Maya Kabkab
Artificial Intelligence · 2018
A researcher in the Science corpus: 1 paper; trusts 31 researchers and is trusted by 26 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 18 citations into it; #8,386 of 35,345 by trust here, #7,945 by citations
- Safety, Robustness & Interpretability — 14 citations into it; #584 of 2,251 by trust here, #684 by citations
- Computer Vision and Pattern Recognition — 8 citations into it; #6,065 of 21,575 by trust here, #5,711 by citations
Whom Maya Kabkab trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Nicolas Papernot
- Ian J. Goodfellow
- Geoffrey E. Hinton
- Yann LeCun
- Christian Szegedy
- Patrick Drew McDaniel
- Alex Krizhevsky
- Yoshua Bengio
- Ilya Sutskever
- Individualized Treat
Who trusts Maya Kabkab overall
- Micah Goldblum
- Ali Shafahi
- David Stutz
- Lukas Schott
- Yang Song
- Mathias Lécuyer
- Yinpeng Dong
- Anirban Chakraborty
- Anish Athalye
- Vishaal Munusamy Kabilan
Papers from Maya Kabkab's perspective
Ordered by how strongly the researchers Maya Kabkab 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
- ImageNet: A large-scale hierarchical image database
- Attention is All you Need (Neural Information Processing Systems)
- Learning Multiple Layers of Features from Tiny Images
- GENERATIVE ADVERSARIAL NETS
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
Their papers
- Defense-GAN: Protecting Classifiers Against Adversarial Attacks Using Generative Models (2018) — 1,276 citations