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Deep contract design via discontinuous networks
Contract design involves a principal who establishes contractual agreements about
payments for outcomes that arise from the actions of an agent. In this paper, we initiate the …
payments for outcomes that arise from the actions of an agent. In this paper, we initiate the …
Manifold learning by mixture models of VAEs for inverse problems
Representing a manifold of very high-dimensional data with generative models has been
shown to be computationally efficient in practice. However, this requires that the data …
shown to be computationally efficient in practice. However, this requires that the data …
Learning with partition of unity-based Kriging estimators
For supervised regression tasks we propose and study a new tool, namely Kriging Estimator
based on the Partition of Unity (KEPU) method. Its background belongs to the framework of …
based on the Partition of Unity (KEPU) method. Its background belongs to the framework of …
A novel target value standardization method based on cumulative distribution functions for training artificial neural networks
Function approximation by artificial neural networks (ANNs) are often carried out via a
collocation grid approach. However, for certain combinations of grids and functions, the …
collocation grid approach. However, for certain combinations of grids and functions, the …
Manifold Learning and Sparsity Priors for Inverse Problems
S Sciutto - 2024 - tesidottorato.depositolegale.it
In this thesis we investigate two distinct regularizing approaches for solving inverse
problems. The first approach involves assuming that the unknown belongs to a manifold …
problems. The first approach involves assuming that the unknown belongs to a manifold …