307 citations · 465 across the 30 of their papers we have counts for
33 papers
Chasing Gravitational Waves with the Cherenkov Telescope Array
Jarred Gershon Green, Alessandro Carosi, Lara Nava +563
The detection of gravitational waves from a binary neutron star merger by Advanced LIGO and Advanced Virgo (GW170817), along with the discovery of the electromagnetic counterparts…
Interpolation of Instrument Response Functions for the Cherenkov Telescope Array in the Context of pyirf
Rune Michael Dominik, Maximilian Linhoff, Julian Sitarek
The Cherenkov Telescope Array (CTA) will be the next generation ground-based very-high-energy gamma-ray observatory, constituted by tens of Imaging Atmospheric Cherenkov Telescopes…
Variability studies of active galactic nuclei from the long-term monitoring program with the Cherenkov Telescope Array
Guillaume Grolleron, Josefa Becerra González, Jonathan Biteau +13
Blazars are active galactic nuclei (AGN) with a relativistic jet oriented toward the observer. This jet is composed of accelerated particles which can display emission over the ent…
Performance update of an event-type based analysis for the Cherenkov Telescope Array
Juan Bernete, Orel Gueta, Tarek Hassan +3
The Cherenkov Telescope Array (CTA) will be the next-generation observatory in the field of very-high-energy (20 GeV to 300 TeV) gamma-ray astroparticle physics. The traditional ap…
Dark matter searches in dwarf spheroidal galaxies with the Cherenkov Telescope Array
Francesco Gabriele Saturni, Michele Doro, Aldo Morselli +1
Dark matter (DM) is one of the major components in the Universe. However, at present its existence is still only inferred through indirect astronomical observations. DM particles c…
Observation of high-energy neutrinos from the Galactic plane
R. Abbasi, M. Ackermann, J. Adams +385
The origin of high-energy cosmic rays, atomic nuclei that continuously impact Earth's atmosphere, has been a mystery for over a century. Due to deflection in interstellar magnetic…
Fast Inference of Tree Ensembles on ARM Devices
Simon Koschel, Sebastian Buschjäger, Claudio Lucchese +1
With the ongoing integration of Machine Learning models into everyday life, e.g. in the form of the Internet of Things (IoT), the evaluation of learned models becomes more and more…
Energy Efficiency Considerations for Popular AI Benchmarks
Raphael Fischer, Matthias Jakobs, Katharina Morik
Advances in artificial intelligence need to become more resource-aware and sustainable. This requires clear assessment and reporting of energy efficiency trade-offs, like sacrifici…
Self-Supervised Pretraining of Graph Neural Network for the Retrieval of Related Mathematical Expressions in Scientific Articles
Lukas Pfahler, Katharina Morik
Given the increase of publications, search for relevant papers becomes tedious. In particular, search across disciplines or schools of thinking is not supported. This is mainly due…
Shrub Ensembles for Online Classification
Sebastian Buschjäger, Sibylle Hess, Katharina Morik
Online learning algorithms have become a ubiquitous tool in the machine learning toolbox and are frequently used in small, resource-constraint environments. Among the most successf…
There is no Double-Descent in Random Forests
Sebastian Buschjäger, Katharina Morik
Random Forests (RFs) are among the state-of-the-art in machine learning and offer excellent performance with nearly zero parameter tuning. Remarkably, RFs seem to be impervious to…
Improving the Accuracy-Memory Trade-Off of Random Forests Via Leaf-Refinement
Sebastian Buschjäger, Katharina Morik
Random Forests (RF) are among the state-of-the-art in many machine learning applications. With the ongoing integration of ML models into everyday life, the deployment and continuou…
Explaining Deep Learning Representations by Tracing the Training Process
Lukas Pfahler, Katharina Morik
We propose a novel explanation method that explains the decisions of a deep neural network by investigating how the intermediate representations at each layer of the deep network w…
Noisy Labels for Weakly Supervised Gamma Hadron Classification
Lukas Pfahler, Mirko Bunse, Katharina Morik
Gamma hadron classification, a central machine learning task in gamma ray astronomy, is conventionally tackled with supervised learning. However, the supervised approach requires a…
Providing Meaningful Data Summarizations Using Exemplar-based Clustering in Industry 4.0
Philipp-Jan Honysz, Alexander Schulze-Struchtrup, Sebastian Buschjäger +1
Data summarizations are a valuable tool to derive knowledge from large data streams and have proven their usefulness in a great number of applications. Summaries can be found by op…
The Care Label Concept: A Certification Suite for Trustworthy and Resource-Aware Machine Learning
Katharina Morik, Helena Kotthaus, Lukas Heppe +4
Machine learning applications have become ubiquitous. This has led to an increased effort of making machine learning trustworthy. Explainable and fair AI have already matured. They…
Yes We Care! -- Certification for Machine Learning Methods through the Care Label Framework
Katharina Morik, Helena Kotthaus, Raphael Fischer +6
Machine learning applications have become ubiquitous. Their applications range from embedded control in production machines over process optimization in diverse areas (e.g., traffi…
Bit Error Tolerance Metrics for Binarized Neural Networks
Sebastian Buschjäger, Jian-Jia Chen, Kuan-Hsun Chen +5
To reduce the resource demand of neural network (NN) inference systems, it has been proposed to use approximate memory, in which the supply voltage and the timing parameters are tu…
GPU-Accelerated Optimizer-Aware Evaluation of Submodular Exemplar Clustering
Philipp-Jan Honysz, Sebastian Buschjäger, Katharina Morik
The optimization of submodular functions constitutes a viable way to perform clustering. Strong approximation guarantees and feasible optimization w.r.t. streaming data make this c…
A Convolutional Neural Network based Cascade Reconstruction for the IceCube Neutrino Observatory
R. Abbasi, M. Ackermann, J. Adams +364
Continued improvements on existing reconstruction methods are vital to the success of high-energy physics experiments, such as the IceCube Neutrino Observatory. In IceCube, further…
Generalized Negative Correlation Learning for Deep Ensembling
Sebastian Buschjäger, Lukas Pfahler, Katharina Morik
Ensemble algorithms offer state of the art performance in many machine learning applications. A common explanation for their excellent performance is due to the bias-variance decom…
Lessons Learned from the 1st ARIEL Machine Learning Challenge: Correcting Transiting Exoplanet Light Curves for Stellar Spots
Nikolaos Nikolaou, Ingo P. Waldmann, Angelos Tsiaras +20
The last decade has witnessed a rapid growth of the field of exoplanet discovery and characterisation. However, several big challenges remain, many of which could be addressed usin…
Very Fast Streaming Submodular Function Maximization
Sebastian Buschjäger, Philipp-Jan Honysz, Lukas Pfahler +1
Data summarization has become a valuable tool in understanding even terabytes of data. Due to their compelling theoretical properties, submodular functions have been in the focus o…
Resource-Constrained On-Device Learning by Dynamic Averaging
Lukas Heppe, Michael Kamp, Linara Adilova +3
The communication between data-generating devices is partially responsible for a growing portion of the world's power consumption. Thus reducing communication is vital, both, from…