Edoardo M. Airoldi
Statistical and Nonlinear Physics · 2007–2017
A researcher in the Science corpus: 7 papers; trusts 87 researchers and is trusted by 19 researchers here, as inferred from the citation graph.
Subfields
- Statistical and Nonlinear Physics — 80 citations into it; #2,999 of 10,127 by trust here, #2,503 by citations
- Artificial Intelligence — 73 citations into it; #20,936 of 35,345 by trust here, #27,551 by citations
- Molecular Biology — 49 citations into it; #11,697 of 17,773 by trust here, #8,381 by citations
- Statistics and Probability — 37 citations into it; #9,070 of 12,932 by trust here, #12,077 by citations
- Probabilistic & Bayesian Methods — 23 citations into it
- Experimental and Cognitive Psychology — 17 citations into it; #3,188 of 9,731 by trust here, #3,491 by citations
- Information Systems — 15 citations into it
- Natural Language Processing — 15 citations into it
Whom Edoardo M. Airoldi trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Donald B. Rubin
- Michael I. Jordan
- David M. Blei
- Peter D. Hoff
- Tom A. B. Snijders
- Mark S. Handcock
- T. Griffiths
- Tom Minka
- Diederik P. Kingma
- Radford M. Neal
Who trusts Edoardo M. Airoldi overall
- Jaewon Yang
- Jure Leskovec
- David M. Blei
- Julian McAuley
- Muhan Zhang
- Eric P. Xing
- Prem Gopalan
- Anima Anandkumar
- Jiaxuan You
- Charu C. Aggarwal
Papers from Edoardo M. Airoldi's perspective
Ordered by how strongly the researchers Edoardo M. Airoldi 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)
- Gradient-based learning applied to document recognition
- Long Short-Term Memory
- Attention is All you Need (Neural Information Processing Systems)
- ImageNet: A large-scale hierarchical image database
- Learning Multiple Layers of Features from Tiny Images
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
Their papers
- A Model of Text for Experimentation in the Social Sciences (2016) — 658 citations
- Analysis and design of RNA sequencing experiments for identifying isoform regulation (2010) — 1,367 citations
- Elements of estimation theory for causal effects in the presence of network interference (2017) — 78 citations
- Predicting traffic volumes and estimating the effects of shocks in massive transportation systems (2015) — 104 citations
- Copula variational inference (2015) — 108 citations
- A Survey of Statistical Network Models (2009) — 1,002 citations
- Mixed Membership Stochastic Blockmodels (2007) — 2,215 citations