Amir Houmansadr
Artificial Intelligence · 2018–2021
A researcher in the Science corpus: 7 papers; trusts 330 researchers and is trusted by 40 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 206 citations into it; #5,188 of 35,345 by trust here, #6,103 by citations
- Safety, Robustness & Interpretability — 99 citations into it; #415 of 2,251 by trust here, #586 by citations
- Privacy & Security — 62 citations into it; #452 of 709 by trust here, #484 by citations
- Federated & Distributed Learning — 52 citations into it; #170 of 319 by trust here, #190 by citations
- Computer Vision and Pattern Recognition — 45 citations into it; #7,021 of 21,575 by trust here, #9,435 by citations
- Optimization & Training — 26 citations into it
- Computer Networks and Communications — 22 citations into it
- Statistics and Probability — 16 citations into it
Whom Amir Houmansadr trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Alex Krizhevsky
- Geoffrey E. Hinton
- Yann LeCun
- H. Brendan McMahan
- Cynthia Dwork
- Karen Simonyan
- Ilya Sutskever
- Kaiming He
- Yoshua Bengio
- Diederik P. Kingma
Who trusts Amir Houmansadr overall
- Liwei Song
- Reza Shokri
- Lingjuan Lyu
- Stephanie L. Hyland
- Nicholas Carlini
- Li Li
- Prateek Mittal
- Ahmed Salem
- Maria Rigaki
- Fan Mo
Papers from Amir Houmansadr's perspective
Ordered by how strongly the researchers Amir Houmansadr 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
- ImageNet: A large-scale hierarchical image database
- Learning Multiple Layers of Features from Tiny Images
- Attention is All you Need (Neural Information Processing Systems)
- 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
- Comprehensive Privacy Analysis of Deep Learning: Passive and Active White-box Inference Attacks against Centralized and Federated Learning (2018) — 1,858 citations
- Machine Learning with Membership Privacy using Adversarial Regularization (2018) — 554 citations
- Manipulating the Byzantine: Optimizing Model Poisoning Attacks and Defenses for Federated Learning (2021) — 684 citations
- Back to the Drawing Board: A Critical Evaluation of Poisoning Attacks on Production Federated Learning (2021) — 321 citations
- Improving Deep Learning with Differential Privacy using Gradient Encoding and Denoising (2020) — 47 citations
- Cronus: Robust and Heterogeneous Collaborative Learning with Black-Box Knowledge Transfer (2019) — 209 citations
- Comprehensive Privacy Analysis of Deep Learning: Stand-alone and Federated Learning under Passive and Active White-box Inference Attacks (2018) — 293 citations