Gabriel Lima Guimaraes

Computational Theory and Mathematics · 2017

A researcher in the Science corpus: 2 papers; trusts 18 researchers and is trusted by 12 researchers here, as inferred from the citation graph.

Subfields

• Computational Theory and Mathematics — 24 citations into it
• Artificial Intelligence — 20 citations into it; #16,885 of 35,345 by trust here, #11,192 by citations
• Computational Chemistry & Drug Discovery — 19 citations into it
• Materials Chemistry — 19 citations into it; #2,509 of 11,513 by trust here, #5,177 by citations
• Computer Vision and Pattern Recognition — 18 citations into it
• Generative Models — 17 citations into it; #985 of 1,956 by trust here, #891 by citations
• Reinforcement Learning — 8 citations into it; #1,258 of 2,242 by trust here, #1,037 by citations
• Model Architectures — 5 citations into it

Whom Gabriel Lima Guimaraes trusts overall

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

1. Ronald J. Williams
2. Diederik P. Kingma
3. Sepp Hochreiter
4. Peter Ertl
5. J. Urgen Schmidhuber
6. Martín Arjovsky
7. Geoffrey E. Hinton
8. R. Ramakrishnan
9. Jimmy Ba
10. Tim Salimans

Who trusts Gabriel Lima Guimaraes overall

1. Yongjun Hong
2. Benjamín Sánchez-Lengeling
3. Tomohide Masuda
4. Nicola De Cao
5. Zaccary Alperstein
6. Austin Tripp
7. Yibo Li
8. Nathan Brown
9. Kristina Preuer
10. Jaechang Lim

Papers from Gabriel Lima Guimaraes's perspective

Ordered by how strongly the researchers Gabriel Lima Guimaraes trusts endorse each paper. Same corpus, a different order for every researcher.

1. Long Short-Term Memory
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. ImageNet classification with deep convolutional neural networks
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. GENERATIVE ADVERSARIAL NETS
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Objective-Reinforced Generative Adversarial Networks (ORGAN) for Sequence Generation Models (2017) — 580 citations
• Optimizing distributions over molecular space. An Objective-Reinforced Generative Adversarial Network for Inverse-design Chemistry (ORGANIC) (2017) — 287 citations