Julian Jara-Ettinger

Developmental and Educational Psychology · 2014–2019

A researcher in the Science corpus: 4 papers; trusts 35 researchers and is trusted by 5 researchers here, as inferred from the citation graph.

Subfields

• Developmental and Educational Psychology — 50 citations into it; #2,370 of 8,223 by trust here, #3,736 by citations
• Artificial Intelligence — 32 citations into it; #16,464 of 35,345 by trust here, #33,294 by citations
• Cognitive Neuroscience — 26 citations into it; #3,947 of 10,852 by trust here, #10,284 by citations
• Cognitive Science & Psychology — 21 citations into it; #554 of 582 by trust here, #475 by citations
• Social Psychology — 21 citations into it; #14,794 of 15,694 by trust here, #11,748 by citations
• Computer Vision and Pattern Recognition — 11 citations into it
• Reinforcement Learning — 10 citations into it
• Cultural Studies — 8 citations into it

Whom Julian Jara-Ettinger trusts overall

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

1. Josh Tenenbaum
2. Richard S. Sutton
3. Noah D. Goodman
4. Chris L. Baker
5. Alison Gopnik
6. Andrew Barto
7. A. Woodward
8. Michael C. Frank
9. Michael L. Littman
10. Henry Wellman

Who trusts Julian Jara-Ettinger overall

1. Tomer D. Ullman
2. Cristian-Paul Bara
3. Sky CH-Wang
4. Josh Tenenbaum
5. Chris L. Baker
6. Shari Liu
7. Brenden M. Lake
8. Joyce Yue Chai
9. Neil C. Rabinowitz
10. Christelle Langley

Papers from Julian Jara-Ettinger's perspective

Ordered by how strongly the researchers Julian Jara-Ettinger 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. Long Short-Term Memory
5. Gradient-based learning applied to document recognition
6. Attention is All you Need (Neural Information Processing Systems)
7. ImageNet: A large-scale hierarchical image database
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. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• Rational quantitative attribution of beliefs, desires and percepts in human mentalizing (2017) — 540 citations
• Theory of mind as inverse reinforcement learning (2019) — 211 citations
• The Naïve Utility Calculus: Computational Principles Underlying Commonsense Psychology. (2016) — 382 citations
• I'd do anything for a cookie (but I won't do that): Children's understanding of the costs and rewards underlying rational action (2014) — 129 citations