Stephen C. Strother
Cognitive Neuroscience · 2005–2016
A researcher in the Science corpus: 5 papers; trusts 15 researchers and is trusted by 6 researchers here, as inferred from the citation graph.
Subfields
- Cognitive Neuroscience — 40 citations into it; #9,444 of 10,852 by trust here, #3,947 by citations
- Artificial Intelligence — 14 citations into it
- Computer Vision and Pattern Recognition — 14 citations into it
- Statistics and Probability — 9 citations into it
- Biophysics — 6 citations into it
- Evaluation & Benchmarks — 6 citations into it
- Optimization & Training — 5 citations into it
- Social Psychology — 5 citations into it
Whom Stephen C. Strother trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Geoffrey E. Hinton
- Bernhard Schölkopf
- David D. Cox
- David Ruppert
- Robert A. Lordo
- Alex Smola
- Yoshua Bengio
- C. Burges
- Vladimir Naumovich Vapni
- Radford M. Neal
Who trusts Stephen C. Strother overall
- Peter Mondrup Rasmussen
- Christos Davatzikos
- Nematollah Kayhan Batmanghelich
- Asja Fischer
- Siqi Nie
- R. Craddock
- Jeanette A. Mumford
- Kenneth A. Norman
- Ze Wang
Papers from Stephen C. Strother's perspective
Ordered by how strongly the researchers Stephen C. Strother 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
- Attention is All you Need (Neural Information Processing Systems)
- Learning Multiple Layers of Features from Tiny Images
- ImageNet: A large-scale hierarchical image database
- Going deeper with convolutions
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
Their papers
- BIDS apps: Improving ease of use, accessibility, and reproducibility of neuroimaging data analysis methods (2016) — 311 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
- Generative versus discriminative training of RBMs for classification of fMRI images (2008) — 83 citations
- Support vector machines for temporal classification of block design fMRI data (2005) — 428 citations