Joost Broekens

Artificial Intelligence · 2009–2017

A researcher in the Science corpus: 5 papers; trusts 94 researchers and is trusted by 5 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 51 citations into it; #23,351 of 35,345 by trust here, #19,018 by citations
• Reinforcement Learning — 36 citations into it; #1,608 of 2,242 by trust here, #1,999 by citations
• Computer Vision and Pattern Recognition — 19 citations into it
• Probabilistic & Bayesian Methods — 18 citations into it
• Generative Models — 16 citations into it
• Management Science and Operations Research — 13 citations into it
• Safety, Robustness & Interpretability — 11 citations into it
• Control and Systems Engineering — 10 citations into it

Whom Joost Broekens trusts overall

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

1. Diederik P. Kingma
2. Geoffrey E. Hinton
3. Max Welling
4. Richard S. Sutton
5. Yoshua Bengio
6. Ian J. Goodfellow
7. Alex Krizhevsky
8. Yann LeCun
9. Radford M. Neal
10. Ilya Sutskever

Who trusts Joost Broekens overall

1. C. Frey
2. Kavosh Asadi
3. Marcel Heerink
4. Michael A. Osborne
5. P. Flandorfer
6. Dipendra Kumar Misra
7. Ben J. A. Kröse
8. Parisa Rashidi

Papers from Joost Broekens's perspective

Ordered by how strongly the researchers Joost Broekens 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. Learning Multiple Layers of Features from Tiny Images
9. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. GENERATIVE ADVERSARIAL NETS

Their papers

• Learning Multimodal Transition Dynamics for Model-Based Reinforcement Learning (2017) — 32 citations
• Efficient exploration with Double Uncertain Value Networks (2017) — 48 citations
• The role of emotion in self-explanations by cognitive agents (2017) — 28 citations
• Do You Get It? User-Evaluated Explainable BDI Agents (2010) — 85 citations
• Assistive social robots in elderly care: a review (2009) — 997 citations