machine learning

The presentation series “Train your engineering network” on diverse topics of Machine Learning addresses all interested persons at TUHH, from MLE partners as well as from the Hamburg region in general and aims at promoting the exchange of information and knowledge between these persons as well as their networking in a relaxed atmosphere. Thereby, the machine learning activities within MLE, TUHH and in the wider environment shall be made more visible, cooperations shall be promoted and also interested students shall be given an insight.

Maximilian Kiener

In this talk, I explore the role of ethics in the development of AI and advanced machine learning. I argue that ethics is deeply integrated into powerful AI systems so that one cannot easily remove it without serious impairment of other aspects of the system’s intelligence and problem-solving capacities. On this basis, I develop a novel and more radical framework for ethics by design.

All talks will be streamed via Zoom using https://tuhh.zoom.us/j/85203195489?pwd=K21saVMvZHc0d2NoNHd2bDZ6TmdDUT09
Meeting-ID: 852 0319 5489
Code: 827469

The presentation series “Train your engineering network” on diverse topics of Machine Learning addresses all interested persons at TUHH, from MLE partners as well as from the Hamburg region in general and aims at promoting the exchange of information and knowledge between these persons as well as their networking in a relaxed atmosphere. Thereby, the machine learning activities within MLE, TUHH and in the wider environment shall be made more visible, cooperations shall be promoted and also interested students shall be given an insight.

Yahya Saleh - Flow-induced bases and application to quantum molecular physics

In analogy to the use of normalizing flows to augment the expressivity of base probability distributions, I propose to augment the expressivity of bases of Hilbert spaces via composition with normalizing flows. I show that the resulting sequences are also bases of the Hilbert space under sufficient and necessary conditions on the flow. This lays a foundation for a theory of spectral learning, a nonlinear extension of spectral methods for solving differential equations. As an application I solve the vibrational molecular Schrödinger equation. The proposed numerical scheme results in several orders of magnitude increased accuracy over the use of standard spectral methods. 

Lectures will be held online via Zoom on Mondays starting at 16:00 in the winter semester 2023 in English. General zoom link for all lectures: Link

The presentation series “Train your engineering network” on diverse topics of Machine Learning addresses all interested persons at TUHH, from MLE partners as well as from the Hamburg region in general and aims at promoting the exchange of information and knowledge between these persons as well as their networking in a relaxed atmosphere. Thereby, the machine learning activities within MLE, TUHH and in the wider environment shall be made more visible, cooperations shall be promoted and also interested students shall be given an insight.

Moritz Braun - Generalizability and explainability of machine learning models for fatigue strength prediction of welded joints

Lectures will be held online via Zoom on Mondays starting at 16:00 in the winter semester 2023 in English. General zoom link for all lectures: Link

The presentation series “Train your engineering network” on diverse topics of Machine Learning addresses all interested persons at TUHH, from MLE partners as well as from the Hamburg region in general and aims at promoting the exchange of information and knowledge between these persons as well as their networking in a relaxed atmosphere. Thereby, the machine learning activities within MLE, TUHH and in the wider environment shall be made more visible, cooperations shall be promoted and also interested students shall be given an insight.

Marius Tacke

All talks will be streamed via Zoom using https://tuhh.zoom.us/j/85203195489?pwd=K21saVMvZHc0d2NoNHd2bDZ6TmdDUT09
Meeting-ID: 852 0319 5489
Code: 827469

The presentation series “Train your engineering network” on diverse topics of Machine Learning addresses all interested persons at TUHH, from MLE partners as well as from the Hamburg region in general and aims at promoting the exchange of information and knowledge between these persons as well as their networking in a relaxed atmosphere. Thereby, the machine learning activities within MLE, TUHH and in the wider environment shall be made more visible, cooperations shall be promoted and also interested students shall be given an insight.

Sören Laue

Most problems from classical machine learning can be cast as an optimization problem. I will present GENO (GENeric Optimization), a framework that lets the user specify a constrained or unconstrained optimization problem in an easy-to-read modeling language. GENO then generates a solver that can solve this class of optimization problems. The generated solver is usually as fast as hand-written, problem-specific, and well-engineered solvers. Often the solvers generated by GENO are faster by a large margin compared to recently developed solvers that are tailored to a specific problem class. I will dig into some of the algorithmic details, e.g., computing derivatives of matrix and tensor expressions, the optimization methods used in GENO, and their implementation in Python.

All talks will be streamed via Zoom using https://tuhh.zoom.us/j/85203195489?pwd=K21saVMvZHc0d2NoNHd2bDZ6TmdDUT09
Meeting-ID: 852 0319 5489
Code: 827469

The presentation series “Train your engineering network” on diverse topics of Machine Learning addresses all interested persons at TUHH, from MLE partners as well as from the Hamburg region in general and aims at promoting the exchange of information and knowledge between these persons as well as their networking in a relaxed atmosphere. Thereby, the machine learning activities within MLE, TUHH and in the wider environment shall be made more visible, cooperations shall be promoted and also interested students shall be given an insight.

