Marcus Olivecrona

Computational Theory and Mathematics · 2017–2018

A researcher in the Science corpus: 3 papers; trusts 30 researchers and is trusted by 24 researchers here, as inferred from the citation graph.

Subfields

• Computational Theory and Mathematics — 60 citations into it
• Molecular Biology — 44 citations into it
• Materials Chemistry — 41 citations into it; #1,390 of 11,513 by trust here, #3,180 by citations
• Computational Chemistry & Drug Discovery — 39 citations into it; #126 of 425 by trust here, #163 by citations
• Artificial Intelligence — 38 citations into it; #11,947 of 35,345 by trust here, #8,810 by citations
• Computer Vision and Pattern Recognition — 32 citations into it
• Generative Models — 17 citations into it
• Model Architectures — 16 citations into it

Whom Marcus Olivecrona trusts overall

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

1. Diederik P. Kingma
2. Sepp Hochreiter
3. J. Urgen Schmidhuber
4. Yoshua Bengio
5. Geoffrey E. Hinton
6. Yann LeCun
7. Ronald J. Williams
8. Ian J. Goodfellow
9. Hans-Joachim Böhm
10. Darko Butina

Who trusts Marcus Olivecrona overall

1. Jiaxuan You
2. Sergey I. Nikolenko
3. Wengong Jin
4. Kristina Preuer
5. Jaechang Lim
6. Kevin Yang
7. Martin Simonovsky
8. Rex Ying
9. Austin Tripp
10. Zaccary Alperstein

Papers from Marcus Olivecrona's perspective

Ordered by how strongly the researchers Marcus Olivecrona 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. Long Short-Term Memory
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Gradient-based learning applied to document recognition
6. Attention is All you Need (Neural Information Processing Systems)
7. ImageNet: A large-scale hierarchical image database
8. Learning Multiple Layers of Features from Tiny Images
9. ImageNet Large Scale Visual Recognition Challenge
10. GENERATIVE ADVERSARIAL NETS

Their papers

• Molecular de-novo design through deep reinforcement learning (2017) — 1,267 citations
• Application of Generative Autoencoder in De Novo Molecular Design (2017) — 375 citations
• The rise of deep learning in drug discovery. (2018) — 1,560 citations