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PublicationAutomatic scoring of Rhizoctonia crown and root rot affected sugar beet fields from orthorectified UAV images using Machine Learning( 2024)Rhizoctonia crown and root rot (RCRR), caused by Rhizoctonia solani, can cause severe yield and quality losses in sugar beet. The most common strategy to control the disease is the development of resistant varieties. In the breeding process, field experiments with artificial inoculation are carried out to evaluate the performance of genotypes and varieties. The phenotyping process in breeding trials requires constant monitoring and scoring by skilled experts. This work is time demanding and shows bias and heterogeneity according to the experience and capacity of each individual person. Optical sensors and artificial intelligence have demonstrated a great potential to achieve higher accuracy than human raters and the possibility to standardize phenotyping applications. A workflow combining red-green-blue (RGB) and multispectral imagery coupled to an unmanned aerial vehicle (UAV), and machine learning techniques was applied to score diseased plants and plots affected by RCRR. Georeferenced annotation of UAV orthorectified images. With the annotated images, five convolutional neural networks were trained to score individual plants. The training was carried out with different image analysis strategies and data augmentation, respectively. The custom convolutional neural network trained from scratch together with a pre-trained MobileNet showed the best precision in scoring RCRR (0.73 to 0.85). The average per plot of spectral information was used to score plots, and the benefit of adding the information obtained from the score of individual plants was compared. For this purpose, machine learning models were trained together with data management strategies, and the best-performing model was chosen. A combined pipeline of Random Forest and k-Nearest neighbors have shown the best weighted precision (0.67). This research provides a reliable workflow for detecting and scoring RCRR based on aerial imagery. RCRR is often distributed heterogeneously in trial plots, therefore, considering the information from individual plants of the plots showed a significant improvement of UAV based automated monitoring routines.
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PublicationHow Does Knowledge Injection Help in Informed Machine Learning?( 2023-08-02)Informed machine learning describes the injection of prior knowledge into learning systems. It can help to improve generalization, especially when training data is scarce. However, the field is so application-driven that general analyses about the effect of knowledge injection are rare. This makes it difficult to transfer existing approaches to new applications, or to estimate potential improvements. Therefore, in this paper, we present a framework for quantifying the value of prior knowledge in informed machine learning. Our main contributions are threefold. Firstly, we propose a set of relevant metrics for quantifying the benefits of knowledge injection, comprising in-distribution accuracy, out-of-distribution robustness, and knowledge conformity. We also introduce a metric that combines performance improvement and data reduction. Secondly, we present a theoretical framework that represents prior knowledge in a function space and relates it to data representations and a trained model. This suggests that the distances between knowledge and data influence potential model improvements. Thirdly, we perform a systematic experimental study with controllable toy problems. All in all, this helps to find general answers to the question how knowledge injection helps in informed machine learning.
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PublicationA New Aligned Simple German Corpus( 2023-07)"Leichte Sprache", the German counterpart to Simple English, is a regulated language aiming to facilitate complex written language that would otherwise stay inaccessible to different groups of people. We present a new sentence-aligned monolingual corpus for Simple German - German. It contains multiple document-aligned sources which we have aligned using automatic sentence-alignment methods. We evaluate our alignments based on a manually labelled subset of aligned documents. The quality of our sentence alignments, as measured by the F1-score, surpasses previous work. We publish the dataset under CC BY-SA and the accompanying code under MIT license.
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PublicationAn Empirical Evaluation of the Rashomon Effect in Explainable Machine Learning( 2023)The Rashomon Effect describes the following phenomenon: for a given dataset there may exist many models with equally good performance but with different solution strategies. The Rashomon Effect has implications for Explainable Machine Learning, especially for the comparability of explanations. We provide a unified view on three different comparison scenarios and conduct a quantitative evaluation across different datasets, models, attribution methods, and metrics. We find that hyperparameter-tuning plays a role and that metric selection matters. Our results provide empirical support for previously anecdotal evidence and exhibit challenges for both scientists and practitioners.
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PublicationTowards Automated Regulatory Compliance Verification in Financial Auditing with Large Language Models( 2023)
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PublicationFrom Open Set Recognition Towards Robust Multi-class Classification( 2022-09-15)The challenges and risks of deploying deep neural networks (DNNs) in the open-world are often overlooked and potentially result in severe outcomes. With our proposed informer approach, we leverage autoencoder-based outlier detectors with their sensitivity to epistemic uncertainty by ensembling multiple detectors each learning a different one-vs-rest setting. Our results clearly show informer’s superiority compared to DNN ensembles, kernel-based DNNs, and traditional multi-layer perceptrons (MLPs) in terms of robustness to outliers and dataset shift while maintaining a competitive classification performance. Finally, we show that informer can estimate the overall uncertainty within a prediction and, in contrast to any of the other baselines, break the uncertainty estimate down into aleatoric and epistemic uncertainty. This is an essential feature in many use cases, as the underlying reasons for the uncertainty are fundamentally different and can require different actions.
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PublicationQuantum Circuit Evolution on NISQ Devices( 2022-09-06)Variational quantum circuits build the foundation for various classes of quantum algorithms. In a nutshell, the weights of a parametrized quantum circuit are varied until the empirical sampling distribution of the circuit is sufficiently close to a desired outcome. Numerical first-order methods are applied frequently to fit the parameters of the circuit, but most of the time, the circuit itself, that is, the actual composition of gates, is fixed. Methods for optimizing the circuit design jointly with the weights have been proposed, but empirical results are rather scarce. Here, we consider a simple evolutionary strategy that addresses the trade-off between finding appropriate circuit architectures and parameter tuning. We evaluate our method both via simulation and on actual quantum hardware. Our benchmark problems include the transverse field Ising Hamiltonian and the Sherrington-Kirkpatrick spin model. Despite the shortcomings of current noisy intermediate-scale quantum hardware, we find only a minor slowdown on actual quantum machines compared to simulations. Moreover, we investigate which mutation operations most significantly contribute to the optimization. The results provide intuition on how randomized search heuristics behave on actual quantum hardware and lay out a path for further refinement of evolutionary quantum gate circuits.
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PublicationTowards Generating Financial Reports from Tabular Data Using Transformers( 2022-08-11)
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PublicationInformed Pre-Training on Prior Knowledge( 2022-05-23T16:24:40Z)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 machine learning approach and suggest to pre-train on prior knowledge. Formal knowledge representations, e.g. graphs or equations, are first transformed into a small and condensed data set of knowledge prototypes. We show that informed pre-training on such knowledge prototypes (i) speeds up the learning processes, (ii) improves generalization capabilities in the regime where not enough training data is available, and (iii) increases model robustness. Analyzing which parts of the model are affected most by the prototypes reveals that improvements come from deeper layers that typically represent high-level features. This confirms that informed pre-training can indeed transfer semantic knowledge. This is a novel effect, which shows that knowledge-based pre-training has additional and complementary strengths to existing approaches.
