80 citations · 179 across the 16 of their papers we have counts for
4 papers · 1 filter
Gradient-based training of Gaussian Mixture Models for High-Dimensional Streaming Data
Alexander Gepperth, Benedikt Pfülb
We present an approach for efficiently training Gaussian Mixture Model (GMM) by Stochastic Gradient Descent (SGD) with non-stationary, high-dimensional streaming data. Our training…
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…
Catastrophic forgetting: still a problem for DNNs
B. Pfülb, A. Gepperth, S. Abdullah +1
We investigate the performance of DNNs when trained on class-incremental visual problems consisting of initial training, followed by retraining with added visual classes. Catastrop…