Moustafa Farid Alzantot
Artificial Intelligence · 2017–2019
A researcher in the Science corpus: 5 papers; trusts 99 researchers and is trusted by 7 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 86 citations into it; #16,373 of 35,345 by trust here, #6,193 by citations
- Safety, Robustness & Interpretability — 57 citations into it; #1,868 of 2,251 by trust here, #593 by citations
- Computer Vision and Pattern Recognition — 50 citations into it; #17,684 of 21,575 by trust here, #17,757 by citations
- Natural Language Processing — 21 citations into it; #4,814 of 5,688 by trust here, #2,082 by citations
- Generative Models — 11 citations into it
- Model Architectures — 11 citations into it
- Biophysics — 7 citations into it
- Representation & Self-Supervised Learning — 7 citations into it
Whom Moustafa Farid Alzantot trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Geoffrey E. Hinton
- Yoshua Bengio
- Sepp Hochreiter
- Ian J. Goodfellow
- Diederik P. Kingma
- Alex Krizhevsky
- Alex Graves
- J. Urgen Schmidhuber
- Yann LeCun
- Ilya Sutskever
Who trusts Moustafa Farid Alzantot overall
- Linyang Li
- Sari Sabban
- Yonatan Belinkov
- Di Jin
- Zonghan Yang
- Alun David Preece
- Avishek Joey Bose
- Xipeng Qiu
- Markus Bayer
- Noam Yefet
Papers from Moustafa Farid Alzantot's perspective
Ordered by how strongly the researchers Moustafa Farid Alzantot 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)
- Long Short-Term Memory
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Attention is All you Need (Neural Information Processing Systems)
- Gradient-based learning applied to document recognition
- 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)
- ImageNet Large Scale Visual Recognition Challenge
Their papers
- Generating Natural Language Adversarial Examples (2018) — 1,038 citations
- Interpretability of deep learning models: A survey of results (2017) — 469 citations
- NeuronInspect: Detecting Backdoors in Neural Networks via Output Explanations (2019) — 122 citations
- Deep learning for situational understanding (2017) — 15 citations
- SenseGen: A deep learning architecture for synthetic sensor data generation (2017) — 143 citations