G. Holland

Reinforcement Learning · with Bowling · 2018

A researcher in the Science corpus: 1 paper; trusts 11 researchers and is trusted by 1 researcher here, as inferred from the citation graph.

Subfields

• Reinforcement Learning — 29 citations into it; #2,063 of 2,242 by trust here, #1,932 by citations
• Artificial Intelligence — 19 citations into it; #31,738 of 35,345 by trust here, #25,809 by citations

Whom G. Holland trusts overall

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

1. Richard S. Sutton
2. Marc G. Bellemare
3. Erik Talvitie
4. Diederik P. Kingma
5. Gavin Adrian Rummery
6. Jing Peng
7. Ronald J. Williams
8. William W. Cohen
9. Joel Veness
10. Peter A. Flach

Who trusts G. Holland overall

1. Kacper Kielak
2. Tianhe Yu
3. Lukasz Kaiser

Papers from G. Holland's perspective

Ordered by how strongly the researchers G. Holland 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. Gradient-based learning applied to document recognition
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Long Short-Term Memory
6. Attention is All you Need (Neural Information Processing Systems)
7. ImageNet: A large-scale hierarchical image database
8. Auto-Encoding Variational Bayes (International Conference on Learning Representations)
9. Learning Multiple Layers of Features from Tiny Images
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• The Effect of Planning Shape on Dyna-style Planning in High-dimensional State Spaces (2018) — 44 citations