Marco Podda

Artificial Intelligence · 2019

A researcher in the Science corpus: 2 papers; trusts 75 researchers and is trusted by 2 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 78 citations into it; #30,481 of 35,345 by trust here, #19,873 by citations
• Graph Learning — 65 citations into it
• Computer Vision and Pattern Recognition — 34 citations into it; #18,787 of 21,575 by trust here, #13,269 by citations
• Molecular Biology — 26 citations into it
• Statistical and Nonlinear Physics — 24 citations into it
• Representation & Self-Supervised Learning — 15 citations into it
• Materials Chemistry — 13 citations into it
• Computational Theory and Mathematics — 12 citations into it

Whom Marco Podda trusts overall

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

1. Thomas Kipf
2. Diederik P. Kingma
3. Max Welling
4. Yoshua Bengio
5. Alessio Micheli
6. Geoffrey E. Hinton
7. Muhan Zhang
8. Martin Simonovsky
9. William L. Hamilton
10. Yann LeCun

Who trusts Marco Podda overall

1. Uri Alon
2. Vijay Prakash Dwivedi
3. Deyu Bo

Papers from Marco Podda's perspective

Ordered by how strongly the researchers Marco Podda 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. Long Short-Term Memory
4. Attention is All you Need (Neural Information Processing Systems)
5. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
6. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
8. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
9. Learning Multiple Layers of Features from Tiny Images
10. Auto-Encoding Variational Bayes (International Conference on Learning Representations)

Their papers

• A Fair Comparison of Graph Neural Networks for Graph Classification (2019) — 527 citations
• A Gentle Introduction to Deep Learning for Graphs (2019) — 326 citations