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Dr Ramsés Sánchez (Lamarr institute, University of Bonn), Paul Roetzer9/3/25, 10:15 AMArea Update
This presentation will introduce the Hybrid ML research area, which aims to integrate deep learning with structured knowledge from mathematics and the natural and social sciences.
Hybrid ML is guided by the observation that both mathematics and the sciences can be seen as generators of compressed pattern representations. We will explain that the central goal of Hybrid ML is to align the...
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Nilah Nair, Alice Kirchheim, Anike Murrenhoff9/3/25, 10:45 AMArea Update
The Planning & Logistics area within the Lamarr Institute focuses on transferring AI research into real-world logistics applications. Logistics offers a rich field for AI with significant impact on both society and sustainability. Key topics include scalable multi-criteria optimization for efficiency and environmental performance (such as route planning, fleet management, and navigation),...
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Jian-Jia Chen, Sebastian Buschjäger (Lamarr Institute for ML and AI, TU Dortmund)9/3/25, 11:15 AMArea Update
Area presentation: "Resource-Aware Machine Learning at Lamarr: A Guided Tour"
In this talk, we offer a guided overview of the resource-aware machine learning (RAML) research taking place at the Lamarr Institute. RAML aims to make machine learning systems not only accurate, but also efficient in terms of energy, latency, and computational resources. We highlight ongoing efforts within the...
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Prof. Wolfgang Rhode (TU Dortmund), Pascal Gutjahr (TU Dortmund University)9/3/25, 11:45 AMArea Update
The interdisciplinary research area of physics at the Lamarr Institute leverages advanced mathematical and machine learning methods to deepen our understanding of nature. By combining simulation-based approaches with sophisticated data analysis techniques, this area addresses fundamental questions across diverse physics domains. This presentation will introduce the research area, its...
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Prof. Jakob Rehof (TU Dortmund), Rebekka Görge, Tim Katzke9/3/25, 12:15 PMArea Update
Within the Lamarr Institute, the topic of trustworthy AI is being explored across diverse application contexts and scientific disciplines. Lamarr researchers focus on areas such as developing effective certification and verification procedures for AI systems, ensuring explainability and robustness, as well as advancing trustworthy AI in domains like physics, life sciences, engineering, and...
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Jonathan Lennartz9/3/25, 2:00 PM
Trustworthy methods for medical image segmentation should come with a reliable mechanism to estimate the quality of their results. Training a separate component for confidence prediction is relatively fast, and can easily be adapted to different quality metrics. However, the resulting estimates are usually not sufficiently reliable under domain shifts, for example when images are taken with...
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Felix Finkeldey9/3/25, 2:00 PM
Using data-based approaches, accurate predictions of thermal deformations, which can significantly affect the quality of manufactured components, can be enabled. However, a sufficient amount of data with maximised information content is necessary for efficient training. In this paper, an approach for optimising sensor configurations for predicting thermal deformations is presented. From...
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Florian Wöste9/3/25, 2:00 PM
Accurate predictions of process characteristics in milling, such as tool vibrations, allow for identifying and avoiding unstable cutting conditions that can lead to excessive tool wear, surface defects or tool breakage. Therefore, considering vibrations during process design is essential to ensure dimensional accuracy, surface integrity and the longevity of cutting tools. Common methods,...
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Anas Gouda (TU Dortmund)9/3/25, 2:00 PM
We address two critical capabilities required for autonomous robots operating in indoor environments, both centered around robust perception of unseen objects. This generalization can support various applications, but here we focus on mobile robotics.
The first focus is on robotic grasping, where 6D pose estimation is needed for successful manipulation. While 6D tracking is now reliable,...
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Ahmed Shokry9/3/25, 2:00 PM
Autonomous assembly is an essential capability for industrial and service robots, with Peg-in-Hole (PiH) insertion being one of the core tasks. However, PiH assembly in unknown environments is still challenging due to uncertainty in task parameters, such as the hole position and orientation, resulting from sensor noise. Although context-based meta reinforcement learning (RL) methods have been...
