Yong Xu
Signal Processing · with Dai · 2014–2020
A researcher in the Science corpus: 5 papers; trusts 85 researchers and is trusted by 39 researchers here, as inferred from the citation graph.
Subfields
- Signal Processing — 118 citations into it; #739 of 7,572 by trust here, #538 by citations
- Artificial Intelligence — 83 citations into it; #29,783 of 35,345 by trust here, #25,040 by citations
- Speech & Audio — 48 citations into it; #254 of 1,076 by trust here, #259 by citations
- Computer Vision and Pattern Recognition — 43 citations into it
- Cognitive Neuroscience — 29 citations into it; #8,290 of 10,852 by trust here, #7,860 by citations
- Computational Mechanics — 25 citations into it; #1,124 of 10,001 by trust here, #746 by citations
- Natural Language Processing — 24 citations into it
- Model Architectures — 23 citations into it
Whom Yong Xu trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Israel Cohen
- Geoffrey E. Hinton
- Yuxuan Wang
- Deliang Wang
- Arun Narayanan
- Yoshua Bengio
- P. Loizou
- Li Deng
- George E. Dahl
- Dong Yu
Who trusts Yong Xu overall
- Cassia Valentini-Botinhao
- Szu-Wei Fu
- Deliang Wang
- Xiao-Lei Zhang
- Yu Tsao
- Jean-Marc Valin
- Aaron Nicolson
- Bo Wu
- John R. Hershey
- Jonathan Le Roux
Papers from Yong Xu's perspective
Ordered by how strongly the researchers Yong Xu 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
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Long Short-Term Memory
- Attention is All you Need (Neural Information Processing Systems)
- Gradient-based learning applied to document recognition
- ImageNet: A large-scale hierarchical image database
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
Their papers
- Audio Set Classification with Attention Model: A Probabilistic Perspective (2017) — 107 citations
- Multi-objective learning and mask-based post-processing for deep neural network based speech enhancement (2017) — 111 citations
- An Overview of Deep-Learning-Based Audio-Visual Speech Enhancement and Separation (2020) — 337 citations
- A Regression Approach to Speech Enhancement Based on Deep Neural Networks (2015) — 1,344 citations
- An Experimental Study on Speech Enhancement Based on Deep Neural Networks (2014) — 926 citations