Yaoyu Zhang
Artificial Intelligence · 2014–2019
A researcher in the Science corpus: 4 papers; trusts 29 researchers and is trusted by 8 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 47 citations into it; #15,069 of 35,345 by trust here, #15,542 by citations
- Scaling & Learning Theory — 33 citations into it; #218 of 428 by trust here, #274 by citations
- Optimization & Training — 20 citations into it; #1,415 of 2,601 by trust here, #1,419 by citations
- Statistical and Nonlinear Physics — 15 citations into it; #8,487 of 10,127 by trust here, #3,979 by citations
- Statistical Learning Theory — 13 citations into it
- Computer Vision and Pattern Recognition — 11 citations into it
- Cognitive Neuroscience — 8 citations into it
- Computational Mechanics — 6 citations into it
Whom Yaoyu Zhang trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Zhi-Qin John Xu
- Diederik P. Kingma
- George V. Cybenko
- Jimmy Ba
- Anthony N. Burkitt
- Yoshua Bengio
- Geoffrey E. Hinton
- Yann LeCun
- Lénaïc Chizat
- Alex Krizhevsky
Who trusts Yaoyu Zhang overall
Papers from Yaoyu Zhang's perspective
Ordered by how strongly the researchers Yaoyu Zhang 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)
- Gradient-based learning applied to document recognition
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- Attention is All you Need (Neural Information Processing Systems)
- Long Short-Term Memory
- Learning Multiple Layers of Features from Tiny Images
- ImageNet: A large-scale hierarchical image database
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
Their papers
- Training behavior of deep neural network in frequency domain (2018) — 413 citations
- Frequency Principle: Fourier Analysis Sheds Light on Deep Neural Networks (2019) — 776 citations
- Analysis of sampling artifacts on the Granger causality analysis for topology extraction of neuronal dynamics (2014) — 14 citations
- A type of generalization error induced by initialization in deep neural networks (2019) — 54 citations