Miaoqiang Su
Artificial Intelligence · 2016–2017
A researcher in the Science corpus: 2 papers; trusts 22 researchers and is trusted by 2 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 43 citations into it; #29,523 of 35,345 by trust here, #14,438 by citations
- Safety, Robustness & Interpretability — 33 citations into it; #1,862 of 2,251 by trust here, #1,162 by citations
- Signal Processing — 26 citations into it; #6,377 of 7,572 by trust here, #1,958 by citations
- Information Systems — 19 citations into it
- Computer Networks and Communications — 15 citations into it
- Computer Vision and Pattern Recognition — 15 citations into it
- Privacy & Security — 8 citations into it
Whom Miaoqiang Su trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Battista Biggio
- Alex Krizhevsky
- Geoffrey E. Hinton
- Nedim Srndic
- Blaine Nelson
- Ilya Sutskever
- Marco Barreno
- Yoshua Bengio
- Nicolas Papernot
- Giorgio Fumera
Who trusts Miaoqiang Su overall
Papers from Miaoqiang Su's perspective
Ordered by how strongly the researchers Miaoqiang Su trusts endorse each paper. Same corpus, a different order for every researcher.
- ImageNet classification with deep convolutional neural networks
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Gradient-based learning applied to document recognition
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Long Short-Term Memory
- Attention is All you Need (Neural Information Processing Systems)
- ImageNet: A large-scale hierarchical image database
- Learning Multiple Layers of Features from Tiny Images
- ImageNet Large Scale Visual Recognition Challenge
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
Their papers
- Deep Text Classification Can be Fooled (2017) — 452 citations
- Cracking Classifiers for Evasion: A Case Study on the Google's Phishing Pages Filter (2016) — 89 citations