Restricted Boltzmann Machines (RBMs) are among the most widely pursed techniques in the context of deep learning-based applications. Their usage enables sundry parallel implementations, which have become pivotal in nowadays ...

In this paper, we deal with the problem of Deep Belief Networks (DBNs) parameters fine-tuning by means of a fast meta-heuristic approach named Harmony Search (HS). Although such deep learning-based technique has been widely ...

In the last few years, metaheuristic-driven optimization has been employed to address deep belief network (DBN) model selection, since it provides simple and elegant solutions in a wide range of applications. In this work, ...

Deep learning techniques have been paramount in the last years, mainly due to their outstanding results in a number of applications. In this paper, we address the issue of fine-tuning parameters of Deep Belief Networks by ...

In this paper, we deal with the problem of Deep Belief Networks (DBNs) parameters fine-tuning by means of a fast meta-heuristic approach named Harmony Search (HS). Although such deep learning-based technique has been widely ...

With the advent of deep learning, the number of works proposing new methods or improving existent ones has grown exponentially in the last years. In this scenario, “very deep” models were emerging, once they were expected ...

Deep learning techniques usually face drawbacks related to the vanishing gradient problem, i.e., the gradient becomes gradually weaker when propagating from one layer to another until it finally vanishes away and no longer ...

The Deep Learning models have drawn ever-increasing research interest owing to their intrinsic capability of overcoming the drawback of traditional algorithm. Hence, we have adopted the representative Deep Learning methods ...

Credal networks generalize Bayesian networks relaxing numerical parameters. This considerably expands expressivity. but makes belief updating a hard task even on polytrees. Nevertheless, if all the variables are binary, ...