Christoph Mathys

Cognitive Neuroscience · 2012–2015

A researcher in the Science corpus: 3 papers; trusts 14 researchers and is trusted by 1 researcher here, as inferred from the citation graph.

Subfields

• Cognitive Neuroscience — 59 citations into it; #9,503 of 10,852 by trust here, #6,723 by citations
• Artificial Intelligence — 23 citations into it
• Reinforcement Learning — 14 citations into it
• Statistics and Probability — 14 citations into it
• Probabilistic & Bayesian Methods — 12 citations into it
• Computer Vision and Pattern Recognition — 8 citations into it
• Neuroscience — 7 citations into it

Whom Christoph Mathys trusts overall

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

1. Karl J. Friston
2. Jean Daunizeau
3. John K. Kruschke
4. Richard S. Sutton
5. Peter S. Dayan
6. Radford M. Neal
7. Nathaniel D. Daw
8. Geoffrey E. Hinton
9. Andrew Barto
10. Timothy Edward John Behrens

Who trusts Christoph Mathys overall

1. Floris P. de Lange
2. Karl J. Friston
3. S. V. D. Cruys
4. Micha Heilbron

Papers from Christoph Mathys's perspective

Ordered by how strongly the researchers Christoph Mathys 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. Reinforcement Learning: An Introduction
4. Gradient-based learning applied to document recognition
5. Long Short-Term Memory
6. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
7. The Helmholtz Machine
8. Emergence of simple-cell receptive field properties by learning a sparse code for natural images
9. Pattern Recognition and Machine Learning
10. Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper

Their papers

• Active inference and epistemic value (2015) — 705 citations
• Uncertainty in perception and the Hierarchical Gaussian Filter (2014) — 418 citations
• Bayesian mixed-effects inference on classification performance in hierarchical data sets (2012) — 29 citations