— Solvers for the Boolean satisfiability problem are an important base technology for many applications. The most efficient SAT solvers for industrial applications are based on...
Abstract. This paper presents a neural-evolutionary framework for the simulation of market models in a bounded rationality scenario. Each agent involved in the scenario make use of...
In this paper, we present an algorithm that minimizes the mutual information between the outputs of a perceptron with two hidden layers. The neural network is then used as separati...
In recent years, gradient-based LSTM recurrent neural networks (RNNs) solved many previously RNN-unlearnable tasks. Sometimes, however, gradient information is of little use for t...
We introduce a framework for syntactic parsing with latent variables based on a form of dynamic Sigmoid Belief Networks called Incremental Sigmoid Belief Networks. We demonstrate ...