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Mr Felix Wersig (TU Dortmund University / LAMARR Institut), Mr Luca Di Bella (TU Dortmund University / LAMARR Institut)9/3/25, 2:00 PM
For more than two decades, the MAGIC telescopes continuously accumulate
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significant amounts of data. However, the analysis of this data poses critical
problems due to its volume exceeding existing data curation capacities. This
criticality induces the demands for the utilization of AI methods to enhance and
accelerate the analysis process. Thus, MAGIC utilizes random forests for an... -
Mr Andrei Kazantsev (Max Planck Institute for Radio Astronomy)9/3/25, 2:00 PM
This work explores the applicability of synthetic data for training deep learning models aimed at real-time classification of astronomical radio signals. Building on previous research where lightweight convolutional neural networks (CNNs) using DM-time representations showed promising performance in detecting transient signals, we now turn to the question of whether synthetic datasets can...
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Justus Bisten (b-it and Institute for Computer Science II & Department for Neuroradiology, University Hospital Bonn)9/3/25, 2:00 PM
Tractography enables the reconstruction of white matter pathways from diffusion MRI and is a key tool for studying brain connectivity in both research and clinical contexts. Within the overall tractography pipeline, the parcellation step assigns individual streamlines to specific anatomical bundles, or discards them as false positive detections. We introduce PETParc (Parallel Efficient...
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Ting Han9/3/25, 2:00 PM
Robust generalization is a foundational requirement for trustworthy artificial intelligence (AI), underpinning the reliability, stability, and fairness of deployed systems. Two geometric phenomena are frequently correlated with generalization: neural collapse, where internal class representations converge to a maximally simple and symmetric structure, and flatness of the loss landscape, where...
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David Kaczér (University of Bonn)9/3/25, 2:00 PM
Stochastically sampling word segmentations from a subword tokeniser, also called subword regularisation, is a known way to increase robustness of language models to out-of-distribution inputs, such as text containing spelling errors. Recent work has observed that usual augmentations that make popular deterministic subword tokenisers stochastic still cause only a handful of all possible...
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Loong Kuan Lee (Fraunhofer IAIS)9/3/25, 2:00 PM
Multi-Agent Path Finding (MAPF) focuses on determining conflict-free paths for multiple agents navigating through a shared space to reach specified goal locations. This problem becomes computationally challenging, particularly when handling large numbers of agents, as frequently encountered in practical applications like coordinating autonomous vehicles. Quantum Computing (QC) is a promising...
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Quentin Führing9/3/25, 2:00 PM
Measurements of neutral, oscillating mesons are a gateway to quantum mechanics and give access to the fundamental interactions of elementary particles. For example, precise measurements of violation in neutral mesons can be taken in order to test the Standard Model of particle physics. These measurements require knowledge of the -meson flavour at the time of its production, which cannot be...
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Jan Robine (TU Dortmund)9/3/25, 2:00 PM
Neural networks often suffer from plasticity loss, which limits their ability to adapt to evolving data distributions in continual learning settings. This results in degraded performance, poor generalization, and inefficient use of model capacity. While recent methods mitigate this by resetting underutilized neurons based on utility scores, the underlying mechanisms remain poorly understood....
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Amal Saadallah (Lamarr Institute-TU Dortmund)9/3/25, 2:00 PM
Forecasting astrophysical flares in blazars presents a unique challenge due to their irregular temporal dynamics and strong variability. While deep neural networks have shown promise for modeling such complex time series, their predictions often lack alignment with established physical knowledge, limiting trust and interpretability. In this work, we propose a domain-informed deep learning...
