Liang Sun

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

A researcher in the Science corpus: 6 papers; trusts 89 researchers and is trusted by 3 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 161 citations into it; #34,754 of 35,345 by trust here, #10,798 by citations
• Signal Processing — 107 citations into it; #5,804 of 7,572 by trust here, #1,120 by citations
• Model Architectures — 66 citations into it
• Computer Vision and Pattern Recognition — 64 citations into it
• Management Science and Operations Research — 53 citations into it; #9,438 of 11,946 by trust here, #2,185 by citations
• Time-Series & Forecasting — 28 citations into it
• Computer Networks and Communications — 24 citations into it
• Electrical and Electronic Engineering — 19 citations into it

Whom Liang Sun 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. Jimmy Ba
10. Alex Graves

Who trusts Liang Sun overall

1. Azal Ahmad Khan
2. Tian Zhou

Papers from Liang Sun's perspective

Ordered by how strongly the researchers Liang Sun 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. Attention is All you Need (Neural Information Processing Systems)
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. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. Learning Multiple Layers of Features from Tiny Images

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
• Transformers in Time Series: A Survey (2022) — 1,549 citations
• DCdetector: Dual Attention Contrastive Representation Learning for Time Series Anomaly Detection (2023) — 376 citations
• Time Series Data Augmentation for Deep Learning: A Survey (2020) — 844 citations