Xue Wang

Artificial Intelligence · Neural Information Processing Systems · 2020–2023

A researcher in the Science corpus: 4 papers; trusts 45 researchers and is trusted by 3 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 94 citations into it; #34,757 of 35,345 by trust here, #13,353 by citations
• Signal Processing — 64 citations into it; #5,850 of 7,572 by trust here, #1,433 by citations
• Model Architectures — 49 citations into it
• Management Science and Operations Research — 38 citations into it; #9,436 of 11,946 by trust here, #2,185 by citations
• Computer Vision and Pattern Recognition — 37 citations into it
• Time-Series & Forecasting — 21 citations into it
• Electrical and Electronic Engineering — 14 citations into it
• Natural Language Processing — 12 citations into it

Whom Xue Wang trusts overall

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

1. Diederik P. Kingma
2. Yoshua Bengio
3. Geoffrey E. Hinton
4. Sepp Hochreiter
5. Alex Krizhevsky
6. Ilya Sutskever
7. Yann LeCun
8. Jimmy Ba
9. J. Urgen Schmidhuber
10. Kaiming He

Who trusts Xue Wang overall

1. Zhijian Xu
2. Nate Gruver
3. Abhimanyu Das

Papers from Xue Wang's perspective

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

1. ImageNet classification with deep convolutional neural networks
2. Attention is All you Need (Neural Information Processing Systems)
3. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Long Short-Term Memory
6. Gradient-based learning applied to document recognition
7. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
8. ImageNet: A large-scale hierarchical image database
9. Learning Multiple Layers of Features from Tiny Images
10. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)

Their papers

• FEDformer: Frequency Enhanced Decomposed Transformer for Long-term Series Forecasting (2022) — 3,326 citations
• FiLM: Frequency improved Legendre Memory Model for Long-term Time Series Forecasting (2022) — 365 citations
• One Fits All: Power General Time Series Analysis by Pretrained LM (2023) — 1,009 citations
• Time Series Data Augmentation for Deep Learning: A Survey (2020) — 844 citations