Giannis Nikolentzos
Artificial Intelligence · 2017–2019
A researcher in the Science corpus: 3 papers; trusts 72 researchers and is trusted by 10 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 54 citations into it; #14,529 of 35,345 by trust here, #18,586 by citations
- Graph Learning — 36 citations into it
- Natural Language Processing — 26 citations into it
- Computer Vision and Pattern Recognition — 20 citations into it
- Molecular Biology — 11 citations into it
- Representation & Self-Supervised Learning — 10 citations into it
- Computational Theory and Mathematics — 8 citations into it
- Model Architectures — 8 citations into it
Whom Giannis Nikolentzos trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Diederik P. Kingma
- Geoffrey E. Hinton
- Karsten M. Borgwardt
- Yoshua Bengio
- Alex Krizhevsky
- Nino Shervashidze
- Ilya Sutskever
- Yann LeCun
- Kaiming He
- Jimmy Ba
Who trusts Giannis Nikolentzos overall
- Christopher Morris
- Nils M. Kriege
- John Boaz Lee
- Hongyun Cai
- Yunsheng Bai
- Chi Zhang
- Zhen Zhang
- Yufeng Zhang
- Vincent Wenchen Zheng
- Fredrik D. Johansson
Papers from Giannis Nikolentzos's perspective
Ordered by how strongly the researchers Giannis Nikolentzos trusts endorse each paper. Same corpus, a different order for every researcher.
- ImageNet classification with deep convolutional neural networks
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Attention is All you Need (Neural Information Processing Systems)
- Long Short-Term Memory
- Gradient-based learning applied to document recognition
- ImageNet: A large-scale hierarchical image database
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
- Learning Multiple Layers of Features from Tiny Images
Their papers
- A Degeneracy Framework for Graph Similarity (2018) — 80 citations
- Message Passing Attention Networks for Document Understanding (2019) — 81 citations
- Matching Node Embeddings for Graph Similarity (2017) — 219 citations