Gradient-based training of Gaussian Mixture Models in High-Dimensional Spaces

By Alexander Rainer Tassilo Gepperth, Benedikt Pfülb. 2019

4 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Benedikt Pfülb
2. Benedikt Bagus
3. Alexander Rainer Tassilo Gepperth
4. Timothée Lesort
5. Marc Masana
6. Martin Mundt
7. Tyler L. Hayes
8. James Smith

Citations in the corpus

• Continual Learning with Fully Probabilistic Models (2021, methodology): “using the efficient training procedure for high-dimensional streaming data described in [38].”
• Image Modeling with Deep Convolutional Gaussian Mixture Models (2021, methodology): “An SGD approach that achieves robust convergence even without k-means-based parameter initialization is presented by [7].”