Michael Pieper

Artificial Intelligence · 2017–2018

A researcher in the Science corpus: 2 papers; trusts 27 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 91 citations into it
• Reinforcement Learning — 72 citations into it
• Natural Language Processing — 48 citations into it
• Evaluation & Benchmarks — 10 citations into it

Whom Michael Pieper trusts overall

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

1. Richard S. Sutton
2. Ronald J. Williams
3. Doina Precup
4. Andrew W. Moore
5. Andrew Barto
6. Satinder Singh
7. Leo Breiman
8. Geoffrey E. Hinton
9. Dimitri P. Bertsekas
10. Diederik P. Kingma

Papers from Michael Pieper's perspective

Ordered by how strongly the researchers Michael Pieper 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. Reinforcement Learning: An Introduction
7. Attention is All you Need (Neural Information Processing Systems)
8. Simple Statistical Gradient-Following Algorithms for Connectionist Reinforcement Learning
9. ImageNet: A large-scale hierarchical image database
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• A Deep Reinforcement Learning Chatbot (2017) — 245 citations
• The Bottleneck Simulator: A Model-based Deep Reinforcement Learning Approach (2018) — 30 citations