21 citations · 25 across the 3 of their papers we have counts for
8 papers · 1 filter
A new perspective on probabilistic image modeling
Alexander Gepperth
We present the Deep Convolutional Gaussian Mixture Model (DCGMM), a new probabilistic approach for image modeling capable of density estimation, sampling and tractable inference. D…
Continual Learning with Fully Probabilistic Models
Benedikt Pfülb, Alexander Gepperth, Benedikt Bagus
We present an approach for continual learning (CL) that is based on fully probabilistic (or generative) models of machine learning. In contrast to, e.g., GANs that are "generative"…
Overcoming Catastrophic Forgetting with Gaussian Mixture Replay
Benedikt Pfülb, Alexander Gepperth
We present Gaussian Mixture Replay (GMR), a rehearsal-based approach for continual learning (CL) based on Gaussian Mixture Models (GMM). CL approaches are intended to tackle the pr…
A Rigorous Link Between Self-Organizing Maps and Gaussian Mixture Models
Alexander Gepperth, Benedikt Pfülb
This work presents a mathematical treatment of the relation between Self-Organizing Maps (SOMs) and Gaussian Mixture Models (GMMs). We show that energy-based SOM models can be inte…
A Study of Deep Learning for Network Traffic Data Forecasting
Benedikt Pfülb, Christoph Hardegen, Alexander Gepperth +1
We present a study of deep learning applied to the domain of network traffic data forecasting. This is a very important ingredient for network traffic engineering, e.g., intelligen…
A comprehensive, application-oriented study of catastrophic forgetting in DNNs
B. Pfülb, A. Gepperth
We present a large-scale empirical study of catastrophic forgetting (CF) in modern Deep Neural Network (DNN) models that perform sequential (or: incremental) learning. A new experi…