Thomas M. Moerland

Artificial Intelligence · 2005–2017

A researcher in the Science corpus: 3 papers; trusts 90 researchers and is trusted by 3 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 40 citations into it; #23,350 of 35,345 by trust here, #25,040 by citations
• Reinforcement Learning — 36 citations into it; #1,606 of 2,242 by trust here, #1,999 by citations
• Probabilistic & Bayesian Methods — 18 citations into it
• Computer Vision and Pattern Recognition — 16 citations into it
• Generative Models — 16 citations into it
• Management Science and Operations Research — 13 citations into it

Whom Thomas M. Moerland trusts overall

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

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

Who trusts Thomas M. Moerland overall

1. Kavosh Asadi
2. Dipendra Kumar Misra
3. Michael L. Littman
4. Stefan Depeweg

Papers from Thomas M. Moerland's perspective

Ordered by how strongly the researchers Thomas M. Moerland 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. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• Learning Multimodal Transition Dynamics for Model-Based Reinforcement Learning (2017) — 32 citations
• Efficient exploration with Double Uncertain Value Networks (2017) — 48 citations
• Fate of unattended fearful faces in the amygdala is determined by both attentional resources and cognitive modulation (2005) — 370 citations