David Eriksson

Artificial Intelligence · with Turner · 2017–2021

A researcher in the Science corpus: 4 papers; trusts 25 researchers and is trusted by 1 researcher here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 63 citations into it; #29,287 of 35,345 by trust here, #20,836 by citations
• Computational Theory and Mathematics — 40 citations into it; #10,064 of 12,182 by trust here, #6,463 by citations
• Management Science and Operations Research — 16 citations into it; #10,925 of 11,946 by trust here, #9,383 by citations
• Probabilistic & Bayesian Methods — 16 citations into it
• Computer Vision and Pattern Recognition — 14 citations into it
• Optimization & Training — 14 citations into it; #2,530 of 2,601 by trust here, #2,259 by citations
• Statistics and Probability — 11 citations into it
• Numerical Methods & Scientific Computing — 10 citations into it

Whom David Eriksson trusts overall

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

1. M. Powell
2. Peter I. Frazier
3. Jasper Snoek
4. Kirthevasan Kandasamy
5. Geoffrey E. Hinton
6. Frank Hutter
7. Andrew Gordon Wilson
8. Rommel G. Regis
9. Yoshua Bengio
10. Alex Krizhevsky

Who trusts David Eriksson overall

1. Shuhei Watanabe
2. Marius Thomas Lindauer
3. Bernd Bischl
4. Ryan D. Turner
5. Jacob V. Gardner
6. Insu Han
7. Katharina Eggensperger

Papers from David Eriksson's perspective

Ordered by how strongly the researchers David Eriksson 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. 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. Going deeper with convolutions
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• Scalable Log Determinants for Gaussian Process Kernel Learning (2017) — 109 citations
• Scalable Global Optimization via Local Bayesian Optimization (2019) — 715 citations
• Bayesian Optimization is Superior to Random Search for Machine Learning Hyperparameter Tuning: Analysis of the Black-Box Optimization Challenge 2020 (2021) — 404 citations
• Latency-Aware Neural Architecture Search with Multi-Objective Bayesian Optimization (2021) — 21 citations