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Akbar Karimi (University of Bonn)9/3/25, 2:00 PM
Hyperbolic representations are effective in modeling knowledge graph data which is prevalently used to facilitate multi-hop reasoning. However, a rigorous and detailed comparison of the two spaces for this task is lacking. In this paper, through a simple integration of hyperbolic representations with an encoder-decoder model, we perform a controlled and comprehensive set of experiments to...
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Nicolo' Brandizzi (Fraunhofer IAIS)9/3/25, 2:00 PM
The AI research ecosystem is a demanding, high-pressure environment that profoundly shapes the future of technology. Its effectiveness and sustainability depend not only on technical innovation but also on the people who sustain its progress. Investigating the psychosocial factors that link individual traits to work experiences and mental health is therefore essential for enabling sustainable,...
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Simon Kurz9/3/25, 2:00 PM
Current pruning methods for large language models (LLMs) achieve high compression post-training while preserving performance. However, most existing work focuses on calibration using English data, despite the multilingual nature of modern LLMs and their widespread use in non-English languages. This poster presents the first study on how the calibration language impacts pruning for multilingual...
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Marena Richter9/3/25, 2:00 PM
We propose a sublinear algorithm for probabilistic testing of the discrete Fréchet distance - a standard similarity measure for curves.
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We assume the algorithm is given access to the input curves via a query oracle that returns the set of vertices of the curve that lie within a radius $\delta$ of a specified vertex of the other curve.
The goal is to use a small number of queries to... -
Shubham Gupta9/3/25, 2:00 PM
Designing stable milling operations is crucial to ensure a high surface quality of the machined workpieces and reduce rejects during production. Stability lobe diagrams can be used to identify stable conditions. Analytical approaches or simulation techniques can be used to reduce the experimental effort for stability evaluation for different process parameter values. However, complex...
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Bahavathy Kathirgamanathan (Fraunhofer IAIS)9/3/25, 2:00 PM
Traditional interpretability techniques such as rule-based models and feature attribution methods, each offer complementary strengths, however are often applied in isolation. Rule-based approaches are intuitive and logically structured, making them easy to understand, but they often struggle to scale effectively. On the other hand, feature attribution techniques like SHAP are well-suited to...
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Rohit Menon9/3/25, 2:00 PM
Service robots operating in dynamic human environments must reliably locate objects that are moved, concealed, or completely novel. Current frameworks often assume static environments, failing to address these real-world uncertainties.
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We present an open-vocabulary framework that combines spatial, semantic, and geometric reasoning to overcome these challenges. By unifying spatial cues about... -
Christian Pionzewski (Fraunhofer IML)9/3/25, 2:00 PM
This abstract outlines my current research for my PhD thesis, focusing specifically on creating a synthetic dataset for multi-camera multi-object tracking (MCMOT) within logistics applications.
Motivation: Tracking moving assets such as trucks, trailers, or containers in logistics yards is crucial for developing digital twins, measuring key performance indicators, and enhancing operational...
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Anike Murrenhoff9/3/25, 2:00 PM
The advancement of artificial intelligence (AI) in intralogistics critically depends on the availability of realistic and diverse datasets. However, existing datasets in this domain often focus on narrow tasks such as object detection or activity recognition, lacking comprehensive three-dimensional (3D) representations of entire intralogistics systems. This paper addresses this gap by...
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Michael Kamp, Osman Mian (The Lamarr Institute for Machine Learning and Artificial Intelligence)9/3/25, 2:00 PM
Understanding causal relationships in oncology is critical for optimizing treatment strategies and generating testable biomedical hypotheses. We present CaDSIm (Causal Discovery with Simultaneous Imputation), a novel method for learning causal structures and associated Structural Equation Models (SEMs) from real-world data.
Our approach addresses three key objectives: Validation,...
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Duygu Ekinci (Paderborn University), Shivam Sharma9/3/25, 2:00 PM
Cross-knowledge-graph (KG) learning is hindered because embeddings trained independently occupy incompatible vector spaces, while pre-merging KGs to enforce consistency is computationally infeasible at web scale. We present WHALE-embeddings, a continuously updated resource derived from Web Data Commons (~98B RDF triples across ~22M domains). By partitioning the corpus by website and training...
