Felix Sattler

Artificial Intelligence · 2018–2019

A researcher in the Science corpus: 3 papers; trusts 74 researchers and is trusted by 18 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 85 citations into it; #9,704 of 35,345 by trust here, #7,531 by citations
• Optimization & Training — 47 citations into it
• Computer Vision and Pattern Recognition — 37 citations into it; #11,812 of 21,575 by trust here, #15,162 by citations
• Federated & Distributed Learning — 24 citations into it
• Efficiency & Compression — 22 citations into it
• Computer Networks and Communications — 10 citations into it
• ML Systems & Hardware — 9 citations into it
• Model Architectures — 9 citations into it

Whom Felix Sattler trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Yann LeCun
2. Geoffrey E. Hinton
3. Alex Krizhevsky
4. Diederik P. Kingma
5. Kaiming He
6. Solomon W. Golomb
7. Ilya Sutskever
8. Karen Simonyan
9. Sergey Ioffe
10. H. Brendan McMahan

Who trusts Felix Sattler overall

1. Mohammad Mohammadi Amiri
2. Deniz Gündüz
3. Hangyu Zhu
4. Dinh C. Nguyen
5. Guodong Long
6. Avishek Ghosh
7. Mang Ye
8. Christopher Briggs
9. Jianyu Wang
10. Simon Wiedemann

Papers from Felix Sattler's perspective

Ordered by how strongly the researchers Felix Sattler trusts endorse each paper. Same corpus, a different order for every researcher.

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. ImageNet classification with deep convolutional neural networks
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Gradient-based learning applied to document recognition
5. Long Short-Term Memory
6. ImageNet: A large-scale hierarchical image database
7. Attention is All you Need (Neural Information Processing Systems)
8. Learning Multiple Layers of Features from Tiny Images
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Clustered Federated Learning: Model-Agnostic Distributed Multitask Optimization Under Privacy Constraints (2019) — 1,446 citations
• Robust and Communication-Efficient Federated Learning From Non-i.i.d. Data (2019) — 1,679 citations
• Sparse Binary Compression: Towards Distributed Deep Learning with minimal Communication (2018) — 243 citations