3 citations · 3 across the 4 of their papers we have counts for
4 papers
The Confusion is Real: GRAPHIC -- A Network Science Approach to Confusion Matrices in Deep Learning
Johanna S. Fröhlich, Bastian Heinlein, Jan U. Claar +3
Explainable artificial intelligence has emerged as a promising field of research to address reliability concerns in artificial intelligence. Despite significant progress in explain…
Coding for Computation: Efficient Compression of Neural Networks for Reconfigurable Hardware
Hans Rosenberger, Rodrigo Fischer, Johanna S. Fröhlich +2
As state of the art neural networks (NNs) continue to grow in size, their resource-efficient implementation becomes ever more important. In this paper, we introduce a compression s…
Graph-based Algorithms for Linear Computation Coding
Hans Rosenberger, Ali Bereyhi, Ralf R. Müller
We revisit existing linear computation coding (LCC) algorithms, and introduce a new framework that measures the computational cost of computing multidimensional linear functions, n…
Linear Computation Coding: Exponential Search and Reduced-State Algorithms
Hans Rosenberger, Johanna S. Fröhlich, Ali Bereyhi +1
Linear computation coding is concerned with the compression of multidimensional linear functions, i.e. with reducing the computational effort of multiplying an arbitrary vector to…