Ida Momennejad

Cognitive Neuroscience · with Daw · 2016–2020

A researcher in the Science corpus: 3 papers; trusts 51 researchers and is trusted by 6 researchers here, as inferred from the citation graph.

Subfields

• Cognitive Neuroscience — 64 citations into it
• Cellular and Molecular Neuroscience — 31 citations into it
• Artificial Intelligence — 24 citations into it
• Reinforcement Learning — 24 citations into it
• Developmental and Educational Psychology — 9 citations into it

Whom Ida Momennejad trusts overall

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

1. Peter S. Dayan
2. Matthew Botvinick
3. Marlos C. Machado
4. Richard S. Sutton
5. Samuel J. Gershman
6. David J. Foster
7. Brad E. Pfeiffer
8. Howard Eichenbaum
9. Nathaniel D. Daw
10. Kimberly L. Stachenfeld

Who trusts Ida Momennejad overall

1. Khimya Khetarpal
2. Tyler L. Hayes
3. Naomi P. Friedman
4. Matthew Riemer
5. Giri Panamoottil Krishnan
6. Irina Rish
7. Maxim Bazhenov
8. Trevor W. Robbins
9. James C. R. Whittington
10. Doina Precup

Papers from Ida Momennejad's perspective

Ordered by how strongly the researchers Ida Momennejad trusts endorse each paper. Same corpus, a different order for every researcher.

1. Reinforcement Learning: An Introduction
2. ImageNet classification with deep convolutional neural networks
3. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Gradient-based learning applied to document recognition
6. Long Short-Term Memory
7. Attention is All you Need (Neural Information Processing Systems)
8. Simple Statistical Gradient-Following Algorithms for Connectionist Reinforcement Learning
9. ImageNet: A large-scale hierarchical image database
10. Asynchronous Methods for Deep Reinforcement Learning

Their papers

• Learning Structures: Predictive Representations, Replay, and Generalization (2020) — 150 citations
• Offline replay supports planning in human reinforcement learning (2018) — 127 citations
• The successor representation in human reinforcement learning (2016) — 409 citations