Jennifer Neville

Artificial Intelligence · 2000–2021

A researcher in the Science corpus: 10 papers; trusts 86 researchers and is trusted by 51 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 74 citations into it; #10,852 of 35,345 by trust here, #11,750 by citations
• Graph Learning — 29 citations into it
• Statistical and Nonlinear Physics — 27 citations into it; #4,427 of 10,127 by trust here, #3,172 by citations
• Computer Vision and Pattern Recognition — 24 citations into it
• Representation & Self-Supervised Learning — 21 citations into it
• Information Systems — 20 citations into it
• Probabilistic & Bayesian Methods — 13 citations into it
• Computer Networks and Communications — 12 citations into it

Whom Jennifer Neville trusts overall

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

1. David D. Jensen
2. Jon M. Kleinberg
3. Julian Besag
4. B. Taskar
5. Sofus A. Macskassy
6. Andrew McCallum
7. Kevin P. Murphy
8. Daphne Koller
9. David E. Heckerman
10. Foster J. Provost

Who trusts Jennifer Neville overall

1. Lei Tang
2. Bryan Perozzi
3. David D. Jensen
4. Huan Liu
5. Charu C. Aggarwal
6. Prithviraj Sen
7. Sofus A. Macskassy
8. Pedro M. Domingos
9. Brian Gallagher
10. Juzheng Li

Papers from Jennifer Neville's perspective

Ordered by how strongly the researchers Jennifer Neville 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. Gradient-based learning applied to document recognition
5. Long Short-Term Memory
6. Attention is All you Need (Neural Information Processing Systems)
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. Learning Multiple Layers of Features from Tiny Images
10. Going deeper with convolutions

Their papers

• A bias/variance decomposition for models using collective inference (2008) — 23 citations
• Guided data repair (2011) — 233 citations
• Adversarial Graph Augmentation to Improve Graph Contrastive Learning (2021) — 456 citations
• Structured Comparative Analysis of Systems Logs to Diagnose Performance Problems (2012) — 314 citations
• Modeling relationship strength in online social networks (2010) — 727 citations
• Relational Dependency Networks (2007) — 349 citations
• Leveraging relational autocorrelation with latent group models (2005) — 147 citations
• Why collective inference improves relational classification (2004) — 316 citations
• Linkage and Autocorrelation Cause Feature Selection Bias in Relational Learning (2002) — 202 citations
• Iterative Classification in Relational Data (2000) — 411 citations