Sebastian Schmitt

Artificial Intelligence · 2021–2022

A researcher in the Science corpus: 2 papers; trusts 38 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 91 citations into it
• Computational Theory and Mathematics — 57 citations into it
• Management Science and Operations Research — 30 citations into it
• Optimization & Training — 18 citations into it
• Probabilistic & Bayesian Methods — 13 citations into it
• Statistics and Probability — 11 citations into it
• Control and Systems Engineering — 5 citations into it

Whom Sebastian Schmitt trusts overall

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

1. Kirthevasan Kandasamy
2. Neil D. Lawrence
3. James Bergstra
4. Geoffrey E. Hinton
5. Jasper Snoek
6. Frank Hutter
7. Yoshua Bengio
8. José Miguel Hernández-Lobato
9. Alex Krizhevsky
10. Radford M. Neal

Papers from Sebastian Schmitt's perspective

Ordered by how strongly the researchers Sebastian Schmitt 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. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. Going deeper with convolutions

Their papers

• Recent Advances in Bayesian Optimization (2022) — 580 citations
• Quantum annealing for industry applications: introduction and review (2021) — 370 citations