2 code implementations • 16 Mar 2020 • Leila Arras, Ahmed Osman, Wojciech Samek
The rise of deep learning in today's applications entailed an increasing need in explaining the model's decisions beyond prediction performances in order to foster trust and accountability.
no code implementations • 25 Sep 2019 • Leila Arras, Jose A. Arjona-Medina, Michael Widrich, Grégoire Montavon, Michael Gillhofer, Klaus-Robert Müller, Sepp Hochreiter, Wojciech Samek
While neural networks have acted as a strong unifying force in the design of modern AI systems, the neural network architectures themselves remain highly heterogeneous due to the variety of tasks to be solved.
1 code implementation • WS 2019 • Leila Arras, Ahmed Osman, Klaus-Robert Müller, Wojciech Samek
Recently, several methods have been proposed to explain the predictions of recurrent neural networks (RNNs), in particular of LSTMs.
1 code implementation • 18 Jul 2017 • Franziska Horn, Leila Arras, Grégoire Montavon, Klaus-Robert Müller, Wojciech Samek
When dealing with large collections of documents, it is imperative to quickly get an overview of the texts' contents.
2 code implementations • 17 Jul 2017 • Franziska Horn, Leila Arras, Grégoire Montavon, Klaus-Robert Müller, Wojciech Samek
When working with a new dataset, it is important to first explore and familiarize oneself with it, before applying any advanced machine learning algorithms.
1 code implementation • WS 2017 • Leila Arras, Grégoire Montavon, Klaus-Robert Müller, Wojciech Samek
Recently, a technique called Layer-wise Relevance Propagation (LRP) was shown to deliver insightful explanations in the form of input space relevances for understanding feed-forward neural network classification decisions.
1 code implementation • 23 Dec 2016 • Leila Arras, Franziska Horn, Grégoire Montavon, Klaus-Robert Müller, Wojciech Samek
Text documents can be described by a number of abstract concepts such as semantic category, writing style, or sentiment.
1 code implementation • WS 2016 • Leila Arras, Franziska Horn, Grégoire Montavon, Klaus-Robert Müller, Wojciech Samek
Layer-wise relevance propagation (LRP) is a recently proposed technique for explaining predictions of complex non-linear classifiers in terms of input variables.