Lifang He

Artificial Intelligence · 2015–2024

A researcher in the Science corpus: 12 papers; trusts 259 researchers and is trusted by 5 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 348 citations into it; #28,133 of 35,345 by trust here, #14,718 by citations
• Computer Vision and Pattern Recognition — 174 citations into it; #17,235 of 21,575 by trust here, #17,258 by citations
• Natural Language Processing — 155 citations into it
• Model Architectures — 58 citations into it
• Representation & Self-Supervised Learning — 48 citations into it
• Information Systems — 40 citations into it
• Large Language Models — 37 citations into it
• Signal Processing — 27 citations into it

Whom Lifang He trusts overall

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

1. Geoffrey E. Hinton
2. Yoshua Bengio
3. Kaiming He
4. Diederik P. Kingma
5. Alex Krizhevsky
6. Thorsten Joachims
7. Yann LeCun
8. Ilya Sutskever
9. Richard Socher
10. Yu Zheng

Who trusts Lifang He overall

1. Senzhang Wang
2. Jie Wen
3. Yijie Lin
4. Xi Zhang

Papers from Lifang He's perspective

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

Their papers

• A generalist vision–language foundation model for diverse biomedical tasks (2023) — 401 citations
• Sora: A Review on Background, Technology, Limitations, and Opportunities of Large Vision Models (2024) — 683 citations
• TrustLLM: Trustworthiness in Large Language Models (2024) — 353 citations
• A Comprehensive Survey on Pretrained Foundation Models: A History from BERT to ChatGPT (2023) — 736 citations
• Multi-level Feature Learning for Contrastive Multi-view Clustering (2021) — 355 citations
• Federated Multi-view Learning for Private Medical Data Integration and Analysis (2021) — 50 citations
• A Survey on Text Classification: From Shallow to Deep Learning (2020) — 173 citations
• A Survey on Text Classification: From Traditional to Deep Learning (2020) — 549 citations
• Computing Urban Traffic Congestions by Incorporating Sparse GPS Probe Data and Social Media Data (2017) — 78 citations
• Estimating Urban Traffic Congestions with Multi-sourced Data (2016) — 38 citations
• Multiple Incomplete Views Clustering via Weighted Nonnegative Matrix Factorization with L2, 1 Regularization (2015) — 311 citations
• Citywide traffic congestion estimation with social media (2015) — 72 citations