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Uttam Kumar9/3/25, 2:00 PM
The onset detection of epileptic seizures from multivariate Electroencephalogram (EEG) data is a challenging task. The variation in seizure patterns across patients and epilepsy types makes it particularly difficult to create a generic solution. Existing approaches indicate low recall due to their inability to capture complex seizure onset patterns. In this paper, we propose VISOR – a novel...
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Patrick Seifner (University of Bonn)9/3/25, 2:00 PM
Dynamical systems governed by ordinary differential equations (ODEs) serve as models for a vast number of natural and social phenomena. In this work, we offer a fresh perspective on the classical problem of imputing missing time series data, whose underlying dynamics are assumed to be determined by ODEs. Specifically, we revisit ideas from amortized inference and neural operators, and propose...
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9/3/25, 5:15 PM
The Lamarr Scientific Forum is rounding off the first day with a closing and all information needed on dinner plans.
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9/4/25, 9:00 AM
We begin program day number 2 with a short look back at the previous day and ahead at today's program.
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Natalia Andrienko (Fraunhofer Institute IAIS)9/4/25, 9:15 AMArea Update
Our research in Human-Centered AI focuses on enabling domain experts to actively guide and interpret machine learning (ML) processes through interactive, knowledge-driven methods. We develop visual analytics (VA) techniques that support expert involvement in both the construction and interpretation of ML models, with the goal of improving transparency, trust, and alignment with human...
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Jürgen Bajorath, Elena Xerxa (University of Bonn), Andrea Mastropietro (University of Bonn)9/4/25, 9:45 AMArea Update
The Lamarr interdisciplinary research area "AI in Life Sciences and Health" will provide an overview of its organization and scientific focal points in the life sciences including drug discovery, medicine, and health. We will introduce research groups participating in this area and key collaborations with external partners and institutions. In addition, recent research progress will be...
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Sven Behnke (Universität Bonn), Julian Eßer9/4/25, 10:15 AMArea Update
This talk provides an overview of recent research in the area of Embodied AI at Lamarr. Embodied Artificial Intelligence refers to AI that is embedded in physical systems, such as robots, and can interact with the surroundings. In contrast to classic Machine Learning in robotics, embodied AI encapsulates all aspects of interacting and learning in an environment: from perception, via...
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Lucie Flek (University of Bonn), Akbar Karimi (University of Bonn), Florian Mai (University of Bonn)9/4/25, 10:45 AMArea Update
Over the past year, the Natural Language Processing (NLP) research area at the Lamarr Institute has made significant strides toward building more robust, context-aware, and aligned language technologies. This talk will provide an overview of key developments in this area and our future plans. We will highlight flagship publications, newly funded projects, strategic collaborations and...
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Felix Finkeldey9/4/25, 11:15 AMArea Update
The Industry and Production research area focuses on the integration of artificial intelligence and machine learning (ML) into production technology. The main objectives are to ensure consistent product quality while minimizing the use of resources such as machine time, tools, materials and energy. This presentation provides an overview of the main research topics of the area, which are...
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Matthias Jakobs (TU Dortmund)9/4/25, 1:00 PM
Recent works for time-series forecasting more and more leverage the high predictive power of Deep Learning models.
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With this increase in model complexity, however, comes a lack in understanding of the underlying model decision process, which is problematic for high-stakes application scenarios. At the same time, simple, interpretable forecasting methods such as ARIMA still perform very... -
Jim Bergmann9/4/25, 1:00 PM
In machining processes, measurement data collected during operation can provide valuable insights into the final quality of the manufactured components. This enables both the reduction of unnecessarily long process chains and the real-time adaptation of process parameters. Machine learning models trained on this data can capture and predict complex process characteristics. However, acquiring...
