Search Results for author:
Found 3 papers, 2 papers with code
Retrospective Uncertainties for Deep Models using Vine Copulas
Despite the major progress of deep models as learning machines, uncertainty estimation remains a major challenge.
Learning Causal Representations of Single Cells via Sparse Mechanism Shift Modeling
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.
A Pareto-optimal compositional energy-based model for sampling and optimization of protein sequences
Deep generative models have emerged as a popular machine learning-based approach for inverse design problems in the life sciences.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
