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Found 8 papers, 1 papers with code
Continual Learning with Deep Learning Methods in an Application-Oriented Context
This dissertation is set in the context of continual machine learning with deep learning methods.
Continual Learning with Fully Probabilistic Models
As a concrete realization of generative continual learning, we propose Gaussian Mixture Replay (GMR).
Overcoming Catastrophic Forgetting with Gaussian Mixture Replay
In addition, task boundaries can be detected by applying GMM density estimation.
Image Modeling with Deep Convolutional Gaussian Mixture Models
For generating sharp images with DCGMMs, we introduce a new gradient-based technique for sampling through non-invertible operations like convolution and pooling.
A Rigorous Link Between Self-Organizing Maps and Gaussian Mixture Models
This work presents a mathematical treatment of the relation between Self-Organizing Maps (SOMs) and Gaussian Mixture Models (GMMs).
Gradient-based training of Gaussian Mixture Models for High-Dimensional Streaming Data
We present an approach for efficiently training Gaussian Mixture Model (GMM) by Stochastic Gradient Descent (SGD) with non-stationary, high-dimensional streaming data.
Gradient-based training of Gaussian Mixture Models in High-Dimensional Spaces
We present an approach for efficiently training Gaussian Mixture Models (GMMs) with Stochastic Gradient Descent (SGD) on large amounts of high-dimensional data (e. g., images).
A Study of Deep Learning for Network Traffic Data Forecasting
We present a study of deep learning applied to the domain of network traffic data forecasting.
