Bohdana Ratitch

Reinforcement Learning · 2003

A researcher in the Science corpus: 1 paper; trusts 5 researchers and is trusted by 2 researchers here, as inferred from the citation graph.

Subfields

• Reinforcement Learning — 8 citations into it

Whom Bohdana Ratitch trusts overall

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

1. Leslie Pack Kaelbling
2. Richard S. Sutton
3. Michael Kearns
4. Andrew Barto
5. Satinder Singh
6. J. Urgen Schmidhuber
7. Richard Dearden
8. Marco A. Wiering
9. Nicolas Meuleau
10. Nir Friedman

Who trusts Bohdana Ratitch overall

1. Lihong Li
2. Susanne Still
3. Valentin Thomas
4. Emmanuel Bengio

Papers from Bohdana Ratitch's perspective

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

1. Reinforcement Learning: An Introduction
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. Gradient-based learning applied to document recognition
4. ImageNet classification with deep convolutional neural networks
5. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
6. Long Short-Term Memory
7. Simple Statistical Gradient-Following Algorithms for Connectionist Reinforcement Learning
8. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
9. Learning Multiple Layers of Features from Tiny Images
10. Learning in embedded systems

Their papers

• Using MDP Characteristics to Guide Exploration in Reinforcement Learning (2003) — 14 citations