Jaewon Yang
Statistical and Nonlinear Physics · 2010–2015
A researcher in the Science corpus: 9 papers; trusts 100 researchers and is trusted by 65 researchers here, as inferred from the citation graph.
Subfields
- Statistical and Nonlinear Physics — 150 citations into it; #368 of 10,127 by trust here, #719 by citations
- Molecular Biology — 65 citations into it; #2,785 of 17,773 by trust here, #12,227 by citations
- Artificial Intelligence — 45 citations into it; #6,082 of 35,345 by trust here, #9,968 by citations
- Computer Networks and Communications — 34 citations into it; #1,347 of 9,886 by trust here, #2,424 by citations
- Graph Learning — 25 citations into it; #475 of 693 by trust here, #662 by citations
- Information Systems — 20 citations into it
- Algorithms & Complexity — 19 citations into it
- Sociology and Political Science — 17 citations into it
Whom Jaewon Yang trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Jure Leskovec
- Mark Newman
- Santo Fortunato
- Martin Rosvall
- Edoardo M. Airoldi
- Yong-Yeol Ahn
- Gergely Palla
- Chih-Jen Lin
- David M. Blei
- Lars Backstrom
Who trusts Jaewon Yang overall
- Jure Leskovec
- Santo Fortunato
- Joan Bruna
- Bryan Perozzi
- Rok Sosic
- Hudson Silva Borges
- Darko Hric
- Mark Newman
- Julian McAuley
- Ninareh Mehrabi
Papers from Jaewon Yang's perspective
Ordered by how strongly the researchers Jaewon Yang 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)
- Long Short-Term Memory
- ImageNet: A large-scale hierarchical image database
- Gradient-based learning applied to document recognition
- Attention is All you Need (Neural Information Processing Systems)
- Learning Multiple Layers of Features from Tiny Images
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
- Going deeper with convolutions
Their papers
- Community-Affiliation Graph Model for Overlapping Network Community Detection (2012) — 309 citations
- Overlapping Communities Explain Core–Periphery Organization of Networks (2014) — 164 citations
- Patterns of temporal variation in online media (2011) — 1,092 citations
- Personalizing LinkedIn Feed (2015) — 41 citations
- Detecting cohesive and 2-mode communities indirected and undirected networks (2014) — 83 citations
- Community Detection in Networks with Node Attributes (2013) — 898 citations
- Overlapping community detection at scale: a nonnegative matrix factorization approach (2013) — 981 citations
- Defining and evaluating network communities based on ground-truth (2012) — 2,350 citations
- Modeling Information Diffusion in Implicit Networks (2010) — 584 citations