Frank Röder - Hindsight Instruction Grounding in Reinforcement Learning

This presentation addresses the challenge of sample inefficiency in robotic reinforcement learning with sparse rewards and natural language goal representations. We introduce a mechanism for hindsight instruction replay, leveraging expert feedback, and a seq2seq model for generating linguistic hindsight instructions. Remarkably, our findings demonstrate that self-supervised language generation, where the agent autonomously generates linguistic instructions, significantly enhances learning performance. These results underscore the promising potential of hindsight instruction grounding in reinforcement learning for robotics.

Lectures will be held online via Zoom on Mondays starting at 16:00 in the winter semester 2023 in English. General zoom link for all lectures: Link

The presentation series “Train your engineering network” on diverse topics of Machine Learning addresses all interested persons at TUHH, from MLE partners as well as from the Hamburg region in general and aims at promoting the exchange of information and knowledge between these persons as well as their networking in a relaxed atmosphere. Thereby, the machine learning activities within MLE, TUHH and in the wider environment shall be made more visible, cooperations shall be promoted and also interested students shall be given an insight.

Lars Stietz - Machine Learning in High Energy Physics

Lectures will be held online via Zoom on Mondays starting at 16:00 in the winter semester 2023 in English. General zoom link for all lectures: Link

The presentation series “Train your engineering network” on diverse topics of Machine Learning addresses all interested persons at TUHH, from MLE partners as well as from the Hamburg region in general and aims at promoting the exchange of information and knowledge between these persons as well as their networking in a relaxed atmosphere. Thereby, the machine learning activities within MLE, TUHH and in the wider environment shall be made more visible, cooperations shall be promoted and also interested students shall be given an insight.

Ana Almeida - Multivariate Time series: Data processing, Imputation and Forecasting

Data is a valuable tool for decision-makers, helping them make informed decisions. We can find multivariate time series in several contexts, such as finances, smart cities, and health. This type of data can bring additional challenges. This presentation will discuss the key concepts and techniques involved in working with multivariate time series data. Specifically, we will focus on the steps of data processing, imputation, and forecasting. 

Lectures will be held online via Zoom on Mondays starting at 16:00 in the winter semester 2023 in English. General zoom link for all lectures: Link

The presentation series “Train your engineering network” on diverse topics of Machine Learning addresses all interested persons at TUHH, from MLE partners as well as from the Hamburg region in general and aims at promoting the exchange of information and knowledge between these persons as well as their networking in a relaxed atmosphere. Thereby, the machine learning activities within MLE, TUHH and in the wider environment shall be made more visible, cooperations shall be promoted and also interested students shall be given an insight.

Abdul Qadir Ibrahim - Parareal with a physics informed neural network as a coarse propagator

Parallel-in-time algorithms provide an additional layer of concurrency for the numerical integration of models based on time-dependent differential equations. Methods like Parareal, which parallelize across multiple time steps, rely on a computationally cheap and coarse integrator to propagate information forward in time, while a parallelizable expensive fine propagator provides accuracy. Typically, the coarse method is a numerical integrator using lower resolution, reduced order or a simplified model. Our paper proposes to use a physics-informed neural network (PINN) instead. We demonstrate for the Black-Scholes equation, a partial differential equation from computational finance, that Parareal with a PINN coarse propagator provides better speedup than a numerical coarse propagator. Training and evaluating a neural network are both tasks whose computing patterns are well suited for GPUs. By contrast, mesh-based algorithms with their low computational intensity struggle to perform well. We show that moving the coarse propagator PINN to a GPU while running the numerical fine propagator on the CPU further improves Parareal's single-node performance. This suggests that integrating machine learning techniques into parallel-in-time integration methods and exploiting their differences in computing patterns might offer a way to better utilize heterogeneous architectures. 

Lectures will be held online via Zoom on Mondays starting at 16:00 in the winter semester 2023 in English. General zoom link for all lectures: Link

The presentation series “Train your engineering network” on diverse topics of Machine Learning addresses all interested persons at TUHH, from MLE partners as well as from the Hamburg region in general and aims at promoting the exchange of information and knowledge between these persons as well as their networking in a relaxed atmosphere. Thereby, the machine learning activities within MLE, TUHH and in the wider environment shall be made more visible, cooperations shall be promoted and also interested students shall be given an insight.

Sebastian Schibsdat & Denys Romanenko - Self-acting anomaly detection and quality estimation for semi-automated drilling with machine learning methods

Due to the high number of rivet holes per aircraft produced, automated process monitoring of the drilling process promises a significant reduction in manual inspection. Advances in sensor technology in new machine tools are greatly expanding the data base. Thus, self-learning can be applied to holistic process monitoring. In this presentation, the authors present approaches to anomaly detection and quality control in the drilling process. Supervised, semi-supervised and unsupervised methods were used for anomaly detection and compared with classical methods of quality control charts. In addition to engineered feature extraction, a new method was used to extract features using a CNN.

