Prof. Dr.-Ing.

Bauckhage, Christian

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Decoupling Autoencoders for Robust One-vs-Rest Classification

2021-10-20 , Lübbering, Max , Gebauer, Michael , Ramamurthy, Rajkumar , Bauckhage, Christian , Sifa, Rafet

One-vs-Rest (OVR) classification aims to distinguish a single class of interest from other classes. The concept of novelty detection and robustness to dataset shift becomes crucial in OVR when the scope of the rest class extends from the classes observed during training to unseen and possibly unrelated classes. In this work, we propose a novel architecture, namely Decoupling Autoencoder (DAE) to tackle the common issue of robustness w.r.t. out-of-distribution samples which is prevalent in classifiers such as multi-layer perceptrons (MLP) and ensemble architectures. Experiments on plain classification, outlier detection, and dataset shift tasks show DAE to achieve robust performance across these tasks compared to the baselines, which tend to fail completely, when exposed to dataset shift. W hile DAE and the baselines yield rather uncalibrated predictions on the outlier detection and dataset shift task, we found that DAE calibration is more stable across all tasks. Therefore, calibration measures applied to the classification task could also improve the calibration of the outlier detection and dataset shift scenarios for DAE.

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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.

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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.

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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.

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Utilizing Representation Learning for Robust Text Classification Under Datasetshift

2021 , Lübbering, Max , Gebauer, Michael , Ramamurthy, Rajkumar , Pielka, Maren , Bauckhage, Christian , Sifa, Rafet

Within One-vs-Rest (OVR) classification, a classifier differentiates a single class of interest (COI) from the rest, i.e. any other class. By extending the scope of the rest class to corruptions (dataset shift), aspects of outlier detection gain relevancy. In this work, we show that adversarially trained autoencoders (ATA) representative of autoencoder-based outlier detection methods, yield tremendous robustness improvements over traditional neural network methods such as multi-layer perceptrons (MLP) and common ensemble methods, while maintaining a competitive classification performance. In contrast, our results also reveal that deep learning methods solely optimized for classification, tend to fail completely when exposed to dataset shift.

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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.

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Automatic Indexing of Financial Documents via Information Extraction

2021 , Ramamurthy, Rajkumar , Lübbering, Max , Bell , Thiago , Gebauer, Michael , Ulusay, Bilge , Uedelhoven, Daniel , Dilmaghani, Tim , Loitz, Rüdiger , Pielka, Maren , Bauckhage, Christian , Sifa, Rafet

The problem of extracting information from large volumes of unstructured documents is pervasive in the domain of financial business. Enterprises and investors need automatic methods that can extract information from these documents, particularly for indexing and efficiently retrieving information. To this end, we present a scalable end-to-end document processing system for indexing and information retrieval from large volumes of financial documents. While we show our system works for the use case of financial document processing, the entire system itself is agnostic of the document type and machine learning model type. Thus, it can be applied to any large-scale document processing task involving domain-specific extractors.

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