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Tim Ruhe (TU Dortmund)9/4/25, 1:00 PM
Over the last decade, AI-algorithms have become a standard tool for data analysis in astroparticle physics. While these efforts were pioneered by the use of ensemble methods for event selection in IceCube, the capabilities of AI in the context of neutrino astronomy have been exemplified by the detection of neutrinos from the Milky Way (also by the IceCube collaboration). This detection was...
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Daniel Kuhse9/4/25, 1:00 PM
Anytime algorithms can be interrupted before completion while still delivering an intermediate result. This is a desirable property for embedded systems where timing is critical, such as object detection in cyber-physical-systems. However it is generally neither supported by models nor inference frameworks. To enable a model to be anytime, early-exits can be added to the network, which allow...
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Sarah Sturm (University of Bonn)9/4/25, 1:00 PM
As a major unsupervised learning method, clustering has received a lot of attention over multiple decades. The various clustering problems that have been studied intensively include, e.g., the k-means problem and the k-center problem. How- ever, in applications, it is common that good clusterings should optimize multiple objectives (e.g., visualizing data on a map by clustering districts into...
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Weikang Wang (Learning and Optimisation for Visual Computing Group, University of Bonn), Tobias Weißberg (Learning and Optimisation for Visual Computing Group, University of Bonn)9/4/25, 1:00 PM
Chirality information (i.e., information that allows distinguishing left from right) is ubiquitous for various data modes in computer vision, including images, videos, point clouds, and meshes. Contrary to symmetry, for which there has been a lot of research in the image domain, chirality information in shape analysis (point clouds and meshes) has remained underdeveloped. Although many shape...
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Mr Vahid Sadiri Javadi (University of Bonn)9/4/25, 1:00 PM
Despite advances in conversational systems, the evaluation of such systems remains a challenging problem. Current evaluation paradigms often rely on costly homogeneous human annotators or oversimplified automated metrics, leading to a critical gap in socially aligned conversational agents, where pluralistic values (i.e., acknowledging diverse human experiences) are essential to reflect the...
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Sebastian Müller (University of Bonn)9/4/25, 1:00 PM
We explore what it means to build a scientific "theory" of a black-box model, drawing on van Fraassen's Constructive Empiricism (CE), and demonstrate how such a theory can be used for explainable AI (XAI).
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A scientific theory is more than just an explanation: it not only has value in its own right, but also serves as a robust framework for answering different questions.
According to CE, a... -
Dr Kevin Schmitz9/4/25, 1:00 PM
In recent years, machine learning and deep learning have revolutionized data analysis across various fields, including particle physics and medical imaging. However, their potential in radio interferometry—a technique used to study the universe through arrays of radio telescopes—remains underexplored. The radionets-project has been pioneering the use of deep learning methods to process data...
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Nils Dengler (University of Bonn)9/4/25, 1:00 PM
Service robots operating in cluttered human environments such as homes, offices, and schools cannot rely on predefined object arrangements and must continuously update their semantic and spatial estimates while dealing with possible frequent rearrangement. Identifying all objects in cluttered, occlusion-heavy environments, such as shelves, requires selecting informative viewpoints and...
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Priya Priya (Fraunhofer IAIS, University of Bonn)9/4/25, 1:00 PM
Surgical gauze is an essential part of surgical procedures, which is primarily used for controlling bleeding and absorbing bodily fluids. The post-surgical retention of gauze can lead to serious complications in the patient’s health and necessitate additional surgery for gauze removal. In the wake of data scarcity, the research on gauze segmentation on the real-world surgical data remains...
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Dr Tiago Janela9/4/25, 1:00 PM
Providing clear explanations is crucial in interdisciplinary research fields like bioinformatics where non-experts in machine learning (ML) must understand model decisions to foster trust in the system. Interactive visualisation can help in enabling the active exploration of model behaviour. In this paper,we present an approach to interpreting compound potency predictions by using RuleSense, a...
