Chenghao Liu

Artificial Intelligence · 2017–2024

A researcher in the Science corpus: 8 papers; trusts 714 researchers and is trusted by 12 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 221 citations into it; #21,392 of 35,345 by trust here, #18,186 by citations
• Computer Vision and Pattern Recognition — 148 citations into it; #13,322 of 21,575 by trust here, #15,529 by citations
• Representation & Self-Supervised Learning — 82 citations into it
• Meta & Continual Learning — 69 citations into it; #413 of 1,041 by trust here, #568 by citations
• Signal Processing — 58 citations into it; #7,332 of 7,572 by trust here, #3,428 by citations
• Management Science and Operations Research — 44 citations into it
• Optimization & Training — 41 citations into it; #2,206 of 2,601 by trust here, #2,313 by citations
• Model Architectures — 37 citations into it

Whom Chenghao Liu trusts overall

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

1. Alex Krizhevsky
2. Geoffrey E. Hinton
3. Kaiming He
4. Diederik P. Kingma
5. Yoshua Bengio
6. Yann LeCun
7. Marc'Aurelio Ranzato
8. Ilya Sutskever
9. Jimmy Ba
10. Oriol Vinyals

Who trusts Chenghao Liu overall

1. Quang Pham
2. Jorge Armando Mendez Mendez
3. Zifeng Wang
4. Da-Wei Zhou
5. Liyuan Wang
6. Eric Eaton
7. Li Xu
8. Haiyan Yin
9. Steven C. H. Hoi
10. Zizhao Zhang

Papers from Chenghao Liu's perspective

Ordered by how strongly the researchers Chenghao Liu 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. 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. 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

• ETSformer: Exponential Smoothing Transformers for Time-series Forecasting (2022) — 321 citations
• CoST: Contrastive Learning of Disentangled Seasonal-Trend Representations for Time Series Forecasting (2022) — 483 citations
• Unified Training of Universal Time Series Forecasting Transformers (2024) — 656 citations
• Continual Normalization: Rethinking Batch Normalization for Online Continual Learning (2022) — 76 citations
• Continual Learning, Fast and Slow (2021) — 57 citations
• Contextual Transformation Networks for Online Continual Learning (2021) — 61 citations
• DualNet: Continual Learning, Fast and Slow Supplementary Materials (2021)
• Malicious URL Detection using Machine Learning: A Survey (2017) — 405 citations