We propose a general-purpose stochastic optimization algorithm, the so-called annealing stochastic approximation Monte Carlo (ASAMC) algorithm, for neural network training. ASAMC c...
We recapitulate regular one-shot learning from membership and equivalence queries, positive and negative finite data. We present a meta-algorithm that generalizes over as many sett...
Abstract. Active language learning is an interesting task for which theoretical results are known and several applications exist. In order to better understand what the better stra...
David Combe, Colin de la Higuera, Jean-Christophe ...
One of the main problems in probabilistic grammatical inference consists in inferring a stochastic language, i.e. a probability distribution, in some class of probabilistic models...
It is known that the complexity of the reinforcement learning algorithms, such as Q-learning, may be exponential in the number of environment’s states. It was shown, however, th...