Marcel A. J. van Gerven

Artificial Intelligence · with Güçlü · 2009–2020

A researcher in the Science corpus: 7 papers; trusts 593 researchers and is trusted by 25 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 330 citations into it; #17,693 of 35,345 by trust here, #12,083 by citations
• Computer Vision and Pattern Recognition — 214 citations into it; #16,943 of 21,575 by trust here, #19,766 by citations
• Safety, Robustness & Interpretability — 174 citations into it; #1,173 of 2,251 by trust here, #1,512 by citations
• Cognitive Neuroscience — 75 citations into it; #2,071 of 10,852 by trust here, #3,321 by citations
• Natural Language Processing — 51 citations into it
• Model Architectures — 34 citations into it
• Signal Processing — 30 citations into it
• Safety Research — 27 citations into it

Whom Marcel A. J. van Gerven trusts overall

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

1. Geoffrey E. Hinton
2. Karen Simonyan
3. Alex Krizhevsky
4. Matthew D. Zeiler
5. Yoshua Bengio
6. Yann LeCun
7. Diederik P. Kingma
8. Ilya Sutskever
9. Kaiming He
10. Rob Fergus

Who trusts Marcel A. J. van Gerven overall

1. Charlotte Caucheteux
2. Nikolaus Kriegeskorte
3. Sarah Wiegreffe
4. Leon A. Gatys
5. Amina Adadi
6. Jonas Kubilius
7. Grace W. Lindsay
8. Santiago A. Cadena
9. Michael Eickenberg
10. Max Bucher

Papers from Marcel A. J. van Gerven's perspective

Ordered by how strongly the researchers Marcel A. J. van Gerven 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. Long Short-Term Memory
5. ImageNet: A large-scale hierarchical image database
6. Gradient-based learning applied to document recognition
7. Attention is All you Need (Neural Information Processing Systems)
8. ImageNet Large Scale Visual Recognition Challenge
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• Explainable Deep Learning: A Field Guide for the Uninitiated (2020) — 452 citations
• Deep disentangled representations for volumetric reconstruction (2016) — 31 citations
• Explainable and Interpretable Models in Computer Vision and Machine Learning (2017) — 117 citations
• Explanation Methods in Deep Learning: Users, Values, Concerns and Challenges (2018) — 245 citations
• Visualizing Apparent Personality Analysis with Deep Residual Networks (2017) — 21 citations
• Deep Neural Networks Reveal a Gradient in the Complexity of Neural Representations across the Ventral Stream (2014) — 1,002 citations
• The brain–computer interface cycle (2009) — 366 citations