4 citations · 4 across the 1 of their papers we have counts for
2 papers
cs.LG2024
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…
cs.LG2022★ 4 cited
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…