Steindór Sæmundsson

Artificial Intelligence · 2018

A researcher in the Science corpus: 1 paper; trusts 24 researchers and is trusted by 5 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 17 citations into it; #17,423 of 35,345 by trust here, #19,018 by citations
• Reinforcement Learning — 13 citations into it; #1,285 of 2,242 by trust here, #1,609 by citations
• Control and Systems Engineering — 6 citations into it; #3,836 of 8,257 by trust here, #2,890 by citations
• Probabilistic & Bayesian Methods — 5 citations into it
• Representation & Self-Supervised Learning — 5 citations into it

Whom Steindór Sæmundsson trusts overall

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

1. Marc Peter Deisenroth
2. Michalis K. Titsias
3. Neil D. Lawrence
4. Diederik P. Kingma
5. Jeff Schneider
6. Richard S. Sutton
7. Finale Doshi-Velez
8. Carl E. Rasmussen
9. George Dimitri Konidaris
10. Chelsea Finn

Who trusts Steindór Sæmundsson overall

1. Chelsea Finn
2. Tianhe Yu
3. Sergey Levine
4. Lantao Yu
5. Glen Berseth
6. Eric Mitchell
7. Kate Rakelly
8. Anusha Nagabandi
9. Mengdi Xu
10. Charles Packer

Papers from Steindór Sæmundsson's perspective

Ordered by how strongly the researchers Steindór Sæmundsson 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. Learning Multiple Layers of Features from Tiny Images
7. Attention is All you Need (Neural Information Processing Systems)
8. ImageNet: A large-scale hierarchical image database
9. GENERATIVE ADVERSARIAL NETS
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Meta Reinforcement Learning with Latent Variable Gaussian Processes (2018) — 148 citations