Ingmar Schuster

Artificial Intelligence · 2015–2021

A researcher in the Science corpus: 3 papers; trusts 87 researchers and is trusted by 3 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 58 citations into it; #21,160 of 35,345 by trust here, #23,731 by citations
• Statistics and Probability — 35 citations into it; #7,728 of 12,932 by trust here, #12,077 by citations
• Probabilistic & Bayesian Methods — 33 citations into it
• Computer Vision and Pattern Recognition — 21 citations into it
• Generative Models — 17 citations into it
• Signal Processing — 13 citations into it
• Management Science and Operations Research — 9 citations into it
• Statistical and Nonlinear Physics — 9 citations into it

Whom Ingmar Schuster trusts overall

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

1. Radford M. Neal
2. Olivier Capp'e
3. Randal Douc
4. Nicolas Chopin
5. Mark A. Girolami
6. Arnaud Guillin
7. Arnaud Doucet
8. Diederik P. Kingma
9. Gareth O. Roberts
10. Matthew D. Hoffman

Who trusts Ingmar Schuster overall

1. Heiko Strathmann
2. Krikamol Muandet
3. Brooks Paige
4. Dino Sejdinovic
5. Kenji Fukumizu

Papers from Ingmar Schuster's perspective

Ordered by how strongly the researchers Ingmar Schuster 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. Attention is All you Need (Neural Information Processing Systems)
5. Long Short-Term Memory
6. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
8. Learning Multiple Layers of Features from Tiny Images
9. GENERATIVE ADVERSARIAL NETS
10. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)

Their papers

• Kernel Sequential Monte Carlo (2015) — 8 citations
• Autoregressive Denoising Diffusion Models for Multivariate Probabilistic Time Series Forecasting (2021) — 554 citations
• Gradient Importance Sampling (2015) — 28 citations