Antonio Garcia Marques

Artificial Intelligence · 2016–2018

A researcher in the Science corpus: 3 papers; trusts 20 researchers and is trusted by 2 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 39 citations into it; #27,207 of 35,345 by trust here, #25,809 by citations
• Statistical and Nonlinear Physics — 24 citations into it; #7,478 of 10,127 by trust here, #6,421 by citations
• Graph Learning — 12 citations into it
• Computer Vision and Pattern Recognition — 11 citations into it
• Molecular Biology — 11 citations into it
• Computational Mechanics — 6 citations into it
• Statistics and Probability — 6 citations into it
• Management Science and Operations Research — 5 citations into it

Whom Antonio Garcia Marques trusts overall

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

1. Aliaksei Sandryhaila
2. Mark Newman
3. Eric D. Kolaczyk
4. Diederik P. Kingma
5. A. Hinge
6. José M. F. Moura
7. Xiaowen Dong
8. Geoffrey E. Hinton
9. David E. Rumelhart
10. Tomas Mikolov

Who trusts Antonio Garcia Marques overall

1. Damian Owerko
2. Fernando Gama
3. Alejandro Ribeiro
4. Gonzalo Mateos
5. Xiaowen Dong
6. Qingbiao Li

Papers from Antonio Garcia Marques's perspective

Ordered by how strongly the researchers Antonio Garcia Marques 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. Gradient-based learning applied to document recognition
5. Long Short-Term Memory
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. ImageNet Large Scale Visual Recognition Challenge
10. Going deeper with convolutions

Their papers

• Convolutional Neural Network Architectures for Signals Supported on Graphs (2018) — 318 citations
• Connecting the Dots: Identifying Network Structure via Graph Signal Processing (2018) — 401 citations
• Network Topology Inference from Spectral Templates (2016) — 309 citations