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Found 7 papers, 3 papers with code
Antibody DomainBed: Out-of-Distribution Generalization in Therapeutic Protein Design
The model predictions are used to determine which designs to evaluate in the lab, and the model is updated on the new measurements to inform the next cycle of decisions.
Uncertainty modeling for fine-tuned implicit functions
In this paper, we introduce Dropsembles, a novel method for uncertainty estimation in tuned implicit functions.
Implicitly Guided Design with PropEn: Match your Data to Follow the Gradient
Matching, combined with an encoder-decoder architecture, forms a domain-agnostic generative framework for property enhancement.
BOtied: Multi-objective Bayesian optimization with tied multivariate ranks
Motivated by this link, we propose the Pareto-compliant CDF indicator and the associated acquisition function, BOtied.
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.
