8 citations · 9 across the 4 of their papers we have counts for
4 papers · 1 filter
FiGuRO: Intrinsic Dimension Estimation for Multi-Modal Data
Viktoria Schuster, Sana Tonekaboni, Caroline Uhler
Determining the complexity, or Intrinsic Dimension (ID), of data is fundamental to efficient and interpretable representation learning. This is particularly challenging in multi-mo…
MultiLoReFT: Decoupling Shared and Modality-Specific Subspaces in Multimodal Learning via Low-Rank Representation Fine-Tuning
Sana Tonekaboni, Viktoria Schuster, Caroline Uhler
Real-world perception and decision making are inherently multimodal, integrating complementary signals across modalities. However, training multimodal models faces two main obstacl…
The Deep Generative Decoder: MAP estimation of representations improves modeling of single-cell RNA data
Viktoria Schuster, Anders Krogh
Learning low-dimensional representations of single-cell transcriptomics has become instrumental to its downstream analysis. The state of the art is currently represented by neural…
A manifold learning perspective on representation learning: Learning decoder and representations without an encoder
Viktoria Schuster, Anders Krogh
Autoencoders are commonly used in representation learning. They consist of an encoder and a decoder, which provide a straightforward way to map n-dimensional data in input space to…