Characterizing the Shape of Activation Space in Deep Neural Networks

The representations learned by deep neural networks are difficult to interpret in part due to their large parameter space and the complexities introduced by their multi-layer structure. We introduce a method for computing persistent homology over the graphical activation structure of neural networks, which provides access to the task-relevant substructures activated throughout the network for a given input. This topological perspective provides unique insights into the distributed representations encoded by neural networks in terms of the shape of their activation structures. We demonstrate the value of this approach by showing an alternative explanation for the existence of adversarial examples. By studying the topology of network activations across multiple architectures and datasets, we find that adversarial perturbations do not add activations that target the semantic structure of the adversarial class as previously hypothesized. Rather, adversarial examples are explainable as alterations to the dominant activation structures induced by the original image, suggesting the class representations learned by deep networks are problematically sparse on the input space

Digital Comics and Critical Librarianship: What, Why and How: A Perspective from the UK

This chapter describes the challenges of collecting digital comics as well as how a critical librarianship approach can maximize an academic library’s potential to be a more inclusive site for collection development and preservation. Digital comics are at the cutting edge of how imaginative, immediate, and emotionally engaging stories can be told in the twenty-first century. The creators of digital comics harness new and emerging technologies to create and distribute innovative forms of storytelling. Digital comics also have the capacity to reflect and contribute to social and cultural change by foregrounding marginalized voices. Just as readers are able to engage with and enjoy these new expressive forms, it is vital that cultural institutions record and recognise these new methods of creative expression

Path homologies of deep feedforward networks

We provide a characterization of two types of directed homology for fully-connected, feedforward neural network architectures. These exact characterizations of the directed homology structure of a neural network architecture are the first of their kind. We show that the directed flag homology of deep networks reduces to computing the simplicial homology of the underlying undirected graph, which is explicitly given by Euler characteristic computations. We also show that the path homology of these networks is non-trivial in higher dimensions and depends on the number and size of the layers within the network. These results provide a foundation for investigating homological differences between neural network architectures and their realized structure as implied by their parameters.Comment: To appear in the proceedings of IEEE ICMLA 201

SoaML-basierter Entwurf eines dienstorientierten Überwachungssystems. [Workshop]

Von einem Überwachungssystem zur Begleitung von Personen innerhalb von Gebäuden wird häufig gefordert, sich in die bestehende Anwendungslandschaft eines Unternehmens integrieren zu lassen und vorhandene Anwendungsfunktionalität möglichst wiederzuverwenden. Anwendungslandschaften werden verstärkt in Form dienstorientierter Architekturen strukturiert, weshalb ein integrierbares Überwachungssystem ebenfalls dienstorientiert entworfen werden sollte. Mit der Service oriented architecture Modeling Language (SoaML) existiert ein Standard, der eine Modellierung eines dienstorientierten Überwachungssystems ermöglicht. In dieser Arbeit werden die hierfür notwendigen Elemente der SoaML identifiziert und in einen Entwicklungsprozess eingeordnet