Lectures will be held online via Zoom on Mondays starting at 16:00 in the winter semester 2023 in English. General zoom link for all lectures: Link

The presentation series “Train your engineering network” on diverse topics of Machine Learning addresses all interested persons at TUHH, from MLE partners as well as from the Hamburg region in general and aims at promoting the exchange of information and knowledge between these persons as well as their networking in a relaxed atmosphere. Thereby, the machine learning activities within MLE, TUHH and in the wider environment shall be made more visible, cooperations shall be promoted and also interested students shall be given an insight.

Johanna Gleichauf

The monitoring of vital signs and increasing patient comfort are cornerstones of modern neonatal intensive care. Commonly used monitoring methods are based on skin contact which can cause irritations and discomfort in preterm neonates. Therefore, non-contact approaches are the subject of current research aiming to resolve this dichotomy. Robust neonatal face detection is essential for the reliable detection of heart rate, respiratory rate and body temperature. While solutions for adult face detection are established, the unique neonatal proportions require a tailored approach. Additionally, sufficient open-source data of neonates on the NICU is lacking. We set out to train neural networks with the thermal-RGB-fusion data of neonates. We propose a novel indirect fusion approach including the sensor fusion of a thermal and RGB camera based on a 3D time-of-flight (ToF) camera. Unlike other approaches, this method is tailored for close distances encountered in neonatal incubators. Two neural networks were used with the fusion data and compared to RGB and thermal networks. For the class “head” we reached average precision values of 0.9958 (RetinaNet) and 0.9455 (YOLOv3) for the fusion data. Compared with the literature, similar precision was achieved, but we are the first to train a neural network with fusion data of neonates. The advantage of this approach is in calculating the detection area directly from the fusion image for the RGB and thermal modality. This increases data efficiency by 66%. Our results will facilitate the future development of non-contact monitoring to further improve the standard of care for preterm neonates.

All talks will be streamed via Zoom
Meeting-ID: 852 0319 5489
Code: 827469

The presentation series “Train your engineering network” on diverse topics of Machine Learning addresses all interested persons at TUHH, from MLE partners as well as from the Hamburg region in general and aims at promoting the exchange of information and knowledge between these persons as well as their networking in a relaxed atmosphere. Thereby, the machine learning activities within MLE, TUHH and in the wider environment shall be made more visible, cooperations shall be promoted and also interested students shall be given an insight.

Marcus Venzke 

The talk presents a new technique for unsupervised learning of repeatedly occurring process states from a suite of time series derived from preprocessed sensor data recorded from a fixed process. As a first application we consider the process of moving a good along a path in an industrial environment. The goal is to identify individual sections of the path while they are being traversed. The technique determines thresholds in time series leading to the same succession of increasing and decreasing intersections for all paths of the training data. The trained model is a so-called “threshold tree”. It consists of thresholds for the different time series splitting a path into its sections to be recognized. The execution of threshold trees has a low CPU and memory footprint allowing their use on micro-controllers, e.g. in embedded systems. Due to their intuitive comprehensibility "threshold trees" belong to the category of explainable AI.

All talks will be streamed via Zoom using https://tuhh.zoom.us/j/85203195489?pwd=K21saVMvZHc0d2NoNHd2bDZ6TmdDUT09
Meeting-ID: 852 0319 5489
Code: 827469

Techniques of machine learning (ML) find a rapidly increasing range of applications touching upon social, economic, and technological aspects of everyday life. They are also being used with great enthusiasm to fill in gaps in our scientific knowledge by data-based modelling approaches. I have followed these developments for a while with interest, concern, and mounting disappointment. When these technologies are employed to take over decisive functionality in safety-critical applications, we would like to exactly know how to guarantee their compliance with pre-defined guardrails and limitations. Moreover, when they are utilized as building blocks in scientific research, it would violate scientific standards -in my opinion- if these building blocks were used without a throrough understanding of their functionality, including inaccuracies, uncertainties, and other pitfalls. In this context, I will juxtapose (a subset of) deep neural network methods with the family of entropy-optimal Sparse Probabilistic Approximation (sSPA) techniques developed recently by Illia Horenko (RPTU Kaiserslautern-Landau) and colleagues.

bAIome Center for biomedical AI (UKE) and Bernhard Nocht Institute for Tropical Medicine (BNITM) will host the seminar series entitled “AI in biology and Medicine”. This series aims to capture a broad audience and promote cross institutional collaboration. Our expert speakers will give an overview and insight into particular AI/data science methods being developed in key areas of biology and medicine. We will have drinks and snacks following each seminar to facilitate exchange.

Olga Zolotareva, Institute for Computational Systems Biology, UHH

For further details and hybrid links, please go to the webpage AI in Biology & Medicine