1 citations · 1 across the 3 of their papers we have counts for
5 papers
GeneICL: A Tabular Foundation Model for Bulk Transcriptomics
Michael Bohl, Alexander Theus, David Wissel +1
Gene expression is widely measured in biomedicine, yet clinical outcome prediction remains challenging due to high dimensionality, strong feature correlations, and limited labeled…
Fixed-Point Reasoners: Stable and Adaptive Deep Looped Transformers
Sajad Movahedi, Vera Milovanović, Shlomo Libo Feigin +5
Looped architectures provide an inductive bias toward learning step-by-step procedures for tasks that require compositional reasoning. The number of effective layers reached by loo…
scSSL-Bench: Benchmarking Self-Supervised Learning for Single-Cell Data
Olga Ovcharenko, Florian Barkmann, Philip Toma +4
Self-supervised learning (SSL) has proven to be a powerful approach for extracting biologically meaningful representations from single-cell data. To advance our understanding of SS…
Generalized Linear Mode Connectivity for Transformers
Alexander Theus, Alessandro Cabodi, Sotiris Anagnostidis +3
Understanding the geometry of neural network loss landscapes is a central question in deep learning, with implications for generalization and optimization. A striking phenomenon is…
Feature Clock: High-Dimensional Effects in Two-Dimensional Plots
Olga Ovcharenko, Rita Sevastjanova, Valentina Boeva
Humans struggle to perceive and interpret high-dimensional data. Therefore, high-dimensional data are often projected into two dimensions for visualization. Many applications benef…