3 accepted papers
Contrastive learning has emerged as a powerful paradigm for self-supervised representation learning, yet the precise conditions under which it recovers meaningful latent structure remain incompletely understood. We develop a measure-theoretic framework that formalizes the diversity condition, a requ…
Generative modeling of single-cell RNA-seq data is crucial for tasks like trajectory inference, batch effect removal, and simulation of realistic cellular data. However, recent deep generative models simulating synthetic single cells from noise operate on pre-processed continuous gene expression app…
Neural Ordinary Differential Equations (NODEs) have proven successful in learning dynamical systems in terms of accurately recovering the observed trajectories. While different types of sparsity have been proposed to improve robustness, the generalization properties of NODEs for dynamical systems be…