Xingjian Shi

Artificial Intelligence · 2015–2023

A researcher in the Science corpus: 10 papers; trusts 470 researchers and is trusted by 25 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 141 citations into it; #11,540 of 35,345 by trust here, #9,170 by citations
• Computer Vision and Pattern Recognition — 126 citations into it; #16,576 of 21,575 by trust here, #7,584 by citations
• Model Architectures — 48 citations into it; #731 of 2,278 by trust here, #347 by citations
• Representation & Self-Supervised Learning — 35 citations into it
• Natural Language Processing — 33 citations into it
• Graph Learning — 22 citations into it
• Information Systems — 19 citations into it; #6,234 of 8,771 by trust here, #5,942 by citations
• Generative Models — 18 citations into it; #1,052 of 1,956 by trust here, #1,681 by citations

Whom Xingjian Shi trusts overall

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

1. Geoffrey E. Hinton
2. Diederik P. Kingma
3. Yoshua Bengio
4. Alex Graves
5. Ruslan Salakhutdinov
6. Ilya Sutskever
7. Alex Krizhevsky
8. Yann LeCun
9. Radford M. Neal
10. Sepp Hochreiter

Who trusts Xingjian Shi overall

1. Hao Wang
2. Dit-Yan Yeung
3. Yunbo Wang
4. Wenguan Wang
5. Jindong Jiang
6. Mengye Ren
7. Chelsea Finn
8. Kevin Trebing
9. Sergey Levine
10. Jiafei Duan

Papers from Xingjian Shi's perspective

Ordered by how strongly the researchers Xingjian Shi 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. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Long Short-Term Memory
5. Attention is All you Need (Neural Information Processing Systems)
6. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. ImageNet Large Scale Visual Recognition Challenge
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• GaAN: Gated Attention Networks for Learning on Large and Spatiotemporal Graphs (2018) — 641 citations
• Towards Geospatial Foundation Models via Continual Pretraining (2023) — 156 citations
• Earthformer: Exploring Space-Time Transformers for Earth System Forecasting (2022) — 356 citations
• Deep Learning for Precipitation Nowcasting: A Benchmark and A New Model (2017) — 1,027 citations
• Relational Deep Learning: A Deep Latent Variable Model for Link Prediction (2017) — 63 citations
• Collaborative Recurrent Autoencoder: Recommend while Learning to Fill in the Blanks (2016) — 107 citations
• Dynamic Key-Value Memory Networks for Knowledge Tracing (2016) — 863 citations
• Natural-Parameter Networks: A Class of Probabilistic Neural Networks (2016) — 89 citations
• Relational Stacked Denoising Autoencoder for Tag Recommendation (2015) — 145 citations
• Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting (2015) — 9,818 citations