# Christian BauckhageUniversity of Bonn | Uni Bonn · Institute for Computer Sciences

Christian Bauckhage

Prof. Dr.

## About

485

Publications

532,511

Reads

**How we measure 'reads'**

A 'read' is counted each time someone views a publication summary (such as the title, abstract, and list of authors), clicks on a figure, or views or downloads the full-text. Learn more

8,196

Citations

Introduction

Christian is a professor of computer science at the University of Bonn and lead scientist for machine learning at Fraunhofer IAIS.
Most of his research addresses questions in data science. In particular, he works on theory and applications of artificial intelligence, machine learning, and data mining in the natural sciences, social media, and finance.

Additional affiliations

October 2008 - present

October 2008 - present

October 2008 - July 2019

## Publications

Publications (485)

In this note, we study k-medoids clustering and show how to implement the algorithm using NumPy. To illustrate potential and practical use of this lesser known clustering method, we discuss an application example where we cluster a data set of strings based on bi-gram distances.

In this note, we show how to use of NumPy mesh-grids and boolean arrays for efficient image processing. As an application example, we compute fractal images that visualize Julia-or Mandelbrot sets.

We revisit the idea of relational clustering and look at NumPy code for spectral clustering that allows us to cluster graphs or networks. In addition, our topic in this note provides us with the opportunity to study the use of NetworkX functions.

Ten days after our last note in this series, worldwide testing for COVID-19 infections has continued and and it seems that current case data can be better explained in terms of Gompertz growth functions rather than in terms of logistic growth functions. In this note, we therefore discuss the Gompertz function and its use in mathematical epidemiolog...

Archetypal analysis is an increasingly popular tool for data mining and pattern recognition. In this note, we first discuss how to solve the underlying optimization problem using plain vanilla Frank-Wolfe optimization and then present an efficient NumPy implementation of this approach.

In preparation for things to come, we discuss the general ideas behind AdaBoost (for binary classifier training) and present efficient NumPy code for boosting pre-trained weak hypotheses.

We present KPI-BERT, a system which employs novel methods of named entity recognition (NER) and relation extraction (RE) to extract and link key performance indicators (KPIs), e.g. "revenue" or "interest expenses", of companies from real-world German financial documents. Specifically, we introduce an end-to-end trainable architecture that is based...

We revisit the minimum set cover problem and formulate it as an integer linear program over binary indicator vectors. Next, we simply adapt our earlier code for greedy set covering to indicator vector representations.

One-vs-Rest (OVR) classification aims to distinguish a single class of interest (COI) from other classes. The concept of novelty detection and robustness to dataset shift becomes crucial in OVR when the scope of the rest class is extended from the classes observed during training to unseen and possibly unrelated classes, a setting referred to as op...

In preparation for things to come, we discuss a plain vanilla Python implementation of "the" greedy approximation algorithm for the set cover problem.

Background
Unmanned aerial vehicle (UAV)–based image retrieval in modern agriculture enables gathering large amounts of spatially referenced crop image data. In large-scale experiments, however, UAV images suffer from containing a multitudinous amount of crops in a complex canopy architecture. Especially for the observation of temporal effects, thi...

Over the past decade, machine learning revolutionized vision-based quality assessment for which convolutional neural networks (CNNs) have now become the standard. In this paper, we consider a potential next step in this development and describe a quanvolutional neural network (QNN) algorithm that efficiently maps classical image data to quantum sta...

When training data is scarce, the incorporation of additional prior knowledge can assist the learning process. While it is common to initialize neural networks with weights that have been pre-trained on other large data sets, pre-training on more concise forms of knowledge has rather been overlooked. In this paper, we propose a novel informed machi...

Although CT and MRI are standard procedures in cirrhosis diagnosis, differentiation of etiology based on imaging is not established. This proof-of-concept study explores the potential of deep learning (DL) to support imaging-based differentiation of the etiology of liver cirrhosis. This retrospective, monocentric study included 465 patients with co...

Given is a set of images, where all images show views of the same area at different points in time and from different viewpoints. The task is the alignment of all images such that relevant information, e.g., poses, changes, and terrain, can be extracted from the fused image. In this work, we focus on quantum methods for keypoint extraction and feat...

