290 citations · 646 across the 8 of their papers we have counts for
9 papers
Leveraging weak complementary labels to improve semantic segmentation of hepatocellular carcinoma and cholangiocarcinoma in H&E-stained slides
Miriam Hägele, Johannes Eschrich, Lukas Ruff +6
In this paper, we present a deep learning segmentation approach to classify and quantify the two most prevalent primary liver cancers - hepatocellular carcinoma and intrahepatic ch…
Balancing the composition of word embeddings across heterogenous data sets
Stephanie Brandl, David Lassner, Maximilian Alber
Word embeddings capture semantic relationships based on contextual information and are the basis for a wide variety of natural language processing applications. Notably these relat…
Explanations can be manipulated and geometry is to blame
Ann-Kathrin Dombrowski, Maximilian Alber, Christopher J. Anders +3
Explanation methods aim to make neural networks more trustworthy and interpretable. In this paper, we demonstrate a property of explanation methods which is disconcerting for both…
Software and application patterns for explanation methods
Maximilian Alber
Deep neural networks successfully pervaded many applications domains and are increasingly used in critical decision processes. Understanding their workings is desirable or even req…
iNNvestigate neural networks!
Maximilian Alber, Sebastian Lapuschkin, Philipp Seegerer +7
In recent years, deep neural networks have revolutionized many application domains of machine learning and are key components of many critical decision or predictive processes. The…
Backprop Evolution
Maximilian Alber, Irwan Bello, Barret Zoph +3
The back-propagation algorithm is the cornerstone of deep learning. Despite its importance, few variations of the algorithm have been attempted. This work presents an approach to d…