Joel W. Burdick

Reinforcement Learning · 2000–2019

A researcher in the Science corpus: 11 papers; trusts 130 researchers and is trusted by 42 researchers here, as inferred from the citation graph.

Subfields

• Reinforcement Learning — 83 citations into it
• Artificial Intelligence — 78 citations into it; #14,030 of 35,345 by trust here, #21,878 by citations
• Control and Systems Engineering — 33 citations into it
• Management Science and Operations Research — 30 citations into it
• Computational Theory and Mathematics — 14 citations into it
• Computer Networks and Communications — 8 citations into it
• Optimization & Training — 8 citations into it; #790 of 2,601 by trust here, #1,195 by citations
• Probabilistic & Bayesian Methods — 8 citations into it; #526 of 1,525 by trust here, #618 by citations

Whom Joel W. Burdick trusts overall

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

1. Richard S. Sutton
2. Andrew Y. Ng
3. Sergey Levine
4. Pieter Abbeel
5. E. Todorov
6. Andrew Barto
7. Geoffrey E. Hinton
8. Chelsea Finn
9. Stéphane Mallat
10. Diederik P. Kingma

Who trusts Joel W. Burdick overall

1. Chris J. Thornton
2. Chao Yu
3. Enric Galceran
4. M. Vidal
5. Kirthevasan Kandasamy
6. H. Rey
7. Dmitri A. Dolgov
8. Tor Lattimore
9. Felix Berkenkamp
10. K. Wise

Papers from Joel W. Burdick's perspective

Ordered by how strongly the researchers Joel W. Burdick 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. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. Reinforcement Learning: An Introduction

Their papers

• Combined shape, appearance and silhouette for simultaneous manipulator and object tracking (2012) — 51 citations
• End-to-end dexterous manipulation with deliberate interactive estimation (2012) — 48 citations
• Dueling Posterior Sampling for Preference-Based Reinforcement Learning (2019) — 75 citations
• Inverse Reinforcement Learning via Function Approximation for Clinical Motion Analysis (2017) — 15 citations
• A Function Approximation Method for Model-based High-Dimensional Inverse Reinforcement Learning (2017) — 6 citations
• End-to-End Safe Reinforcement Learning through Barrier Functions for Safety-Critical Continuous Control Tasks (2019) — 805 citations
• Safe Exploration for Optimization with Gaussian Processes (2015) — 461 citations
• Parallelizing Exploration-Exploitation Tradeoffs with Gaussian Process Bandit Optimization (2012) — 511 citations
• Effect of Epidural stimulation of the lumbosacral spinal cord on voluntary movement, standing, and assisted stepping after motor complete paraplegia: a case study (2011) — 1,063 citations
• Spike detection using the continuous wavelet transform (2005) — 397 citations
• Sensor-Based Exploration: The Hierarchical Generalized Voronoi Graph (2000) — 323 citations