Carey E. Priebe

Artificial Intelligence · Cell · 2001–2021

A researcher in the Science corpus: 20 papers; trusts 46 researchers and is trusted by 15 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 82 citations into it; #14,403 of 35,345 by trust here, #14,718 by citations
• Statistical and Nonlinear Physics — 56 citations into it; #5,246 of 10,127 by trust here, #4,820 by citations
• Computer Vision and Pattern Recognition — 50 citations into it
• Representation & Self-Supervised Learning — 39 citations into it
• Statistics and Probability — 36 citations into it; #4,385 of 12,932 by trust here, #3,513 by citations
• Statistical Learning Theory — 21 citations into it
• Molecular Biology — 18 citations into it
• Geometry and Topology — 16 citations into it

Whom Carey E. Priebe trusts overall

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

1. Ulrike von Luxburg
2. Brian Karrer
3. Mikhail Belkin
4. Mark Newman
5. Jianbo Shi
6. Miroslav Fiedler
7. Frank McSherry
8. Tom A. B. Snijders
9. Peter J. Bickel
10. Daniel Lewis Sussman

Who trusts Carey E. Priebe overall

1. Charu C. Aggarwal
2. Jason G. DeVinney
3. Avanti Athreya
4. Oscar Li
5. Hiroyuki Kasahara
6. Leman Akoglu
7. Ichigaku Takigawa
8. Jana Diesner
9. Hayden S. Helm
10. Manish Gupta

Papers from Carey E. Priebe's perspective

Ordered by how strongly the researchers Carey E. Priebe trusts endorse each paper. Same corpus, a different order for every researcher.

1. Gradient-based learning applied to document recognition
2. Learning Multiple Layers of Features from Tiny Images
3. Attention is All you Need (Neural Information Processing Systems)
4. ImageNet classification with deep convolutional neural networks
5. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
6. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
7. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
8. ImageNet: A large-scale hierarchical image database
9. Normalized cuts and image segmentation
10. Long Short-Term Memory

Their papers

• Consistent estimation of mixture complexity (2001) — 98 citations
• A partition-based similarity for classification distributions (2020) — 6 citations
• Limit theorems for eigenvectors of the normalized Laplacian for random graphs (2016) — 109 citations
• A Limit Theorem for Scaled Eigenvectors of Random Dot Product Graphs (2013) — 125 citations
• Out-of-sample extension of graph adjacency spectral embedding (2018)
• Statistical Inference on Random Dot Product Graphs: a Survey (2017) — 290 citations
• A central limit theorem for an omnibus embedding of multiple random graphs and implications for multiscale network inference (2017)
• Inducing a hierarchy for multi-class classification problems (2021)
• Omnidirectional Transfer for Quasilinear Lifelong Learning (2021)
• Towards a theory of out-of-distribution learning (2021)
• Dynamic Silos: Modularity in intra-organizational communication networks before and during the Covid-19 pandemic (2021)
• A general approach to progressive learning (2020) — 10 citations
• Modern Machine Learning: Partition & Vote (2020)
• The complete connectome of a learning and memory centre in an insect brain (2017) — 479 citations
• Saturated Reconstruction of a Volume of Neocortex. (2015) — 1,027 citations
• A Consistent Adjacency Spectral Embedding for Stochastic Blockmodel Graphs (2011) — 318 citations
• Manifold Matching: Joint Optimization of Fidelity and Commensurability (2011)
• A new family of random graphs for testing spatial segregation (2007) — 25 citations
• Scan Statistics on Enron Graphs (2005) — 365 citations
• Classification Using Class Cover Catch Digraphs (2003) — 79 citations