Natasha M. J. Padfield

Cognitive Neuroscience · 2019

A researcher in the Science corpus: 1 paper; trusts 3 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Cognitive Neuroscience — 24 citations into it
• Cellular and Molecular Neuroscience — 12 citations into it
• Electrical and Electronic Engineering — 12 citations into it

Whom Natasha M. J. Padfield trusts overall

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

1. Gert Pfurtscheller
2. Dervis Karaboga
3. Yann LeCun
4. Gernot R. Müller-Putz
5. Yousef Rezaei Tabar
6. Luis F. Nicolás-Alonso
7. Bernhard Graimann
8. A. K. Qin
9. Yoshua Bengio
10. Pouya Bashivan

Papers from Natasha M. J. Padfield's perspective

Ordered by how strongly the researchers Natasha M. J. Padfield 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. Gradient-based learning applied to document recognition
4. Long Short-Term Memory
5. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
6. Attention is All you Need (Neural Information Processing Systems)
7. ImageNet: A large-scale hierarchical image database
8. Going deeper with convolutions
9. ImageNet Large Scale Visual Recognition Challenge
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• EEG-Based Brain-Computer Interfaces Using Motor-Imagery: Techniques and Challenges (2019) — 508 citations