Olga Fink

Artificial Intelligence · 2020–2022

A researcher in the Science corpus: 4 papers; trusts 250 researchers and is trusted by 2 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 118 citations into it
• Computer Vision and Pattern Recognition — 83 citations into it
• Representation & Self-Supervised Learning — 45 citations into it
• Control and Systems Engineering — 37 citations into it; #6,946 of 8,257 by trust here, #4,045 by citations
• Model Architectures — 22 citations into it
• Natural Language Processing — 20 citations into it
• Optimization & Training — 17 citations into it
• Mechanical Engineering — 16 citations into it

Whom Olga Fink trusts overall

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

1. Geoffrey E. Hinton
2. Alex Krizhevsky
3. Alex Graves
4. Yann LeCun
5. Yoshua Bengio
6. Ilya Sutskever
7. Diederik P. Kingma
8. Kaiming He
9. Radford M. Neal
10. J. Urgen Schmidhuber

Who trusts Olga Fink overall

1. Manuel Arias Chao
2. Arash Heidari
3. Lukas Hoyer

Papers from Olga Fink's perspective

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

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

Their papers

• Continual Test-Time Domain Adaptation (2022) — 754 citations
• Aircraft Engine Run-to-Failure Dataset under Real Flight Conditions for Prognostics and Diagnostics (2021) — 312 citations
• Fusing Physics-based and Deep Learning Models for Prognostics (2020) — 388 citations
• Potential, challenges and future directions for deep learning in prognostics and health management applications (2020) — 488 citations