We present a survey of ways in which domain-knowledge has been included when
constructing models with neural networks. The inclusion of domain-knowledge is of special …

We present a survey of ways in which existing scientific knowledge are included when
constructing models with neural networks. The inclusion of domain-knowledge is of special …

We present a short survey of ways in which existing scientific knowledge are included when
constructing models with neural networks. The inclusion of domain-knowledge is of special …

Recent research has shown that integrating domain knowledge into deep learning
architectures is effective; It helps reduce the amount of required data, improves the accuracy …

We propose a novel way to incorporate expert knowledge into the training of deep neural
networks. Many approaches encode domain constraints directly into the network …

Deep Learning (DL) models proved themselves to perform extremely well on a wide variety
of learning tasks, as they can learn useful patterns from large data sets. However, purely …

We demonstrate a library for the integration of domain knowledge in deep learning
architectures. Using this library, the structure of the data is expressed symbolically via graph …

Incorporating domain knowledge into the modeling process is an effective way to improve
learning accuracy. However, as it is provided by humans, domain knowledge can only be …

There is a concerted effort to build domain-general artificial intelligence in the form of
universal neural network models with sufficient computational flexibility to solve a wide …

Deep neural networks (DNNs) have become the gold standard for solving challenging
classification problems, especially given complex sensor inputs (eg, images and video) …