5 papers
Beyond Linear and Overcomplete Regimes: A Mean-Field Analysis of Bottleneck Autoencoders
Santanu Das, Ramyak Bilas, Pascal Esser +1
Autoencoders (AEs) learn low-dimensional representations by mapping data into a latent space while minimizing reconstruction error. Despite their empirical success, theoretical und…
Characterizing Learning Dynamics under Relative Reparameterization of Singular Models
Pascal Mattia Esser, Frank Nielsen
A common way to analyze learning of statistical models is to consider operations in the models parameter space, however this becomes challenging when there is no one-to-one mapping…
Theoretical Foundations of Representation Learning using Unlabeled Data: Statistics and Optimization
Pascal Esser, Maximilian Fleissner, Debarghya Ghoshdastidar
Representation learning from unlabeled data has been extensively studied in statistics, data science and signal processing with a rich literature on techniques for dimension reduct…
A Probabilistic Model for Non-Contrastive Learning
Maximilian Fleissner, Pascal Esser, Debarghya Ghoshdastidar
Self-supervised learning (SSL) aims to find meaningful representations from unlabeled data by encoding semantic similarities through data augmentations. Despite its current popular…
Cluster Specific Representation Learning
Mahalakshmi Sabanayagam, Omar Al-Dabooni, Pascal Esser
Representation learning aims to extract meaningful lower-dimensional embeddings from data, known as representations. Despite its widespread application, there is no established def…