Jean-Benoit Delbrouck

Artificial Intelligence · 2017–2023

A researcher in the Science corpus: 3 papers; trusts 311 researchers and is trusted by 2 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 70 citations into it; #28,858 of 35,345 by trust here, #26,682 by citations
• Computer Vision and Pattern Recognition — 41 citations into it; #17,721 of 21,575 by trust here, #19,310 by citations
• Natural Language Processing — 35 citations into it; #4,717 of 5,688 by trust here, #5,296 by citations
• Evaluation & Benchmarks — 20 citations into it
• Model Architectures — 16 citations into it
• Multimodal Learning — 10 citations into it; #944 of 1,211 by trust here, #1,154 by citations
• Optimization & Training — 7 citations into it

Whom Jean-Benoit Delbrouck trusts overall

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

1. Kyunghyun Cho
2. Yoshua Bengio
3. Matthew D. Zeiler
4. Ilya Sutskever
5. Geoffrey E. Hinton
6. Kaiming He
7. Oriol Vinyals
8. Dzmitry Bahdanau
9. Rico Sennrich
10. Alex Krizhevsky

Who trusts Jean-Benoit Delbrouck overall

1. Bei Li
2. Stéphane Dupont
3. Chuanhao Lv
4. Omar Seddati

Papers from Jean-Benoit Delbrouck's perspective

Ordered by how strongly the researchers Jean-Benoit Delbrouck trusts endorse each paper. Same corpus, a different order for every researcher.

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. ImageNet classification with deep convolutional neural networks
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Long Short-Term Memory
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. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Adapted large language models can outperform medical experts in clinical text summarization (2023) — 793 citations
• Visually Grounded Word Embeddings and Richer Visual Features for Improving Multimodal Neural Machine Translation (2017) — 9 citations
• An empirical study on the effectiveness of images in Multimodal Neural Machine Translation (2017) — 43 citations