Patricia Wollstadt, Sebastian Schmitt, Michael Wibral,
"A Rigorous Information-Theoretic Definition of Redundancy and Relevancy in Feature Selection Based on (Partial) Information Decomposition",
Journal of Machine Learning Research, vol. 24, no. 131, pp. 1-44, 2023.
Abstract
Selecting a minimal feature set that is maximally informative about a target variable is a central task in machine learning and statistics. Information theory provides a powerful framework for formulating feature selection algorithms---yet, a rigorous, information-theoretic definition of feature relevancy, which accounts for feature interactions such as redundant and synergistic contributions, is still missing. We argue that this lack is inherent...
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Andrea Castellani,
"Dealing with Inaccurate and Incomplete Labels in Industrial Streaming Data - Talk at Uni Creete (27.09.2023)",
Crete University, Crete University, 2023.
Abstract
Machine learning techniques are an essential option for processing large volumes of data and are capable to capture complex relationships within it. However, obtaining meaningfully annotated data is a real challenge and typically incurs large costs. Especially, in an industrial setting where few labelled data samples are available and drifting data features poses a severe challenge. In this talk, I will address: (1) how to efficiently train model...
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Andrea Castellani,
"Dealing with Inaccurate and Incomplete Labels in Industrial Streaming Data",
Uni Bielefeld, Uni Bielefeld, 2023.
Abstract
The pressure to increase the energetic efficiency of industrial facilities has led to a strong
increase in the number of installed measurement sensors. These collect large volumes
of data that need to be processed and analyzed. As manual data processing methods are
not appropriate due to the sheer amount of data, automated and intelligent solutions are
needed. Machine learning techniques are a viable option for processing large volumes
of da...
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Daniel Gordon, Andreas Christou, Michael Gienger, Sethu Vijayakumar,
"Adaptive Assistive Robotics: A Framework For Triadic Collaboration Between Humans and Robots",
Royal Society Open Science, 2023.
Abstract
Robots and other assistive technologies have a
huge potential to help society in domains ranging
from factory work to healthcare. However, safe
and eff ective control of robotic agents in these
environments is complex, especially when it involves
close interactions and multiple actors. We propose an
eff ective framework for optimising the behaviour of
robots and complementary assistive technologies in
systems comprising a mix of human and...
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Jan Leusmann, Chao Wang, Sven Mayer, Michael Gienger, Albrecht Schmidt,
"Understanding the Uncertainty Loop of Human-Robot Interaction",
Workshop paper of CHI 23, 2023.
Abstract
Recently the field of Human-Robot Interaction gained popularity, due to the wide range of possibilities of how robots can support humans during daily tasks. One form of supportive robots are socially assistive robots which are specifically built for communicating with humans, e.g., as service robots or personal companions. As they understand humans through artificial intelligence, these robots will at some point make wrong assumptions about the h...
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Noah Wach,
" Data re-uploading with a single qudit",
Quantum Research Seminars Toronto, 2023.
Abstract
Invited Talk at Quantum Research Seminar Toronto...
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Zhenpeng Shi, Nikolay Matyunin, Kalman Graffi, David Starobinski,
"Uncovering CWE-CVE-CPE Relations with Threat Knowledge Graphs",
arXiv, 2023.
Abstract
Security assessment relies on public information about products, vulnerabilities, and weaknesses. So far, databases in these categories have rarely been analyzed in combination. Yet, doing so could help predict unreported vulnerabilities and identify common threat patterns. In this paper, we propose a methodology for producing and optimizing a knowledge graph that aggregates knowledge from common threat databases (CVE, CWE, and CPE). We apply the...
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Anna Belardinelli,
"Gaze-based intention estimation: principles, methodologies, and applications in HRI",
arxiv, 2023.
Abstract
Intention prediction has become a relevant field of research in Human-Machine and Human-Robot Interaction. Indeed, any artificial
system (co)-operating with and along humans, designed to assist and coordinate its actions with a human partner, would benefit from
first inferring the human’s current intention. To spare the user the cognitive burden of explicitly uttering their goals, this inference
relies mostly on behavioral cues deemed indica...
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Sebastian Schmitt,
"Data re-uploading with qudits and other qudit applications",
Fermilab QIS/HEP seminar, 2023.
Abstract
Invited talk on published papers at the Fermilab QIS/HEP seminar. ...
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Frank Joublin, Antonello Ceravola, Jörg Deigmöller, Michael Gienger, Mathias Franzius, Julian Eggert,
"A Glimpse in ChatGPT Capabilities and its impact for AI research",
arXiv, 2023.
Abstract
Large language models (LLMs) have recently become a popular topic in the field of Artificial Intelligence (AI) research, with companies such as Google, Amazon, Facebook, and Apple (GAFA) investing heavily in their development. These models are trained on massive amounts of data and can be used for a wide range of tasks, including language translation, text generation, and question answering. However, the computational resources required to train ...
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