5 papers · 1 filter
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…
Improved Representation Learning Through Tensorized Autoencoders
Pascal Mattia Esser, Satyaki Mukherjee, Mahalakshmi Sabanayagam +1
The central question in representation learning is what constitutes a good or meaningful representation. In this work we argue that if we consider data with inherent cluster struct…
Near-Optimal Comparison Based Clustering
Michaël Perrot, Pascal Mattia Esser, Debarghya Ghoshdastidar
The goal of clustering is to group similar objects into meaningful partitions. This process is well understood when an explicit similarity measure between the objects is given. How…