We show that the fundamental tasks of sorting lists and building search trees or heaps can be modeled as quadratic unconstrained binary optimization problems (QUBOs). The idea is to understand these tasks as permutation problems and to devise QUBOs whose solutions represent appropriate permutation matrices. We discuss how to construct such QUBOs an...

The automatization and digitalization of business processes have led to an increase in the need for efficient information extraction from business documents. However, financial and legal documents are often not utilized effectively by text processing or machine learning systems, partly due to the presence of sensitive information in these documents...

Digital twins enable the modeling and simulation of real-world entities (objects, processes or systems), resulting in improvements in the associated value chains. The emerging field of quantum computing holds tremendous promise for evolving this virtualization towards Quantum (Digital) Twins (QDT) and ultimately Quantum Twins (QT). The quantum (dig...

The most damaging foliar disease in sugar beet is Cercospora leaf spot (CLS), caused by Cercospora beticola Sacc. The pathogen is expanding its territory due to climate conditions, generating the need for early and accurate detection to avoid yield losses. In Germany, monitoring and control strategies are based on visual field assessments, with the...

div>
Many applications in automated auditing and the analysis and consistency check of financial documents can be formulated in part as the subset sum problem: Given a set of numbers and a target sum, find the subset of numbers that sums up to the target. The problem is NP-hard and classical solving algorithms are therefore not practical to use in...

div>
Many applications in automated auditing and the analysis and consistency check of financial documents can be formulated in part as the subset sum problem: Given a set of numbers and a target sum, find the subset of numbers that sums up to the target. The problem is NP-hard and classical solving algorithms are therefore not practical to use in...

UAV-based image retrieval in modern agriculture enables gathering large amounts of spatially referenced crop image data. In large-scale experiments, however, UAV images suffer from containing a multitudinous amount of crops in a complex canopy architecture. Especially for the observation of temporal effects, this complicates the recognition of indi...

We consider L2 support vector machines for binary classification. These are as robust as other kinds of SVMs but can be trained almost effortlessly. Indeed, having previously derived the corresponding dual training problem, we now show how to solve it using the Frank-Wolfe algorithm. In short, we show that it requires only a few lines of plain vani...

In times of climate change, growing world population, and the resulting scarcity of resources, efficient and economical usage of agricultural land is increasingly important and challenging at the same time. To avoid disadvantages of monocropping for soil and environment, it is advisable to practice intercropping of various plant species whenever po...

In order to avoid disadvantages of monocropping for soil and environment, it is advisable to practice intercropping of various plant species whenever possible. However, intercropping is challenging as it requires a balanced planting schedule due to individual cultivation time frames. Maintaining a continuous harvest reduces logistical costs and rel...

In this note, we introduce some of the common terminology in digital image processing. We also have a very first look at how to work with digital images in Python and discuss how to read and write them from-and to disc.

Financial reports are commonplace in the business world, but are long and tedious to produce. These reports mostly consist of tables with written sections describing these tables. Automating the process of creating these reports, even partially has the potential to save a company time and resources that could be spent on more creative tasks. We imp...

Financial reports are commonplace in the business world, but are long and tedious to produce. These reports mostly consist of tables with written sections describing these tables. Automating the process of creating these reports, even partially has the potential to save a company time and resources that could be spent on more creative tasks. We imp...

Neural networks have the potential to be extremely powerful for computer vision related tasks, but can be computationally expensive. Classical methods, by comparison, tend to be relatively light weight, albeit not as powerful. In this paper, we propose a method of combining parts from a classical system, called the Viola-Jones Object Detection Fram...

Just as user preferences change with time, item reviews also reflect those same preference changes. In a nutshell, if one is to sequentially incorporate review content knowledge into recommender systems, one is naturally led to dynamical models of text. In the present work we leverage the known power of reviews to enhance rating predictions in a wa...

Pre-symptomatic drought stress prediction is of great relevance in precision plant protection, ultimately helping to meet the challenge of "How to feed a hungry world?". Unfortunately, it also presents unique computational problems in scale and interpretability: it is a temporal, large-scale prediction task, e.g., when monitoring plants over time u...

We demonstrate that Hopfield networks can be used for hard vector quantization. To this end, we first formulate vector quantization as the problem of minimizing the mean discrepancy between kernel density estimates of two data distributions and then express it as a quadratic unconstrained binary optimization problem that can be solved by a Hopfield...

