Zoe McCarthy

Reinforcement Learning · 2011–2017

A researcher in the Science corpus: 5 papers; trusts 53 researchers and is trusted by 10 researchers here, as inferred from the citation graph.

Subfields

• Reinforcement Learning — 72 citations into it; #1,298 of 2,242 by trust here, #843 by citations
• Artificial Intelligence — 64 citations into it; #14,166 of 35,345 by trust here, #7,945 by citations
• Control and Systems Engineering — 37 citations into it; #3,688 of 8,257 by trust here, #906 by citations
• Computer Vision and Pattern Recognition — 24 citations into it
• Robotics & Control — 12 citations into it; #266 of 609 by trust here, #184 by citations
• Aerospace Engineering — 9 citations into it
• Biomedical Engineering — 9 citations into it

Whom Zoe McCarthy trusts overall

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

1. Sergey Levine
2. Jan Peters
3. Stefan Schaal
4. Pieter Abbeel
5. Diederik P. Kingma
6. Marc Peter Deisenroth
7. Geoffrey E. Hinton
8. Richard S. Sutton
9. John Schulman
10. Yoshua Bengio

Who trusts Zoe McCarthy overall

1. Ajay Mandlekar
2. Lei Tai
3. Corey Lynch
4. Jonathan Booher
5. Peter R. Florence
6. Chelsea Finn
7. Sergey Levine
8. Tianhe Yu
9. Fereshteh Sadeghi
10. Soroush Nasiriany

Papers from Zoe McCarthy's perspective

Ordered by how strongly the researchers Zoe McCarthy 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. 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. Learning Multiple Layers of Features from Tiny Images
9. Going deeper with convolutions
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Deep Imitation Learning for Complex Manipulation Tasks from Virtual Reality Teleoperation (2017) — 778 citations
• Probabilistically safe policy transfer (2017) — 22 citations
• Learning deep neural network policies with continuous memory states (2015) — 95 citations
• Policy Learning with Continuous Memory States for Partially Observed Robotic Control (2015) — 19 citations
• Laplace transforms for the nabla-difference operator (2011) — 79 citations