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Federico Spurio (University of Bonn)9/4/25, 1:00 PM
In this work, we address unsupervised temporal action segmentation, which segments a set of long, untrimmed videos into semantically meaningful segments that are consistent across videos. While recent approaches combine representation learning and clustering in a single step for this task, they do not cope with large variations within temporal segments of the same class. To address this...
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David Kaczér (University of Bonn)9/4/25, 1:00 PM
Fine-tuning lets practitioners repurpose aligned large language models (LLMs) for new domains, yet recent work reveals emergent misalignment (EMA): Even a small, domain-specific fine-tune can induce harmful behaviors far outside the target domain. Even in the case where model weights are hidden behind a fine-tuning API, this gives attackers inadvertent access to a broadly misaligned model in a...
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Zhakshylyk Nurlanov (Learning and Optimisation for Visual Computing Group, University of Bonn)9/4/25, 1:00 PM
Large Language Models (LLMs) remain vulnerable to adversarial jailbreaks, yet existing attacks rely on handcrafted priors or require white-box access for gradient propagation. We show that token-level iterative optimization can succeed without gradients and introduce RAILS (RAndom Iterative Local Search), a simple yet effective method using only model logits with a query budget comparable to...
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Sami Azirar9/4/25, 1:00 PM
Traditional Task test and Motion Planning (TAMP) systems integrate physics simulators for motion planning with discrete symbolic models for task planning. However, because these symbolic models are not derived from data, they must be meticulously handcrafted, requiring manually designed classifiers to bridge the gap with the physics simulator. This process is both resource-intensive and...
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Amr Abourayya (Trustworthy AI)9/4/25, 1:00 PM
In the healthcare domain, sensitive patient data is inherently decentralized across institutions and cannot be centralized due to strict privacy regulations. Federated learning offers a collaborative model training without explicitly sharing patient data by communicating model parameters or soft labels. These approaches, however, are still vulnerable to privacy leakage and often limit model...
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Shangrui Nie (Bonn-Aachen International Center for Information Technology (b-it))9/4/25, 1:00 PM
Social sciences define values as preferred behaviors or outcomes that motivate an individual's actions or judgments.
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While LLMs often reflect biases from their training data, it remains unclear what values underlie their generation processes, and whether such internal value systems can be measured or modified.
In this paper, we investigate whether fine-tuning can steer a model’s internal... -
Zeyu Ding (TU Dortmund)9/4/25, 1:00 PM
In this paper, we present MCBench, a benchmark suite designed to assess the quality of Monte Carlo (MC) samples. The benchmark suite enables quantitative comparisons of samples by applying different metrics, including basic statistical metrics as well as more complex measures, in particular the sliced Wasserstein distance and the maximum mean discrepancy. We apply these metrics to point clouds...
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Marc Höftmann9/4/25, 1:00 PM
Reinforcement learning traditionally learns absolute state values, estimating how good a particular situation is in isolation. Yet in both biological systems and practical decision-making, what often matters is not the absolute value of a state, but how it compares to alternatives. Motivated by empirical findings in neuroscience, we introduce \textbf{Pairwise-TD}, a novel framework that learns...
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Simon Klüttermann9/4/25, 1:00 PM
In this poster, I show Polyra Swarms, a novel approach to machine learning that shifts focus from function approximation to shape approximation. While these swarms are still less developed, I show that they can still hold their own when compared to neural networks and on some tasks outperform them. I also present an automated abstraction mechanism that enhances generalization and...
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Loong Kuan Lee (Fraunhofer IAIS)9/4/25, 1:00 PM
In this article, we propose a novel quantum regression model by extending the Real-Part Quantum SVM. We apply our model to the problem of stability limit prediction in milling processes, a key component in high-precision manufacturing. To train our model, we use a custom data set acquired by an extensive series of milling experiments using different spindle speeds, enhanced with a custom...
