Dr. Robert Vandermeulen
Dr. Robert Vandermeulen
Postdoctoral Researcher
Dr. Robert Vandermeulen is a Postdoctoral Researcher at BIFOLD. He earned his PhD in Electrical Engineering at the University of Michigan in 2016. Prior to working at BIFOLD Robert was a postdoctoral researcher at Technische Universität Kaiserslautern. His work focuses on deep anomaly detection and nonparametric statistics.
- Deep Anomaly Detection
- Nonparametric Density Estimation
- Nonparametric Tensor Methods
- Nonparametric Statistics
- Human vs. Neural Network Alignment
Robert A. Vandermeulen, René Saitenmacher
Generalized Identifiability Bounds for Mixture Models with Grouped Samples
Lukas Muttenthaler, Lorenz Linhardt, Jonas Dippel, Robert A. Vandermeulen, Katherine Hermann, Andrew K. Lampinen, Simon Kornblith
Improving neural network representations using human similarity judgments
Lukas Muttenthaler, Robert A. Vandermeulen, Qiuyi Zhang, Thomas Unterthiner, Klaus-Robert Müller
Set Learning for Accurate and Calibrated Models
Philipp Liznerski, Lukas Ruff, Robert A. Vandermeulen, Billy Joe Franks, Klaus-Robert Müller, Marius Kloft
Exposing Outlier Exposure: What Can Be Learned From Few, One, and Zero Outlier Images
Lukas Muttenthaler, Charles Yang Zheng, Patrick McClure, Robert A. Vandermeulen, Martin N. Hebart, Francisco Pereira
VICE: Variational Interpretable Concept Embeddings
Do computers and humans "see" alike?
The field of computer vision has long since left the realm of research and is now used in countless daily applications, such as object recognition and measuring geometric structures of objects. One question that is not or only rarely asked is: To what extent do computer vision systems see the world in the same way that humans do?
Lifting the curse of dimensionality for statistics in ML
The paper “Beyond Smoothness: Incorporating Low-Rank Analysis into Nonparametric Density Estimation” by BIFOLD researcher Dr. Robert A. Vandermeulen and his colleague Dr. Antoine Ledent, Technical University Kaiserslautern, was presented at the Conference on Neural Information Processing Systems (NeurIPS 2021). Their paper provides the first solid theoretical foundations for applying low-rank methods to nonparametric density estimation.