Sparse dictionary learning (DL) has emerged as a powerful approach to extract semantically meaningful concepts from the internals of large language models (LLMs) trained mainly in the text domain. In this work, we explore whether DL can extract meaningful concepts from less human-interpretable scientific data, such as vision foundation models trained on cell microscopy images, where limited prior knowledge exists about which high-level concepts should arise. We propose a novel combination of a sparse DL algorithm, Iterative Codebook Feature Learning (ICFL), with a PCA whitening pre-processing step derived from control data. Using this combined approach, we successfully retrieve biologically meaningful concepts, such as cell types and genetic perturbations. Moreover, we demonstrate how our method reveals subtle morphological changes arising from human-interpretable interventions, offering a promising new direction for scientific discovery via mechanistic interpretability in bioimaging.

Recent years have seen growing interest in deterministic search approaches to spike-and-slab Bayesian variable selection. Such methods have focused on the goal of finding a global mode to identify a “best model”. However, the report of a single model will be a misleading reflection of the model uncertainty inherent in a highly multimodal posterior. Motivated by non-parametric variational Bayes strategies, we move beyond this limitation by proposing an ensemble optimization approach to identify a collection of representative posterior modes. By deploying determinantal penalty functions as diversity regularizers, our approach performs regularization over multiple locations of the posterior. The key driver of these determinantal penalties is a kernel function that induces repulsion in the latent model space domain.