Thomas Bo Schön

Computer Vision and Pattern Recognition · 2005–2023

A researcher in the Science corpus: 20 papers; trusts 1,068 researchers and is trusted by 51 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 257 citations into it; #6,258 of 21,575 by trust here, #6,873 by citations
• Artificial Intelligence — 213 citations into it; #6,451 of 35,345 by trust here, #7,657 by citations
• Probabilistic & Bayesian Methods — 124 citations into it; #1,490 of 1,525 by trust here, #1,274 by citations
• Aerospace Engineering — 65 citations into it; #5,473 of 6,421 by trust here, #2,921 by citations
• Reinforcement Learning — 43 citations into it; #758 of 2,242 by trust here, #1,227 by citations
• Statistics and Probability — 42 citations into it; #5,278 of 12,932 by trust here, #7,914 by citations
• Control and Systems Engineering — 39 citations into it; #3,144 of 8,257 by trust here, #2,528 by citations
• Optimization & Training — 37 citations into it

Whom Thomas Bo Schön trusts overall

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

1. Arnaud Doucet
2. Geoffrey E. Hinton
3. Alex Krizhevsky
4. Diederik P. Kingma
5. Yoshua Bengio
6. Kaiming He
7. Radford M. Neal
8. Yann LeCun
9. J. Urgen Schmidhuber
10. Ilya Sutskever

Who trusts Thomas Bo Schön overall

1. Fredrik Lindsten
2. Simo Särkkä
3. Niklas Wahlstrom
4. Andreas Svensson
5. Marc Peter Deisenroth
6. Le Yang
7. Kai Arulkumaran
8. Feng Ding
9. David Törnqvist
10. Sergey Levine

Papers from Thomas Bo Schön's perspective

Ordered by how strongly the researchers Thomas Bo Schön 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. Gradient-based learning applied to document recognition
5. Attention is All you Need (Neural Information Processing Systems)
6. ImageNet: A large-scale hierarchical image database
7. Long Short-Term Memory
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Learning Multiple Layers of Features from Tiny Images
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• From Pixels to Torques: Policy Learning with Deep Dynamical Models (2015) — 192 citations
• Data-Efficient Learning of Feedback Policies from Image Pixels using Deep Dynamical Models (2015) — 39 citations
• Evaluating Scalable Bayesian Deep Learning Methods for Robust Computer Vision (2019) — 349 citations
• Learning deep dynamical models from image pixels (2014) — 67 citations
• The Eighth Visual Object Tracking VOT2020 Challenge Results (2020) — 345 citations
• How to Train Your Energy-Based Model for Regression (2020) — 44 citations
• Energy-Based Models for Deep Probabilistic Regression (2020) — 75 citations
• Bayesian Inference and Learning in Gaussian Process State-Space Models with Particle MCMC (2013) — 167 citations
• Nested Sequential Monte Carlo Methods (2015) — 88 citations
• System identification of nonlinear state-space models (2011) — 623 citations
• Particle gibbs with ancestor sampling (2014) — 279 citations
• Particle Filter SLAM with High Dimensional Vehicle Model (2009) — 55 citations
• Automatic diagnosis of the 12-lead ECG using a deep neural network (2019) — 1,027 citations
• Image Restoration with Mean-Reverting Stochastic Differential Equations (2023) — 318 citations
• Evaluating model calibration in classification (2019) — 273 citations
• Using Inertial Sensors for Position and Orientation Estimation (2017) — 513 citations
• Delayed Sampling and Automatic Rao-Blackwellization of Probabilistic Programs (2017) — 62 citations
• Identification of jump Markov linear models using particle filters (2014) — 30 citations
• Identification of Hammerstein-Wiener models (2013) — 306 citations
• Marginalized particle filters for mixed linear/nonlinear state-space models (2005) — 693 citations