Deirdre Quillen

Artificial Intelligence · 2016–2019

A researcher in the Science corpus: 6 papers; trusts 263 researchers and is trusted by 653 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 135 citations into it; #7,376 of 35,345 by trust here, #4,682 by citations
• Reinforcement Learning — 116 citations into it; #413 of 2,242 by trust here, #233 by citations
• Control and Systems Engineering — 109 citations into it; #206 of 8,257 by trust here, #57 by citations
• Computer Vision and Pattern Recognition — 74 citations into it; #1,739 of 21,575 by trust here, #1,957 by citations
• Robotics & Control — 42 citations into it; #52 of 609 by trust here, #46 by citations
• Biomedical Engineering — 37 citations into it; #208 of 10,734 by trust here, #470 by citations
• Aerospace Engineering — 24 citations into it
• Evaluation & Benchmarks — 22 citations into it; #1,789 of 3,147 by trust here, #1,050 by citations

Whom Deirdre Quillen trusts overall

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

1. Sergey Levine
2. Chelsea Finn
3. Richard S. Sutton
4. Geoffrey E. Hinton
5. Alex Krizhevsky
6. Diederik P. Kingma
7. Ronald J. Williams
8. Pieter Abbeel
9. Yoshua Bengio
10. John Schulman

Who trusts Deirdre Quillen overall

1. Sergey Levine
2. Chelsea Finn
3. Annie Xie
4. Tianhe Yu
5. Lerrel Pinto
6. Valts Blukis
7. Pieter Abbeel
8. A. Irpan
9. Aviral Kumar
10. Abhinav Kumar Gupta

Papers from Deirdre Quillen's perspective

Ordered by how strongly the researchers Deirdre Quillen 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. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Learning hand-eye coordination for robotic grasping with deep learning and large-scale data collection (2016) — 2,254 citations
• QT-Opt: Scalable Deep Reinforcement Learning for Vision-Based Robotic Manipulation (2018) — 1,735 citations
• Meta-World: A Benchmark and Evaluation for Multi-Task and Meta Reinforcement Learning (2019) — 1,715 citations
• Efficient Off-Policy Meta-Reinforcement Learning via Probabilistic Context Variables (2019) — 832 citations
• Deep Reinforcement Learning for Vision-Based Robotic Grasping: A Simulated Comparative Evaluation of Off-Policy Methods (2018) — 219 citations
• Learning Hand-Eye Coordination for Robotic Grasping with Large-Scale Data Collection (2016) — 241 citations