Daliang Li

Artificial Intelligence · with Bhojanapalli · 2019–2021

A researcher in the Science corpus: 3 papers; trusts 57 researchers and is trusted by 23 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 88 citations into it; #7,857 of 35,345 by trust here, #12,244 by citations
• Computer Vision and Pattern Recognition — 45 citations into it
• Federated & Distributed Learning — 10 citations into it; #50 of 319 by trust here, #215 by citations
• Representation & Self-Supervised Learning — 10 citations into it
• Efficiency & Compression — 8 citations into it; #287 of 1,264 by trust here, #840 by citations
• Optimization & Training — 8 citations into it
• Meta & Continual Learning — 7 citations into it

Whom Daliang Li trusts overall

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

1. Alex Krizhevsky
2. Geoffrey E. Hinton
3. Luca Corinzia
4. Diederik P. Kingma
5. Yoshua Bengio
6. Oriol Vinyals
7. Yann LeCun
8. Mikhail Khodak
9. Kaiming He
10. Ilya Sutskever

Who trusts Daliang Li overall

1. Lichao Sun
2. Mang Ye
3. Hong-You Chen
4. Zhuangdi Zhu
5. Wenke Huang
6. Alysa Ziying Tan
7. Omar Abdel Wahab
8. Guodong Long
9. Yue Tan
10. Lingjuan Lyu

Papers from Daliang Li's perspective

Ordered by how strongly the researchers Daliang Li 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. Gradient-based learning applied to document recognition
4. Attention is All you Need (Neural Information Processing Systems)
5. Long Short-Term Memory
6. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
7. Learning Multiple Layers of Features from Tiny Images
8. ImageNet: A large-scale hierarchical image database
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)

Their papers

• Understanding Robustness of Transformers for Image Classification (2021) — 515 citations
• FedMD: Heterogenous Federated Learning via Model Distillation (2019) — 1,187 citations
• Modifying Memories in Transformer Models (2020) — 278 citations