Jean-Emmanuel Bibault

Cancer Letters · 2016

A researcher in the Science corpus: 1 paper; trusts 2 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Whom Jean-Emmanuel Bibault trusts overall

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

1. Vladimir Naumovich Vapnik
2. David E. Rumelhart
3. J. Ross Quinlan

Papers from Jean-Emmanuel Bibault's perspective

Ordered by how strongly the researchers Jean-Emmanuel Bibault trusts endorse each paper. Same corpus, a different order for every researcher.

1. Estimation of Dependences Based on Empirical Data
2. Large-scale deep unsupervised learning using graphics processors
3. Securing Personal Health Records in Cloud Computing: Patient-Centric and Fine-Grained Data Access Control in Multi-owner Settings (Security and Privacy in Communication Networks)
4. Parallel Distributed Processing: Explorations in the Micro-structure of Cognition
5. Class prediction and discovery using gene microarray and proteomics mass spectroscopy data: curses, caveats, cautions (Bioinform.)
6. Improved Survival with Ipilimumab in Patients with Metastatic Melanoma (New England Journal of Medicine)
7. Machine learning applications in cancer prognosis and prediction
8. Induction of decision trees
9. Gradient-based learning applied to document recognition
10. Distilling the Knowledge in a Neural Network (arXiv.org)

Their papers

• Big Data and machine learning in radiation oncology: State of the art and future prospects. (2016) — 307 citations