307 citations · 465 across the 30 of their papers we have counts for
17 papers · 1 filter
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…
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…
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…
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…
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…
Towards Explainable Bit Error Tolerance of Resistive RAM-Based Binarized Neural Networks
Sebastian Buschjäger, Jian-Jia Chen, Kuan-Hsun Chen +6
Non-volatile memory, such as resistive RAM (RRAM), is an emerging energy-efficient storage, especially for low-power machine learning models on the edge. It is reported, however, t…
The Trustworthy Pal: Controlling the False Discovery Rate in Boolean Matrix Factorization
Sibylle Hess, Nico Piatkowski, Katharina Morik
Boolean matrix factorization (BMF) is a popular and powerful technique for inferring knowledge from data. The mining result is the Boolean product of two matrices, approximating th…
The SpectACl of Nonconvex Clustering: A Spectral Approach to Density-Based Clustering
Sibylle Hess, Wouter Duivesteijn, Philipp Honysz +1
When it comes to clustering nonconvex shapes, two paradigms are used to find the most suitable clustering: minimum cut and maximum density. The most popular algorithms incorporatin…
C-SALT: Mining Class-Specific ALTerations in Boolean Matrix Factorization
Sibylle Hess, Katharina Morik
Given labeled data represented by a binary matrix, we consider the task to derive a Boolean matrix factorization which identifies commonalities and specifications among the classes…