897 citations · 1.2k across the 22 of their papers we have counts for
8 papers · 1 filter
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…
Set Learning for Accurate and Calibrated Models
Lukas Muttenthaler, Robert A. Vandermeulen, Qiuyi Zhang +2
Model overconfidence and poor calibration are common in machine learning and difficult to account for when applying standard empirical risk minimization. In this work, we propose a…
VICE: Variational Interpretable Concept Embeddings
Lukas Muttenthaler, Charles Y. Zheng, Patrick McClure +3
A central goal in the cognitive sciences is the development of numerical models for mental representations of object concepts. This paper introduces Variational Interpretable Conce…
Deep Anomaly Detection by Residual Adaptation
Lucas Deecke, Lukas Ruff, Robert A. Vandermeulen +1
Deep anomaly detection is a difficult task since, in high dimensions, it is hard to completely characterize a notion of "differentness" when given only examples of normality. In th…
Input Hessian Regularization of Neural Networks
Waleed Mustafa, Robert A. Vandermeulen, Marius Kloft
Regularizing the input gradient has shown to be effective in promoting the robustness of neural networks. The regularization of the input's Hessian is therefore a natural next step…
A Unifying Review of Deep and Shallow Anomaly Detection
Lukas Ruff, Jacob R. Kauffmann, Robert A. Vandermeulen +5
Deep learning approaches to anomaly detection have recently improved the state of the art in detection performance on complex datasets such as large collections of images or text.…
Rethinking Assumptions in Deep Anomaly Detection
Lukas Ruff, Robert A. Vandermeulen, Billy Joe Franks +2
Though anomaly detection (AD) can be viewed as a classification problem (nominal vs. anomalous) it is usually treated in an unsupervised manner since one typically does not have ac…
Deep Semi-Supervised Anomaly Detection
Lukas Ruff, Robert A. Vandermeulen, Nico Görnitz +4
Deep approaches to anomaly detection have recently shown promising results over shallow methods on large and complex datasets. Typically anomaly detection is treated as an unsuperv…