3 papers
cs.LG2025
PMODE: Theoretically Grounded and Modular Mixture Modeling
Robert A. Vandermeulen
We introduce PMODE (Partitioned Mixture Of Density Estimators), a general and modular framework for mixture modeling with both parametric and nonparametric components. PMODE builds…
cs.CV2025
Human alignment of neural network representations
Lukas Muttenthaler, Jonas Dippel, Lorenz Linhardt +2
Today's computer vision models achieve human or near-human level performance across a wide variety of vision tasks. However, their architectures, data, and learning algorithms diff…
stat.ML2024
Dimension-independent rates for structured neural density estimation
Robert A. Vandermeulen, Wai Ming Tai, Bryon Aragam
We show that deep neural networks achieve dimension-independent rates of convergence for learning structured densities such as those arising in image, audio, video, and text applic…