High Dimensional
High-dimensional data analysis focuses on extracting meaningful information and building predictive models from datasets with numerous variables, often exceeding the number of observations. Current research emphasizes developing computationally efficient algorithms, such as stochastic gradient descent and its variants, and novel model architectures like graph neural networks and deep learning approaches tailored to handle the unique challenges posed by high dimensionality, including issues of sparsity and missing data. These advancements are crucial for addressing complex problems across diverse fields, including scientific modeling, robotics, and financial risk assessment, where high-dimensional data are increasingly prevalent.
Papers
ImplicitSLIM and How it Improves Embedding-based Collaborative Filtering
Ilya Shenbin, Sergey Nikolenko
VENI, VINDy, VICI: a variational reduced-order modeling framework with uncertainty quantification
Paolo Conti, Jonas Kneifl, Andrea Manzoni, Attilio Frangi, Jörg Fehr, Steven L. Brunton, J. Nathan Kutz
The future of cosmological likelihood-based inference: accelerated high-dimensional parameter estimation and model comparison
Davide Piras, Alicja Polanska, Alessio Spurio Mancini, Matthew A. Price, Jason D. McEwen
Comprehensive Multimodal Deep Learning Survival Prediction Enabled by a Transformer Architecture: A Multicenter Study in Glioblastoma
Ahmed Gomaa, Yixing Huang, Amr Hagag, Charlotte Schmitter, Daniel Höfler, Thomas Weissmann, Katharina Breininger, Manuel Schmidt, Jenny Stritzelberger, Daniel Delev, Roland Coras, Arnd Dörfler, Oliver Schnell, Benjamin Frey, Udo S. Gaipl, Sabine Semrau, Christoph Bert, Rainer Fietkau, Florian Putz