91 citations · 441 across the 19 of their papers we have counts for
20 papers
COVID-19 adaptive humoral immunity models: weakly neutralizing versus antibody-disease enhancement scenarios
Antoine Danchin, Oriane Pagani-Azizi, Gabriel Turinici +1
The interplay between the virus, infected cells and the immune responses to SARS-CoV-2 is still under debate. Extending the basic model of viral dynamics we propose here a formal a…
Diversity in deep generative models and generative AI
Gabriel Turinici
The decoder-based machine learning generative algorithms such as Generative Adversarial Networks (GAN), Variational Auto-Encoders (VAE), Transformers show impressive results when c…
Architectures of epidemic models: accommodating constraints from empirical and clinical data
Gabriel Turinici
Deterministic compartmental models have been used extensively in modeling epidemic propagation. These models are required to fit available data and numerical procedures are often i…
Convergence dynamics of Generative Adversarial Networks: the dual metric flows
Gabriel Turinici
Fitting neural networks often resorts to stochastic (or similar) gradient descent which is a noise-tolerant (and efficient) resolution of a gradient descent dynamics. It outputs a…
Contact rate epidemic control of COVID-19: an equilibrium view
Romuald Elie, Emma Hubert, Gabriel Turinici
We consider the control of the COVID-19 pandemic through a standard SIR compartmental model. This control is induced by the aggregation of individuals' decisions to limit their soc…
Stochastic Runge-Kutta methods and adaptive SGD-G2 stochastic gradient descent
Imen Ayadi, Gabriel Turinici
The minimization of the loss function is of paramount importance in deep neural networks. On the other hand, many popular optimization algorithms have been shown to correspond to s…