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In this paper, we investigate the memory properties of two popular gated units: long short term memory (LSTM) and gated recurrent units (GRU), which have been used in recurrent neural networks (RNN) to achieve state-of-the-art performance on several machine learning tasks. We propose five basic tasks for isolating and examining specific capabilities relating to the implementation of memory. Results...
The recent success of representation learning is built upon the learning of relevant features, in particular from unlabelled data available in different domains. This raises the question of how to transfer and reuse such knowledge effectively so that the learning of a new task can be made easier or be improved. This poses a difficult challenge for the area of transfer learning where there is no label...
Representation learning has emerged recently as a useful tool in the extraction of features from data. In a range of applications, features learned from data have been shown superior to their hand-crafted counterpart. Many deep learning approaches have taken advantage of such feature extraction. However, further research is needed on how such features can be evaluated for re-use in related applications,...
We are interested in modelling musical pitch sequences in melodies in the symbolic form. The task here is to learn a model to predict the probability distribution over the various possible values of pitch of the next note in a melody, given those leading up to it. For this task, we propose the Recurrent Temporal Discriminative Restricted Boltzmann Machine (RTDRBM). It is obtained by carrying out discriminative...
Runtime monitors check the execution of a system under scrutiny against a set of formal specifications describing a prescribed behaviour. The two core properties for monitoring systems are scalability and adaptability. In this paper we show how RuleRunner, our previous neural-symbolic monitoring system, can exploit learning strategies in order to integrate desired deviations with the initial set of...
A recent trend in High-Performance Computation is parallel computing, and the field of Neural Networks is showing impressive improvements in performance, especially with the use of GPU accelerators. In this paper, we use neural networks to improve the performance of Runtime Verification. Runtime verification is used in a variety of domains -from policy enforcement to electronic fraud detection-to...
Learning visual words from video frames is challenging because deciding which word to assign to each subset of frames is a difficult task. For example, two similar frames may have different meanings in describing human actions such as starting to run and starting to walk. In order to associate richer information to vector-quantization and generate visual words, several approaches have been proposed...
We propose a novel framework for adapting and evolving software requirements models. The framework uses model checking and machine learning techniques for verifying properties and evolving model descriptions. The paper offers two novel contributions and a preliminary evaluation and application of the ideas presented. First, the framework is capable of coping with errors in the specification process...
This paper presents a novel approach to knowledge extraction from large-scale datasets using a neural network when applied to the real-world problem of payment card fraud detection. Fraud is a serious and long term threat to a peaceful and democratic society. We present SOAR (Sparse Oracle-based Adaptive Rule) extraction, a practical approach to process large datasets and extract key generalizing...
Artificial Neural Networks have previously been applied in neuro-symbolic learning to learn ground logic program rules. However, there are few results of learning relations using neuro-symbolic learning. This paper presents the system PAN, which can learn relations. The inputs to PAN are one or more atoms, representing the conditions of a logic rule, and the output is the conclusion of the rule. The...
We present a new approach to incorporate a temporal dimension into a hybrid system, by integrating a symbolic model and recurrent neural networks. This combination is supported by an algorithm to perform empirical learning. Further, the network is submitted to testbeds to analyse the influence of background knowledge insertion in the experiments and to validate the algorithm?s learning capability...
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