Benedek Rozemberczki

Artificial Intelligence · 2018–2022

A researcher in the Science corpus: 5 papers; trusts 84 researchers and is trusted by 7 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 116 citations into it; #17,102 of 35,345 by trust here, #14,718 by citations
• Graph Learning — 77 citations into it
• Statistical and Nonlinear Physics — 53 citations into it; #4,282 of 10,127 by trust here, #2,849 by citations
• Representation & Self-Supervised Learning — 37 citations into it
• Molecular Biology — 18 citations into it
• Computer Vision and Pattern Recognition — 17 citations into it
• Computer Networks and Communications — 13 citations into it
• Management Science and Operations Research — 13 citations into it

Whom Benedek Rozemberczki trusts overall

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

1. Diederik P. Kingma
2. Tomas Mikolov
3. Bryan Perozzi
4. Geoffrey E. Hinton
5. Yoshua Bengio
6. Ilya Sutskever
7. Thomas Kipf
8. Jimmy Ba
9. Max Welling
10. Aditya Grover

Who trusts Benedek Rozemberczki overall

1. Sitao Luan
2. Xiyuan Wang
3. Yujun Yan
4. Yao Ma
5. Yue Gao
6. Mingguo He
7. Jiong Zhu
8. Zhewei Wei
9. Oleg Platonov
10. Ming Chen

Papers from Benedek Rozemberczki's perspective

Ordered by how strongly the researchers Benedek Rozemberczki 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. Attention is All you Need (Neural Information Processing Systems)
5. Long Short-Term Memory
6. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
8. Learning Multiple Layers of Features from Tiny Images
9. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• GEMSEC: Graph Embedding with Self Clustering (2018) — 365 citations
• Characteristic Functions on Graphs: Birds of a Feather, from Statistical Descriptors to Parametric Models (2020) — 429 citations
• Multi-scale Attributed Node Embedding (2019) — 1,111 citations
• The Shapley Value in Machine Learning (2022) — 361 citations
• Scaling Graph Neural Networks with Approximate PageRank (2020) — 434 citations