Edwin V. Bonilla

Artificial Intelligence · 2006–2018

A researcher in the Science corpus: 6 papers; trusts 66 researchers and is trusted by 66 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 63 citations into it; #30,676 of 35,345 by trust here, #21,316 by citations
• Probabilistic & Bayesian Methods — 41 citations into it; #256 of 1,525 by trust here, #294 by citations
• Control and Systems Engineering — 14 citations into it
• Computer Vision and Pattern Recognition — 13 citations into it; #19,074 of 21,575 by trust here, #18,266 by citations
• Computational Theory and Mathematics — 12 citations into it
• Representation & Self-Supervised Learning — 9 citations into it
• Statistical Learning Theory — 8 citations into it
• Optimization & Training — 8 citations into it

Whom Edwin V. Bonilla trusts overall

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

1. Zoubin Ghahramani
2. Diederik P. Kingma
3. Carl E. Rasmussen
4. Alex Graves
5. Geoffrey E. Hinton
6. Neil D. Lawrence
7. R. Caruana
8. Wei Chu
9. Radford M. Neal
10. David John Cameron MacKay

Who trusts Edwin V. Bonilla overall

1. Yu Zhang
2. Yu Zhang
3. Mauricio A Álvarez
4. Dit-Yan Yeung
5. Qiang Yang
6. Kian Ming Adam Chai
7. Michalis K. Titsias
8. Haitao Liu
9. Neil D. Lawrence
10. Yew-Soon Ong

Papers from Edwin V. Bonilla's perspective

Ordered by how strongly the researchers Edwin V. Bonilla trusts endorse each paper. Same corpus, a different order for every researcher.

1. ImageNet classification with deep convolutional neural networks
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
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. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Random Feature Expansions for Deep Gaussian Processes (2016) — 153 citations
• Using machine learning to focus iterative optimization (2006) — 500 citations
• Calibrating Deep Convolutional Gaussian Processes (2018) — 45 citations
• Scalable Inference for Gaussian Process Models with Black-Box Likelihoods (2015) — 68 citations
• Gaussian Process Preference Elicitation (2010) — 96 citations
• Multi-task Gaussian Process Prediction (2007) — 1,357 citations