Nicholas Rhinehart

Artificial Intelligence · 2016–2021

A researcher in the Science corpus: 9 papers; trusts 565 researchers and is trusted by 39 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 214 citations into it; #6,444 of 35,345 by trust here, #7,531 by citations
• Computer Vision and Pattern Recognition — 130 citations into it; #5,631 of 21,575 by trust here, #8,831 by citations
• Reinforcement Learning — 129 citations into it; #568 of 2,242 by trust here, #827 by citations
• Control and Systems Engineering — 46 citations into it
• Model Architectures — 30 citations into it
• Generative Models — 28 citations into it
• Automotive Engineering — 27 citations into it; #1,302 of 3,081 by trust here, #1,511 by citations
• Optimization & Training — 20 citations into it

Whom Nicholas Rhinehart trusts overall

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

1. Diederik P. Kingma
2. Geoffrey E. Hinton
3. Brian D. Ziebart
4. Alex Krizhevsky
5. Ronald J. Williams
6. Yann LeCun
7. Yoshua Bengio
8. Sergey Levine
9. Richard S. Sutton
10. Kaiming He

Who trusts Nicholas Rhinehart overall

1. Sergey Levine
2. Antonino Furnari
3. Yuning Chai
4. Rowan Thomas McAllister
5. Kashyap Chitta
6. Kris M. Kitani
7. Andrey Rudenko
8. Daniel Dauner
9. Aditya Prakash
10. Sergio Casas

Papers from Nicholas Rhinehart's perspective

Ordered by how strongly the researchers Nicholas Rhinehart 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. Long Short-Term Memory
5. Attention is All you Need (Neural Information Processing Systems)
6. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. ImageNet Large Scale Visual Recognition Challenge
10. Going deeper with convolutions

Their papers

• First-Person Activity Forecasting with Online Inverse Reinforcement Learning (2016) — 149 citations
• N2N Learning: Network to Network Compression via Policy Gradient Reinforcement Learning (2017) — 182 citations
• Directed-Info GAIL: Learning Hierarchical Policies from Unsegmented Demonstrations using Directed Information (2018) — 76 citations
• Predictive-State Decoders: Encoding the Future into Recurrent Networks (2017) — 46 citations
• PRECOG: PREdiction Conditioned on Goals in Visual Multi-Agent Settings (2019) — 414 citations
• Parrot: Data-Driven Behavioral Priors for Reinforcement Learning (2020) — 172 citations
• SMiRL: Surprise Minimizing Reinforcement Learning in Unstable Environments (2021) — 44 citations
• Deep Imitative Models for Flexible Inference, Planning, and Control (2018) — 160 citations
• r2p2: A ReparameteRized Pushforward Policy for Diverse, Precise Generative Path Forecasting (2018) — 257 citations