Marie-Pier Scott-Boyer

Molecular Biology · 2021

A researcher in the Science corpus: 1 paper; trusts 3 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Molecular Biology — 28 citations into it
• Statistical Learning Theory — 5 citations into it

Whom Marie-Pier Scott-Boyer trusts overall

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

1. Markus Ringnér
2. Eric J. Beh
3. Ingo Steinwart
4. Daniel D. Lee
5. Bernhard Schölkopf
6. Marinka Zitnik
7. David Roi Hardoon
8. Yan V. Sun
9. Hui Zou
10. Ronglai Shen

Papers from Marie-Pier Scott-Boyer's perspective

Ordered by how strongly the researchers Marie-Pier Scott-Boyer 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. Learning the parts of objects by non-negative matrix factorization
4. Integrative clustering of multiple genomic data types using a joint latent variable model with application to breast and lung cancer subtype analysis (Bioinform.)
5. Learning the Kernel Matrix with Semidefinite Programming
6. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
7. Nonlinear Component Analysis as a Kernel Eigenvalue Problem
8. Regression Shrinkage and Selection via the Lasso
9. Methods of integrating data to uncover genotype–phenotype interactions (Nature reviews genetics)
10. Attention is All you Need (Neural Information Processing Systems)

Their papers

• Integration strategies of multi-omics data for machine learning analysis (2021) — 525 citations