Robots are becoming increasingly accessible to both experts and non-experts. Therefore, establishing a method for learning robot operations that can be easily mastered by non-experts is important. With this in mind, we aimed to develop a method that ...
Recent work on intelligent agents is a popular topic among the artificial intelligence community and robotic system design. The complexity of designing a framework as a guide for intelligent agents in an unknown built environment suggests a pressing ...
IEEE transactions on neural networks and learning systems
Aug 31, 2022
Efficient neural architecture search (ENAS) achieves novel efficiency for learning architecture with high-performance via parameter sharing and reinforcement learning (RL). In the phase of architecture search, ENAS employs deep scalable architecture ...
IEEE transactions on neural networks and learning systems
Aug 31, 2022
Graph-based learning in semisupervised models provides an effective tool for modeling big data sets in high-dimensional spaces. It has been useful for propagating a small set of initial labels to a large set of unlabeled data. Thus, it meets the requ...
IEEE transactions on neural networks and learning systems
Aug 31, 2022
In this article, we propose a novel model-parallel learning method, called local critic training, which trains neural networks using additional modules called local critic networks. The main network is divided into several layer groups, and each laye...
IEEE transactions on neural networks and learning systems
Aug 31, 2022
Enabling a neural network to sequentially learn multiple tasks is of great significance for expanding the applicability of neural networks in real-world applications. However, artificial neural networks face the well-known problem of catastrophic for...
In real industrial scenarios, intelligent fault diagnosis based on data-driven methods has been widely researched in the past decade. However, data scarcity is widespread in fault diagnosis tasks owning to the difficulties in collecting adequate data...
Automated, data-driven construction and evaluation of scientific models and theories is a long-standing challenge in artificial intelligence. We present a framework for algorithmically synthesizing models of a basic part of human language: morpho-pho...
Computational intelligence and neuroscience
Aug 29, 2022
This exploration aims to study the value orientation and essence of early childhood enlightenment education based on the deep neural network (DNN). Based on the acquisition and feature learning of cross-media education big data, the DNN correlation l...
Computational intelligence and neuroscience
Aug 29, 2022
Current educational resources do not maximize energy efficiency, and scientific and proven teaching methods are necessary for today's university education to help achieve the integration of teaching resources and improve teaching quality. This study ...
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