Kathrin Grosse
Artificial Intelligence · 2016–2020
A researcher in the Science corpus: 6 papers; trusts 65 researchers and is trusted by 29 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 138 citations into it; #7,359 of 35,345 by trust here, #4,489 by citations
- Safety, Robustness & Interpretability — 84 citations into it; #568 of 2,251 by trust here, #388 by citations
- Computer Vision and Pattern Recognition — 42 citations into it
- Signal Processing — 41 citations into it; #1,591 of 7,572 by trust here, #459 by citations
- Computer Networks and Communications — 33 citations into it; #5,009 of 9,886 by trust here, #1,515 by citations
- Privacy & Security — 25 citations into it
- Information Systems — 18 citations into it
- Optimization & Training — 10 citations into it
Whom Kathrin Grosse trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Alex Krizhevsky
- Nicolas Papernot
- Geoffrey E. Hinton
- Yann LeCun
- Yoshua Bengio
- Christian Szegedy
- Ian J. Goodfellow
- Ilya Sutskever
- Kaiming He
- Patrick Drew McDaniel
Who trusts Kathrin Grosse overall
- Nicolas Papernot
- Nicholas Carlini
- Naveed Akhtar
- Florian Tramèr
- Pin-Yu Chen
- Tao Yu
- W. Zhang
- Patrick Drew McDaniel
- Nina Narodytska
- Weiwei Hu
Papers from Kathrin Grosse's perspective
Ordered by how strongly the researchers Kathrin Grosse 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)
- Gradient-based learning applied to document recognition
- 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
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
Their papers
- Adversarial Perturbations Against Deep Neural Networks for Malware Classification (2016) — 451 citations
- On the (Statistical) Detection of Adversarial Examples (2017) — 768 citations
- Adversarial Examples for Malware Detection (2017) — 589 citations
- On the security relevance of weights in deep learning. (2020)
- Adversarial Initialization - when your network performs the way I want (2019) — 8 citations
- MLCapsule: Guarded Offline Deployment of Machine Learning as a Service (2018) — 124 citations