Nino Shervashidze

Artificial Intelligence · 2009–2013

A researcher in the Science corpus: 5 papers; trusts 24 researchers and is trusted by 91 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 24 citations into it; #17,872 of 35,345 by trust here, #13,163 by citations
• Graph Learning — 21 citations into it; #41 of 693 by trust here, #73 by citations
• Computer Vision and Pattern Recognition — 12 citations into it; #16,963 of 21,575 by trust here, #18,266 by citations
• Molecular Biology — 12 citations into it
• Computational Theory and Mathematics — 10 citations into it
• Statistical Learning Theory — 10 citations into it
• Computational Chemistry & Drug Discovery — 8 citations into it
• Evaluation & Benchmarks — 7 citations into it

Whom Nino Shervashidze trusts overall

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

1. Karsten M. Borgwardt
2. Pierre Mahé
3. Thomas Gärtner
4. Risi Kondor
5. Chih-Jen Lin
6. Paul D. Dobson
7. Nikil Wale
8. Hisashi Kashima
9. Jean-Philippe Vert
10. Hans-Peter Kriegel

Who trusts Nino Shervashidze overall

1. Christopher Morris
2. William L. Hamilton
3. Giovanni Da San Martino
4. Nils M. Kriege
5. Saurabh Verma
6. Giannis Nikolentzos
7. Muhan Zhang
8. Aynaz Taheri
9. Fredrik D. Johansson
10. Nicoló Navarin

Papers from Nino Shervashidze's perspective

Ordered by how strongly the researchers Nino Shervashidze 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. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Marginalized Kernels Between Labeled Graphs
5. Attention is All you Need (Neural Information Processing Systems)
6. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
8. Long Short-Term Memory
9. Learning Multiple Layers of Features from Tiny Images
10. Learning with Kernels: support vector machines, regularization, optimization, and beyond

Their papers

• Distributed large-scale natural graph factorization (2013) — 706 citations
• Weisfeiler-Lehman Graph Kernels (2011) — 2,430 citations
• Fast subtree kernels on graphs (2009) — 268 citations
• The graphlet spectrum (2009) — 82 citations
• Efficient graphlet kernels for large graph comparison (2009) — 1,128 citations