Felipe A. Tobar

Artificial Intelligence · 2015–2018

A researcher in the Science corpus: 2 papers; trusts 30 researchers and is trusted by 5 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 18 citations into it; #27,585 of 35,345 by trust here, #30,347 by citations
• Applied Mathematics — 16 citations into it
• Computational Theory and Mathematics — 12 citations into it
• Probabilistic & Bayesian Methods — 10 citations into it
• Statistics and Probability — 10 citations into it; #11,579 of 12,932 by trust here, #12,077 by citations
• Optimization & Training — 7 citations into it
• Geometry and Topology — 5 citations into it
• Statistical and Nonlinear Physics — 5 citations into it; #7,632 of 10,127 by trust here, #8,120 by citations

Whom Felipe A. Tobar trusts overall

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

1. Michalis K. Titsias
2. David John Cameron MacKay
3. Radford M. Neal
4. Andrew Gordon Wilson
5. Mehmet Gönen
6. David Kristjanson Duvenaud
7. Yoshua Bengio
8. Geoffrey E. Hinton
9. Christopher K. I. Williams
10. Iain Murray

Who trusts Felipe A. Tobar overall

1. Chao Ma
2. Haitao Liu
3. Thang D. Bui
4. Yingzhen Li
5. Yew-Soon Ong

Papers from Felipe A. Tobar's perspective

Ordered by how strongly the researchers Felipe A. Tobar 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. Learning Multiple Layers of Features from Tiny Images
6. Long Short-Term Memory
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

• Bayesian Learning with Wasserstein Barycenters (2018) — 40 citations
• Learning Stationary Time Series using Gaussian Processes with Nonparametric Kernels (2015) — 77 citations