Qingsong Wen

Artificial Intelligence · International Joint Conference on Artificial Intelligence · 2020–2023

A researcher in the Science corpus: 6 papers; trusts 73 researchers and is trusted by 3 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 213 citations into it; #34,753 of 35,345 by trust here, #10,667 by citations
• Signal Processing — 129 citations into it; #5,805 of 7,572 by trust here, #1,241 by citations
• Computer Vision and Pattern Recognition — 71 citations into it
• Model Architectures — 66 citations into it
• Management Science and Operations Research — 53 citations into it; #9,441 of 11,946 by trust here, #2,667 by citations
• Computer Networks and Communications — 33 citations into it
• Time-Series & Forecasting — 27 citations into it
• Electrical and Electronic Engineering — 23 citations into it

Whom Qingsong Wen trusts overall

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

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

Who trusts Qingsong Wen overall

1. Tian Zhou
2. Donghao Luo
3. Azal Ahmad Khan
4. Guillermo Alonso Iglesias
5. Mononito Goswami
6. Xue Wang
7. Zhijian Xu
8. Abhimanyu Das
9. Yuqi Nie
10. Ziqing Ma

Papers from Qingsong Wen's perspective

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

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. ImageNet classification with deep convolutional neural networks
3. Attention is All you Need (Neural Information Processing Systems)
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. ImageNet: A large-scale hierarchical image database
8. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
9. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

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
• Transformers in Time Series: A Survey (2022) — 1,549 citations
• DCdetector: Dual Attention Contrastive Representation Learning for Time Series Anomaly Detection (2023) — 376 citations
• A Survey on Graph Neural Networks for Time Series: Forecasting, Classification, Imputation, and Anomaly Detection (2023) — 498 citations
• Time Series Data Augmentation for Deep Learning: A Survey (2020) — 844 citations