11 citations · 23 across the 5 of their papers we have counts for
5 papers
Industry-Scale Orchestrated Federated Learning for Drug Discovery
Martijn Oldenhof, Gergely Ács, Balázs Pejó +44
To apply federated learning to drug discovery we developed a novel platform in the context of European Innovative Medicines Initiative (IMI) project MELLODDY (grant n°831472), whic…
Substra: a framework for privacy-preserving, traceable and collaborative Machine Learning
Mathieu N Galtier, Camille Marini
Machine learning is promising, but it often needs to process vast amounts of sensitive data which raises concerns about privacy. In this white-paper, we introduce Substra, a distri…
Machine learning for classification and quantification of monoclonal antibody preparations for cancer therapy
Laetitia Le, Camille Marini, Alexandre Gramfort +9
Monoclonal antibodies constitute one of the most important strategies to treat patients suffering from cancers such as hematological malignancies and solid tumors. In order to guar…
Morpheo: Traceable Machine Learning on Hidden data
Mathieu Galtier, Camille Marini
Morpheo is a transparent and secure machine learning platform collecting and analysing large datasets. It aims at building state-of-the art prediction models in various fields wher…
Relative entropy minimizing noisy non-linear neural network to approximate stochastic processes
Mathieu N. Galtier, Camille Marini, Gilles Wainrib +1
A method is provided for designing and training noise-driven recurrent neural networks as models of stochastic processes. The method unifies and generalizes two known separate mode…