Jun Yue

Artificial Intelligence · 2015–2017

A researcher in the Science corpus: 3 papers; trusts 69 researchers and is trusted by 13 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 36 citations into it
• Computer Vision and Pattern Recognition — 33 citations into it; #9,110 of 21,575 by trust here, #9,722 by citations
• Media Technology — 14 citations into it; #1,920 of 3,621 by trust here, #1,017 by citations
• Natural Language Processing — 10 citations into it
• Atmospheric Science — 9 citations into it; #2,179 of 4,804 by trust here, #1,620 by citations
• Model Architectures — 8 citations into it
• Probabilistic & Bayesian Methods — 5 citations into it

Whom Jun Yue trusts overall

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

1. Yoshua Bengio
2. Farid Melgani
3. Clément Farabet
4. Geoffrey E. Hinton
5. Kaiming He
6. Yann LeCun
7. Xueyun Chen
8. Alex Krizhevsky
9. Lorenzo Bruzzone
10. Ilya Sutskever

Who trusts Jun Yue overall

1. Ying Li
2. Mengmeng Zhang
3. Alberto Signoroni
4. Weiwei Jiang
5. Saejoon Kim
6. Keiller Nogueira
7. Guangrui Zhao
8. Anastasia Ioannidou
9. Aaron E. Maxwell
10. Mingyi He

Papers from Jun Yue's perspective

Ordered by how strongly the researchers Jun Yue trusts endorse each paper. Same corpus, a different order for every researcher.

1. ImageNet classification with deep convolutional neural networks
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. Long Short-Term Memory
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Gradient-based learning applied to document recognition
6. Attention is All you Need (Neural Information Processing Systems)
7. ImageNet: A large-scale hierarchical image database
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Going deeper with convolutions
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• A deep learning framework for financial time series using stacked autoencoders and long-short term memory (2017) — 1,013 citations
• Spectral–spatial classification of hyperspectral images using deep convolutional neural networks (2015) — 465 citations
• A deep learning framework for hyperspectral image classification using spatial pyramid pooling (2016) — 135 citations