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pro vyhledávání: '"Angaji, Arman"'
Group-equivariant neural networks have emerged as a data-efficient approach to solve classification and regression tasks, while respecting the relevant symmetries of the data. However, little work has been done to extend this paradigm to the unsuperv
Externí odkaz:
http://arxiv.org/abs/2209.15567
Publikováno v:
J. Stat. Mech. 103502 (2021)
We consider an exponentially growing population of cells undergoing mutations and ask about the effect of reproductive fluctuations (genetic drift) on its long-term evolution. We combine first step analysis with the stochastic dynamics of a birth-dea
Externí odkaz:
http://arxiv.org/abs/2106.14236