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Shubham Gupta, Mr Daniil Kaminskyi (TU Dortmund University)9/4/25, 1:00 PM
Detecting temporal abnormal patterns over streaming data is challenging due to volatile data properties and the lack of real-time labels. The abnormal patterns are usually hidden in the temporal context, which cannot be detected by evaluating single points. Furthermore, the normal state evolves over time due to concept drifts. A single model does not fit all data over time. Autoencoders are...
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Raphael Fischer (TU Dortmund University)9/4/25, 1:00 PM
While many have analyzed the resource efficiency of trained models, an important question remains: How can one be sustainable and resource-aware during AI development, or in other words, when looking for a suitable model to train on a specific learning task? AutoML can help with finding well-performing models on given data, however these frameworks overly focus on predictive quality and...
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Alexander Krooß (Fraunhofer IML), Jan Jäkel, Julian Brandt9/4/25, 1:00 PM
Pallets are one of the most important load carriers for international supply chains. Yet, continuously tracking activities such as driving, lifting or standing along their life cycle is hardly possible. As part of a preliminary project, it was shown that it is possible to develop a prediction model for pallet activities using data from inertial measurements units mounted on a pallet. A...
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Katharina Beckh9/4/25, 1:00 PM
Automatic medical coding has the potential to ease documentation and billing processes. For this task, transparency plays an important role for medical coders and regulatory bodies, which can be achieved using explainability methods. However, the evaluation of these approaches has been mostly limited to short text and binary settings due to a scarcity of annotated data. Recent efforts by Cheng...
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Nilah Nair9/4/25, 1:00 PM
Human motion is of interest to industrial simulations for process optimisation, ergonomic evaluation and visualisation of digital-twin environments. Furthermore, it is of interest for simulation-based reinforcement learning in human-robot interaction and humanoid robotics for industrial scenarios. Human motion data created algorithmically lacks the variability and naturalness of real human...
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Maren Bennewitz9/4/25, 2:30 PM
The RIG comprises 14 German universities and research institutions and focuses on research clusters that cover all central areas of development and innovation in AI-powered robotics, enabling the translation of innovations into practice through close dialogue with industry.
Maren Bennewitz will present the RIG and its opportunities for collaboration.
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Prof. Cyrill Stachniss9/4/25, 2:45 PM
PhenoRob is one of six Clusters of Excellence at the University of Bonn and the only Cluster of Excellence in agriculture in Germany. It performs world-leading research in robotics and phenotyping for sustainable crop production. It hereby transforms crop production by optimizing breeding and farming management through developing and deploying new technologies.
Lamarr PI and PhenoRob...
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Emmanuel Müller9/4/25, 3:00 PM
As part of the University Alliance Ruhr and founded in 2021, the RC Trust follows an interdisciplinary research approach that covers the entire spectrum of research challenges in all facets of trustworthy and privacy-aware technologies. Main foci include Artificial Intelligence and Machine Learning, Psychology and Social Sciences, Data Science and Statistical Learning as well as Cybersecurity...
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Dr Thomas Metcalf (University of Bonn)9/4/25, 3:15 PM
As part of the The Institute for Science and Ethics (IWE), the Bonn Sustainable AI Lab postualtes sustainable AI as AI for sustainability and sustainability of AI. It aims to measure and assess the diverse environmental impacts of AI, research ways of making AI systems more sustainable, and address AI in the context of the Sustainable Development Goals.
The Bonn Sustainable AI Lab and its...
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9/4/25, 3:30 PM
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Networking and growing together: The Lamarr Institute is embedded in and constantly growing its network. Get to know some of our partners that you can cooperate with through the Lamarr Institute.
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Evolving and growing together: The Lamarr Institute is shaping the future of AI. Join in to learn more about the Lamarr Grand Challenge and how you can contribute to move the Lamarr vision forward.
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