It has been observed that representations learned by distinct neural networks conceal
structural similarities when the models are trained under similar inductive biases. From a …

Neural models learn data representations that lie on low-dimensional manifolds, yet
modeling the relation between these representational spaces is an ongoing challenge. By …

Aligning data from different domains is a fundamental problem in machine learning with
broad applications across very different areas, most notably aligning experimental readouts …

To gain insight into the mechanisms behind machine learning methods, it is crucial to
establish connections among the features describing data points. However, these …

With the wide adaption of deep learning and pre-trained models rises the question of how to
effectively reuse existing latent spaces for new applications. One important question is how …