Galileo Namata

Artificial Intelligence · 2008–2012

A researcher in the Science corpus: 5 papers; trusts 15 researchers and is trusted by 96 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 23 citations into it
• Probabilistic & Bayesian Methods — 14 citations into it
• Information Systems — 10 citations into it
• Computer Vision and Pattern Recognition — 8 citations into it
• Natural Language Processing — 7 citations into it
• Graph Learning — 6 citations into it; #58 of 693 by trust here, #59 by citations
• Computer Networks and Communications — 5 citations into it
• Representation & Self-Supervised Learning — 5 citations into it

Whom Galileo Namata trusts overall

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

1. B. Taskar
2. Ray R. Larson
3. Julian Besag
4. Jennifer Neville
5. Thomas M. Cover
6. Andrew McCallum
7. David D. Jensen
8. Daphne Koller
9. Qing Lu
10. Tom Minka

Who trusts Galileo Namata overall

1. Thomas Kipf
2. Johannes Klicpera
3. Liyu Gong
4. Benjamin Paul Chamberlain
5. Jiaxuan You
6. Shirui Pan
7. Matthias Fey
8. Stephan Günnemann
9. Aleksandar Bojchevski
10. Michael M. Bronstein

Papers from Galileo Namata's perspective

Ordered by how strongly the researchers Galileo Namata 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. Gradient-based learning applied to document recognition
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
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. Going deeper with convolutions
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Learning from Labeled and Unlabeled Data (2010) — 355 citations
• Query-driven Active Surveying for Collective Classification (2012) — 616 citations
• Learning in Logic (2010) — 104 citations
• Collective Classification in Network Data (2008) — 4,707 citations
• Collective Classi!cation in Network Data (2008) — 1,758 citations