Eva Cernadas

Artificial Intelligence · 2014–2019

A researcher in the Science corpus: 2 papers; trusts 29 researchers and is trusted by 7 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 37 citations into it
• Statistics and Probability — 34 citations into it
• Computer Vision and Pattern Recognition — 17 citations into it
• Statistical Learning Theory — 12 citations into it
• Evaluation & Benchmarks — 8 citations into it
• Molecular Biology — 7 citations into it
• Information Systems — 5 citations into it; #3,210 of 8,771 by trust here, #895 by citations
• Optimization & Training — 5 citations into it

Whom Eva Cernadas trusts overall

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

1. Jerome H. Friedman
2. Leo Breiman
3. Max Kuhn
4. J. Ross Quinlan
5. Robert Tibshirani
6. Eric R. Ziegel
7. Trevor J. Hastie
8. Nicolai Meinshausen
9. Eibe Frank
10. Yoav Freund

Who trusts Eva Cernadas overall

1. Momin M. Malik
2. Laura Gustafson
3. Chintan Parmar
4. Le Zhang
5. Lucas K. Mentch
6. Andreas Henelius
7. Philipp Probst
8. Louis J. M. Aslett
9. Jan Philip Göpfert
10. Günter Klambauer

Papers from Eva Cernadas's perspective

Ordered by how strongly the researchers Eva Cernadas 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. Gradient-based learning applied to document recognition
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Long Short-Term Memory
6. Attention is All you Need (Neural Information Processing Systems)
7. ImageNet: A large-scale hierarchical image database
8. Bagging Predictors
9. Learning Multiple Layers of Features from Tiny Images
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• An extensive experimental survey of regression methods (2019) — 317 citations
• Do we need hundreds of classifiers to solve real world classification problems? (2014) — 3,241 citations