Hugh Salimbeni

Artificial Intelligence · 2016–2017

A researcher in the Science corpus: 2 papers; trusts 77 researchers and is trusted by 6 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 40 citations into it; #19,445 of 35,345 by trust here, #11,921 by citations
• Computer Vision and Pattern Recognition — 25 citations into it; #16,411 of 21,575 by trust here, #9,563 by citations
• Generative Models — 17 citations into it
• Probabilistic & Bayesian Methods — 13 citations into it
• Optimization & Training — 8 citations into it
• Control and Systems Engineering — 7 citations into it; #5,522 of 8,257 by trust here, #4,962 by citations

Whom Hugh Salimbeni trusts overall

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

1. Neil D. Lawrence
2. Diederik P. Kingma
3. Andreas C. Damianou
4. James Hensman
5. Geoffrey E. Hinton
6. José Miguel Hernández-Lobato
7. Kaiming He
8. Danilo Jimenez Rezende
9. Radford M. Neal
10. Yoshua Bengio

Who trusts Hugh Salimbeni overall

1. Kenneth Blomqvist
2. Maximilian Balandat
3. Andreas Doerr
4. Samuel Kaski
5. Steven Atkinson
6. Christian Daniel
7. Brian Karrer
8. M. Heinonen
9. Guodong Zhang

Papers from Hugh Salimbeni's perspective

Ordered by how strongly the researchers Hugh Salimbeni 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. Gradient-based learning applied to document recognition
5. Long Short-Term Memory
6. Learning Multiple Layers of Features from Tiny Images
7. Attention is All you Need (Neural Information Processing Systems)
8. ImageNet: A large-scale hierarchical image database
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. GENERATIVE ADVERSARIAL NETS

Their papers

• Deep Unsupervised Clustering with Gaussian Mixture Variational Autoencoders (2016) — 734 citations
• Doubly Stochastic Variational Inference for Deep Gaussian Processes (2017) — 472 citations