Reinforcement learning algorithms that employ neural networks as function approximators have proven to be powerful tools for solving optimal control problems. However, their traini...
Many black box optimization algorithms have sufcient exibility to allow them to adapt to the varying circumstances they encounter. These capabilities are of two primary sorts: 1) ...
Our participation in TREC 2003 aims to adapt the use of the DFR (Divergence From Randomness) models with Query Expansion (QE) to the robust track and the topic distillation task o...
Giambattista Amati, Claudio Carpineto, Giovanni Ro...
We address the problem of rate allocation and network/path selection for multiple users, running simultaneous applications over multiple parallel access networks. Our joint optimi...
Dan Jurca, Wolfgang Kellerer, Eckehard G. Steinbac...
Non-linear dimensionality reduction of noisy data is a challenging problem encountered in a variety of data analysis applications. Recent results in the literature show that spect...