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This work presents the application of a probabilistic approach to an already existing deep learning model for weather and climate prediction. Probabilistic deep learning allows to capture and address the uncertainties related to the data given as input and ...
The accuracy required for a correct interpretation of differential reflectivity (ZDR) is typically estimated to be between 0.1 and 0.2 dB. This is achieved through calibration, defined as the identification of the constant or time-varying offset to be subt ...
When learning skills from demonstrations, one is often required to think in advance about the appropriate task representation (usually in either operational or configuration spaces). We here propose a probabilistic approach for simultaneously learning and ...
The accuracy required for a correct interpretation of differential reflectivity (ZDR) is typically estimated to be between 0.1 and 0.2 dB. This is achieved through calibration, defined as the identification of the constant or time-varying offset to be subt ...
Fusion of very high spatial resolution multispectral (VHR) images and lower spatial resolution image time series with more spectral bands can improve land cover classification, combining geometric and semantic advantages of both sources. This study present ...
We introduce a probabilistic modeling approach for pedestrian speed density relationship. It is motivated by a high scatter in real data that precludes the use of traditional equilibrium relationships. To characterize the observed pattern, we relax the hom ...
A non-intrusive reduced basis (RB) method is proposed for parametrized nonlinear structural analysis undergoing large deformations and with elasto-plastic constitutive relations. In this method, a reduced basis is constructed from a set of full-order snaps ...
Non-parametric probabilistic classification models are increasingly being investigated as an
alternative to Discrete Choice Models (DCMs), e.g. for predicting mode choice. There exist many strategies within the literature for model selection between DCMs, ...
Mobile ground robots operating on unstructured terrain must predict which areas of the environment they are able to pass in order to plan feasible paths. We address traversability estimation as a heightmap classification problem: we build a convolutional n ...
The performance of a classifier trained on data coming from a specific domain typically degrades when applied to a related but different one. While annotating many samples from the new domain would address this issue, it is often too expensive or impractic ...