Abstract. We investigate the question of what constitutes a proof when quantifiers and multiplicative units are both present. On the technical level this paper provides two new as...
Deep-layer machine learning architectures continue to emerge as a promising biologically-inspired framework for achieving scalable perception in artificial agents. State inference ...
Deep Belief Networks (DBN's) are generative models that contain many layers of hidden variables. Efficient greedy algorithms for learning and approximate inference have allow...
The key limiting factor in graphical model inference and learning is the complexity of the partition function. We thus ask the question: what are the most general conditions under...
Abstract. In this paper we introduce a modular framework which allows to infer (feasible) upper bounds on the (derivational) complexity of term rewrite systems by combining differ...