Manuel Gomez Rodriguez

Artificial Intelligence · with Aodha · 2012–2021

A researcher in the Science corpus: 5 papers; trusts 36 researchers and is trusted by 9 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 49 citations into it; #17,864 of 35,345 by trust here, #20,377 by citations
• Generative Models — 17 citations into it
• Graph Learning — 17 citations into it
• Computational Theory and Mathematics — 15 citations into it
• Statistical and Nonlinear Physics — 15 citations into it; #4,500 of 10,127 by trust here, #8,981 by citations
• Molecular Biology — 12 citations into it
• Modeling and Simulation — 10 citations into it
• AI Ethics, Fairness & Society — 7 citations into it

Whom Manuel Gomez Rodriguez trusts overall

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

1. Jure Leskovec
2. Manuel Gomez-Rodriguez
3. Yajun Wang
4. David Kempe
5. Jon M. Kleinberg
6. Jacco Wallinga
7. Chi Wang
8. Aaron Clauset
9. George L. Nemhauser
10. Shishir Bharathi

Who trusts Manuel Gomez Rodriguez overall

1. Manuel Gomez-Rodriguez
2. Yuchen Li
3. Jure Leskovec
4. Ju Fan
5. Aditya Krishna Menon
6. Bernhard Schölkopf
7. Robert C. Williamson
8. Yanhao Wang

Papers from Manuel Gomez Rodriguez's perspective

Ordered by how strongly the researchers Manuel Gomez Rodriguez 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. ImageNet classification with deep convolutional neural networks
3. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
4. Long Short-Term Memory
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. Auto-Encoding Variational Bayes (International Conference on Learning Representations)
9. Learning Multiple Layers of Features from Tiny Images
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Influence Maximization in Continuous Time Diffusion Networks (2012) — 100 citations
• NeVAE: A Deep Generative Model for Molecular Graphs (2018) — 241 citations
• Listening to bluetooth beacons for epidemic risk mitigation (2021) — 15 citations
• Teaching Multiple Concepts to a Forgetful Learner (2018) — 40 citations
• Learning Fair Classifiers (2015) — 50 citations