Jorge Aguilera-Iparraguirre

Computational Theory and Mathematics · 2015–2016

A researcher in the Science corpus: 5 papers; trusts 6 researchers and is trusted by 44 researchers here, as inferred from the citation graph.

Subfields

• Computational Theory and Mathematics — 65 citations into it; #999 of 12,182 by trust here, #40 by citations
• Computational Chemistry & Drug Discovery — 43 citations into it; #168 of 425 by trust here, #47 by citations
• Artificial Intelligence — 25 citations into it
• Model Architectures — 15 citations into it
• Organic Chemistry — 13 citations into it

Whom Jorge Aguilera-Iparraguirre trusts overall

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

1. Diederik P. Kingma
2. Geoffrey E. Hinton
3. Yoshua Bengio
4. Yann LeCun
5. Alex Graves
6. Richard Socher
7. Sepp Hochreiter
8. Alex Krizhevsky
9. J. Urgen Schmidhuber
10. Ruslan Salakhutdinov

Who trusts Jorge Aguilera-Iparraguirre overall

1. Wengong Jin
2. Ryan-Rhys Griffiths
3. José Miguel Hernández-Lobato
4. Thomas Kipf
5. Regina Barzilay
6. Rex Ying
7. Yujia Li
8. Tommi S. Jaakkola
9. Garrett B. Goh
10. Risi Kondor

Papers from Jorge Aguilera-Iparraguirre's perspective

Ordered by how strongly the researchers Jorge Aguilera-Iparraguirre trusts endorse each paper. Same corpus, a different order for every researcher.

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

Their papers

• Convolutional Networks on Graphs for Learning Molecular Fingerprints (2015) — 3,666 citations
• Automatic Chemical Design Using a Data-Driven Continuous Representation of Molecules (2016) — 3,556 citations
• Design of efficient molecular organic light-emitting diodes by a high-throughput virtual screening and experimental approach. (2016) — 926 citations
• What Is High-Throughput Virtual Screening? A Perspective from Organic Materials Discovery (2015) — 286 citations
• Design of e cient molecular organic light-emitting diodes by a high-throughput virtual screening and experimental approach (2016)