Daniel Cyrus

With the ever-increasing potential of AI to perform personalised tasks, it is becoming essential to develop new machine learning techniques which are data-efficient and do not require hundreds or thousands of training data. In this paper, we explore an Inductive Logic Programming approach for one-shot text classification. In particular, we explore the framework of Meta-Interpretive Learning (MIL), along with using common-sense background knowledge extracted from ConceptNet. Results indicate that MIL can learn text classification rules from a small number of training examples, even one example. Moreover, the higher complexity of chosen example for one-shot learning, the higher accuracy of the outcome. Finally, we utilise two approaches, Background Knowledge Splitting and Average One-Shot Learning, to evaluate our model on a public News Category dataset. The outcomes validate MIL's superior performance to the Siamese net for one-shot learning from text.

Fractals are geometric patterns with identical characteristics in each of their component parts. They are used to depict features which have recurring patterns at ever-smaller scales. This study offers a technique for learning from fractal images using Meta-Interpretative Learning (MIL). MIL has previously been employed for few-shot learning from geometrical shapes (e.g. regular polygons) and has exhibited significantly higher accuracy when compared to Convolutional Neural Networks (CNN). Our objective is to illustrate the application of MIL in learning from fractal images. We first generate a dataset of images of simple fractal and non-fractal geometries and then we implement a technique to learn recursive rules which describe fractal geometries. Our approach uses graphs extracted from images as background knowledge. Finally, we evaluate our approach against CNN-based approaches, such as Siamese Net, VGG19, ResNet50 and DenseNet169.