Xiaoyong Jin

Artificial Intelligence · 2019–2020

A researcher in the Science corpus: 3 papers; trusts 12 researchers and is trusted by 3 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 29 citations into it; #27,626 of 35,345 by trust here, #11,624 by citations
• Time-Series & Forecasting — 15 citations into it; #324 of 381 by trust here, #51 by citations
• Management Science and Operations Research — 13 citations into it; #9,108 of 11,946 by trust here, #2,016 by citations
• Generative Models — 7 citations into it
• Statistics and Probability — 5 citations into it

Whom Xiaoyong Jin trusts overall

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

1. Charles C. Holt
2. Alex Graves
3. Yoshua Bengio
4. Ilya Sutskever
5. Hsiang-Fu Yu
6. Geoffrey E. Hinton
7. Rose Yu
8. Kyunghyun Cho
9. Diederik P. Kingma
10. Guokun Lai

Who trusts Xiaoyong Jin overall

1. Tian Zhou
2. Haixu Wu
3. Gerald Woo
4. Jiehui Xu
5. Ailing Zeng
6. Bryan Lim
7. Yunhao Zhang
8. Hao Xue
9. Juan Miguel Lopez Alcaraz
10. Qingsong Wen

Papers from Xiaoyong Jin's perspective

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

1. Long Short-Term Memory
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. ImageNet classification with deep convolutional neural networks
4. Attention is All you Need (Neural Information Processing Systems)
5. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
6. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
7. Gradient-based learning applied to document recognition
8. ImageNet: A large-scale hierarchical image database
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

• Enhancing the Locality and Breaking the Memory Bottleneck of Transformer on Time Series Forecasting (2019) — 1,983 citations
• Inter-Series Attention Model for COVID-19 Forecasting (2020) — 49 citations
• Multi-step Deep Autoregressive Forecasting with Latent States (2019)