no code implementations • 19 Oct 2022 • Nataša Tagasovska, Nathan C. Frey, Andreas Loukas, Isidro Hötzel, Julien Lafrance-Vanasse, Ryan Lewis Kelly, Yan Wu, Arvind Rajpal, Richard Bonneau, Kyunghyun Cho, Stephen Ra, Vladimir Gligorijević
Deep generative models have emerged as a popular machine learning-based approach for inverse design problems in the life sciences.
no code implementations • 1 Jun 2023 • Ji Won Park, Nataša Tagasovska, Michael Maser, Stephen Ra, Kyunghyun Cho
At the heart of MOBO is the acquisition function, which determines the next candidate to evaluate by navigating the best compromises among the objectives.
1 code implementation • 24 Feb 2023 • Nataša Tagasovska, Firat Ozdemir, Axel Brando
Despite the major progress of deep models as learning machines, uncertainty estimation remains a major challenge.
1 code implementation • 7 Nov 2022 • Romain Lopez, Nataša Tagasovska, Stephen Ra, Kyunghyn Cho, Jonathan K. Pritchard, Aviv Regev
Instead, recent methods propose to leverage non-stationary data, as well as the sparse mechanism shift assumption in order to learn disentangled representations with a causal semantic.