Jiawei Su
Artificial Intelligence · 2017–2019
A researcher in the Science corpus: 2 papers; trusts 86 researchers and is trusted by 26 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 64 citations into it; #7,734 of 35,345 by trust here, #7,112 by citations
- Safety, Robustness & Interpretability — 59 citations into it; #596 of 2,251 by trust here, #625 by citations
- Computer Vision and Pattern Recognition — 28 citations into it; #6,104 of 21,575 by trust here, #5,236 by citations
- Signal Processing — 16 citations into it
- Computer Networks and Communications — 8 citations into it
- Computational Theory and Mathematics — 7 citations into it
Whom Jiawei Su trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Nikolaus Hansen
- Alex Krizhevsky
- Karen Simonyan
- Seyed-Mohsen Moosavi-Dezfooli
- Nicolas Papernot
- Nicholas Carlini
- Geoffrey E. Hinton
- Andrew Zisserman
- Ian J. Goodfellow
- Christian Szegedy
Who trusts Jiawei Su overall
- Elvis Dohmatob
- Danilo Vasconcellos Vargas
- Francesco Croce
- Yuhang Zhou
- Shashank Kotyan
- Fatima Hussain
- Matthias Hein
- A. Serban
- Anurag Arnab
- Vishaal Munusamy Kabilan
Papers from Jiawei Su's perspective
Ordered by how strongly the researchers Jiawei 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
- Learning Multiple Layers of Features from Tiny Images
- ImageNet: A large-scale hierarchical image database
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- Attention is All you Need (Neural Information Processing Systems)
- Going deeper with convolutions
Their papers
- One Pixel Attack for Fooling Deep Neural Networks (2017) — 2,581 citations
- Understanding the One Pixel Attack: Propagation Maps and Locality Analysis (2019) — 40 citations