4 citations · 4 across the 3 of their papers we have counts for
3 papers
Implicitly Guided Design with PropEn: Match your Data to Follow the Gradient
Nataša Tagasovska, Vladimir Gligorijević, Kyunghyun Cho +1
Across scientific domains, generating new models or optimizing existing ones while meeting specific criteria is crucial. Traditional machine learning frameworks for guided design u…
Retrospective Uncertainties for Deep Models using Vine Copulas
Nataša Tagasovska, Firat Ozdemir, Axel Brando
Despite the major progress of deep models as learning machines, uncertainty estimation remains a major challenge. Existing solutions rely on modified loss functions or architectura…
A Pareto-optimal compositional energy-based model for sampling and optimization of protein sequences
Nataša Tagasovska, Nathan C. Frey, Andreas Loukas +9
Deep generative models have emerged as a popular machine learning-based approach for inverse design problems in the life sciences. However, these problems often require sampling ne…