Unsupervised learning is becoming an essential tool to analyze the increasingly large
amounts of data produced by atomistic and molecular simulations, in material science, solid …

Collective variable-based enhanced sampling methods have been widely used to study
thermodynamic properties of complex systems. Efficiency and accuracy of these enhanced …

The effectiveness of machine learning algorithms arises from being able to extract useful
features from large amounts of data. As model and dataset sizes increase, dataset …

In this paper, we address the problem of image anomaly detection and segmentation.
Anomaly detection involves making a binary decision as to whether an input image contains …

Rapidly increasing quality of AI-generated content makes it difficult to distinguish between
human and AI-generated texts, which may lead to undesirable consequences for society …

Classically, data interpolation with a parametrized model class is possible as long as the
number of parameters is larger than the number of equations to be satisfied. A puzzling …

Deep neural networks progressively transform their inputs across multiple processing layers.
What are the geometrical properties of the representations learned by these networks? Here …

Classically, data interpolation with a parametrized model class is possible as long as the
number of parameters is larger than the number of equations to be satisfied. A puzzling …

Large transformers are powerful architectures used for self-supervised data analysis across
various data types, including protein sequences, images, and text. In these models, the …

In many applications, it is important to reconstruct a fluid flow field, or some other high-
dimensional state, from limited measurements and limited data. In this work, we propose a …