In this talk, I will discuss the challenges and opportunities of applying machine learning to streaming data. To illustrate key concepts, I will introduce CapyMOA, a new open-source library designed for efficient real-time learning.
Thanks to their inherent interpretability, tree models are widely utilized in various learning tasks, including time series forecasting. However, single tree models often suffer from overfitting, limiting their applicability to real-world scenarios. To address this issue, ensembles of tree models are commonly employed. Yet, ensemble construction must account for the dynamic nature of time...
Presentation by Sabine Hunze (Research Support Service, TU Dortmund) discussing key aspects of the funding landscape and available funding opportunities.
Researchers at Hybrid-ML tackle a wide range of applied and theoretical problems, characterized by different data modalities, such as time series, graphs, natural language, images, and their combinations. Solving these problems requires drawing from an equally broad spectrum of background knowledge, from abstract algebra and statistical physics to cognitive psychology. Yet, despite this...
As machine learning models become increasingly integrated into various applications, the need for resource-aware deployment strategies becomes paramount. One promising approach for optimizing resource consumption is rejection ensembles. Rejection ensembles combine a small model deployed to an edge device with a large model deployed in the cloud, with a rejector tasked to determine the most...
A long, long time ago, in a far away land, some smart people thought about how to connect hardware and machine learning to make ML and hardware more resource-aware. At this time, the term "resource-aware ML" came along. While our roots go back some 10-15 years now, the term "resource-aware ML" only partially reflects the current trend in ML research. In fact, most new projects and ideas...
The study of sunspot numbers is crucial for understanding solar activity and its impact on Earth's climate and space weather. This research analyzes the temporal patterns in historical sunspot data and develops predictive models for long-term forecasting. Using statistical and deep learning techniques, we identify key trends, periodicities, and anomalies in sunspot cycles. The proposed models...
Anomaly and signal detection is one of the most important use cases of machine learning (ML) both in scientific and in commercial applications. Anomalous signals are measured relative to an expected behavior of data, i.e., relative to the background or to the priors. Relevant examples of anomalies and signals in physics can be: an excess of gamma-rays near the center of our Galaxy (a possible...
Plasma fusion has the potential to provide an efficient and safe energy source, however, several technological challenges still remain before fusion reactors can be realized at large scale. One promising direction is called stellarator in which the plasma is guided into a possibly complex equilibrium flow by magnetic fields. The optimal form for this flow is still unknown and only very sparse...
Looking into the future
High-energy particle physics experiments are on the brink of facing significant challenges in reconstructing complex events due to increasing intensities and energies. The scientific aim of the presented work is to address the growing computational complexity of event reconstruction while enhancing efficiency and improving the precision of analyses in the ATLAS, LHCb, and Belle II experiments....
Lecture in the AI Colloquium by Prof. Gregor Kasieczka (Universität Hamburg)
Abstract: Machine learning and AI have quickly turned into indispensable tools for modern particle physics. They both greatly amplify the power of existing techniques - such as supercharging supervised classification - and enable qualitatively new ways...
