Prof. Dr.-Ing.

Bauckhage, Christian

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  • Publication
    Auto Encoding Explanatory Examples with Stochastic Paths
    ( 2021-05-05)
    Ojeda, César
    ;
    Sánchez, Ramsés J.
    ;
    Cvejoski, Kostadin
    ;
    Schücker, Jannis
    ;
    Bauckhage, Christian
    ;
    Georgiev, Bogdan
    In this paper we ask for the main factors that determine a classifiers decision making process and uncover such factors by studying latent codes produced by auto-encoding frameworks. To deliver an explanation of a classifiers behaviour, we propose a method that provides series of examples highlighting semantic differences between the classifiers decisions. These examples are generated through interpolations in latent space. We introduce and formalize the notion of a semantic stochastic path, as a suitable stochastic process defined in feature (data) space via latent code interpolations. We then introduce the concept of semantic Lagrangians as a way to incorporate the desired classifiers behaviour and find that the solution of the associated variational problem allows for highli ghting differences in the classifier decision. Very importantly, within our framework the classifier is used as a black-box, and only its evaluation is required.
  • Publication
    Decision Snippet Features
    ( 2021-05-05)
    Welke, Pascal
    ;
    Alkhoury, Fouad
    ;
    Bauckhage, Christian
    ;
    Wrobel, Stefan
    Decision trees excel at interpretability of their prediction results. To achieve required prediction accuracies, however, often large ensembles of decision trees random forests are considered, reducing interpretability due to large size. Additionally, their size slows down inference on modern hardware and restricts their applicability in low-memory embedded devices. We introduce Decision Snippet Features, which are obtained from small subtrees that appear frequently in trained random forests. We subsequently show that linear models on top of these features achieve comparable and sometimes even better predictive performance than the original random forest, while reducing the model size by up to two orders of magnitude.
  • Publication
    Switching Dynamical Systems with Deep Neural Networks
    ( 2021-05-05)
    Ojeda, César
    ;
    Georgiev, Bogdan
    ;
    Cvejoski, Kostadin
    ;
    Schücker, Jannis
    ;
    Bauckhage, Christian
    ;
    Sánchez, Ramsés J.
    The problem of uncovering different dynamical regimes is of pivotal importance in time series analysis. Switching dynamical systems provide a solution for modeling physical phenomena whose time series data exhibit different dynamical modes. In this work we propose a novel variational RNN model for switching dynamics allowing for both non-Markovian and nonlinear dynamical behavior between and within dynamic modes. Attention mechanisms are provided to inform the switching distribution. We evaluate our model on synthetic and empirical datasets of diverse nature and successfully uncover different dynamical regimes and predict the switching dynamics.
  • Publication
    Learning Deep Generative Models for Queuing Systems
    ( 2021)
    Ojeda, César
    ;
    Cvejoski, Kostadin
    ;
    Georgiev, Bogdan
    ;
    Bauckhage, Christian
    ;
    Schücker, Jannis
    ;
    Sánchez, Ramsés J.
    Modern society is heavily dependent on large scale client-server systems with applications ranging from Internet and Communication Services to sophisticated logistics and deployment of goods. To maintain and improve such a system, a careful study of client and server dynamics is needed e.g. response/service times, aver-age number of clients at given times, etc. To this end, one traditionally relies, within the queuing theory formalism, on parametric analysis and explicit distribution forms. However, parametric forms limit the models expressiveness and could struggle on extensively large datasets. We propose a novel data-driven approach towards queuing systems: the Deep Generative Service Times. Our methodology delivers a flexible and scalable model for service and response times. We leverage the representation capabilities of Recurrent Marked Point Processes for the temporal dynamics of clients, as well as Wasserstein Generative Adversarial Network techniques, to learn deep generative models which are able to represent complex conditional service time distributions. We provide extensive experimental analysis on both empirical and synthetic datasets, showing the effectiveness of the proposed models.
  • Publication
    Toxicity Detection in Online Comments with Limited Data: A Comparative Analysis
    ( 2021)
    Lübbering, Max
    ;
    Pielka, Maren
    ;
    Das, Kajaree
    ;
    Gebauer, Michael
    ;
    Ramamurthy, Rajkumar
    ;
    Bauckhage, Christian
    ;
    Sifa, Rafet
    We present a comparative study on toxicity detection, focusing on the problem of identifying toxicity types of low prevalence and possibly even unobserved at training time. For this purpose, we train our models on a dataset that contains only a weak type of toxicity, and test whether they are able to generalize to more severe toxicity types. We find that representation learning and ensembling exceed the classification performance of simple classifiers on toxicity detection, while also providing significantly better generalization and robustness. All models benefit from a larger training set size, which even extends to the toxicity types unseen during training.
  • Publication
    Problem Solving with Hopfield Networks and Adiabatic Quantum Computing
    ( 2020-09-28)
    Bauckhage, Christian
    ;
    Sánchez, Ramsés J.
