Kimberly L. Stachenfeld

Cognitive Neuroscience · with Agarwal · 2014–2018

A researcher in the Science corpus: 5 papers; trusts 57 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Cognitive Neuroscience — 71 citations into it
• Artificial Intelligence — 43 citations into it
• Cellular and Molecular Neuroscience — 33 citations into it
• Reinforcement Learning — 33 citations into it
• Computer Vision and Pattern Recognition — 26 citations into it
• Representation & Self-Supervised Learning — 24 citations into it
• Neuroscience — 21 citations into it
• Computational Theory and Mathematics — 10 citations into it

Whom Kimberly L. Stachenfeld trusts overall

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

1. Richard S. Sutton
2. Peter S. Dayan
3. Andrew Barto
4. Jianbo Shi
5. Mathias Franzius
6. Nathaniel D. Daw
7. Matthew Botvinick
8. Özgür Simsek
9. Yael Niv
10. Jitendra Malik

Who trusts Kimberly L. Stachenfeld overall

1. Ida Momennejad
2. Marlos C. Machado
3. Yifan Wu
4. Mona M. Garvert
5. Jacob L. S. Bellmund
6. Clemens Rosenbaum
7. Samuel J. Gershman
8. Timothy Edward John Behrens
9. Marc G. Bellemare
10. George Tucker

Papers from Kimberly L. Stachenfeld's perspective

Ordered by how strongly the researchers Kimberly L. Stachenfeld 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. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Gradient-based learning applied to document recognition
5. Long Short-Term Memory
6. ImageNet: A large-scale hierarchical image database
7. Attention is All you Need (Neural Information Processing Systems)
8. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
9. Learning Multiple Layers of Features from Tiny Images
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• What Is a Cognitive Map? Organizing Knowledge for Flexible Behavior. (2018) — 941 citations
• Spectral Inference Networks: Unifying Spectral Methods With Deep Learning (2018) — 10 citations
• Spectral Inference Networks: Unifying Deep and Spectral Learning (2018) — 50 citations
• The hippocampus as a predictive map (2017) — 927 citations
• Design Principles of the Hippocampal Cognitive Map (2014) — 114 citations