5 citations · 14 across the 5 of their papers we have counts for
5 papers
A High-Order Cumulant Extension of Quasi-Linkage Equilibrium
Kai S. Shimagaki, Jorge Fernandez-de-Cossio-Diaz, Mauro Pastore +3
A central question in evolutionary biology is how to quantitatively understand the dynamics of genetically diverse populations. Modeling the genotype distribution is challenging, a…
Task learning through stimulation-induced plasticity in neural networks
Francesco Borra, Simona Cocco, Rémi Monasson
Synaptic plasticity dynamically shapes the connectivity of neural systems and is key to learning processes in the brain. To what extent the mechanisms of plasticity can be exploite…
Restoring balance: principled under/oversampling of data for optimal classification
Emanuele Loffredo, Mauro Pastore, Simona Cocco +1
Class imbalance in real-world data poses a common bottleneck for machine learning tasks, since achieving good generalization on under-represented examples is often challenging. Mit…
Unlearning regularization for Boltzmann Machines
Enrico Ventura, Simona Cocco, Rémi Monasson +1
Boltzmann Machines (BMs) are graphical models with interconnected binary units, employed for the unsupervised modeling of data distributions. When trained on real data, BMs show th…
Gaussian Closure Scheme in the Quasi-Linkage Equilibrium Regime of Evolving Genome Populations
Eugenio Mauri, Simona Cocco, Rémi Monasson
Describing the evolution of a population of genomes evolving in a complex fitness landscape is generally very hard. We here introduce an approximate Gaussian closure scheme to char…