Kristoffer Hougaard Madsen
Cognitive Neuroscience · 2006–2018
A researcher in the Science corpus: 7 papers; trusts 46 researchers and is trusted by 6 researchers here, as inferred from the citation graph.
Subfields
- Cognitive Neuroscience — 81 citations into it; #4,899 of 10,852 by trust here, #818 by citations
- Radiology, Nuclear Medicine and Imaging — 34 citations into it; #2,450 of 3,265 by trust here, #311 by citations
- Computer Vision and Pattern Recognition — 22 citations into it
- Artificial Intelligence — 19 citations into it
- Signal Processing — 10 citations into it
- Social Psychology — 9 citations into it
- Optimization & Training — 7 citations into it
- Statistics and Probability — 7 citations into it
Whom Kristoffer Hougaard Madsen trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Diederik P. Kingma
- Geoffrey E. Hinton
- Alex Krizhevsky
- Yoshua Bengio
- Kaiming He
- Orestis Tsinalis
- John Shawe-Taylor
- Xavier Glorot
- Karen Simonyan
- Ilya Sutskever
Who trusts Kristoffer Hougaard Madsen overall
- R. Craddock
- Erico Tjoa
- Bilwaj Gaonkar
- Lune P Bellec
- Kevin Murphy
- Albert Vilamala
- Rasmus M. Birn
- Yannick Roy
- Yashar Behzadi
Papers from Kristoffer Hougaard Madsen's perspective
Ordered by how strongly the researchers Kristoffer Hougaard Madsen trusts endorse each paper. Same corpus, a different order for every researcher.
- ImageNet classification with deep convolutional neural networks
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Long Short-Term Memory
- Gradient-based learning applied to document recognition
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- Going deeper with convolutions
- Attention is All you Need (Neural Information Processing Systems)
- ImageNet: A large-scale hierarchical image database
- Learning Multiple Layers of Features from Tiny Images
Their papers
- SimNIBS 2.1: A Comprehensive Pipeline for Individualized Electric Field Modelling for Transcranial Brain Stimulation (2018) — 350 citations
- Deep convolutional neural networks for interpretable analysis of EEG sleep stage scoring (2017) — 150 citations
- Visualization of Nonlinear Classification Models in Neuroimaging - Signed Sensitivity Maps (2012) — 22 citations
- Model sparsity and brain pattern interpretation of classification models in neuroimaging (2012) — 144 citations
- Visualization of nonlinear kernel models in neuroimaging by sensitivity maps (2011) — 75 citations
- Toward discovery science of human brain function (2010) — 3,011 citations
- Non-white noise in fMRI: Does modelling have an impact? (2006) — 458 citations