Lei Chen

Artificial Intelligence · with Bruna · 2019–2020

A researcher in the Science corpus: 2 papers; trusts 26 researchers and is trusted by 4 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 54 citations into it; #23,036 of 35,345 by trust here, #14,718 by citations
• Graph Learning — 44 citations into it
• Model Architectures — 17 citations into it
• Molecular Biology — 16 citations into it
• Statistical and Nonlinear Physics — 15 citations into it
• Representation & Self-Supervised Learning — 9 citations into it
• Optimization & Training — 6 citations into it

Whom Lei Chen trusts overall

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

1. Haggai Maron
2. Zhengdao Chen
3. Jin-Yi Cai
4. William L. Hamilton
5. Keyulu Xu
6. Sepp Hochreiter
7. Diederik P. Kingma
8. Geoffrey E. Hinton
9. Joan Bruna
10. Jure Leskovec

Who trusts Lei Chen overall

1. Xiyuan Wang
2. Vijay Prakash Dwivedi
3. Zhengdao Chen
4. Vikas K. Garg
5. Pan Li
6. Christopher Morris
7. Gabriele Corso

Papers from Lei Chen's perspective

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

1. ImageNet classification with deep convolutional neural networks
2. Long Short-Term Memory
3. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
6. Attention is All you Need (Neural Information Processing Systems)
7. Gradient-based learning applied to document recognition
8. ImageNet: A large-scale hierarchical image database
9. Learning Multiple Layers of Features from Tiny Images
10. GENERATIVE ADVERSARIAL NETS

Their papers

• On the equivalence between graph isomorphism testing and function approximation with GNNs (2019) — 311 citations
• Can graph neural networks count substructures? (2020) — 381 citations