Taxonomy Induction for Fictional Domains (TiFi)
Taxonomies are important building blocks of structured knowledge bases, and their construction from text sources and Wikipedia has received much attention. In this paper we focus on the construction of taxonomies for fictional domains, using noisy category systems from fan wikis or text extraction as input. Such fictional domains are archetypes of entity universes that are poorly covered by Wikipedia, such as also enterprise-specific knowledge bases or highly specialized verticals. Our fiction-targeted approach, called TiFi, consists of three phases: (i) category cleaning, by identifying candidate categories that truly represent classes in the domain of interest, (ii) edge cleaning, by selecting subcategory relationships that correspond to class subsumption, and (iii) top-level construction, by mapping classes onto a subset of high-level WordNet categories. A comprehensive evaluation shows that TiFi is able to construct taxonomies for a diverse range of fictional domains such as Lord of the Rings, The Simpsons or Greek Mythology with very high precision and that it outperforms state-of-the-art baselines for taxonomy induction by a substantial margin. …

Fast Multipole Network (FMN)
Two fundamental prerequisites for robotic multiagent systems are mobility and communication. We propose \emph{fast multipole networks} (FMNs) to achieve both ends within a unified framework. FMNs can be organized very efficiently in a distributed way from local information and are ideally suited for motion planning using artificial potentials. We compare FMNs to conventional communication topologies, and find that FMNs offer favorable communication performance in addition to their intrinsic advantages for mobility. …

Reversible Neural Network (RevNet)
Generative models with an encoding component such as autoencoders currently receive great interest. However, training of autoencoders is typically complicated by the need for training of a separate encoder and decoder model that have to be enforced to be reciprocal to each other. Here, we propose to use the by-design reversible neural networks (RevNets) as a new class of generative models. We investigate the generative performance of RevNets on the CelebA dataset, showing that generative RevNets can indeed generate coherent faces with similar quality as Variational Autoencoders. This first attempt to use RevNets as a generative model still slightly underperformed relative to recent advanced generative models using an autoencoder component on CelebA, but this gap may diminish with further optimization of the training setup of generative RevNets. In addition to the experiments on CelebA, we show a proof-of-principle experiment on the MNIST dataset suggesting that adversary-free trained RevNets can discover meaningful dimensions without pre-specifying the number of latent dimensions of the sampling distribution. In summary, this study shows that RevNets enable generative applications with an encoding component while overcoming the need of training separate encoder and decoder models. …

Relation Structure-Aware Heterogeneous Information Network Embedding Model (RHINE)
Heterogeneous information network (HIN) embedding aims to embed multiple types of nodes into a low-dimensional space. Although most existing HIN embedding methods consider heterogeneous relations in HINs, they usually employ one single model for all relations without distinction, which inevitably restricts the capability of network embedding. In this paper, we take the structural characteristics of heterogeneous relations into consideration and propose a novel Relation structure-aware Heterogeneous Information Network Embedding model (RHINE). By exploring the real-world networks with thorough mathematical analysis, we present two structure-related measures which can consistently distinguish heterogeneous relations into two categories: Affiliation Relations (ARs) and Interaction Relations (IRs). To respect the distinctive characteristics of relations, in our RHINE, we propose different models specifically tailored to handle ARs and IRs, which can better capture the structures and semantics of the networks. At last, we combine and optimize these models in a unified and elegant manner. Extensive experiments on three real-world datasets demonstrate that our model significantly outperforms the state-of-the-art methods in various tasks, including node clustering, link prediction, and node classification. …