David F. Gleich

Statistical and Nonlinear Physics · 2014–2017

A researcher in the Science corpus: 4 papers; trusts 48 researchers and is trusted by 58 researchers here, as inferred from the citation graph.

Subfields

• Statistical and Nonlinear Physics — 53 citations into it; #1,295 of 10,127 by trust here, #2,437 by citations
• Artificial Intelligence — 31 citations into it; #8,778 of 35,345 by trust here, #21,878 by citations
• Information Systems — 25 citations into it; #1,374 of 8,771 by trust here, #5,713 by citations
• Molecular Biology — 25 citations into it; #8,253 of 17,773 by trust here, #11,410 by citations
• Computational Theory and Mathematics — 16 citations into it; #7,151 of 12,182 by trust here, #4,365 by citations
• Computer Networks and Communications — 15 citations into it
• Graph Learning — 13 citations into it; #435 of 693 by trust here, #481 by citations
• Computer Vision and Pattern Recognition — 12 citations into it

Whom David F. Gleich trusts overall

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

1. Fan Chung
2. Olaf Sporns
3. Pavel Berkhin
4. Eugene Garfield
5. Glen Jeh
6. Lars Backstrom
7. Jon M. Kleinberg
8. Zoltán Gyöngyi
9. Hazel Victoria Campbell
10. Dengyong Zhou

Who trusts David F. Gleich overall

1. Leonard Adolphs
2. James Martens
3. Jonas Köhler
4. Aurélien Lucchi
5. Jorge Nocedal
6. Simon Schmitt
7. Michael T. Schaub
8. Jose H. Blanchet
9. Shreyas Hampali
10. Maher Nouiehed

Papers from David F. Gleich's perspective

Ordered by how strongly the researchers David F. Gleich 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. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Long Short-Term Memory
5. Attention is All you Need (Neural Information Processing Systems)
6. Gradient-based learning applied to document recognition
7. Authoritative sources in a hyperlinked environment
8. ImageNet: A large-scale hierarchical image database
9. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. Going deeper with convolutions

Their papers

• Higher-order organization of complex networks (2016) — 1,353 citations
• TRUST REGION METHODS (2017)
• Local Higher-Order Graph Clustering (2017) — 696 citations
• PageRank beyond the Web (2014) — 731 citations