Relational Reasoning Using Neural Networks: A Survey

Abstract

Relational Networks (RN), as one of the most widely used relational reasoning techniques, have achieved great success in many applications such as action and image analysis, speech recognition and text understanding. The use of relational reasoning via RN in neural networks has often been used in recent years. In these instances, RN is composed of various deep learning-based algorithms in simple plug-and-play modules. This is quite advantageous since it circumvents the need for features engineering. This paper surveys the emerging research of deep learning models that make use of RN in tasks such as Natural Language Processing (NLP), Action Recognition, Temporal Relational Reasoning as well as Facial Emotion Recognition (FER). Since, RNs are easy to integrate they have been used in various tasks such as NLP, Recurrent Neural Networks (RNN), Action Recognition, Image Analysis, Object Detection, Temporal Relational Reasoning, as well as for FER. This is due to the fact that RNs use bidirectional LSTM and CNN to solve relational reasoning problems at character and word level. In this paper a comparative review of all relational reasoning-based RN models using deep learning techniques is presented.