Markus Kaiser

2019

A researcher in the Science corpus: 1 paper; trusts 3 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Whom Markus Kaiser trusts overall

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

1. Martin A. Riedmiller
2. Marc Peter Deisenroth
3. James Hensman
4. Sascha Lange
5. Carl E. Rasmussen
6. Richard S. Sutton
7. Alexander G. de G. Matthews
8. Thomas Gabel
9. Zoubin Ghahramani

Papers from Markus Kaiser's perspective

Ordered by how strongly the researchers Markus Kaiser trusts endorse each paper. Same corpus, a different order for every researcher.

1. Reinforcement Learning: An Introduction
2. ImageNet classification with deep convolutional neural networks
3. Learning Multiple Layers of Features from Tiny Images
4. Gradient-based learning applied to document recognition
5. Neural Fitted Q Iteration - First Experiences with a Data Efficient Neural Reinforcement Learning Method
6. PILCO: A Model-Based and Data-Efficient Approach to Policy Search
7. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
8. Learning Deep Architectures for AI
9. Batch Reinforcement Learning
10. This PDF file includes: Materials and Methods

Their papers

• Interpretable Dynamics Models for Data-efficient Reinforcement Learning (2019) — 4 citations