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08/26/2023
Large-scale gradient-based training of Mixtures of Factor Analyzers
Gaussian Mixture Models (GMMs) are a standard tool in data analysis. How...
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03/23/2023
Adiabatic replay for continual learning
Conventional replay-based approaches to continual learning (CL) require,...
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08/30/2022
Beyond Supervised Continual Learning: a Review
Continual Learning (CL, sometimes also termed incremental learning) is a...
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06/08/2022
A Study of Continual Learning Methods for Q-Learning
We present an empirical study on the use of continual learning (CL) meth...
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03/21/2022
A new perspective on probabilistic image modeling
We present the Deep Convolutional Gaussian Mixture Model (DCGMM), a new ...
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08/15/2021
An Investigation of Replay-based Approaches for Continual Learning
Continual learning (CL) is a major challenge of machine learning (ML) an...
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04/19/2021
Continual Learning with Fully Probabilistic Models
We present an approach for continual learning (CL) that is based on full...
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04/19/2021
Overcoming Catastrophic Forgetting with Gaussian Mixture Replay
We present Gaussian Mixture Replay (GMR), a rehearsal-based approach for...
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09/24/2020
A Rigorous Link Between Self-Organizing Maps and Gaussian Mixture Models
This work presents a mathematical treatment of the relation between Self...
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12/18/2019
Gradient-based training of Gaussian Mixture Models in High-Dimensional Spaces
We present an approach for efficiently training Gaussian Mixture Models ...
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09/10/2019
A Study of Deep Learning for Network Traffic Data Forecasting
We present a study of deep learning applied to the domain of network tra...
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10/29/2018