1 citations · 1 across the 3 of their papers we have counts for
5 papers
Contrastive Linearized Laplace: Loss Geometry, Valid Repairs, and Reliable Cores
Francisco J. Sáez-Maldonado, Luis A. Ortega, Andrés R. Masegosa +1
While contrastive learning is widely used to learn deterministic representations, it does not inherently provide estimates of uncertainty. Post-hoc uncertainty estimation methods,…
Generalized Matheron Variational Implicit Processes
Luis A. Ortega, Andrés R. Masegosa, Thomas D. Nielsen
Implicit-process priors specify distributions over functions through sample-forward mechanisms such as Bayesian neural networks and stochastic simulators, but their function-space…
Flow-Transformed Implicit Processes for Function-Space Variational Inference
Luis A. Ortega, Andrés R. Masegosa, Thomas D. Nielsen
Implicit-process priors define distributions over functions through flexible generative mechanisms, making them attractive for Bayesian function-space modelling. However, performin…
UncertainGen: Uncertainty-Aware Representations of DNA Sequences for Metagenomic Binning
Abdulkadir Celikkanat, Andres R. Masegosa, Mads Albertsen +1
Metagenomic binning aims to cluster DNA fragments from mixed microbial samples into their respective genomes, a critical step for downstream analyses of microbial communities. Exis…
Revisiting K-mer Profile for Effective and Scalable Genome Representation Learning
Abdulkadir Celikkanat, Andres R. Masegosa, Thomas D. Nielsen
Obtaining effective representations of DNA sequences is crucial for genome analysis. Metagenomic binning, for instance, relies on genome representations to cluster complex mixtures…