A Hybrid NWP–Analog Ensemble

Towards superconductor-spin ensemble hybrid quantum systems

Complex information systems, whether natural or manmade, rely on specialization of functionality in order to efficiently carry out information processing tasks. In the quintessential model used by computer scientists, called the Turing machine, computation proceeds using a “tape,” or long term memory, and a “processor” that consists of a malleable state register coupled to a read/write head. To...

The Hybrid Local Ensemble Transform Kalman Filter

Hybrid data assimilation methods combine elements of ensemble Kalman filters (EnKF) and variational methods. While most approaches have focused on augmenting an operational variational system with dynamic error covariance information from an EnKF [1][2][4][5][8], we take the opposite perspective of augmenting an operational EnKF with information from a simple 3D-Variational (3D-Var) method [7]....

Ensemble Learning for Hybrid Music Recommendation

We investigate ensemble learning methods for hybrid music recommenders, combining a social and a content-based recommender algorithm in an initial experiment by applying a simple combination rule to merge recommender results. A first experiment suggests that such a combination can reduce the mean absolute prediction error compared to the used recommenders’ individual errors.

Ensemble of hybrid networks with strong regularization

A Study of Ensemble of Hybrid Networks with Strong Regularization

We study various ensemble methods for hybrid neural networks. The hybrid networks are composed of radial and projection units and are trained using a deterministic algorithm that completely defines the parameters of the network for a given data set. Thus, there is no random selection of the initial (and final) parameters as in other training algorithms. Network independent is achieved by using ...