Steven Lemm

Cognitive Neuroscience · 2005–2011

A researcher in the Science corpus: 4 papers; trusts 37 researchers and is trusted by 31 researchers here, as inferred from the citation graph.

Subfields

• Cognitive Neuroscience — 75 citations into it; #923 of 10,852 by trust here, #1,346 by citations
• Cellular and Molecular Neuroscience — 36 citations into it
• Artificial Intelligence — 20 citations into it; #24,075 of 35,345 by trust here, #16,771 by citations
• Electrical and Electronic Engineering — 17 citations into it
• Signal Processing — 15 citations into it; #983 of 7,572 by trust here, #841 by citations
• Statistical Learning Theory — 12 citations into it
• Statistics and Probability — 12 citations into it
• Computer Vision and Pattern Recognition — 10 citations into it

Whom Steven Lemm trusts overall

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

1. K. Müller
2. Benjamin Blankertz
3. Guido Dornhege
4. Jonathan R. Wolpaw
5. Herbert Ramoser
6. Gabriel Curio
7. Vladimir Naumovich Vapni
8. Eric R. Ziegel
9. Bernhard Schölkopf
10. K. Jellinger

Who trusts Steven Lemm overall

1. Benjamin Blankertz
2. Christian Andreas Kothe
3. Fabien Lotte
4. K. Müller
5. Luis F. Nicolás-Alonso
6. Cuntai Guan
7. Kai Keng Ang
8. Wojciech Samek
9. Scott Makeig
10. Désirée Baumann

Papers from Steven Lemm's perspective

Ordered by how strongly the researchers Steven Lemm 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. ImageNet: A large-scale hierarchical image database
6. Long Short-Term Memory
7. Attention is All you Need (Neural Information Processing Systems)
8. Learning Multiple Layers of Features from Tiny Images
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Going deeper with convolutions

Their papers

• Introduction to machine learning for brain imaging (2011) — 659 citations
• Single-trial analysis and classification of ERP components - A tutorial (2011) — 1,195 citations
• Optimizing Spatial filters for Robust EEG Single-Trial Analysis (2008) — 2,111 citations
• Spatio-spectral filters for improving the classification of single trial EEG (2005) — 663 citations