Bart van Merrienboer

Artificial Intelligence · 2014–2016

A researcher in the Science corpus: 5 papers; trusts 35 researchers and is trusted by 2,415 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 39 citations into it; #185 of 35,345 by trust here, #228 by citations
• Natural Language Processing — 27 citations into it; #110 of 5,688 by trust here, #63 by citations
• Computer Vision and Pattern Recognition — 24 citations into it; #16,540 of 21,575 by trust here, #9,079 by citations
• Model Architectures — 22 citations into it; #168 of 2,278 by trust here, #106 by citations
• Optimization & Training — 12 citations into it
• Representation & Self-Supervised Learning — 6 citations into it
• Large Language Models — 5 citations into it

Whom Bart van Merrienboer trusts overall

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

1. Yoshua Bengio
2. Alex Graves
3. Ilya Sutskever
4. Geoffrey E. Hinton
5. Matthew D. Zeiler
6. Holger Schwenk
7. Kyunghyun Cho
8. Alex Krizhevsky
9. Yann LeCun
10. Nal Kalchbrenner

Who trusts Bart van Merrienboer overall

1. Yoshua Bengio
2. Kyunghyun Cho
3. Zhe Gan
4. Quoc V. Le
5. Ruslan Salakhutdinov
6. Mohit Bansal
7. Jianfeng Gao
8. Yoav Goldberg
9. Percy Liang
10. Sergey Levine

Papers from Bart van Merrienboer's perspective

Ordered by how strongly the researchers Bart van Merrienboer 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. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
9. Going deeper with convolutions
10. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)

Their papers

• Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation (2014) — 26,848 citations
• On the Properties of Neural Machine Translation: Encoder–Decoder Approaches (2014) — 7,554 citations
• Blocks and Fuel: Frameworks for deep learning (2015) — 188 citations
• Overcoming the Curse of Sentence Length for Neural Machine Translation using Automatic Segmentation (2014) — 84 citations
• Theano: A Python framework for fast computation of mathematical expressions (2016) — 2,380 citations