B. Eskofier

Artificial Intelligence · 2017–2022

A researcher in the Science corpus: 2 papers; trusts 7 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 54 citations into it
• Computer Vision and Pattern Recognition — 16 citations into it
• Federated & Distributed Learning — 8 citations into it
• Optimization & Training — 8 citations into it
• Health Information Management — 7 citations into it
• Medical & Health AI — 5 citations into it

Whom B. Eskofier trusts overall

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

1. Dianbo Liu
2. Geoffrey E. Hinton
3. Radford M. Neal

Papers from B. Eskofier's perspective

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

1. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
2. ImageNet classification with deep convolutional neural networks
3. Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification
4. Improving neural networks by preventing co-adaptation of feature detectors
5. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
6. Gradient-based learning applied to document recognition
7. Pattern Recognition and Machine Learning
8. What is the best multi-stage architecture for object recognition?
9. Deep Learning with Differential Privacy
10. Attention is All you Need (Neural Information Processing Systems)

Their papers

• Activity recognition in beach volleyball using a Deep Convolutional Neural Network (2017) — 151 citations
• Federated Learning for Healthcare: Systematic Review and Architecture Proposal (2022) — 598 citations