Marc Claesen

Artificial Intelligence · 2014–2015

A researcher in the Science corpus: 2 papers; trusts 50 researchers and is trusted by 6 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 26 citations into it; #16,841 of 35,345 by trust here, #23,077 by citations
• Optimization & Training — 13 citations into it
• Computational Theory and Mathematics — 10 citations into it
• Computer Vision and Pattern Recognition — 9 citations into it
• Management Science and Operations Research — 6 citations into it
• Neural Architecture Search & AutoML — 6 citations into it
• Evaluation & Benchmarks — 5 citations into it
• Statistical Learning Theory — 5 citations into it

Whom Marc Claesen trusts overall

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

1. Geoffrey E. Hinton
2. James Bergstra
3. Christopher K. I. Williams
4. Frank Hutter
5. Jasper Snoek
6. Ron Kohavi
7. Yoshua Bengio
8. Trevor J. Hastie
9. Alex Krizhevsky
10. Hugo Larochelle

Who trusts Marc Claesen overall

1. Li Yang
2. Abdallah Shami
3. Antoine Jean-Pierre Tixier
4. Pingchuan Ma
5. Jason P. C. Chiu
6. Matthew R. Hallowell
7. Eric Nichols
8. Yunsheng Tian
9. Pablo Ribalta Lorenzo
10. Ying Sun

Papers from Marc Claesen's perspective

Ordered by how strongly the researchers Marc Claesen 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. Learning Multiple Layers of Features from Tiny Images
8. ImageNet: A large-scale hierarchical image database
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Going deeper with convolutions

Their papers

• Easy Hyperparameter Search Using Optunity (2014) — 86 citations
• Hyperparameter Search in Machine Learning (2015) — 484 citations