Roxana Geambasu
Artificial Intelligence · 2009–2018
A researcher in the Science corpus: 5 papers; trusts 114 researchers and is trusted by 1 researcher here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 173 citations into it; #34,736 of 35,345 by trust here, #8,525 by citations
- Safety, Robustness & Interpretability — 105 citations into it; #2,183 of 2,251 by trust here, #1,023 by citations
- Computer Vision and Pattern Recognition — 49 citations into it
- Computer Networks and Communications — 30 citations into it
- Signal Processing — 22 citations into it
- Privacy & Security — 20 citations into it; #696 of 709 by trust here, #292 by citations
- Statistics and Probability — 20 citations into it
- AI Ethics, Fairness & Society — 19 citations into it
Whom Roxana Geambasu trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Geoffrey E. Hinton
- Alex Krizhevsky
- Ian J. Goodfellow
- Christian Szegedy
- Diederik P. Kingma
- William W. Pugh
- Yann LeCun
- Yoshua Bengio
- Ross B. Girshick
- Kaiming He
Who trusts Roxana Geambasu overall
Papers from Roxana Geambasu's perspective
Ordered by how strongly the researchers Roxana Geambasu 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)
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- ImageNet: A large-scale hierarchical image database
- Gradient-based learning applied to document recognition
- Long Short-Term Memory
- Learning Multiple Layers of Features from Tiny Images
- Attention is All you Need (Neural Information Processing Systems)
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
Their papers
- Certified Robustness to Adversarial Examples with Differential Privacy (2018) — 1,063 citations
- FairTest: Discovering Unwarranted Associations in Data-Driven Applications (2015) — 191 citations
- On the Connection between Differential Privacy and Adversarial Robustness in Machine Learning (2018) — 20 citations
- Discovering Unwarranted Associations in Data-Driven Applications with the FairTest Testing Toolkit (2015) — 36 citations
- Vanish: Increasing Data Privacy with Self-Destructing Data (2009) — 454 citations