Matthias Poloczek

Artificial Intelligence · 2016–2019

A researcher in the Science corpus: 3 papers; trusts 24 researchers and is trusted by 7 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 33 citations into it; #15,518 of 35,345 by trust here, #20,836 by citations
• Computational Theory and Mathematics — 26 citations into it; #4,921 of 12,182 by trust here, #5,046 by citations
• Management Science and Operations Research — 22 citations into it; #4,494 of 11,946 by trust here, #5,092 by citations
• Optimization & Training — 12 citations into it
• Statistics and Probability — 12 citations into it
• Mathematical Optimization — 8 citations into it
• Probabilistic & Bayesian Methods — 8 citations into it

Whom Matthias Poloczek trusts overall

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

1. Peter I. Frazier
2. Kevin Swersky
3. Jasper Snoek
4. Deng Huang
5. Philipp Hennig
6. Kirthevasan Kandasamy
7. Marc C. Kennedy
8. José Miguel Hernández-Lobato
9. Yee Whye Teh
10. Geoffrey E. Hinton

Who trusts Matthias Poloczek overall

1. Stefan Falkner
2. Kirthevasan Kandasamy
3. Aaron Klein
4. Peter I. Frazier
5. Frank Hutter
6. Maximilian Balandat
7. Jialei Wang
8. Gautam Dasarathy
9. Valerio Perrone
10. Brian Karrer

Papers from Matthias Poloczek's perspective

Ordered by how strongly the researchers Matthias Poloczek 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. Learning Multiple Layers of Features from Tiny Images
6. Long Short-Term Memory
7. ImageNet: A large-scale hierarchical image database
8. Attention is All you Need (Neural Information Processing Systems)
9. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• Scalable Global Optimization via Local Bayesian Optimization (2019) — 715 citations
• Warm starting Bayesian optimization (2016) — 74 citations
• Multi-Information Source Optimization (2016) — 230 citations