Anders Jonsson

Artificial Intelligence · with Aguas · 2017–2020

A researcher in the Science corpus: 3 papers; trusts 59 researchers and is trusted by 6 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 48 citations into it; #16,535 of 35,345 by trust here, #18,186 by citations
• Reinforcement Learning — 37 citations into it; #1,234 of 2,242 by trust here, #1,586 by citations
• Computer Networks and Communications — 13 citations into it
• Management Science and Operations Research — 11 citations into it
• Optimization & Training — 10 citations into it
• Computer Vision and Pattern Recognition — 7 citations into it
• Computational Theory and Mathematics — 6 citations into it; #5,541 of 12,182 by trust here, #4,531 by citations

Whom Anders Jonsson trusts overall

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

1. Richard S. Sutton
2. Csaba Szepesvari
3. Shai Shalev-Shwartz
4. Martin L. Puterman
5. Elad Hazan
6. Michael L. Littman
7. Brian D. Ziebart
8. Andrew Barto
9. Lin Xiao
10. Geoffrey E. Hinton

Who trusts Anders Jonsson overall

1. Ofir Nachum
2. Bo Dai
3. Joan Bas-Serrano
4. Zafarali Ahmed

Papers from Anders Jonsson's perspective

Ordered by how strongly the researchers Anders Jonsson 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. Gradient-based learning applied to document recognition
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Long Short-Term Memory
6. Attention is All you Need (Neural Information Processing Systems)
7. Reinforcement Learning: An Introduction
8. ImageNet: A large-scale hierarchical image database
9. Learning Multiple Layers of Features from Tiny Images
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• A unified view of entropy-regularized Markov decision processes (2017) — 318 citations
• A review of generalized planning (2019) — 62 citations
• Hierarchical reinforcement learning for efficient exploration and transfer (2020) — 10 citations