Lei Ma

Artificial Intelligence · International Conference on Automated Software Engineering · 2017–2020

A researcher in the Science corpus: 6 papers; trusts 96 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 106 citations into it
• Computer Vision and Pattern Recognition — 97 citations into it
• Safety, Robustness & Interpretability — 72 citations into it
• Media Technology — 29 citations into it
• Signal Processing — 27 citations into it
• Generative Models — 13 citations into it
• Model Architectures — 8 citations into it

Whom Lei Ma trusts overall

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

1. Yann LeCun
2. Geoffrey E. Hinton
3. Alex Krizhevsky
4. Nicholas Carlini
5. Ian J. Goodfellow
6. Yoshua Bengio
7. Karen Simonyan
8. Kaiming He
9. Kexin Pei
10. Christian Szegedy

Who trusts Lei Ma overall

1. Jie M. Zhang
2. Xiaofei Xie
3. Matthew Sotoudeh
4. H. Yao
5. Jinhan Kim
6. Bo Li
7. Mark Harman
8. Swapan Talukdar
9. Yang Liu
10. Felix Juefei-Xu

Papers from Lei Ma's perspective

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

Their papers

• FakeSpotter: A Simple Baseline for Spotting AI-Synthesized Fake Faces (2019) — 60 citations
• Stealthy and Efficient Adversarial Attacks against Deep Reinforcement Learning (2020) — 149 citations
• DeepMutation: Mutation Testing of Deep Learning Systems (2018) — 401 citations
• DeepHunter: a coverage-guided fuzz testing framework for deep neural networks (2019) — 472 citations
• DeepGauge: Multi-Granularity Testing Criteria for Deep Learning Systems (2018) — 708 citations
• A review of supervised object-based land-cover image classification (2017) — 825 citations