Gabrielle Ras
Artificial Intelligence · 2018–2020
A researcher in the Science corpus: 2 papers; trusts 264 researchers and is trusted by 7 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 206 citations into it; #17,701 of 35,345 by trust here, #14,438 by citations
- Safety, Robustness & Interpretability — 146 citations into it; #1,172 of 2,251 by trust here, #1,512 by citations
- Computer Vision and Pattern Recognition — 98 citations into it
- Natural Language Processing — 31 citations into it
- Safety Research — 23 citations into it
- AI Ethics, Fairness & Society — 21 citations into it
- Signal Processing — 12 citations into it
- Biophysics — 11 citations into it
Whom Gabrielle Ras trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Geoffrey E. Hinton
- Yoshua Bengio
- Alex Krizhevsky
- Matthew D. Zeiler
- Karen Simonyan
- Yann LeCun
- Diederik P. Kingma
- Ilya Sutskever
- Kaiming He
- Christian Szegedy
Who trusts Gabrielle Ras overall
- Sarah Wiegreffe
- Amina Adadi
- Max Bucher
- Chiara Zucco
- Ana Marasovic
- Mohammed Berrada
- Yonatan Belinkov
- Carlos A. Zednik
- Stéphane Herbin
- Huizhi Liang
Papers from Gabrielle Ras's perspective
Ordered by how strongly the researchers Gabrielle Ras 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)
- Long Short-Term Memory
- Gradient-based learning applied to document recognition
- Attention is All you Need (Neural Information Processing Systems)
- ImageNet: A large-scale hierarchical image database
- ImageNet Large Scale Visual Recognition Challenge
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- Learning Multiple Layers of Features from Tiny Images
Their papers
- Explainable Deep Learning: A Field Guide for the Uninitiated (2020) — 452 citations
- Explanation Methods in Deep Learning: Users, Values, Concerns and Challenges (2018) — 245 citations