This note demonstrates that Hopfield nets can solve Sudoku puzzles. We discuss how to represent Sudokus in terms of binary vectors and how to express their rules and hints in terms of matrix-vector equations. This allows us to set up energy functions whose global minima encode the solution to a given puzzle. However, as these energy functions typic...

We approach least squares optimization from the point of view of gradient flows. As a practical example, we consider a simple linear regression problem, set up the corresponding differential equation, and show how to solve it using SciPy.

Objective
To identify non-EEG based signals and algorithms for detection of motor and non-motor seizures in people lying in bed during video-EEG (VEEG) monitoring and to test whether these algorithms work in freely moving people during mobile EEG recordings.
Methods
Data of three groups of adult people with epilepsy (PwE) were analyzed. Group 1 un...

We revisit Hopfield nets for bipartition clustering and tweak the underlying energy function such that it has a unique global minimum. In other words, we show how to remove ambiguity from the bipartition clustering problem. Our corresponding NumPy code is short and simple.

We show how max-sum diversification can be used to solve the-clique problem, a well-known NP-complete problem. This reduction proves that max-sum diversification is NP-hard and provides a simple and practical method to find cliques of a given size using Hopfield networks.

We derive the dual problem of L2 support vector machine training. This involves setting up the Lagrangian of the primal problem and working with the Karush-Kuhn-Tucker conditions. As a payoff, we find that the dual poses a rather simple optimization problem that can be solved by the Frank-Wolfe algorithm.

We revisit Hopfield nets for bipartition clustering and show how to invoke the kernel trick to increase robustness and versatility. Our corresponding NumPy code is short and simple.

We show that Hopfield networks can cluster numerical data into two salient clusters. Our derivation of a corresponding energy function is based on properties of the specific problem of 2-means clustering. Our corresponding NumPy code is short and simple.

We demonstrate that Hopfield networks can tackle the max-sum diversification problem. To this end, we express max-sum diversification as a quadratic unconstrained binary optimization problem which can be cast as a Hopfield energy minimization problem. Since max-sum diversification is an NP-hard subset selection problem, we cannot guarantee that Hop...

Natural Language Inference, and specifically Contradiction Detection, is still an unexplored topic with respect to German text. In this paper, we apply Recurrent Neural Network (RNN) methods to learn contradiction-specific sentence embeddings. Our data set for evaluation is a machine-translated version of the Stanford Natural Language Inference (SN...

We show how to use Hopfield networks for sorting. We first derive a corresponding energy function, then present an efficient algorithm for its minimization, and finally implement our ideas in NumPy.

Having previously considered sorting as a linear programming problem, we now cast it as a quadratic unconstrained binary optimization problem (QUBO). Deriving this formulation is a bit cumbersome but it allows for implementing neural networks or even quantum computing algorithms that sort. Here, however, we consider a simple greedy QUBO solver and...

Despite its great success, machine learning can have its limits when dealing with insufficient training data. A potential solution is the additional integration of prior knowledge into the training process which leads to the notion of informed machine learning. In this paper, we present a structured overview of various approaches in this field. We...

Ojas' rule for neural principal component learning has a continuous analog called the Oja flow. This is a gradient flow on the unit sphere whose equilibrium points indicate the principal eigenspace of the training data. We briefly discuss characteristics of this flow and show how to solve its differential equation using SciPy.

Linear programming is a surprisingly versatile tool. That is, many problems we would not usually think of in terms of a linear programming problem can actually be expressed as such. In this note, we show that sorting is such a problem and discuss how to solve linear programs for sorting using SciPy.

Artificial intelligence for autonomous driving must meet strict requirements on safety and robustness, which motivates the thorough validation of learned models. However, current validation approaches mostly require ground truth data and are thus both cost-intensive and limited in their applicability. We propose to overcome these limitations by a m...

Discuss how machine learning and deep learning methods may be made transparent and reliable. Discuss general capabilities of deep learning.

We revisit the problem of numerically solving the Schrödinger equation for a one-dimensional quantum harmonic oscillator. We reconsider our previous finite difference scheme and discuss how higher order finite differences can lead to more accurate solutions. In particular, we will consider a five point stencil to approximate second order derivative...

