Tom Lenaerts

Artificial Intelligence · with Coppens · 2019

A researcher in the Science corpus: 1 paper; trusts 6 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 9 citations into it
• Reinforcement Learning — 7 citations into it

Whom Tom Lenaerts trusts overall

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

1. Ronald J. Williams
2. Geoffrey E. Hinton
3. Diederik P. Kingma
4. Michael I. Jordan

Papers from Tom Lenaerts's perspective

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

1. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
2. Distilling the Knowledge in a Neural Network (arXiv.org)
3. Simple Statistical Gradient-Following Algorithms for Connectionist Reinforcement Learning
4. Reinforcement Learning: An Introduction
5. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
6. ImageNet classification with deep convolutional neural networks
7. Proximal Policy Optimization Algorithms (arXiv.org)
8. Gradient-based learning applied to document recognition
9. Attention is All you Need (Neural Information Processing Systems)
10. Long Short-Term Memory

Their papers

• Distilling Deep Reinforcement Learning Policies in Soft Decision Trees (2019) — 115 citations