    ;
    Sifa, Rafet
    Our goal with this paper is to elucidate the close connection between Hopfield networks and adiabatic quantum computing. Focusing on their use in problem solving, we point out that the energy functions minimized by Hopfield networks are essentially identical to those minimized by adiabatic quantum computers. To practically illustrate this, we consider a simple textbook problem, namely the k-rooks problem, and discuss how to set it up for solution via a Hopfield network or adiabatic quantum computing.
  • Publication
    Triple Classification Using Regions and Fine-Grained Entity Typing
    ( 2019)
    Dong, Tiansi
    ;
    Wang, Zhigang
    ;
    Li, Juanzi
    ;
    Bauckhage, Christian
    ;
    Cremers, Armin B.
    A Triple in knowledge-graph takes a form that consists of head, relation, tail. Triple Classification is used to determine the truth value of an unknown Triple. This is a hard task for 1-to-N relations using the vector-based embedding approach. We propose a new region-based embedding approach using fine-grained type chains. A novel geometric process is presented to extend the vectors of pre-trained entities into n-balls (n-dimensional balls) under the condition that head balls shall contain their tail balls. Our algorithm achieves zero energy loss, therefore, serves as a case study of perfectly imposing tree structures into vector space. An unknown Triple (h, r, x) will be predicted as true, when x's n-ball is located in the r-subspace of h's n-ball, following the same construction of know n tails of h. The experiments are based on large datasets derived from the benchmark datasets WN11, FB13, and WN18. Our results show that the performance of the new method is related to the length of the type chain and the quality of pre-trained entity-embeddings, and that performances of long chains with well-trained entity-embeddings outperform other methods in the literature.
  • Publication
    Recurrent Adversarial Service Times
    ( 2019)
    Ojeda, César
    ;
    Cvejosky, Kostadin
    ;
    Sánchez, Ramsés J.
    ;
    Schücker, Jannis
    ;
    Georgiev, Bogdan
    ;
    Bauckhage, Christian
    Service system dynamics occur at the interplay between customer behaviour and a service provider's response. This kind of dynamics can effectively be modeled within the framework of queuing theory where customers' arrivals are described by point process models. However, these approaches are limited by parametric assumptions as to, for example, inter-event time distributions. In this paper, we address these limitations and propose a novel, deep neural network solution to the queuing problem. Our solution combines a recurrent neural network that models the arrival process with a recurrent generative adversarial network which models the service time distribution. We evaluate our methodology on various empirical datasets ranging from internet services (Blockchain, GitHub, Stackoverflow) to mobility service systems (New York taxi cab).
  • Publication
    Imposing Category Trees Onto Word-Embeddings Using a Geometric Construction
    ( 2019)
    Dong, Tiansi
    ;
    Bauckhage, Christian
    ;
    Jin, Hailong
    ;
    Li, J.
    ;
    Cremers, O.
    ;
    Speicher, D.
    ;
    Cremers, Armin B.
    ;
    Zimmermann, J.
    We present a novel method to precisely impose tree-structured category information onto word-embeddings, resulting in ball embeddings in higher dimensional spaces (N-balls for short). Inclusion relations among N-balls implicitly encode subordinate relations among categories. The similarity measurement interms of the cosine function is enriched by category information. Using a geometric construction method instead of back-propagation, we create large N-ball embeddings that satisfy two conditions: (1) category trees are precisely imposed onto word embeddings at zero energy cost; (2) pre-trained word embeddings are well preserved. A new benchmark data set is created for validating the category of unknown words. Experiments show that N-ball embeddings, carrying category information, significantly outperform word embeddings in the test of nearest neighborhoods, and demonstrate surprisingly good performance in validating categories of unknown words. Source codes and data-sets are free for public access https://github.com/GnodIsNait/nball4tree.git and https://github.com/GnodIsNait/bp94nball.git.
  • Publication
    Matrix and Tensor Factorization Based Game Content Recommender Systems: A Bottom-Up Architecture and a Comparative Online Evaluation
    ( 2018)
    Sifa, Rafet
    ;
    Yawar, Raheel
    ;
    Ramamurthy, Rajkumar
    ;
    Bauckhage, Christian
    Players of digital games face numerous choices as to what kind of games to play and what kind of game content or in-game activities to opt for. Among these, game content plays an important role in keeping players engaged so as to increase revenues for the gaming industry. However, while nowadays a lot of game content is generated using procedural content generation, automatically determining the kind of content that suits players' skills still poses challenges to game developers. Addressing this challenge, we present matrix- and tensor factorization based game content recommender systems for recommending quests in a single player role-playing game. We discuss the theory behind latent factor models for recommender systems and derive an algorithm for tensor factorizations to decompose collections of bipartite matrices. Extensive online bucket type tests reveal that our novel recommender system retained more players and recommended more engaging quests than handcrafted content-based and previous collaborative filtering approaches.