13 citations · 29 across the 3 of their papers we have counts for
3 papers
cs.LG2021★ 5 cited
Learning Equivariant Energy Based Models with Equivariant Stein Variational Gradient Descent
Priyank Jaini, Lars Holdijk, Max Welling
We focus on the problem of efficient sampling and learning of probability densities by incorporating symmetries in probabilistic models. We first introduce Equivariant Stein Variat…
cs.LG2019★ 11 cited
Robustness of Generalized Learning Vector Quantization Models against Adversarial Attacks
Sascha Saralajew, Lars Holdijk, Maike Rees +1
Adversarial attacks and the development of (deep) neural networks robust against them are currently two widely researched topics. The robustness of Learning Vector Quantization (LV…
cs.LG2019★ 13 cited
Prototype-based Neural Network Layers: Incorporating Vector Quantization
Sascha Saralajew, Lars Holdijk, Maike Rees +1
Neural networks currently dominate the machine learning community and they do so for good reasons. Their accuracy on complex tasks such as image classification is unrivaled at the…