Everything is Connected: Graph Neural Networks

In many ways, graphs are the main modality of data we receive from nature. This is due to the fact that most of the patterns we see, both in natural and artificial systems, are elegantly representable using the language of graph structures. Prominent examples include molecules (represented as graphs of atoms and bonds), social networks and transportation networks. This potential has already been seen by key scientific and industrial groups, with already-impacted application areas including traffic forecasting, drug discovery, social network analysis and recommender systems. Further, some of the most successful domains of application for machine learning in previous years -- images, text and speech processing -- can be seen as special cases of graph representation learning, and consequently there has been significant exchange of information between these areas. The main aim of this short survey is to enable the reader to assimilate the key concepts in the area, and position graph representation learning in a proper context with related fields.Comment: To appear in Current Opinion in Structural Biology. 14 pages, 1 figur

X-CNN: Cross-modal Convolutional Neural Networks for Sparse Datasets

In this paper we propose cross-modal convolutional neural networks (X-CNNs), a novel biologically inspired type of CNN architectures, treating gradient descent-specialised CNNs as individual units of processing in a larger-scale network topology, while allowing for unconstrained information flow and/or weight sharing between analogous hidden layers of the network---thus generalising the already well-established concept of neural network ensembles (where information typically may flow only between the output layers of the individual networks). The constituent networks are individually designed to learn the output function on their own subset of the input data, after which cross-connections between them are introduced after each pooling operation to periodically allow for information exchange between them. This injection of knowledge into a model (by prior partition of the input data through domain knowledge or unsupervised methods) is expected to yield greatest returns in sparse data environments, which are typically less suitable for training CNNs. For evaluation purposes, we have compared a standard four-layer CNN as well as a sophisticated FitNet4 architecture against their cross-modal variants on the CIFAR-10 and CIFAR-100 datasets with differing percentages of the training data being removed, and find that at lower levels of data availability, the X-CNNs significantly outperform their baselines (typically providing a 2--6% benefit, depending on the dataset size and whether data augmentation is used), while still maintaining an edge on all of the full dataset tests.Comment: To appear in the 7th IEEE Symposium Series on Computational Intelligence (IEEE SSCI 2016), 8 pages, 6 figures. Minor revisions, in response to reviewers' comment

The resurgence of structure in deep neural networks

Machine learning with deep neural networks ("deep learning") allows for learning complex features directly from raw input data, completely eliminating hand-crafted, "hard-coded" feature extraction from the learning pipeline. This has lead to state-of-the-art performance being achieved across several---previously disconnected---problem domains, including computer vision, natural language processing, reinforcement learning and generative modelling. These success stories nearly universally go hand-in-hand with availability of immense quantities of labelled training examples ("big data") exhibiting simple grid-like structure (e.g. text or images), exploitable through convolutional or recurrent layers. This is due to the extremely large number of degrees-of-freedom in neural networks, leaving their generalisation ability vulnerable to effects such as overfitting. However, there remain many domains where extensive data gathering is not always appropriate, affordable, or even feasible. Furthermore, data is generally organised in more complicated kinds of structure---which most existing approaches would simply discard. Examples of such tasks are abundant in the biomedical space; with e.g. small numbers of subjects available for any given clinical study, or relationships between proteins specified via interaction networks. I hypothesise that, if deep learning is to reach its full potential in such environments, we need to reconsider "hard-coded" approaches---integrating assumptions about inherent structure in the input data directly into our architectures and learning algorithms, through structural inductive biases. In this dissertation, I directly validate this hypothesis by developing three structure-infused neural network architectures (operating on sparse multimodal and graph-structured data), and a structure-informed learning algorithm for graph neural networks, demonstrating significant outperformance of conventional baseline models and algorithms.The work depicted in this dissertation was in part supported by funding from the European Union's Horizon 2020 research and innovation programme PROPAG-AGEING under grant agreement No 634821

Parallel Algorithms Align with Neural Execution

Neural algorithmic reasoners are parallel processors. Teaching them sequential algorithms contradicts this nature, rendering a significant share of their computations redundant. Parallel algorithms however may exploit their full computational power, therefore requiring fewer layers to be executed. This drastically reduces training times, as we observe when comparing parallel implementations of searching, sorting and finding strongly connected components to their sequential counterparts on the CLRS framework. Additionally, parallel versions achieve strongly superior predictive performance in most cases.Comment: 8 pages, 5 figures, To appear at the KLR Workshop at ICML 202