Objective
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 neural network and do not reach the full flexibility of the deep learning tool kit available to date. This calls for hybrid learning systems which exploit the superior inference properties of SPNs within other deep learning approaches.
In this project, I will investigate two such approaches. First, I will structurally combine a deep learning architecture (front-end), which extracts a representation from a set of inputs, controlling the parameters of an SPN (back-end) over a set of outputs. This yields a hybrid conditional SPN which facilitates full inference over the output space, and which is naturally applied in structural prediction tasks. Such hybrid SPNs can be expected to be highly expressive and to set new state-of-the-art results in e.g. semantic image segmentation.
The second approach is to use SPNs as variational distributions, i.e. for approximating a given target distribution by minimizing Kullback-Leibler divergence. On the one hand, this allows to capture intractable models with SPNs, with the goal to enable fast amortized approximate inference. On the other hand, this approach allows to use hybrid conditional SPNs as so-called inference networks for intractable generative models with latent variables, for the purpose of variational posterior inference and learning. This approach would represent a substantial improvement over state-of-the-art approaches, which are usually limited to expensive inference via Monte Carlo estimation.
Fields of science (EuroSciVoc)
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: The European Science Vocabulary.
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: The European Science Vocabulary.
- natural sciences computer and information sciences artificial intelligence generative artificial intelligence
- natural sciences computer and information sciences artificial intelligence machine learning deep learning
- natural sciences computer and information sciences artificial intelligence computational intelligence
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Programme(s)
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
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H2020-EU.1.3. - EXCELLENT SCIENCE - Marie Skłodowska-Curie Actions
MAIN PROGRAMME
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H2020-EU.1.3.2. - Nurturing excellence by means of cross-border and cross-sector mobility
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Topic(s)
Calls for proposals are divided into topics. A topic defines a specific subject or area for which applicants can submit proposals. The description of a topic comprises its specific scope and the expected impact of the funded project.
Calls for proposals are divided into topics. A topic defines a specific subject or area for which applicants can submit proposals. The description of a topic comprises its specific scope and the expected impact of the funded project.
Funding Scheme
Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
MSCA-IF-EF-ST - Standard EF
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Call for proposal
Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
(opens in new window) H2020-MSCA-IF-2017
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Net EU financial contribution. The sum of money that the participant receives, deducted by the EU contribution to its linked third party. It considers the distribution of the EU financial contribution between direct beneficiaries of the project and other types of participants, like third-party participants.
CB2 1TN CAMBRIDGE
United Kingdom
The total costs incurred by this organisation to participate in the project, including direct and indirect costs. This amount is a subset of the overall project budget.