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LRP-Based path relevances for global explanation of deep architectures
(Elsevier B.V., 2020)
Learning Parameters in Deep Belief Networks Through Firefly Algorithm
(Springer, 2016-01-01)
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 ...
Deep Belief Network and Auto-Encoder for Face Classification
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 ...
A Layer-Wise Information Reinforcement Approach to Improve Learning in Deep Belief Networks
(2020-01-01)
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 ...
Recognition of Hand Gestures Based on EMG Signals with Deep and Double-Deep Q-Networks
(MDPI, 2023-04)
In recent years, hand gesture recognition (HGR) technologies that use electromyography (EMG) signals have been of considerable interest in developing human–machine interfaces. Most state-of-the-art HGR approaches are based ...
Reinforcing learning in Deep Belief Networks through nature-inspired optimization
(2021-09-01)
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 ...
Fine-tuning Deep Belief Networks using Harmony Search
(2016-09-01)
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 ...
Quaternion-based Deep Belief Networks fine-tuning
(Elsevier B.V., 2017-11-01)
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 ...
Fine-tuning Deep Belief Networks using Harmony Search
(Elsevier B.V., 2016-09-01)
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 ...
Deep neural network approaches for Spanish sentiment analysis of short texts
(Springer Verlag, 2018)
Sentiment Analysis has been extensively researched in the last years. While important theoretical and practical results have been obtained, there is still room for improvement. In particular, when short sentences and low ...