8 citations · 13 across the 5 of their papers we have counts for
5 papers
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…
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…
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…