Prashant Doshi

Artificial Intelligence · 2005–2018

A researcher in the Science corpus: 5 papers; trusts 90 researchers and is trusted by 20 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 43 citations into it; #7,101 of 35,345 by trust here, #13,962 by citations
• Reinforcement Learning — 42 citations into it
• Control and Systems Engineering — 17 citations into it
• Education — 15 citations into it
• Safety Research — 13 citations into it
• Developmental and Educational Psychology — 11 citations into it
• Social Psychology — 11 citations into it
• Computer Vision and Pattern Recognition — 9 citations into it

Whom Prashant Doshi trusts overall

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

1. Sergey Levine
2. Brian D. Ziebart
3. Pieter Abbeel
4. Andrew Y. Ng
5. E. Todorov
6. Martin L. Puterman
7. Michael L. Littman
8. Richard S. Sutton
9. Geoffrey E. Hinton
10. Yoshua Bengio

Who trusts Prashant Doshi overall

1. Frans Adriaan Oliehoek
2. Pablo Hernandez-Leal
3. Stefano V. Albrecht
4. Daniel S. Brown
5. Alessandro Panella
6. Nikos A. Vlassis
7. Georgios Papoudakis
8. Peter Stone
9. Takayuki Osa
10. Xingjiao Wu

Papers from Prashant Doshi's perspective

Ordered by how strongly the researchers Prashant Doshi 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. Attention is All you Need (Neural Information Processing Systems)
7. ImageNet: A large-scale hierarchical image database
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. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• A Framework for Sequential Planning in Multi-Agent Settings (2005) — 523 citations
• Levels of theory-of-mind reasoning in competitive games (2012) — 91 citations
• A Survey of Inverse Reinforcement Learning: Challenges, Methods and Progress (2018) — 794 citations
• Robotics to promote elementary education pre-service teachers' STEM engagement, learning, and teaching (2015) — 300 citations
• Multi-robot inverse reinforcement learning under occlusion with interactions (2014) — 42 citations