Kai Olav Ellefsen

Artificial Intelligence · 2013–2018

A researcher in the Science corpus: 5 papers; trusts 24 researchers and is trusted by 7 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 27 citations into it; #14,252 of 35,345 by trust here, #12,387 by citations
• Molecular Biology — 15 citations into it
• Cognitive Neuroscience — 14 citations into it; #3,368 of 10,852 by trust here, #6,360 by citations
• Sociology and Political Science — 12 citations into it
• Cellular and Molecular Neuroscience — 11 citations into it
• Genetics — 10 citations into it
• Electrical and Electronic Engineering — 6 citations into it
• Mechanical Engineering — 6 citations into it

Whom Kai Olav Ellefsen trusts overall

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

1. R. M. French
2. Kenneth O. Stanley
3. Janet F. Werker
4. John A. Bullinaria
5. Andrea Soltoggio
6. Eric I. Knudsen
7. Santo Fortunato
8. Risto Miikkulainen
9. R. C. Tees
10. Dario Floreano

Who trusts Kai Olav Ellefsen overall

1. Jeff Clune
2. Andrea Soltoggio
3. Kenneth O. Stanley
4. Roby Velez
5. Benno Lüders
6. Sebastian Risi
7. Mikkel Schläger
8. Giacomo Spigler
9. Joel Lehman
10. Olaf Sporns

Papers from Kai Olav Ellefsen's perspective

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

Their papers

• The Surprising Creativity of Digital Evolution: A Collection of Anecdotes from the Evolutionary Computation and Artificial Life Research Communities (2018) — 317 citations
• Neural Modularity Helps Organisms Evolve to Learn New Skills without Forgetting Old Skills (2015) — 177 citations
• The Evolution of Learning Under Environmental Variability (2014) — 5 citations
• Evolved Sensitive Periods in Learning (2013) — 5 citations
• Different regulation mechanisms for evolved critical periods in learning (2013)