Sebastian Garcia
Artificial Intelligence · 2014–2020
A researcher in the Science corpus: 2 papers; trusts 147 researchers and is trusted by 0 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 85 citations into it
- Safety, Robustness & Interpretability — 35 citations into it
- Computer Vision and Pattern Recognition — 30 citations into it
- Privacy & Security — 15 citations into it
- Information Systems — 9 citations into it
- Signal Processing — 9 citations into it
- Optimization & Training — 8 citations into it
- Natural Language Processing — 6 citations into it
Whom Sebastian Garcia trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Diederik P. Kingma
- Alex Krizhevsky
- Geoffrey E. Hinton
- Yann LeCun
- Yoshua Bengio
- Vladimir Naumovich Vapnik
- Reza Shokri
- Kaiming He
- Ilya Sutskever
- Todd K. Moon
Who trusts Sebastian Garcia overall
- Ke He
- Mohamed Amine Ferrag
- Daniel S. Berman
- Zhibo Zhang
- Markus Ring
- Dan Dongseong Kim
- Leandros A. Maglaras
- Anna L. Buczak
- Hussam Al Hamadi
Papers from Sebastian Garcia's perspective
Ordered by how strongly the researchers Sebastian Garcia 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
- ImageNet: A large-scale hierarchical image database
- Long Short-Term Memory
- Attention is All you Need (Neural Information Processing Systems)
- 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
- A Survey of Privacy Attacks in Machine Learning (2020) — 345 citations
- An empirical comparison of botnet detection methods (2014) — 927 citations