CORDIS Project
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This project explores hybrid learning systems that combine deep learning with probabilistic models. It aims to enhance inference capabilities in neural networks, particularly for tasks like image segmentation and generative modeling.
We have recently witnessed a considerable interest in probabilistic models within deep learning, leading to e.g. generative adversarial networks, deep generative networks, neural auto-regressive density estimators and Pixel-RNNs/CNNs.
Furthermore, sum-product networks (SPNs) are a recent deep architecture with a unique advantage over the aforementioned models: they allow both exact and efficient inference, implemented in terms of simple network passes.
However, SPNs are a constrained type of neu…
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