3 citations · 5 across the 6 of their papers we have counts for
8 papers
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…
Give more data, awareness and control to individual citizens, and they will help COVID-19 containment
Mirco Nanni, Gennady Andrienko, Albert-László Barabási +36
The rapid dynamics of COVID-19 calls for quick and effective tracking of virus transmission chains and early detection of outbreaks, especially in the phase 2 of the pandemic, when…
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…