Interpretable Machine Learning via Linear Temporal Logic

In recent years, deep neural networks have shown excellent performance, outperforming even human experts in various tasks. However, their inherent complexity and black-box nature often make it hard, if not impossible, to understand the decisions made by these models, hindering their practical application in high-stakes scenarios. We propose a framework for learning LTL formulas as inherently interpretable machine learning models. These models can be trained both in a supervised and unsupervised setting. Furthermore, they can easily be extended to handle noisy data and to incorporate expert knowledge

Finding Commonalities in Dynamical Systems with Gaussian Processes

Gaussian processes can be utilized in the area of equation discovery to identify differential equations describing the physical processes present in time series data.Furthermore, automatically constructed models can be split into components that facilitate comparisons between time series on a structural level. We consider the potential combination of these two methods and describe how they could be used to detect shared physical properties in multiple recordings of dynamical systems as time series. This approach provides insights into the underlying dynamics of the observed systems, facilitating a deeper understanding of complex processes

Bioinspired Decentralized Hexapod Control with a Graph Neural Network

Legged locomotion enables animals to navigate challenging terrains. However, it demands intricate coordination between the legs, with varying levels of information exchange depending on the task. For instance, in more demanding scenarios such as an insect climbing on a twig, greater coordination between the legs is necessary to achieve adaptive behavior. To address this challenge for legged robots, we present a concept and preliminary results of a decentralized biologically inspired controller for a hexapod robot: Based on insights of coordination influences between legs in stick insects, our approach models inter-leg information flow as message passing through a Graph Neural Network

de.NBI & ELIXIR Germany - eine Forschungs- und Serviceinfrastruktur für Bioinformatik in den Lebenswissenschaften

Das deutsche Netzwerk für Bioinformatikinfrastruktur (de.NBI) ist eine nationale, akademische und gemeinnützige Forschungsinfrastruktur, die von 2015 - 2021 durch das BMBF gefördert und im Anschluss über die Helmholtz Gemeinschaft mit dem Forschungszentrum Jülich als Träger verstetigt wurde. Ziel von de.NBI mit seinen 24 nationalen Partnern ist es, erstklassige Bioinformatikwerkzeuge, -ressourcen und -dienstleistungen für die Forschung in den Lebenswissenschaften und der Biomedizin bereitzustellen, sowie umfassende Schulungen anzubieten und den Transfer von Fachwissen zwischen Wissenschaft und Industrie zu fördern. Für den akademischen Bereich stellt die föderierte de.NBI Cloud als Teil von de.NBI kostenlose Cloud-Ressourcen für die Verarbeitung großer Datenmengen, für das maschinelle Lernen, sowie für den Betrieb von Webservices bereit. Durch eine Kooperation mit Netzwerken wie ELIXIR Europe und EOSC stärkt de.NBI in Form von ELIXIR Deutschland seit 2016 die internationale Zusammenarbeit in der Bioinformatik-Gemeinschaft, insbesondere in den Bereichen FAIR Data, Research Data Management, Training und Software. Dieser Vortrag gibt einen Überblick über den Werdegang, die Strukturen und Aktivitäten von de.NBI und ELIXIR Deutschland im nationalen und europäischen Rahmen. Er gibt einen Überblick über vergangene, sowie einen Ausblick auf momentan anlaufende und zukünftige Aktivitäten

Einblicke in die ‚Alma-SAP-Werkstatt‘

Der Wunsch eines automatisierten Datenaustauschs zwischen SAP und dem Bibliothekssystem bestand schon sehr lange. Mit einem pragmatischen Ansatz konnte dieses Projekt nun endlich bewerkstelligt werden. In diesem „Werkstattbericht“ soll es mehr um die technischen Aspekte dieses Vorhabens gehen

Provable Guarantees for Deep Learning-Based Anomaly Detection through Logical Constraints

Incorporating constraints expressed as logical formulas and based on foundational prior knowledge into deep learning models can provide formal guarantees for the fulfillment of critical model properties, improve model performance, and ensure that relevant structures can be inferred from less data. We propose to thoroughly explore such logical constraints over input-output relations in the context of deep learning-based anomaly detection, specifically by extending the capabilities of the MultiplexNet framework

Advancements in Neural Network Generations

Innovations in Neural Network Generation demonstrate the continual evolution, optimization, and development of artificial neural networks (ANNs) over periods. These improvements include a combination of methodologies, approaches, and technical breakthroughs aimed at increasing the efficiency and abilities of neural network models. Researchers and engineers have repeatedly attempted to push the boundaries of neural network performance, scalability, and applicability across multiple fields. These improvements usually involve changes to network designs, training algorithms, optimization methodologies, and hardware acceleration methods. Moreover, the neural network generations are closely related to key achievements in the machine learning (ML) research domain, such as the development of deep learning (DL) designs like convolutional neural network (CNN) or spiking neural network (SNN) and using both neural generations to introduce natural language processing and advances in computer vision applications. Thus, in the field of neural network study, researchers have categorized ANN models into generations based on their computational design and capabilities. Therefore, this research study explores the continual evolution and optimization of ANNs, highlighting advancements in methodologies and technical innovation. We discuss the different generations of ANN, based on computational design and capabilities, emphasizing their role in shaping achievements in ML research. The study underscores the significance of these generational milestones in enhancing the adaptability and efficacy of neural network models for computational tasks, such as image classification

Closing the Loop with Concept Regularization

Convolutional Neural Networks (CNNs) are widely adopted in industrial settings, but are prone to biases and lack transparency. Explainable Artificial Intelligence (XAI), particularly through concept extraction (CE), allows for global explanations and bias detection, yet fails to offer corrective measures for identified biases. To bridge this gap, we introduce Concept Regularization (CoRe), which uses CE capabilities alongside human feedback to embed a regularization term during retraining. CoRe allows for the adjustments in model sensitivities based on identified biases, aligning model prediction process with expert human assessments. Our evaluations on a modified metal casting dataset demonstrate CoRe's efficacy in bias mitigation, highlighting its potential to refine models in practical applications

Is it Possible to Characterize Group Fairness in Rankings in Terms of Individual Fairness and Diversity?

Rankings are ever-present in everyday life. Examples are the results of personalized recommendations and web search queries. Rankings can result from an algorithm, importance scores and human-based rankings of items. Till we are not concerned with societal applications, the “fairness“ of the ranking is often irrelevant; however, problems appear when switching from depersonalized items to individuals. Then, suddenly, fairness becomes an issue. We investigate the relationships among group fairness, individual fairness, diversity, and Shapley values. Far from being a comprehensive survey of fairness-related papers or proposing a new method, we want to raise awareness of the chaos we are trying to navigate and propose some new research direction we are trying to follow