Catherine Matias

Statistical and Nonlinear Physics · 2005–2019

A researcher in the Science corpus: 4 papers; trusts 31 researchers and is trusted by 36 researchers here, as inferred from the citation graph.

Subfields

• Statistical and Nonlinear Physics — 32 citations into it
• Artificial Intelligence — 20 citations into it; #6,719 of 35,345 by trust here, #12,974 by citations
• Statistics and Probability — 10 citations into it; #1,682 of 12,932 by trust here, #2,800 by citations
• Molecular Biology — 9 citations into it
• Computer Networks and Communications — 8 citations into it
• Graph Learning — 7 citations into it

Whom Catherine Matias trusts overall

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

1. Petter Holme
2. Jean-Jacques Daudin
3. Michael I. Jordan
4. Eric P. Xing
5. Brian Karrer
6. Arthur P. Dempster
7. Eric D. Kolaczyk
8. Mark Newman
9. Elizabeth S. Allman
10. Edoardo M. Airoldi

Who trusts Catherine Matias overall

1. A. Meister
2. Hiroyuki Kasahara
3. Cristina Butucea
4. Gongjun Xu
5. John A. Rhodes
6. Michael Levine
7. Didier Chauveau
8. Daniel J. Hsu
9. Fabienne Comte
10. Vincent Miele

Papers from Catherine Matias's perspective

Ordered by how strongly the researchers Catherine Matias 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. Learning Multiple Layers of Features from Tiny Images
9. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• Identifiability of parameters in latent structure models with many observed variables (2008) — 589 citations
• Minimax estimation of the noise level and of the deconvolution density in a semiparametric convolution model (2005) — 78 citations
• Nine quick tips for analyzing network data (2019) — 30 citations
• Statistical clustering of temporal networks through a dynamic stochastic block model (2015) — 351 citations