Austin R. Benson

Statistical and Nonlinear Physics · 2016–2020

A researcher in the Science corpus: 10 papers; trusts 95 researchers and is trusted by 9 researchers here, as inferred from the citation graph.

Subfields

• Statistical and Nonlinear Physics — 135 citations into it; #2,972 of 10,127 by trust here, #1,381 by citations
• Artificial Intelligence — 101 citations into it
• Computer Vision and Pattern Recognition — 66 citations into it; #18,458 of 21,575 by trust here, #13,269 by citations
• Molecular Biology — 55 citations into it; #8,118 of 17,773 by trust here, #10,193 by citations
• Computational Theory and Mathematics — 37 citations into it; #6,371 of 12,182 by trust here, #2,931 by citations
• Graph Learning — 34 citations into it; #436 of 693 by trust here, #481 by citations
• Computer Networks and Communications — 28 citations into it
• Information Systems — 25 citations into it

Whom Austin R. Benson trusts overall

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

1. Mark Newman
2. Jure Leskovec
3. Matthieu Latapy
4. Linyuan Lu
5. Duncan J. Watts
6. Lada A. Adamic
7. Jon M. Kleinberg
8. Geoffrey E. Hinton
9. Aaron Clauset
10. Réka Albert

Who trusts Austin R. Benson overall

1. Marinka Zitnik
2. Jure Leskovec
3. Michael T. Schaub
4. Rok Sosic
5. Federico Monti
6. Yanbang Wang
7. Fabrizio Frasca
8. Lek-Heng Lim
9. S. Majhi
10. William L. Hamilton

Papers from Austin R. Benson's perspective

Ordered by how strongly the researchers Austin R. Benson 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. Long Short-Term Memory
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. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
9. Et al
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• Combining Label Propagation and Simple Models Out-performs Graph Neural Networks (2020) — 328 citations
• Higher-order organization of complex networks (2016) — 1,353 citations
• Simplicial closure and higher-order link prediction (2018) — 619 citations
• Random Walks on Simplicial Complexes and the normalized Hodge Laplacian (2018) — 339 citations
• Neural Jump Stochastic Differential Equations (2019) — 281 citations
• Higher-order clustering in networks (2017) — 113 citations
• Set-Structured Latent Representations (2020)
• Better Set Representations For Relational Reasoning (2020) — 22 citations
• Local Higher-Order Graph Clustering (2017) — 696 citations
• Motifs in Temporal Networks (2016) — 801 citations