Simo Särkkä

Artificial Intelligence · 2007–2019

A researcher in the Science corpus: 8 papers; trusts 70 researchers and is trusted by 50 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 66 citations into it; #3,435 of 35,345 by trust here, #9,465 by citations
• Probabilistic & Bayesian Methods — 32 citations into it; #443 of 1,525 by trust here, #878 by citations
• Statistics and Probability — 26 citations into it
• Aerospace Engineering — 22 citations into it; #1,178 of 6,421 by trust here, #5,172 by citations
• Control and Systems Engineering — 22 citations into it; #1,537 of 8,257 by trust here, #5,227 by citations
• Statistical and Nonlinear Physics — 17 citations into it; #1,562 of 10,127 by trust here, #3,010 by citations
• Finance — 16 citations into it
• Mathematical Physics — 11 citations into it

Whom Simo Särkkä trusts overall

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

1. Radford M. Neal
2. Bernt Øksendal
3. Arnaud Doucet
4. Simon Justin Julier
5. Haikady N. Nagaraja
6. Christophe Andrieu
7. Rudolph van der Merwe
8. Ienkaran Arasaratnam
9. Neil J. Gordon
10. Jeffrey K. Uhlmann

Who trusts Simo Särkkä overall

1. Bryan Lim
2. Wagner Hugo Bonat
3. Stefano Peluchetti
4. Philippe Moireau
5. Stefan Zohren
6. Arno Solin
7. Arash Vahdat
8. Ienkaran Arasaratnam
9. A. Kamariotis
10. Maizar Raissi

Papers from Simo Särkkä's perspective

Ordered by how strongly the researchers Simo Särkkä trusts endorse each paper. Same corpus, a different order for every researcher.

1. ImageNet classification with deep convolutional neural networks
2. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
3. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
4. Learning Multiple Layers of Features from Tiny Images
5. Attention is All you Need (Neural Information Processing Systems)
6. Gradient-based learning applied to document recognition
7. Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper
8. Long Short-Term Memory
9. ImageNet: A large-scale hierarchical image database
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• Bayesian Filtering and Smoothing (2013) — 1,934 citations
• Applied Stochastic Differential Equations (2019) — 553 citations
• Spatiotemporal Learning via Infinite-Dimensional Bayesian Filtering and Smoothing: A Look at Gaussian Process Regression Through Kalman Filtering (2013) — 279 citations
• Advances in Neural Information Processing Systems 25 (NIPS 2012) (2012) — 14 citations
• Kalman filtering and smoothing solutions to temporal Gaussian process regression models (2010) — 304 citations
• Recursive Noise Adaptive Kalman Filtering by Variational Bayesian Approximations (2009) — 644 citations
• Rao-Blackwellized particle filter for multiple target tracking (2007) — 318 citations
• On Unscented Kalman Filtering for State Estimation of Continuous-Time Nonlinear Systems (2007) — 460 citations