Prof. Dr.-Ing.

Bauckhage, Christian

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  • 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
    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
    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
    Ising models for binary clustering via adiabatic quantum computing
    ( 2018)
    Bauckhage, Christian
    ;
    Brito, Eduardo
    ;
    Cvejoski, Kostadin
    ;
    Ojeda, César
    ;
    Sifa, Rafet
    ;
    Wrobel, Stefan
    Existing adiabatic quantum computers are tailored towards minimizing the energies of Ising models. The quest for implementations of pattern recognition or machine learning algorithms on such devices can thus be seen as the quest for Ising model (re-)formulations of their objective functions. In this paper, we present Ising models for the tasks of binary clustering of numerical and relational data and discuss how to set up corresponding quantum registers and Hamiltonian operators. In simulation experiments, we numerically solve the respective Schrödinger equations and observe our approaches to yield convincing results.
  • Publication
    Towards German Word Embeddings: A Use Case with Predictive Sentiment Analysis
    ( 2017)
    Brito, Eduardo
    ;
    Sifa, Rafet
    ;
    Cvejoski, Kostadin
    ;
    Ojeda, César
    ;
    Bauckhage, Christian
    Despite the research boom on words embeddings and their text mining applications from the last years, the vast majority of publications focus only on the English language. Furthermore, hyperparameter tuning is a rarely well documented process (specially for non English text) that is necessary to obtain high quality word representations. In this work, we present how different hyperparameter combinations impact the resulting German word vectors and how these word representations can be part of more complex models. In particular, we perform first an intrinsic evaluation of our German word embeddings, which are later used within a predictive sentiment analysis model. The latter does not only serve as an extrinsic evaluation of the German word embeddings but also shows the feasibility of predic ting preferences only from document embeddings.
  • Publication
    Inverse dynamical inheritance in stack exchange taxonomies
    ( 2017)
    Ojeda, César
    ;
    Cvejoski, Kostadin
    ;
    Sifa, Rafet
    ;
    Bauckhage, Christian
    Question Answering websites are popular repositories of expert knowledge and cover areas as diverse as linguistics, computer science, or mathematics. Knowledge is commonly organized via user defined tags which implicitly create population folksonomies. However, the interplay between latent knowledge structures and the answering behavior of users has not been fully explored yet. Here, we propose a model of a dynamical tagging process guided by taxonomies, devise a robust algorithm that allow us to uncover hidden topic hierarchies, apply our method to analyze several Stack Exchange websites. Our results show that the dynamics of the system strongly correlate with uncovered taxonomies.