Jelle Munk

Reinforcement Learning · 2016

A researcher in the Science corpus: 1 paper; trusts 26 researchers and is trusted by 12 researchers here, as inferred from the citation graph.

Subfields

• Reinforcement Learning — 16 citations into it
• Artificial Intelligence — 11 citations into it; #14,436 of 35,345 by trust here, #27,551 by citations
• Cognitive Neuroscience — 6 citations into it

Whom Jelle Munk trusts overall

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

1. Diederik P. Kingma
2. Richard S. Sutton
3. Pawel Wawrzynski
4. Andrew Barto
5. Yann LeCun
6. Laurenz Wiskott
7. Sergey Levine
8. Chelsea Finn
9. Geoffrey E. Hinton
10. Yoshua Bengio

Who trusts Jelle Munk overall

1. Alexander Sax
2. Timothée Lesort
3. Vivek Veeriah
4. Debidatta Dwibedi
5. Antonin Raffin
6. Denis Yarats
7. Bradley Emi
8. Natalia Díaz Rodríguez
9. Amir Zamir
10. Junhyuk Oh

Papers from Jelle Munk's perspective

Ordered by how strongly the researchers Jelle Munk 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. Attention is All you Need (Neural Information Processing Systems)
7. ImageNet: A large-scale hierarchical image database
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. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• Learning state representation for deep actor-critic control (2016) — 70 citations