Konstantin Kirchheim

I am currently a PhD student at the Otto-von-Guericke University Magdeburg (Germany) in the Department of Computer Science.

My research focuses on anomaly detection in high-dimensional data with deep learning models, and more recently with neuro-symbolic methods, mostly on image, video and text data. I also did some work on reproducibility of experiments involving deep learning models, as well as mining of academic literature.

I maintain pytorch-ood, a Python library for out-of-distribution detection with deep neural networks.

In my spare time, I like to scrape data from the web and mine it. This hobby spawned some interesting projects with dedicated websites, such as sworm and extra-mining.

On the right, you can find a rather dated picture of me in the style of an image from Picasso’s blue period. It was generated with Neural Style Transfer.

Latest Updates

Improving Out-of-Distribution Detection with Markov Logic Networks, 06 Jun. 2025 (papers)
Our paper Improving Out-of-Distribution Detection with Markov Logic Network has been accepted at the ICML. Abstract § Out-of-distribution (OOD) detection is essential for ensuring the reliability of deep learning models operating in open-world scenarios. Current OOD detectors mainly rely on statistical models to identify unusual patterns in the latent representations of a deep neural network. This work proposes to augment existing OOD detectors with probabilistic reasoning, utilizing Markov …
Categories: Neuro-Symbolic
145 Words, Tagged with: ICML · Neuro-Symbolic · Anomaly Detection
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Out-of-Distribution Detection with Adversarial Outlier Exposure, 06 Jun. 2025 (papers)
Our paper Out-of-Distribution Detection with Adversarial Outlier Exposure has been accepted on the SAIAD Workshop at CVPR. Abstract § Machine learning models typically perform reliably only on inputs drawn from the distribution they were trained on, making Out-of-Distribution (OOD) detection essential for safety-critical applications. While exposing models to example outliers during training is one of the most effective ways to enhance OOD detection, recent studies suggest that synthetically …
Categories: Anomaly Detection
137 Words, Tagged with: CVPR · Generative Models · Anomaly Detection
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On the Implementation of AI Ethics, 24 Feb. 2025 (blog) 🇩🇪
This is a (german) term paper that I wrote in 2019 (in a pre-LLM era) for a seminar on the philosophical aspects of AI. It discusses general strategies for implementing ethical behavior in AI systems at the example of autonomous vehicles. While somewhat outdated, it still constitutes a reasonable introduction to the topic. Einleitung § Lange Zeit galten menschliche Individuen und Gesellschaften als die einzigen intelligenten Entscheidungsträger. Durch die Fortschritte in der Informatik, …
Categories: Philosophy
2893 Words, Tagged with: Ethics · Autonomous Vehicles
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Home Server Setup 2025, 26 Jan. 2025 (blog)
In this post, I want to present my current home server setup, including the hardware, the virtualized infrastructure (Networks, VMs), and the services (Containers) I am running.1 The goal is to give you some inspiration and also to have some more thorough documentation for myself. While writing, I noticed some possible improvements, so there is value in the documentation process itself. This post will be quite long as the infrastructure evolved over a prolonged period. To avoid convoluting it …
Categories: Homeserver
3113 Words, Tagged with: Homeserver · Virtualization
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Training a German LLM from Scratch, 14 Nov. 2024 (blog)
This article is not finished and will be updated. The research group I work with has access to a small GPU cluster, which occasionally sits idle. To avoid wasting valuable compute resources (IDLE GPUs essentially burn money through opportunity costs), I decided to train a German GPT-2-style model from scratch, using only German text. Existing German models available on Hugging Face have 137M parameters and a context length of 1024 tokens1, which is quite limited compared to recently released …
Categories: Deep Learning
2794 Words, Tagged with: Deep Learning · Generative Models · LLM
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Language Models as Reasoners for Out-of-Distribution Detection, 17 Sep. 2024 (papers)
Our paper, Language Models as Reasoners for Out-of-Distribution Detection, was presented at the Workshop on AI Safety Engineering (WAISE) 2024 and received the best paper award by popular vote. It constitutes an extension of our idea of Out-of-Distribution Detection with Logical Reasoning, where we replaced the prolog-based reasoning component with an LLM. Abstract § Deep neural networks (DNNs) are prone to making wrong predictions with high confidence for data that does not stem from their …
Categories: Anomaly Detection Neuro-Symbolic
195 Words, Tagged with: SafeComp · Anomaly Detection · Large Language Models · Neuro-Symbolic
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Deep learning-based harmonization and super-resolution of Landsat-8 and Sentinel-2 images, 17 May. 2024 (papers)
Our paper Deep learning-based harmonization and super-resolution of Landsat-8 and Sentinel-2 images, which is based on the masters thesis of my colleague Venkatesh Thirugnana Sambandham, has been published in the ISPRS Journal of Photogrammetry and Remote Sensing. This work is an extension of our previous workshop paper on transformers for satellite homogenization. In summary, we find that a simple UNet model provides surprisingly good performance for the satellite homogenization task. We …
Categories: Deep Learning
344 Words, Tagged with: Deep Learning · Superresolution
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Out-of-Distribution Detection with Logical Reasoning, 04 Jan. 2024 (papers)
Our paper Out-of-Distribution Detction with Logical Reasoning has been accepted on the WACV 2024. Abstract § Machine Learning models often only generalize reliably to samples from the training distribution. Consequentially, detecting when input data is out-of-distribution (OOD) is crucial, especially in safety-critical applications. Current OOD detection methods, however, tend to be domain agnostic and often fail to incorporate valuable prior knowledge about the structure of the training …
Categories: Anomaly Detection Neuro-Symbolic
226 Words, Tagged with: WACV · Anomaly Detection · Neuro-Symbolic
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Towards Deep Anomaly Detection with Structured Knowledge Representations, 15 Jun. 2023 (papers)
My paper Towards Deep Anomaly Detection with Structured Knowledge Representations has been accepted on the Workshop on AI Safety Engineering at SafeComp. Abstract § Machine learning models tend to only make reliable predictions for inputs that are similar to the training data. Consequentially, anomaly detection, which can be used to detect unusual inputs, is critical for ensuring the safety of machine learning agents operating in open environments. In this work, we identify and discuss several …
Categories: Anomaly Detection Neuro-Symbolic
181 Words, Tagged with: SafeComp · Anomaly Detection · Neuro-Symbolic
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Mining the Bundestag, 22 Jan. 2023 (blog)
Did you know that the German parliament publishes protocols for all of its proceedings in PDF format? It is relatively straightforward to download and parse them, so we can easily collect a dataset of transcripts of what seems to be every speech in the Bundestag since the Second World War. My original idea was to mine the speeches for word associations. Some words will be associated with other words based on the intended connotation, and this association might change over time as the …
Categories: Data Mining
1025 Words, Tagged with: Bundestag · Data Mining · Generative Models
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