Robin Devooght

Artificial Intelligence · 2016–2017

A researcher in the Science corpus: 2 papers; trusts 40 researchers and is trusted by 10 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 25 citations into it; #12,540 of 35,345 by trust here, #29,412 by citations
• Information Systems — 13 citations into it; #2,279 of 8,771 by trust here, #7,107 by citations
• Management Science and Operations Research — 10 citations into it; #5,481 of 11,946 by trust here, #10,893 by citations
• Model Architectures — 8 citations into it
• Natural Language Processing — 7 citations into it
• Optimization & Training — 6 citations into it
• Recommender Systems — 6 citations into it

Whom Robin Devooght trusts overall

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

1. Steffen Rendle
2. Zeno Gantner
3. John C. Duchi
4. Ilya Sutskever
5. Matthew D. Zeiler
6. Christoph Freudenthaler
7. Tomas Mikolov
8. Alex Graves
9. Balázs Hidasi
10. Geoffrey E. Hinton

Who trusts Robin Devooght overall

1. Balázs Hidasi
2. Malte Ludewig
3. Alexandros Karatzoglou
4. Massimo Quadrana
5. Zeynep Batmaz
6. Han Zhu
7. Dietmar Jannach
8. Ali Yurekli
9. Pengye Zhang
10. Alper Bilge

Papers from Robin Devooght's perspective

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

Their papers

• Long and Short-Term Recommendations with Recurrent Neural Networks (2017) — 133 citations
• Collaborative Filtering with Recurrent Neural Networks (2016) — 90 citations