Despite the success of deep learning in various domains such as natural language processing, speech recognition, and computer vision, learning from a limited amount of samples and generalizing to unseen data still pose challenges. Notably, in the tasks of outlier detection and imbalanced dataset classification, the label of interest is either scarc...

Most quantum mechanical systems cannot be solved analytically and therefore require numerical solution strategies. In this note, we consider a simple such strategy and discretize the Schrodinger equation that governs the behavior of a one-dimensional quantum harmonic oscillator. This leads to an eigenvalue / eigenvector problem over finite matrices...

Having previously discussed how SciPy allows us to solve linear programs, we can study further applications of linear programming. Here, we consider least absolute deviation regression and solve a simple parameter estimation problem deliberately chosen to expose potential pitfalls in using SciPy's optimization functions.

This note discusses how to solve linear programming problems with SciPy. As a practical use case, we consider the task of computing the Chebyshev center of a bounded convex polytope.

Unsupervised topic extraction is a vital step in automatically extracting concise contentual information from large text corpora. Existing topic extraction methods lack the capability of linking relations between these topics which would further help text understanding. Therefore we propose utilizing the Decomposition into Directional Components (D...

Artificial intelligence for autonomous driving must meet strict requirements on safety and robustness, which motivates the thorough validation of learned models. However, current validation approaches mostly require ground truth data and are thus both cost-intensive and limited in their applicability. We propose to overcome these limitations by a m...

The hyperbolic tangent (tanh) is a traditional choice for the activation function of the neurons of an artificial neural network. Here, we go through a simple calculation that shows that this modeling choice is linked to Bayesian decision theory. Our brief, tutorial-like discussion is intended as a reference to an observation rarely mentioned in st...

This book constitutes the refereed proceedings of the 43rd DAGM German Conference on Pattern Recognition, DAGM GCPR 2021, which was held during September 28 – October 1, 2021. The conference was planned to take place in Bonn, Germany, but changed to a virtual event due to the COVID-19 pandemic.
The 46 papers presented in this volume were carefully...

Variational Quantum Eigensolvers (VQEs) have recently attracted considerable attention. Yet, in practice, they still suffer from the efforts for estimating cost function gradients for large parameter sets or resource-demanding reinforcement strategies. Here, we therefore consider recent advances in weight-agnostic learning and propose a strategy th...

The FinCausal 2020 shared task aims to detect causality on financial news and identify those parts of the causal sentences related to the underlying cause and effect. We apply ensemble-based and sequence tagging methods for identifying causality, and extracting causal subsequences. Our models yield promising results on both sub-tasks, with the pros...

Deep neural network models represent the state-of-the-art methodologies for natural language processing. Here we build on top of these methodologies to incorporate temporal information and model how review data changes with time. Specifically, we use the dynamic representations of recurrent point process models, which encode the history of how busi...

Deep neural network models represent the state-of-the-art methodologies for natural language processing. Here we build on top of these methodologies to incorporate temporal information and model how to review data changes with time. Specifically, we use the dynamic representations of recurrent point process models, which encode the history of how b...

Reinforcement learning (RL) has recently shown impressive performance in complex game AI and robotics tasks. To a large extent, this is thanks to the availability of simulated environments such as OpenAI Gym, Atari Learning Environment, or Malmo which allow agents to learn complex tasks through interaction with virtual environments. While RL is als...

Artificial intelligence for autonomous driving must meet strict requirements on safety and robustness. We propose to validate machine learning models for self-driving vehicles not only with given ground truth labels, but also with additional a-priori knowledge. In particular, we suggest to validate the drivable area in semantic segmentation masks u...

In this note, we revisit the problem of computing archetypal analysis and show how to solve it by means of mirror descent. Just as our earlier solutions, this approach, too, is easily implemented in NumPy.

In this short note, we discuss the use of archetypal analysis in clustering. The underlying ideas are straightforward and very easy to implement in NumPy.

This strategic paper highlights selected research initiatives and success stories of technology transfer in the domain of artificial intelligence that have been driven by researchers in North Rhine-Westphalia (NRW). It was inspired by a round table on Artificial Intelligence (AI) initiated by the Ministry of Culture and Science (MKW) of NRW.

In this short note, we discuss archetypal analysis for (binary) classification. Building on top of our previous solutions for computing archetypal analysis, our NumPy implementation of an archetypal hull classifier is downright trivial.