Qi Lei

Artificial Intelligence · 2016–2020

A researcher in the Science corpus: 5 papers; trusts 42 researchers and is trusted by 15 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 54 citations into it; #19,982 of 35,345 by trust here, #11,490 by citations
• Computer Vision and Pattern Recognition — 27 citations into it; #8,526 of 21,575 by trust here, #7,938 by citations
• Computational Mechanics — 17 citations into it
• Safety, Robustness & Interpretability — 11 citations into it
• Hardware and Architecture — 6 citations into it
• Generative Models — 5 citations into it
• Statistical Learning Theory — 5 citations into it
• Statistical and Nonlinear Physics — 5 citations into it

Whom Qi Lei trusts overall

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

1. Paul Hand
2. Individualized Treat
3. Viraj Shah
4. Yoshua Bengio
5. Geoffrey E. Hinton
6. Alex Krizhevsky
7. Lisandro Dalcin
8. Wen Huang
9. Ian J. Goodfellow
10. Vladislav Voroninski

Who trusts Qi Lei overall

1. Deepesh Data
2. Zachary B. Charles
3. Sanghamitra Dutta
4. Aviad Aberdam
5. Amirhossein Reisizadeh
6. Songze Li
7. Suhas N. Diggavi
8. Can Bülent Karakus
9. Qian Yu
10. Dimitris Papailiopoulos

Papers from Qi Lei's perspective

Ordered by how strongly the researchers Qi Lei 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. Gradient-based learning applied to document recognition
5. Long Short-Term Memory
6. ImageNet: A large-scale hierarchical image database
7. Attention is All you Need (Neural Information Processing Systems)
8. GENERATIVE ADVERSARIAL NETS
9. Learning Multiple Layers of Features from Tiny Images
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Hessian-based Analysis of Large Batch Training and Robustness to Adversaries (2018) — 194 citations
• Few-Shot Learning via Learning the Representation, Provably (2020) — 298 citations
• Inverting Deep Generative models, One layer at a time (2019) — 58 citations
• Gradient Coding: Avoiding Stragglers in Distributed Learning (2017) — 541 citations
• Gradient Coding (2016) — 78 citations