 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
- Attention is All you Need (Neural Information Processing Systems)
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Long Short-Term Memory
- Gradient-based learning applied to document recognition
- Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
- ImageNet: A large-scale hierarchical image database
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
Their papers
- A New GAN-based End-to-End TTS Training Algorithm (2019) — 49 citations
- Exploiting Syntactic Features in a Parsed Tree to Improve End-to-End TTS (2019) — 31 citations