Anders Eklund
Cognitive Neuroscience · with Abramian · 2013–2018
A researcher in the Science corpus: 4 papers; trusts 32 researchers and is trusted by 11 researchers here, as inferred from the citation graph.
Subfields
- Cognitive Neuroscience — 38 citations into it; #4,308 of 10,852 by trust here, #4,716 by citations
- Radiology, Nuclear Medicine and Imaging — 37 citations into it; #899 of 3,265 by trust here, #1,299 by citations
- Computer Vision and Pattern Recognition — 29 citations into it; #19,203 of 21,575 by trust here, #17,258 by citations
- Artificial Intelligence — 7 citations into it
- Statistics and Probability — 7 citations into it
- Biomedical Engineering — 6 citations into it
- Computer Networks and Communications — 6 citations into it
- Biophysics — 5 citations into it
Whom Anders Eklund trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Thomas E. Nichols
- Russell A. Poldrack
- Michael Elad
- Gitta Kutyniok
- John Ashburner
- Karl J. Friston
- Matthew D. Lieberman
- Paul A. Viola
- Christoph Vetter
- Thomas Schiwietz
Who trusts Anders Eklund overall
- Shiva Maleki Varnosfaderani
- Henk R. Cremers
- Qiang Sun
- Benjamin Floyd
- Prantik Kundu
- Aaron F. Alexander-Bloch
- Claudia D van Borkulo
- Matthew F. Glasser
- Mohamad Forouzanfar
- Tor D. Wager
Papers from Anders Eklund's perspective
Ordered by how strongly the researchers Anders Eklund 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)
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- Gradient-based learning applied to document recognition
- ImageNet: A large-scale hierarchical image database
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
- Fully convolutional networks for semantic segmentation
- Long Short-Term Memory
- Going deeper with convolutions
Their papers
- Refacing: Reconstructing Anonymized Facial Features Using Gans (2018)
- BIDS apps: Improving ease of use, accessibility, and reproducibility of neuroimaging data analysis methods (2016) — 311 citations
- Cluster failure: Why fMRI inferences for spatial extent have inflated false-positive rates (2015) — 3,333 citations
- Medical image processing on the GPU - Past, present and future (2013) — 415 citations