Publications

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 …

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 …

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 …

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 …

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 …

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 …

Our paper On Outlier Exposure with Generative Models has been accepted on the NeurIPS Machine Learning Safety Workshop. Abstract § While Outlier Exposure reliably increases the performance of Out-of-Distribution detectors, it requires a set of available outliers during training. In this paper, we propose Generative Outlier Exposure (GOE), which alleviates the need for available outliers by using generative models to sample synthetic outliers from low-density regions of the data distribution. The …

During the last weeks, I worked with some colleagues on a website that aims to improve access to social work literature. We described the results in out paper Social Work Research Map – ein niederschwelliger Zugang zu internationalen Publikationen der Sozialen Arbeit, which has been published in the journal Soziale Passagen. While the paper is written in german, there is also a technical report in english. Abstract § Internationalization is a central topic in higher education policy in Germany. …

Our abstract Towards Transformer-based Homogenization of Satellite Imagery for Landsat-8 and Sentinel-2 was accepted for presentation on the Transformers Workshop for Environmental Science. In summary, we somewhat surprisingly found that transformers, a neural network architecture that achieves state-of-the-art results on most tasks it is applied to, does not outperform a vanilla U-Net model on our particular superresolution task.

Our Paper Multi-Class Hypersphere Anomaly Detection (MCHAD) has been accepted for presentation at the ICPR 2022. In summary, we propose a new loss function for learning neural networks that are able to detect anomalies in their inputs. Poster for MCHAD (PDF). MACHAD is available via pytorch-ood. You can find example code here. How does it work? § The general idea is that we want a neural network $f_{\theta}: \mathcal{X} \rightarrow \mathcal{Z}$ that maps inputs from the input space to some lower …