Despite the well-known performances and the theoretical power of neural networks, learning and generalizing are sometimes very difficult. In this article, we investigate how short ...
A hybrid algorithm is devised to boost the performance of complete search on under-constrained problems. We suggest to use random variable selection in combination with restarts, ...
This paper addresses the task of trajectory cost prediction, a new learning task for trajectories. The goal of this task is to predict the cost for an arbitrary (possibly unknown)...
We propose Tree Sequence Kernel (TSK), which implicitly exhausts the structure features of a sequence of subtrees embedded in the phrasal parse tree. By incorporating the capabili...
This paper concerns learning and prediction with probabilistic models where the domain sizes of latent variables have no a priori upper-bound. Current approaches represent prior d...