Charalampos E. Tsourakakis
Computer Networks and Communications · 2009–2014
A researcher in the Science corpus: 5 papers; trusts 52 researchers and is trusted by 20 researchers here, as inferred from the citation graph.
Subfields
- Computer Networks and Communications — 25 citations into it
- Computational Theory and Mathematics — 23 citations into it; #11,237 of 12,182 by trust here, #8,756 by citations
- Statistical and Nonlinear Physics — 22 citations into it; #1,931 of 10,127 by trust here, #2,503 by citations
- Artificial Intelligence — 19 citations into it; #22,550 of 35,345 by trust here, #26,682 by citations
- Computational Mathematics — 19 citations into it
- Signal Processing — 15 citations into it
- Information Systems — 13 citations into it; #2,180 of 8,771 by trust here, #3,198 by citations
- Computer Vision and Pattern Recognition — 11 citations into it; #6,489 of 21,575 by trust here, #9,079 by citations
Whom Charalampos E. Tsourakakis trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Muthu Dayalan
- Mark Newman
- Tamás Sarlós
- Petros Drineas
- Michael W. Mahoney
- Paula Fenger
- Jimeng Sun
- Lieven De Lathauwer
- Tamara G. Kolda
- Brett W. Bader
Who trusts Charalampos E. Tsourakakis overall
- Bryan Perozzi
- U. Kang
- Yucheng Low
- Anand Rajaraman
- Warren Schudy
- Xin Huang
- Seshadhri Comandur
- Aydin Buluç
- Rong Chen
- Joseph E. Gonzalez
Papers from Charalampos E. Tsourakakis's perspective
Ordered by how strongly the researchers Charalampos E. Tsourakakis 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)
- Gradient-based learning applied to document recognition
- Long Short-Term Memory
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Attention is All you Need (Neural Information Processing Systems)
- ImageNet: A large-scale hierarchical image database
- Learning Multiple Layers of Features from Tiny Images
- 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)
Their papers
- PEGASUS: A Peta-Scale Graph Mining System Implementation and Observations (2009) — 733 citations
- Denser than the densest subgraph: extracting optimal quasi-cliques with quality guarantees (2013) — 340 citations
- FENNEL: streaming graph partitioning for massive scale graphs (2014) — 372 citations
- DOULION: counting triangles in massive graphs with a coin (2009) — 361 citations
- MACH: Fast Randomized Tensor Decompositions (2009) — 83 citations